lr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.3\n"
     ]
    }
   ],
   "source": [
    "#Checking the version of Python\n",
    "\n",
    "from platform import python_version\n",
    "print(python_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Acquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "plt.style.use('seaborn-bright')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CaseOrder</th>\n",
       "      <th>Customer_id</th>\n",
       "      <th>Interaction</th>\n",
       "      <th>UID</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>...</th>\n",
       "      <th>MonthlyCharge</th>\n",
       "      <th>Bandwidth_GB_Year</th>\n",
       "      <th>Item1</th>\n",
       "      <th>Item2</th>\n",
       "      <th>Item3</th>\n",
       "      <th>Item4</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>K409198</td>\n",
       "      <td>aa90260b-4141-4a24-8e36-b04ce1f4f77b</td>\n",
       "      <td>e885b299883d4f9fb18e39c75155d990</td>\n",
       "      <td>Point Baker</td>\n",
       "      <td>AK</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "      <td>99927</td>\n",
       "      <td>56.25100</td>\n",
       "      <td>-133.37571</td>\n",
       "      <td>...</td>\n",
       "      <td>172.455519</td>\n",
       "      <td>904.536110</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>S120509</td>\n",
       "      <td>fb76459f-c047-4a9d-8af9-e0f7d4ac2524</td>\n",
       "      <td>f2de8bef964785f41a2959829830fb8a</td>\n",
       "      <td>West Branch</td>\n",
       "      <td>MI</td>\n",
       "      <td>Ogemaw</td>\n",
       "      <td>48661</td>\n",
       "      <td>44.32893</td>\n",
       "      <td>-84.24080</td>\n",
       "      <td>...</td>\n",
       "      <td>242.632554</td>\n",
       "      <td>800.982766</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>K191035</td>\n",
       "      <td>344d114c-3736-4be5-98f7-c72c281e2d35</td>\n",
       "      <td>f1784cfa9f6d92ae816197eb175d3c71</td>\n",
       "      <td>Yamhill</td>\n",
       "      <td>OR</td>\n",
       "      <td>Yamhill</td>\n",
       "      <td>97148</td>\n",
       "      <td>45.35589</td>\n",
       "      <td>-123.24657</td>\n",
       "      <td>...</td>\n",
       "      <td>159.947583</td>\n",
       "      <td>2054.706961</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>D90850</td>\n",
       "      <td>abfa2b40-2d43-4994-b15a-989b8c79e311</td>\n",
       "      <td>dc8a365077241bb5cd5ccd305136b05e</td>\n",
       "      <td>Del Mar</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>92014</td>\n",
       "      <td>32.96687</td>\n",
       "      <td>-117.24798</td>\n",
       "      <td>...</td>\n",
       "      <td>119.956840</td>\n",
       "      <td>2164.579412</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>K662701</td>\n",
       "      <td>68a861fd-0d20-4e51-a587-8a90407ee574</td>\n",
       "      <td>aabb64a116e83fdc4befc1fbab1663f9</td>\n",
       "      <td>Needville</td>\n",
       "      <td>TX</td>\n",
       "      <td>Fort Bend</td>\n",
       "      <td>77461</td>\n",
       "      <td>29.38012</td>\n",
       "      <td>-95.80673</td>\n",
       "      <td>...</td>\n",
       "      <td>149.948316</td>\n",
       "      <td>271.493436</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 50 columns</p>\n",
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       "   CaseOrder Customer_id                           Interaction  \\\n",
       "0          1     K409198  aa90260b-4141-4a24-8e36-b04ce1f4f77b   \n",
       "1          2     S120509  fb76459f-c047-4a9d-8af9-e0f7d4ac2524   \n",
       "2          3     K191035  344d114c-3736-4be5-98f7-c72c281e2d35   \n",
       "3          4      D90850  abfa2b40-2d43-4994-b15a-989b8c79e311   \n",
       "4          5     K662701  68a861fd-0d20-4e51-a587-8a90407ee574   \n",
       "\n",
       "                                UID         City State                 County  \\\n",
       "0  e885b299883d4f9fb18e39c75155d990  Point Baker    AK  Prince of Wales-Hyder   \n",
       "1  f2de8bef964785f41a2959829830fb8a  West Branch    MI                 Ogemaw   \n",
       "2  f1784cfa9f6d92ae816197eb175d3c71      Yamhill    OR                Yamhill   \n",
       "3  dc8a365077241bb5cd5ccd305136b05e      Del Mar    CA              San Diego   \n",
       "4  aabb64a116e83fdc4befc1fbab1663f9    Needville    TX              Fort Bend   \n",
       "\n",
       "     Zip       Lat        Lng  ...  MonthlyCharge Bandwidth_GB_Year Item1  \\\n",
       "0  99927  56.25100 -133.37571  ...     172.455519        904.536110     5   \n",
       "1  48661  44.32893  -84.24080  ...     242.632554        800.982766     3   \n",
       "2  97148  45.35589 -123.24657  ...     159.947583       2054.706961     4   \n",
       "3  92014  32.96687 -117.24798  ...     119.956840       2164.579412     4   \n",
       "4  77461  29.38012  -95.80673  ...     149.948316        271.493436     4   \n",
       "\n",
       "  Item2  Item3  Item4  Item5 Item6 Item7 Item8  \n",
       "0     5      5      3      4     4     3     4  \n",
       "1     4      3      3      4     3     4     4  \n",
       "2     4      2      4      4     3     3     3  \n",
       "3     4      4      2      5     4     3     3  \n",
       "4     4      4      3      4     4     4     5  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the raw pandas dataframe\n",
    "\n",
    "df = pd.read_csv(\"C:/Users/bozor/Documents/WGU MSDA/Predictive Modeling/churn_clean.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 50 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   CaseOrder             10000 non-null  int64  \n",
      " 1   Customer_id           10000 non-null  object \n",
      " 2   Interaction           10000 non-null  object \n",
      " 3   UID                   10000 non-null  object \n",
      " 4   City                  10000 non-null  object \n",
      " 5   State                 10000 non-null  object \n",
      " 6   County                10000 non-null  object \n",
      " 7   Zip                   10000 non-null  int64  \n",
      " 8   Lat                   10000 non-null  float64\n",
      " 9   Lng                   10000 non-null  float64\n",
      " 10  Population            10000 non-null  int64  \n",
      " 11  Area                  10000 non-null  object \n",
      " 12  TimeZone              10000 non-null  object \n",
      " 13  Job                   10000 non-null  object \n",
      " 14  Children              10000 non-null  int64  \n",
      " 15  Age                   10000 non-null  int64  \n",
      " 16  Income                10000 non-null  float64\n",
      " 17  Marital               10000 non-null  object \n",
      " 18  Gender                10000 non-null  object \n",
      " 19  Churn                 10000 non-null  object \n",
      " 20  Outage_sec_perweek    10000 non-null  float64\n",
      " 21  Email                 10000 non-null  int64  \n",
      " 22  Contacts              10000 non-null  int64  \n",
      " 23  Yearly_equip_failure  10000 non-null  int64  \n",
      " 24  Techie                10000 non-null  object \n",
      " 25  Contract              10000 non-null  object \n",
      " 26  Port_modem            10000 non-null  object \n",
      " 27  Tablet                10000 non-null  object \n",
      " 28  InternetService       10000 non-null  object \n",
      " 29  Phone                 10000 non-null  object \n",
      " 30  Multiple              10000 non-null  object \n",
      " 31  OnlineSecurity        10000 non-null  object \n",
      " 32  OnlineBackup          10000 non-null  object \n",
      " 33  DeviceProtection      10000 non-null  object \n",
      " 34  TechSupport           10000 non-null  object \n",
      " 35  StreamingTV           10000 non-null  object \n",
      " 36  StreamingMovies       10000 non-null  object \n",
      " 37  PaperlessBilling      10000 non-null  object \n",
      " 38  PaymentMethod         10000 non-null  object \n",
      " 39  Tenure                10000 non-null  float64\n",
      " 40  MonthlyCharge         10000 non-null  float64\n",
      " 41  Bandwidth_GB_Year     10000 non-null  float64\n",
      " 42  Item1                 10000 non-null  int64  \n",
      " 43  Item2                 10000 non-null  int64  \n",
      " 44  Item3                 10000 non-null  int64  \n",
      " 45  Item4                 10000 non-null  int64  \n",
      " 46  Item5                 10000 non-null  int64  \n",
      " 47  Item6                 10000 non-null  int64  \n",
      " 48  Item7                 10000 non-null  int64  \n",
      " 49  Item8                 10000 non-null  int64  \n",
      "dtypes: float64(7), int64(16), object(27)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking dataset general info\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>48</td>\n",
       "      <td>18925.23</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "      <td>40074.19</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.147417</td>\n",
       "      <td>16</td>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Children  Age    Income    Marital  Gender Churn  Outage_sec_perweek  \\\n",
       "0         0   68  28561.99    Widowed    Male    No            7.978323   \n",
       "1         1   27  21704.77    Married  Female   Yes           11.699080   \n",
       "2         4   50   9609.57    Widowed  Female    No           10.752800   \n",
       "3         1   48  18925.23    Married    Male    No           14.913540   \n",
       "4         0   83  40074.19  Separated    Male   Yes            8.147417   \n",
       "\n",
       "   Email  Contacts  Yearly_equip_failure  ... MonthlyCharge Bandwidth_GB_Year  \\\n",
       "0     10         0                     1  ...    172.455519        904.536110   \n",
       "1     12         0                     1  ...    242.632554        800.982766   \n",
       "2      9         0                     1  ...    159.947583       2054.706961   \n",
       "3     15         2                     0  ...    119.956840       2164.579412   \n",
       "4     16         2                     1  ...    149.948316        271.493436   \n",
       "\n",
       "  Item1 Item2 Item3 Item4 Item5 Item6 Item7 Item8  \n",
       "0     5     5     5     3     4     4     3     4  \n",
       "1     3     4     3     3     4     3     4     4  \n",
       "2     4     4     2     4     4     3     3     3  \n",
       "3     4     4     4     2     5     4     3     3  \n",
       "4     4     4     4     3     4     4     4     5  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eliminating variables that have no importance in our analysis\n",
    "\n",
    "column = ['CaseOrder', 'Customer_id', 'Interaction', 'UID', 'City', 'State', 'County', \n",
    "          'Zip', 'Lat', 'Lng', 'Population', 'Area', 'TimeZone','Job'\n",
    "         ]\n",
    "df.drop(columns=column, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Children', 'Age', 'Income', 'Marital', 'Gender', 'Churn',\n",
       "       'Outage_sec_perweek', 'Email', 'Contacts', 'Yearly_equip_failure',\n",
       "       'Techie', 'Contract', 'Port_modem', 'Tablet', 'InternetService',\n",
       "       'Phone', 'Multiple', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'PaperlessBilling', 'PaymentMethod', 'Tenure', 'MonthlyCharge',\n",
       "       'Bandwidth_GB_Year', 'Item1', 'Item2', 'Item3', 'Item4', 'Item5',\n",
       "       'Item6', 'Item7', 'Item8'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List of remaining features\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Univariate Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous variables are:\n",
      "\n",
      " ['Children', 'Age', 'Income', 'Outage_sec_perweek', 'Email', 'Contacts', 'Yearly_equip_failure', 'Tenure', 'MonthlyCharge', 'Bandwidth_GB_Year', 'Item1', 'Item2', 'Item3', 'Item4', 'Item5', 'Item6', 'Item7', 'Item8'] \n",
      "\n",
      " and Categorical variables are:\n",
      "\n",
      " ['Marital', 'Gender', 'Churn', 'Techie', 'Contract', 'Port_modem', 'Tablet', 'InternetService', 'Phone', 'Multiple', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'PaperlessBilling', 'PaymentMethod']\n"
     ]
    }
   ],
   "source": [
    "#Creating two lists, containing continuous and categorical variables\n",
    "\n",
    "cont_var = ['Children', 'Age', 'Income', 'Outage_sec_perweek', 'Email', 'Contacts', 'Yearly_equip_failure',\n",
    "            'Tenure', 'MonthlyCharge', 'Bandwidth_GB_Year', 'Item1', 'Item2', 'Item3', 'Item4', 'Item5', 'Item6',\n",
    "            'Item7', 'Item8'\n",
    "           ]\n",
    "cat_var = [i for i in df.columns if i not in cont_var]\n",
    "\n",
    "print('Continuous variables are:\\n\\n {} \\n\\n and Categorical variables are:\\n\\n {}'.format(cont_var, cat_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1. Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1584 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list1 = np.array(cont_var).reshape(6,3)\n",
    "\n",
    "fig, axes = plt.subplots(6, 3, figsize=(16,22))\n",
    "fig.suptitle('Univariate Visualization of Continuous Variables', fontsize=20)\n",
    "for index, var in np.ndenumerate(list1):\n",
    "    sns.histplot(ax=axes[index[0], index[1]], data=df, x=var, kde=True, color='green', bins='sqrt')\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Count')\n",
    "plt.savefig('fig.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Matirx of the continuous variables\n",
    "\n",
    "corr_mat = round(df[cont_var].corr(), 3)\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(corr_mat, annot=True, linewidth=0.01)\n",
    "plt.savefig('fig.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2. Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1584 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing categorical variables\n",
    "\n",
    "\n",
    "list2 = np.array(cat_var, dtype='object').reshape(6,3)\n",
    "\n",
    "fig, axes = plt.subplots(6, 3, figsize=(16,22))\n",
    "fig.suptitle('Univariate Visualization of Categorical Variables', fontsize=20)\n",
    "for index, var in np.ndenumerate(list2):\n",
    "    sns.countplot(ax=axes[index[0], index[1]], data=df, x=var)\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Count')\n",
    "    \n",
    "plt.savefig('fig.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Bivariate Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1584 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Continuous Variables VS. Tenure\n",
    "\n",
    "\n",
    "list1 = np.array(cont_var).reshape(6,3)\n",
    "\n",
    "fig, axes = plt.subplots(6, 3, figsize=(16,22))\n",
    "fig.suptitle('Continuous Variables vs. Churn', fontsize=20)\n",
    "for index, var in np.ndenumerate(list1):\n",
    "    sns.histplot(ax=axes[index[0], index[1]], data=df, x=var, hue='Churn')\n",
    "    plt.xlabel(var)\n",
    "    \n",
    "plt.savefig('fig.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1584 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical Variables VS. Tenure\n",
    "\n",
    "\n",
    "list2 = np.array(cat_var, dtype='object').reshape(6,3)\n",
    "\n",
    "fig, axes = plt.subplots(6, 3, sharey=False, figsize=(16,22))\n",
    "fig.suptitle('Categorical Variables vs. Tenure', fontsize=20)\n",
    "for index, var in np.ndenumerate(list2):\n",
    "    sns.countplot(ax=axes[index[0], index[1]], data=df, x=var, hue='Churn')\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Tenure')\n",
    "    \n",
    "plt.savefig('fig.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a new feature by averaging all the 8 questions in survey:\n",
    "\n",
    "surv = ['Item1', 'Item2', 'Item3', 'Item4', 'Item5', 'Item6', 'Item7', 'Item8']\n",
    "df['Survey'] = df[surv].astype(float).mean(axis=1).round()\n",
    "df.drop(columns=surv, inplace=True)\n",
    "\n",
    "#Graphing the new feature\n",
    "\n",
    "sns.histplot(data=df, x='Survey', kde=True, color='blue', bins='sqrt')\n",
    "plt.xticks(range(1,8,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Droping one variable due to Multicollinearity and creating the cleaned dataset\n",
    "\n",
    "cleaned_df = df.drop(columns=['Bandwidth_GB_Year'])\n",
    "cleaned_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Children</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Outage_sec_perweek</th>\n",
       "      <th>Email</th>\n",
       "      <th>Contacts</th>\n",
       "      <th>Yearly_equip_failure</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>MonthlyCharge</th>\n",
       "      <th>Survey</th>\n",
       "      <th>...</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit Card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic Check</th>\n",
       "      <th>PaymentMethod_Mailed Check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>28561.99</td>\n",
       "      <td>7.978323</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.795513</td>\n",
       "      <td>172.455519</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>21704.77</td>\n",
       "      <td>11.699080</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.156681</td>\n",
       "      <td>242.632554</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>9609.57</td>\n",
       "      <td>10.752800</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.754144</td>\n",
       "      <td>159.947583</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>18925.23</td>\n",
       "      <td>14.913540</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17.087227</td>\n",
       "      <td>119.956840</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>40074.19</td>\n",
       "      <td>8.147417</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.670972</td>\n",
       "      <td>149.948316</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Children  Age    Income  Outage_sec_perweek  Email  Contacts  \\\n",
       "0         0   68  28561.99            7.978323     10         0   \n",
       "1         1   27  21704.77           11.699080     12         0   \n",
       "2         4   50   9609.57           10.752800      9         0   \n",
       "3         1   48  18925.23           14.913540     15         2   \n",
       "4         0   83  40074.19            8.147417     16         2   \n",
       "\n",
       "   Yearly_equip_failure     Tenure  MonthlyCharge  Survey  ...  \\\n",
       "0                     1   6.795513     172.455519     4.0  ...   \n",
       "1                     1   1.156681     242.632554     4.0  ...   \n",
       "2                     1  15.754144     159.947583     3.0  ...   \n",
       "3                     0  17.087227     119.956840     4.0  ...   \n",
       "4                     1   1.670972     149.948316     4.0  ...   \n",
       "\n",
       "   OnlineSecurity_Yes  OnlineBackup_Yes  DeviceProtection_Yes  \\\n",
       "0                   1                 1                     0   \n",
       "1                   1                 0                     0   \n",
       "2                   0                 0                     0   \n",
       "3                   1                 0                     0   \n",
       "4                   0                 0                     0   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_Yes  StreamingMovies_Yes  \\\n",
       "0                0                0                    1   \n",
       "1                0                1                    1   \n",
       "2                0                0                    1   \n",
       "3                0                1                    0   \n",
       "4                1                1                    0   \n",
       "\n",
       "   PaperlessBilling_Yes  PaymentMethod_Credit Card (automatic)  \\\n",
       "0                     1                                      1   \n",
       "1                     1                                      0   \n",
       "2                     1                                      1   \n",
       "3                     1                                      0   \n",
       "4                     0                                      0   \n",
       "\n",
       "   PaymentMethod_Electronic Check  PaymentMethod_Mailed Check  \n",
       "0                               0                           0  \n",
       "1                               0                           0  \n",
       "2                               0                           0  \n",
       "3                               0                           1  \n",
       "4                               0                           1  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dummy variables for all categorical features \n",
    "\n",
    "cleaned_df = pd.get_dummies(cleaned_df, drop_first=True)\n",
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 36)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the cleaned dataset into a csv file.\n",
    "\n",
    "cleaned_df.to_csv('LR_cleaned_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Creating the Initial Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 35)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seaparating independent and dependent variables\n",
    "\n",
    "X = cleaned_df.drop(columns='Churn_Yes')\n",
    "y = cleaned_df[['Churn_Yes']].values.ravel()\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Observations: 7000\n",
      "Test Set Observations: 3000\n"
     ]
    }
   ],
   "source": [
    "#Splitting data into training and test sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=110)\n",
    "\n",
    "print('Training Set Observations: %.f' % X_train.shape[0]) \n",
    "print('Test Set Observations: %.f' % X_test.shape[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing the features\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "index_train = X_train.index\n",
    "index_test = X_test.index\n",
    "\n",
    "\n",
    "n_scaler = MinMaxScaler()\n",
    "X_train = pd.DataFrame(n_scaler.fit_transform(X_train), index=index_train, columns=X.columns)\n",
    "X_test = pd.DataFrame(n_scaler.transform(X_test), index=index_test, columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is: 0.907\n",
      "The recall score is: 0.805\n",
      "The precision score is: 0.836\n"
     ]
    }
   ],
   "source": [
    "#Creating the initial LR model:\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
    "\n",
    "init_mod = LogisticRegression().fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('The accuracy score is: %.3f' % accuracy_score(y_train, init_mod.predict(X_train)))\n",
    "print('The recall score is: %.3f' %recall_score(y_train, init_mod.predict(X_train)))\n",
    "print('The precision score is: %.3f' %precision_score(y_train, init_mod.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.2</td>\n",
       "      <td>Outage_sec_perweek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.4</td>\n",
       "      <td>Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Contacts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.4</td>\n",
       "      <td>Yearly_equip_failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.6</td>\n",
       "      <td>Tenure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>135.6</td>\n",
       "      <td>MonthlyCharge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.6</td>\n",
       "      <td>Survey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Marital_Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Marital_Never Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Marital_Separated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Marital_Widowed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Gender_Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Gender_Nonbinary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.2</td>\n",
       "      <td>Techie_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.4</td>\n",
       "      <td>Contract_One year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Contract_Two Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Port_modem_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.4</td>\n",
       "      <td>Tablet_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.5</td>\n",
       "      <td>InternetService_Fiber Optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>InternetService_None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9.0</td>\n",
       "      <td>Phone_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.3</td>\n",
       "      <td>Multiple_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.6</td>\n",
       "      <td>OnlineSecurity_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.8</td>\n",
       "      <td>OnlineBackup_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.7</td>\n",
       "      <td>DeviceProtection_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.4</td>\n",
       "      <td>TechSupport_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>13.5</td>\n",
       "      <td>StreamingTV_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>19.5</td>\n",
       "      <td>StreamingMovies_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2.4</td>\n",
       "      <td>PaperlessBilling_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.9</td>\n",
       "      <td>PaymentMethod_Credit Card (automatic)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2.5</td>\n",
       "      <td>PaymentMethod_Electronic Check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.0</td>\n",
       "      <td>PaymentMethod_Mailed Check</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VIF Factor                               features\n",
       "0          1.9                               Children\n",
       "1          3.7                                    Age\n",
       "2          2.9                                 Income\n",
       "3         10.2                     Outage_sec_perweek\n",
       "4         11.4                                  Email\n",
       "5          2.0                               Contacts\n",
       "6          1.4                   Yearly_equip_failure\n",
       "7          2.6                                 Tenure\n",
       "8        135.6                          MonthlyCharge\n",
       "9          6.6                                 Survey\n",
       "10         1.9                        Marital_Married\n",
       "11         1.9                  Marital_Never Married\n",
       "12         1.9                      Marital_Separated\n",
       "13         1.9                        Marital_Widowed\n",
       "14         1.9                            Gender_Male\n",
       "15         1.0                       Gender_Nonbinary\n",
       "16         1.2                             Techie_Yes\n",
       "17         1.4                      Contract_One year\n",
       "18         1.5                      Contract_Two Year\n",
       "19         1.9                         Port_modem_Yes\n",
       "20         1.4                             Tablet_Yes\n",
       "21         4.5            InternetService_Fiber Optic\n",
       "22         2.0                   InternetService_None\n",
       "23         9.0                              Phone_Yes\n",
       "24         8.3                           Multiple_Yes\n",
       "25         1.6                     OnlineSecurity_Yes\n",
       "26         4.8                       OnlineBackup_Yes\n",
       "27         2.7                   DeviceProtection_Yes\n",
       "28         2.4                        TechSupport_Yes\n",
       "29        13.5                        StreamingTV_Yes\n",
       "30        19.5                    StreamingMovies_Yes\n",
       "31         2.4                   PaperlessBilling_Yes\n",
       "32         1.9  PaymentMethod_Credit Card (automatic)\n",
       "33         2.5         PaymentMethod_Electronic Check\n",
       "34         2.0             PaymentMethod_Mailed Check"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Claculating Variance Inflation Factor (VIF):\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n",
    "vif[\"features\"] = X_train.columns\n",
    "\n",
    "vif.round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Creating the Reduced Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for Stepwise Feature Selection:\n",
    "\n",
    "def stepwise_selection(data, target,SL_in=0.05,SL_out = 0.05):\n",
    "    initial_features = data.columns.tolist()\n",
    "    best_features = []\n",
    "    while (len(initial_features)>0):\n",
    "        remaining_features = list(set(initial_features)-set(best_features))\n",
    "        new_pval = pd.Series(index=remaining_features, dtype='float64')\n",
    "        for new_column in remaining_features:\n",
    "            model = sm.Logit(target, sm.add_constant(data[best_features+[new_column]])).fit()\n",
    "            new_pval[new_column] = model.pvalues[new_column]\n",
    "        min_p_value = new_pval.min()\n",
    "        if(min_p_value<SL_in):\n",
    "            best_features.append(new_pval.idxmin())\n",
    "            while(len(best_features)>0):\n",
    "                best_features_with_constant = sm.add_constant(data[best_features])\n",
    "                p_values = sm.Logit(target, best_features_with_constant).fit().pvalues[1:]\n",
    "                max_p_value = p_values.max()\n",
    "                if(max_p_value >= SL_out):\n",
    "                    excluded_feature = p_values.idxmax()\n",
    "                    best_features.remove(excluded_feature)\n",
    "                else:\n",
    "                    break \n",
    "        else:\n",
    "            break\n",
    "    return best_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 192.596713\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 218.748114\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 212.662965\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.438922\n",
      "         Iterations 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 193.481881\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.569872\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 193.059425\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.433098\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 194.059286\n",
      "         Iterations 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.618952\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.460061\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.532344\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.438127\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.535789\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.689066\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 193.077895\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.435790\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 194.485521\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.581117\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.531624\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.614407\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 199.741265\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 202.718306\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.436945\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 238.726429\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 194.020709\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 192.465927\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.481314\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.511935\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.558953\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.557192\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.425962\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 192.483061\n",
      "         Iterations 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
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      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
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      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
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      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bozor\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    }
   ],
   "source": [
    "# Stepwise Feature Selection:\n",
    "\n",
    "features = stepwise_selection(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Tenure', 'MonthlyCharge', 'InternetService_Fiber Optic', 'Contract_Two Year', 'Contract_One year', 'Techie_Yes', 'OnlineBackup_Yes', 'TechSupport_Yes', 'PaymentMethod_Electronic Check', 'InternetService_None', 'Multiple_Yes', 'Gender_Male', 'DeviceProtection_Yes', 'OnlineSecurity_Yes', 'Port_modem_Yes']\n"
     ]
    }
   ],
   "source": [
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducing training and test set to the selected features:\n",
    "\n",
    "re_X_train = X_train[features]\n",
    "re_X_test = X_test[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Tenure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4</td>\n",
       "      <td>MonthlyCharge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.3</td>\n",
       "      <td>InternetService_Fiber Optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.4</td>\n",
       "      <td>Contract_Two Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.3</td>\n",
       "      <td>Contract_One year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.2</td>\n",
       "      <td>Techie_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>OnlineBackup_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.6</td>\n",
       "      <td>TechSupport_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.5</td>\n",
       "      <td>PaymentMethod_Electronic Check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.5</td>\n",
       "      <td>InternetService_None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Multiple_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Gender_Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.8</td>\n",
       "      <td>DeviceProtection_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.5</td>\n",
       "      <td>OnlineSecurity_Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Port_modem_Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VIF Factor                        features\n",
       "0          2.3                          Tenure\n",
       "1          7.4                   MonthlyCharge\n",
       "2          2.3     InternetService_Fiber Optic\n",
       "3          1.4               Contract_Two Year\n",
       "4          1.3               Contract_One year\n",
       "5          1.2                      Techie_Yes\n",
       "6          2.0                OnlineBackup_Yes\n",
       "7          1.6                 TechSupport_Yes\n",
       "8          1.5  PaymentMethod_Electronic Check\n",
       "9          1.5            InternetService_None\n",
       "10         2.3                    Multiple_Yes\n",
       "11         1.8                     Gender_Male\n",
       "12         1.8            DeviceProtection_Yes\n",
       "13         1.5              OnlineSecurity_Yes\n",
       "14         1.8                  Port_modem_Yes"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Claculating VIF for the reduced features:\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(re_X_train.values, i) for i in range(re_X_train.shape[1])]\n",
    "vif[\"features\"] = re_X_train.columns\n",
    "\n",
    "vif.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is: 0.904\n",
      "The recall score is: 0.795\n",
      "The precision score is: 0.833\n"
     ]
    }
   ],
   "source": [
    "#Creating the reduced model: \n",
    "\n",
    "re_mod = LogisticRegression().fit(re_X_train, y_train)\n",
    "\n",
    "print('The accuracy score is: %.3f' % accuracy_score(y_train, re_mod.predict(re_X_train)))\n",
    "print('The recall score is: %.3f' % recall_score(y_train, re_mod.predict(re_X_train)))\n",
    "print('The precision score is: %.3f' % precision_score(y_train, re_mod.predict(re_X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.24, 11.27, -2.25, -3.04, -2.85,  0.76, -0.46, -0.45,  0.4 ,\n",
       "        -0.55, -0.34,  0.33, -0.26, -0.25,  0.18]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New variables' coeficients:\n",
    "\n",
    "np.round(re_mod.coef_, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.65])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(re_mod.intercept_, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is: 0.899\n",
      "The recall score is: 0.790\n",
      "The precision score is: 0.825\n"
     ]
    }
   ],
   "source": [
    "# Model scores with the test datapoints:\n",
    "\n",
    "print('The accuracy score is: %.3f' % accuracy_score(y_test, re_mod.predict(re_X_test)))\n",
    "print('The recall score is: %.3f' % recall_score(y_test, re_mod.predict(re_X_test)))\n",
    "print('The precision score is: %.3f' % precision_score(y_test, re_mod.predict(re_X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.94      0.93      2200\n",
      "           1       0.83      0.79      0.81       800\n",
      "\n",
      "    accuracy                           0.90      3000\n",
      "   macro avg       0.87      0.86      0.87      3000\n",
      "weighted avg       0.90      0.90      0.90      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report:\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, re_mod.predict(re_X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix:\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = re_mod.predict(re_X_test)\n",
    "\n",
    "logistic_cm = confusion_matrix(y_pred, y_test)\n",
    "sns.heatmap(logistic_cm, annot=True, fmt='.2f',xticklabels = [\"Stayed\", \"Churned\"] , yticklabels = [\"Stayed\", \"Churned\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('Logisitic Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}