Estate_management.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix, accuracy_score, mean_squared_error,plot_confusion_matrix\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import argparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   gender  marital  religion  ethnicity  family  age  education  occupation  \\\n",
      "0       0        1         0          0       1    1          0           2   \n",
      "1       0        1         1          1       1    1          0           2   \n",
      "2       0        1         1          1       1    1          1           1   \n",
      "3       0        0         1          1       1    1          0           2   \n",
      "4       1        1         1          1       2    1          0           1   \n",
      "\n",
      "   salary  reference  status  property  relationship  duration  history  \\\n",
      "0       0          1       0         0             5         3        0   \n",
      "1       0          1       0         0             5         3        0   \n",
      "2       0          1       0         0             5         3        0   \n",
      "3       0          1       0         1             5         3        0   \n",
      "4       0          0       0         1             5         3        0   \n",
      "\n",
      "   status2  \n",
      "0        1  \n",
      "1        1  \n",
      "2        1  \n",
      "3        1  \n",
      "4        1  \n",
      "status2\n",
      "(724, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gender              AxesSubplot(0.125,0.71587;0.168478x0.16413)\n",
       "marital          AxesSubplot(0.327174,0.71587;0.168478x0.16413)\n",
       "religion         AxesSubplot(0.529348,0.71587;0.168478x0.16413)\n",
       "ethnicity        AxesSubplot(0.731522,0.71587;0.168478x0.16413)\n",
       "family             AxesSubplot(0.125,0.518913;0.168478x0.16413)\n",
       "age             AxesSubplot(0.327174,0.518913;0.168478x0.16413)\n",
       "education       AxesSubplot(0.529348,0.518913;0.168478x0.16413)\n",
       "occupation      AxesSubplot(0.731522,0.518913;0.168478x0.16413)\n",
       "salary             AxesSubplot(0.125,0.321957;0.168478x0.16413)\n",
       "reference       AxesSubplot(0.327174,0.321957;0.168478x0.16413)\n",
       "status          AxesSubplot(0.529348,0.321957;0.168478x0.16413)\n",
       "property        AxesSubplot(0.731522,0.321957;0.168478x0.16413)\n",
       "relationship          AxesSubplot(0.125,0.125;0.168478x0.16413)\n",
       "duration           AxesSubplot(0.327174,0.125;0.168478x0.16413)\n",
       "history            AxesSubplot(0.529348,0.125;0.168478x0.16413)\n",
       "status2            AxesSubplot(0.731522,0.125;0.168478x0.16413)\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tQ+ZlW9FUoy4qM5t097G86+hGyjZbyjc7RcpWZ32IiASnRi0iElyvNOpteRfQxZRttpRvdgqTbU8coxYRKbJemVGLiBSWGrWISHBd3ajNrOalXmZmna6liCo5mdlFZvaXC+0jx6fWWK03fqVxzYxPS4TMvNkrE8MysxtJrpDqB5a7+43l7etJTsafBkaAF4HTzezzwC3ufldOJYdlZreVP+0zsxeAM4H/NbPrgZOBZ4DVJFn+xszeA/wSeAN4V/njS8AngR8DLwMfBN5093s6+b1EVx63pwLrzOxbwGXAWcBWYNTMVgMfIMn6TWAjMOnu38ip5NDKY/cFkj4wAHzTzD5E0ht+APwBMANMAJ8B3gLuBf4WeIAk8/Uk4/VfgfXu/k8d/jbmafsfEwcGBrxUKrX1NVtx+PBhnn/+eUqlEieddBKvvvoq+/bt4/TTT6e/vz/X2qampg7WuwJpMVHyjazVfKNkOzU1Vfe50dHRDlYyX9GzjWyhbNs+oy6VSkxOTrb7ZZt2zjnn8PDDD3PppZfObtu9ezc33HBD7vWZWcuX0kbJN7Ja+ZrZJcB17n5Vva+Lkm3l3Xp6ElXZlnd9Rc82soX6Qtcc+qg2PT3NunXr5mxbt24d09PTOVVUfCPbR5raP9JCQ+7+iJldUL090gpvRRU92yKP24qubdRDQ0M89thjc2bUjz32GENDQzlWVWwRB/DxirLCWzeKku0r0xNzV9BbQGU51Gi69qyPLVu2cO2117J7925mZmbYvXs31157LVu2bMm7NMmBma0BLjKzDXnX0igzm/2IrIjZFk3Xzqg/9rGPAXDDDTcwPT3N0NAQd9111+x26S3u/gTwkbzraIS712zOUa8iLlK2RdW1jRqSZq3GLEUUtSlLPrr20IeISLdQoxYRCU6NWkQkODVqEZHg1KhFRIJToxYRCa6rT8+T7BTpPF+RotOMWpqWbtLXXXddze0i0j6aUQdhZlcC5wFPufuDedfTiMoM+r777lOTFsmQZtRBuPsOksXiz6h+zsw2mdmkmU0eOHCg88XVkJ5J13osIu2zaKM2syvN7DNmdruZ3VnvlkxyfMysD7gFuK/6OXff5u5j7j62YkVL9xtou69+9asLPhaR9lm0Uadmer8BPgecmHVRPeqzJNmuzbuQRpkZ119/vQ57iGRs0WPUqZnemwvsE2KB8CJz91vzrqFR6dXd0jNpnfUhko1GjlFXZnovAluA16t3iPjWXLLl7vM+RCQbi86oizTTExHpRjrrQ0QkODVqEZHg1KhFRIJToxYRCU6NWkQkOK31IS3R6nkinaMZtTQt3aTvvffemttFpH3UqKVl7s5NN92kmbRIxtSopSXpmXStxyLSPmrU0pKbb755wcci0j5q1NIyM2Pr1q06Ni2SMTVqaVr6mHR6Jq1j1SLZ0Ol50hI1ZZHO0YxaRCQ4NWoRkeDUqEVEguuqY9Qj20ea2n/P1XsyqkREpH26qlG/Mj3BvokrGtq3NL4r42pERNpDhz5ERIJToxYRCa6rDn1A44c0Tlm2NONKupuWORXpnKYatZldDFwIvOTu92dTUuvqHZ8uje9q+Nh1XqJnm5Zu0itXruS5556b3R6xWRcp2yJSvtlr9tDHWnefAFZkUUyPK1y27s6zzz4bsjlXKVy2BaN8M9bsoY+aP5FmtgnYBDA4OHi8NbVF9Vtzu/vY50EbS92iIua7cuXKeY8rM+uAwmdb8FNLw/eFyiHR/XdvrLvPqs07wx4StWaaVvktzlqStzgP1NpnbGzMJycn21RedzKzKXcfq9q2aLYQI9/KL8H02Km1LS/V+RYp2+haHbvKdnG1sp19rt0/WGZ2ANjf1hc9fgPAwbyLSFnl7i29TQyS72jq818D70g9nupwLbW0lG+QbKt1xdhVtg2pm23bG3VEZjZZ7zeVHB9lmy3lm50iZavzqEVEglOjFhEJrlca9ba8C+hiyjZbyjc7hcm2J45Ri4gUWa/MqEVECkuNWkQkuMI1aitfWWFm15jZaVXPjZf/u6HB1xpPfX6hmb2znbV2g3RGqW2W+rxkZleZ2XIz+2Bnqyu21HhdbWZnprZfYmYX1Pma+athSV11xu+8bannLjWzLWZ2Y7aVNacwq+eZ2W3lT/vM7AXgjfL2TcC5wF8Do2Y2Cqwxsz7g/cCbwP8B7yp/7AB+B/gfYIWZfar8ukeAX5jZ14DvAM+6+3925JsLyMz+FFgG3AlMlAf3D4ENwDfMbC3J2g7fBdYDzwJnlLcvBaaB3wd+AvyWu3+5899FeH3l8fdHwC3lsfwUcBQ4y8xOBH6X5BLtH5Bk/5yZ/RdJ5l9391/lU3ps5UZ7KrDOzL4FXAacBWwl6ROrgQ8AL5L0iI3ApLt/w8y+D9yeT+W1FWlG/RZwOsnVTSeVt/UBbwdmgOXAlLtXrowbdfcvlrcD/DPJVUg/J/m+3wEccfcvlV+j4hfu/nWg5oymhwy6+1eAt5Uf95X/uxN4gaR5nEny7/Eo8Hz5+VPdfStwPnConOWyjlVdLA58GfiP8uOngFNIMt1FkuFWjv0b7AT+DvgQsExNekHTJH3hUZLxegJJ4z5CcvXs64CRjOuj5X0q76hvAb7W4XoXVLRG/QjQTzKgK9sGSN4ZnACcYmbnl5+bNLNPc+wS0aPl/w4ArwDvJflNCnMXlXmfmd1MMuPuZfvM7CrgaTP7C2CovH2G5BfmWyS/4A4BFwFnlJ8/YmY3keR3FFnIW+7+Fsn4M5IJyLtJmsaVwPeAmyi/ewRmyvsvAx7rfLmF8tvl//4eycx5GckvvBOAleXnzyeZSUPSC1aa2SdIJiBrO1rtInR6XhUzGy8v2SgSjpm9G/iUu9c9zirdR41aRCS4Ih36EBHpSWrUIiLBqVGLiASnRi0iEpwatYhIcP8PLxV3KSdDYvIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.read_csv('C:\\\\Users\\\\User\\\\Desktop\\\\datasets\\\\janetdata.csv')\n",
    "new=data.drop('ID',axis=1)\n",
    "print(new.head())\n",
    "X=new[['gender','marital','religion','ethnicity','family','age','education','occupation','salary','reference','property','relationship','duration','history']]\n",
    "\n",
    "y= new['status2']\n",
    "print(y.name)\n",
    "print(X.shape)\n",
    "y.shape\n",
    "new.plot(kind='box', subplots='True', layout=(4,4), sharex=False, fontsize=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>gender</th>\n",
       "      <th>marital</th>\n",
       "      <th>religion</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>family</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>occupation</th>\n",
       "      <th>salary</th>\n",
       "      <th>reference</th>\n",
       "      <th>status</th>\n",
       "      <th>property</th>\n",
       "      <th>relationship</th>\n",
       "      <th>duration</th>\n",
       "      <th>history</th>\n",
       "      <th>status2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "      <td>724.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.332873</td>\n",
       "      <td>0.640884</td>\n",
       "      <td>0.200276</td>\n",
       "      <td>0.711326</td>\n",
       "      <td>0.577348</td>\n",
       "      <td>1.053867</td>\n",
       "      <td>0.885359</td>\n",
       "      <td>1.191989</td>\n",
       "      <td>0.392265</td>\n",
       "      <td>0.784530</td>\n",
       "      <td>0.266575</td>\n",
       "      <td>0.715470</td>\n",
       "      <td>3.675414</td>\n",
       "      <td>2.506906</td>\n",
       "      <td>0.225138</td>\n",
       "      <td>0.733425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.471567</td>\n",
       "      <td>0.480073</td>\n",
       "      <td>0.420696</td>\n",
       "      <td>0.471405</td>\n",
       "      <td>0.634059</td>\n",
       "      <td>0.686663</td>\n",
       "      <td>0.628380</td>\n",
       "      <td>0.742238</td>\n",
       "      <td>0.576867</td>\n",
       "      <td>0.766146</td>\n",
       "      <td>0.442474</td>\n",
       "      <td>0.451502</td>\n",
       "      <td>2.231028</td>\n",
       "      <td>1.027267</td>\n",
       "      <td>0.417962</td>\n",
       "      <td>0.442474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gender     marital    religion   ethnicity      family         age  \\\n",
       "count  724.000000  724.000000  724.000000  724.000000  724.000000  724.000000   \n",
       "mean     0.332873    0.640884    0.200276    0.711326    0.577348    1.053867   \n",
       "std      0.471567    0.480073    0.420696    0.471405    0.634059    0.686663   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "50%      0.000000    1.000000    0.000000    1.000000    0.000000    1.000000   \n",
       "75%      1.000000    1.000000    0.000000    1.000000    1.000000    2.000000   \n",
       "max      1.000000    1.000000    2.000000    2.000000    2.000000    2.000000   \n",
       "\n",
       "        education  occupation      salary   reference      status    property  \\\n",
       "count  724.000000  724.000000  724.000000  724.000000  724.000000  724.000000   \n",
       "mean     0.885359    1.191989    0.392265    0.784530    0.266575    0.715470   \n",
       "std      0.628380    0.742238    0.576867    0.766146    0.442474    0.451502   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      1.000000    1.000000    0.000000    1.000000    0.000000    1.000000   \n",
       "75%      1.000000    2.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "max      3.000000    2.000000    2.000000    3.000000    1.000000    1.000000   \n",
       "\n",
       "       relationship    duration     history     status2  \n",
       "count    724.000000  724.000000  724.000000  724.000000  \n",
       "mean       3.675414    2.506906    0.225138    0.733425  \n",
       "std        2.231028    1.027267    0.417962    0.442474  \n",
       "min        0.000000    0.000000    0.000000    0.000000  \n",
       "25%        2.000000    3.000000    0.000000    0.000000  \n",
       "50%        5.000000    3.000000    0.000000    1.000000  \n",
       "75%        5.000000    3.000000    0.000000    1.000000  \n",
       "max       33.000000    3.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(new.head(20))\n",
    "new.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train and validation splitting \n",
    "(train_X, test_X, train_y, test_y)=train_test_split(X,y,test_size=0.30, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_folds=7\n",
    "scoring=\"accuracy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 91.897695, DEVIATION: (0.017224)\n",
      "knn: 86.744401, DEVIATION: (0.035017)\n",
      "naive_bayes: 91.101326, DEVIATION: (0.027610)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm: 92.294521, DEVIATION: (0.021876)\n",
      "decision_tree: 89.511307, DEVIATION: (0.042767)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_forest: 92.291803, DEVIATION: (0.021900)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models=[]\n",
    "models.append(('LR',LogisticRegression()))\n",
    "models.append(('knn',KNeighborsClassifier()))\n",
    "models.append(('naive_bayes',GaussianNB()))\n",
    "models.append(('svm',SVC()))\n",
    "models.append(('decision_tree',DecisionTreeClassifier()))\n",
    "models.append(('random_forest',RandomForestClassifier()))\n",
    "results=[]\n",
    "names=[]\n",
    "\n",
    "\n",
    "for name, model in models:\n",
    "    kfold= KFold(n_splits=num_folds, random_state=7)\n",
    "    cv_results=cross_val_score(model,train_X,train_y, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "   \n",
    "    res=\"%s: %f, DEVIATION: (%f)\" %(name,cv_results.mean()*100, cv_results.std())\n",
    "    print(res)\n",
    "    \n",
    "    \n",
    "fig=plt.figure(figsize=(12,4))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax=fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "   \n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.4954128440367\n",
      "Mean_Squared_Error: 0.05504587155963303\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.79      0.88        57\n",
      "           1       0.93      1.00      0.96       161\n",
      "\n",
      "    accuracy                           0.94       218\n",
      "   macro avg       0.97      0.89      0.92       218\n",
      "weighted avg       0.95      0.94      0.94       218\n",
      "\n",
      "[[ 45  12]\n",
      " [  0 161]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mod=SVC()\n",
    "mod.fit(train_X,train_y)\n",
    "pred=mod.predict(test_X)\n",
    "Acc=classification_report(test_y,pred)\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "svc_disp=plot_roc_curve(mod,test_X,test_y)\n",
    "print(\"Accuracy:\",accuracy_score(test_y,pred)*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "print(Acc)\n",
    "print(matrix)\n",
    "plot_confusion_matrix(cm = np.array([[ 45,  12],\n",
    " [  0, 161]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "plt.show()\n",
    "plt.figure()\n",
    "plt.plot(test_y,pred)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('predicted')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.4954128440367\n",
      "Mean_Squared_Error: 0.05504587155963303\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.81      0.88        57\n",
      "           1       0.94      0.99      0.96       161\n",
      "\n",
      "    accuracy                           0.94       218\n",
      "   macro avg       0.96      0.90      0.92       218\n",
      "weighted avg       0.95      0.94      0.94       218\n",
      "\n",
      "[[ 46  11]\n",
      " [  1 160]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "\n",
    "mod=LogisticRegression()\n",
    "mod.fit(train_X,train_y)\n",
    "pred=mod.predict(test_X)\n",
    "Acc=classification_report(test_y,pred)\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "svc_disp=plot_roc_curve(mod,test_X,test_y)\n",
    "print(\"Accuracy:\",accuracy_score(test_y,pred)*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "print(Acc)\n",
    "print(matrix)\n",
    "\n",
    "plot_confusion_matrix(cm = np.array([[ 46,  11],\n",
    " [  1,160]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "plt.figure()\n",
    "plt.plot(test_y,pred)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('predicted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 88.53211009174312\n",
      "Mean_Squared_Error: 0.11467889908256881\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.67      0.75        57\n",
      "           1       0.89      0.96      0.93       161\n",
      "\n",
      "    accuracy                           0.89       218\n",
      "   macro avg       0.88      0.81      0.84       218\n",
      "weighted avg       0.88      0.89      0.88       218\n",
      "\n",
      "[[ 38  19]\n",
      " [  6 155]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mod=KNeighborsClassifier()\n",
    "mod.fit(train_X,train_y)\n",
    "pred=mod.predict(test_X)\n",
    "Acc=classification_report(test_y,pred)\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "svc_disp=plot_roc_curve(mod,test_X,test_y)\n",
    "print(\"Accuracy:\",accuracy_score(test_y,pred)*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "print(Acc)\n",
    "print(matrix)\n",
    "plot_confusion_matrix(cm = np.array([[ 38, 19],\n",
    " [  6, 155]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "plt.figure()\n",
    "plt.plot(test_y,pred)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('predicted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 89.44954128440367\n",
      "Mean_Squared_Error: 0.10550458715596331\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.86      0.81        57\n",
      "           1       0.95      0.91      0.93       161\n",
      "\n",
      "    accuracy                           0.89       218\n",
      "   macro avg       0.86      0.88      0.87       218\n",
      "weighted avg       0.90      0.89      0.90       218\n",
      "\n",
      "[[ 49   8]\n",
      " [ 15 146]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mod=DecisionTreeClassifier()\n",
    "mod.fit(train_X,train_y)\n",
    "pred=mod.predict(test_X)\n",
    "Acc=classification_report(test_y,pred)\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "svc_disp=plot_roc_curve(mod,test_X,test_y)\n",
    "print(\"Accuracy:\",accuracy_score(test_y,pred)*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "print(Acc)\n",
    "print(matrix)\n",
    "plot_confusion_matrix(cm = np.array([[ 48,   9],\n",
    " [ 14, 147]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "plt.figure()\n",
    "plt.plot(test_y,pred)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('predicted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.11926605504587\n",
      "Mean_Squared_Error: 0.06880733944954129\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.81      0.86        57\n",
      "           1       0.93      0.98      0.95       161\n",
      "\n",
      "    accuracy                           0.93       218\n",
      "   macro avg       0.93      0.89      0.91       218\n",
      "weighted avg       0.93      0.93      0.93       218\n",
      "\n",
      "[[ 46  11]\n",
      " [  4 157]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mod=RandomForestClassifier()\n",
    "mod.fit(train_X,train_y)\n",
    "pred=mod.predict(test_X)\n",
    "Acc=classification_report(test_y,pred)\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "svc_disp=plot_roc_curve(mod,test_X,test_y)\n",
    "print(\"Accuracy:\",accuracy_score(test_y,pred)*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "print(Acc)\n",
    "print(matrix)\n",
    "plot_confusion_matrix(cm = np.array([[ 46,  11],\n",
    " [  2, 159]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "plt.figure()\n",
    "plt.plot(test_y,pred)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('predicted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.11926605504587\n",
      "Mean_Squared_Error: 0.06880733944954129\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.82      0.86        57\n",
      "           1       0.94      0.97      0.95       161\n",
      "\n",
      "    accuracy                           0.93       218\n",
      "   macro avg       0.92      0.90      0.91       218\n",
      "weighted avg       0.93      0.93      0.93       218\n",
      "\n",
      "[[ 47  10]\n",
      " [  5 156]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAak0lEQVR4nO3debQU9Z338fdHRONCNAoug5CLijrqEaMX1GgUd+CZidGZiUtGxyUxjKLzxIwjZlTiFnF0PO4ij+OWRSYaJ6IiaNxwVBRcQFxQxO2qOYBG3EXg+/zRjXY1zb0NdHV1d31e53C49avqvt/yYn/ut5ZfKSIwM7P8Wi3rAszMLFsOAjOznHMQmJnlnIPAzCznHARmZjm3etYFrKiePXtGW1tb1mWYmTWVp556an5E9Kq0rumCoK2tjWnTpmVdhplZU5H0xvLW+dCQmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlXGpBIOl6SXMlzVzOekm6XNJsSTMk7ZRWLWZmtnxpdgQ3AkM6WT8U6F/8czxwTYq1mJnZcqR2H0FETJbU1skmBwE3R2Ee7CmS1pe0aUS8m0Y9x904lftfmpvGW5uZ1c3ro/9Pzd8zy3MEvYG3SpY7imPLkHS8pGmSps2bN2+lvplDwMyssizvLFaFsYpPyYmIscBYgPb29lV6ks7J+/bnlP23WpW3MDNL3ag7ZnLT48u9GbimsgyCDqBPyfJmwDsZ1WJm1jDaRt5d1++XZRCMB0ZIGgfsAixI6/yAmVkzaD/vPuZ/vDAxtvScQJrhkFoQSLoFGAz0lNQBjAK6A0TEGGACMAyYDXwKHJNWLWZmja78g37zXuvwwM8H1+V7p3nV0OFdrA/gxLS+v5lZM6j0m34aVwZ1xncWm5llICKWCYETBm9R9xCAJnwegZlZs2uELqCUg8DMrE4+XbiIbc+alBi78ZiBDN56o4wqKnAQmJnVQaN1AaUcBGZmKZo99yP2u2RyYuzx0/dh0/XWyqiiZTkIzMxS0shdQCkHgZlZjd3+dAen/H56YuyV84fSvVtjXqjpIDAzq6Fm6QJKOQjMzGrgZ//9LP/zzNuJsUYPgKUcBGZmq6gZu4BSDgIzs5XU7/S7ibKJ8ZspAJZqzDMXZmYNrm1kMgR27LN+U4YAuCMwM1shzX4YqBIHgZlZFSKCfqdPSIydeuDWnLj3lhlVVDsOAjOzLrRiF1DKQWBmthwLPvuSAWffmxj7/U93Y1C/DTKqKB0OAjOzClq9CyjlIDAzKzHz7QX8zRX/mxibdsZ+9Fx3zYwqSp+DwMysKE9dQCkHgZnl3q+nvMGZf5yZGHv1V8Potpoyqqi+HARmlmt57QJKOQjMLJeOu3Eq9780NzGWtwBYykFgZrnjLiDJQWBmueEAqMyTzplZLpSHwOCtezkEitwRmFlLcxfQNQeBmbWkJUuCzX+RnCTunIO246jd2rIpqIE5CMys5bgLWDEOAjNrGe99/AU7n/enxNhdJ+3B9r3Xy6ii5uAgMLOW4C5g5TkIzKypTX39ff5hzOOJsemjDmC9tbpnVFHzSTUIJA0BLgO6AddFxOiy9esBvwH6Fmu5OCJuSLMmM2sd7gJqI7UgkNQNuArYH+gApkoaHxEvlGx2IvBCRPytpF7ALEm/jYiFadVlZs3v6odm8x8TZyXGXrtgGFI+JomrtTQ7gkHA7IiYAyBpHHAQUBoEAfRQ4ae3LvA+sCjFmsysybkLqL00g6A38FbJcgewS9k2VwLjgXeAHsChEbGk/I0kHQ8cD9C3b99UijWzxvZ31zzGU2/8JTHmAKiNNKeYqNSjRdnygcCzwF8BOwJXSvrmMi+KGBsR7RHR3qtXr9pXamYNrW3k3YkQWE0OgVpKsyPoAPqULG9G4Tf/UscAoyMigNmSXgO2AZ5MsS4zaxI+DFQfaXYEU4H+kvpJWgM4jMJhoFJvAvsCSNoY2BqYk2JNZtYkykPgBzv+lUMgJal1BBGxSNIIYBKFy0evj4jnJQ0vrh8DnAvcKOk5CoeSTouI+WnVZGaNz11A/aV6H0FETAAmlI2NKfn6HeCANGsws+bw5eIl9P/3exJjl/xwAIfstFlGFeWH7yw2s8y5C8iWg8DMMvPnBZ+z6wX3J8bu/dmebLVxj4wqyicHgZllwl1A43AQmFldPfzyPP7p+uQV4s+ffSDrrOmPo6z4v7yZ1Y27gMbkIDCz1F048SWueejVxJgniWscDgIzS5W7gMbnIDCzVOxz8UPMmf9JYswB0JgcBGZWc+VdwLfW7s4zZ/ne0UblIDCzmvFhoOaU5qRzZpYj5SHwj7v2dQg0CXcEZrZK3AU0PweBma2Uz79czDZnTkyMXXvkzhy43SYZVWQry0FgZivMXUBrcRCYWdXeeO8T9rroocTY5FP3pu+Ga2dTkNWEg8DMquIuoHU5CMysUxNnvsvw3zydGHvp3CF8o3u3jCqyWnMQmNlyuQvIBweBmS3jrDtmcvPjbyTGHACty0FgZgnuAvLHQWBmAHznnHv5y6dfJsYcAPngIDCzZbqAzXutwwM/H5xNMVZ3DgKzHPNhIANPOmeWSxGxTAicuPcWDoGcckdgljPuAqycg8AsJz75YhHbjZqUGLvp2EHstVWvjCqyRuEgMMsBdwHWGQeBWQub9eePOPDSyYmxx0/fh03XWyujiqwROQjMWpS7AKuWg8Csxdz2VAf/euv0xNgr5w+lezdfJGiVpRoEkoYAlwHdgOsiYnSFbQYDlwLdgfkRsVeaNZm1MncBtjJSCwJJ3YCrgP2BDmCqpPER8ULJNusDVwNDIuJNSRulVY9ZKzv5lmcYP/2dxJgDwKqVZkcwCJgdEXMAJI0DDgJeKNnmCOD2iHgTICLmpliPWUtyF2CrqtMgkHRKZ+sj4pJOVvcG3ipZ7gB2KdtmK6C7pIeAHsBlEXFzhTqOB44H6Nu3b2clmeWGA8BqpauOoEfx762BgcD44vLfApMrvuJrqjAWFb7/zsC+wFrA45KmRMTLiRdFjAXGArS3t5e/h1nulIfATn3X5/YTds+oGmt2nQZBRJwNIOleYKeI+Ki4/Evg1i7euwPoU7K8GfBOhW3mR8QnwCeSJgMDgJcxs2W4C7A0VHs9WV9gYcnyQqCti9dMBfpL6idpDeAwvu4olroD+J6k1SWtTeHQ0YtV1mSWG5UmiRs5dBuHgNVEtSeLfw08Kel/KBzeORhY5lh+qYhYJGkEMInC5aPXR8TzkoYX14+JiBclTQRmAEsoXGI6cyX3xawluQuwtFUVBBFxvqR7gO8Vh46JiGeqeN0EYELZ2Jiy5YuAi6or1yw/Fnz6JQPOuTcxduvw3RjYtkFGFVmrWpHLR9cGPoyIGyT1ktQvIl5LqzCzPHMXYPVUVRBIGgW0U7h66AYKdwH/BvBlCmY1NKPjA75/5aOJsWln7EfPddfMqCLLg2o7goOB7wBPA0TEO5J6dP4SM1sR7gIsK9UGwcKICEkBIGmdFGsyy5VfP/46Z97xfGJszq+GsdpqlW7FMau9aoPg95KuBdaX9BPgWOC69Moyywd3AdYIqr1q6GJJ+wMfUjhPcFZE3JdqZWYt7J+uf5KHX56XGHMAWFaqPVl8YUScBtxXYczMVoC7AGs01R4a2h8o/9AfWmHMzJbDAWCNqqvZR/8ZOAHYQtKMklU9gMfSLMyslZSHwL7bbMR/HT0wo2rMkrrqCH4H3ANcAIwsGf8oIt5PrSqzFuEuwJpBV7OPLgAWSLoMeL9k9tEeknaJiCfqUaRZs1m8JNjiF4nZVTjvB9vzj7t+O6OKzJav2nME1wA7lSx/UmHMzHAXYM2n2iBQRHz1QJiIWCIp1QffmzWbeR99wcDz/5QYu+ukPdi+93oZVWRWnWo/zOdIOplCFwCFE8hz0inJrPm4C7BmVm0QDAcuB86g8DyC+yk+Q9gsz56Y8x6Hjp2SGJs+6gDWW6t7RhWZrbhq7yyeS+EJY2ZW5C7AWkVX9xH8W0T8h6QrWPbB80TEyalVZtagrnpwNhdNmpUYe+2CYUieJM6aU1cdwdLnB09LuxCzZuAuwFpRV/cR3Fn8+6b6lGPWmL5/5f8yo2NBYswBYK2iq0NDd1LhkNBSEfH9mldk1mDKu4DVBHMucAhY6+jq0NDFxb8PATah8HhKgMOB11Oqyawh+DCQ5UVXh4YeBpB0bkTsWbLqTkmTU63MLEPlIXDId3pzyaE7ZlSNWbqqvY+gl6TNI2IOgKR+QK/0yjLLhrsAy6Nqg+BnwEOSlt5N3Ab8NJWKzDKwcNEStjrjnsTYJT8cwCE7bZZRRWb1U+0NZRMl9Qe2KQ69FBFfpFeWWf24C7C8q/ZRlWsDpwDfjoifSOovaeuIuCvd8szS8/YHn7H76AcSY/f+bE+22rhHRhWZZaPaQ0M3AE8BuxWXO4BbAQeBNSV3AWZfqzYItoiIQyUdDhARn8n301sTenDWXI65YWpi7IVzDmTtNTyruuVXtf/6F0pai+LNZZK2AHyOwJqKuwCzyqoNglHARKCPpN8CuwNHp1WUWS1dcM+LXPtw8vEZDgCzr3UZBJJWA75F4e7iXQEB/xIR81OuzWyVuQsw61qXQVB8LOWIiPg9sOz/VZ2QNAS4DOgGXBcRo5ez3UBgCnBoRNy2It/DrJLBFz3I6+99mhhzAJhVtlqV290n6V8l9ZG0wdI/nb1AUjfgKmAosC1wuKRtl7PdhcCkFazdrKK2kXcnQqDnums6BMw6Ue05gmMpnCg+oWx8805eMwiYXTItxTjgIOCFsu1OAv4ADKyyFrOKfBjIbOVU2xFsS+G3++nAs8AVwHZdvKY38FbJckdx7CuSegMHA2M6eyNJx0uaJmnavHnzqizZ8qQ8BI7+bptDwKxK1XYENwEfUniAPRSmob4J+GEnr6l0n0H5sw0uBU6LiMWd3ZYQEWOBsQDt7e3LfT6C5Y+7ALNVV20QbB0RA0qWH5Q0vYvXdAB9SpY3A94p26YdGFcMgZ7AMEmLIuKPVdZlOfX5l4vZ5syJibFrj9yZA7fbJKOKzJpXtUHwjKRdI2IKgKRdgEe7eM1UoH9xyuq3gcOAI0o3iIh+S7+WdCNwl0PAuuIuwKy2qg2CXYCjJL1ZXO4LvCjpOSAiYofyF0TEIkkjKFwN1A24PiKelzS8uL7T8wJm5V6f/wmDL34oMTb51L3pu+Ha2RRk1iKqDYIhK/PmETEBmFA2VjEAIuLolfkelg/uAszSU+3zCN5IuxCzSu6e8S4n/u7pxNis84aw5urdMqrIrPV4ykVrWO4CzOrDQWAN5/Tbn+OWJ99MjDkAzNLjILCG4i7ArP4cBNYQdvjlJD78fFFizAFgVh8OAstceRfQf6N1ue+UvTKqxix/HASWGR8GMmsM1U46Z1YzEbFMCIzYe0uHgFlG3BFYXbkLMGs8DgKri4+/WMT2o5LPHrr52EHsuVWvjCoys6UcBJY6dwFmjc1BYKmZ9eePOPDSyYmxJ36xLxt/8xsZVWRmlTgILBXuAsyah4PAaurWaW9x6m0zEmOzzx/K6t18gZpZo3IQWM24CzBrTg4CW2Un3fIMd05PPoXUAWDWPBwEtkrcBZg1PweBrRQHgFnr8Bk8W2HlITCobQOHgFkTc0dgVXMXYNaaHATWpYig3+kTEmOnD92Gn+61RUYVmVktOQisU+4CzFqfg8AqWvDplww4597E2K3Dd2Ng2wYZVWRmaXEQ2DLcBZjli4PAvvLsWx/wg6seTYw9feb+bLDOGhlVZGb14CAwwF2AWZ45CHLuhkdf4+w7X0iMzfnVMFZbTRlVZGb15iDIMXcBZgYOglw68r+e4JFX5ifGHABm+eUgyBl3AWZWLtUgkDQEuAzoBlwXEaPL1v8IOK24+DHwzxExPc2a8soBYGbLk9qkc5K6AVcBQ4FtgcMlbVu22WvAXhGxA3AuMDatevKsPAT233Zjh4CZfSXNjmAQMDsi5gBIGgccBHx1iUpEPFay/RRgsxTryR13AWZWjTSnoe4NvFWy3FEcW57jgHsqrZB0vKRpkqbNmzevhiW2psVLYpkQOP/g7R0CZlZRmh1BpQvRo+KG0t4UgmCPSusjYizFw0bt7e0V38MK3AWY2YpKMwg6gD4ly5sB75RvJGkH4DpgaES8l2I9LW3uR58z6Pz7E2N3nbQH2/deL6OKzKxZpBkEU4H+kvoBbwOHAUeUbiCpL3A7cGREvJxiLS3NXYCZrYrUgiAiFkkaAUyicPno9RHxvKThxfVjgLOADYGrJQEsioj2tGpqNY+9Op8j/t8TibEZvzyAb36je0YVmVkzSvU+goiYAEwoGxtT8vWPgR+nWUOrchdgZrXiO4ubzBX3v8J/3pc8ivbaBcModlRmZivMQdBE3AWYWRocBE3gb654hJlvf5gYcwCYWa04CBpceRfQvZt45fxhGVVjZq3IQdCgfBjIzOolzSkmbCWVh8Ah3+ntEDCz1LgjaCDuAswsCw6CBrBw0RK2OiM5394lPxzAITt5MlYzS5+DIGPuAswsaw6CjLz9wWfsPvqBxNifTtmLLTdaN6OKzCyvHAQZcBdgZo3EQVBHD740l2NunJoYe+GcA1l7Df8YzCw7/gSqE3cBZtaoHAQpu+CeF7n24TmJMQeAmTUSB0GK3AWYWTNwEKRg99EP8PYHnyXGHABm1qgcBDVW3gVs1GNNnvz3/TKqxsysaw6CGvFhIDNrVp50rgbKQ+Do77Y5BMysabgjWAXuAsysFTgIVsJnCxfz12dNTIyNPXJnDthuk4wqMjNbeQ6CFeQuwMxajYOgSnPmfcw+//lwYuyRf9ubPhusnVFFZma14SCogrsAM2tlDoJO3DXjHUb87pnE2KzzhrDm6t0yqsjMrPYcBMvhLsDM8sJBUGbkH2YwbupbiTEHgJm1MgdBCXcBZpZHDgJg+1GT+PiLRYkxB4CZ5UXug6C8C+i/0brcd8peGVVjZlZ/uQ0CHwYyMytIddI5SUMkzZI0W9LICusl6fLi+hmSdkqzHgAilgmBk/fZ0iFgZrmVWkcgqRtwFbA/0AFMlTQ+Il4o2Wwo0L/4ZxfgmuLfqbn8gdmJZQeAmeVdmoeGBgGzI2IOgKRxwEFAaRAcBNwcEQFMkbS+pE0j4t0U6wLgtz/ehd237Jn2tzEza3hpBkFvoPSC/A6W/W2/0ja9gUQQSDoeOB6gb9++q1yYuwAzs6+lGQSqMBYrsQ0RMRYYC9De3r7M+mr4w9/Mmlman2FpnizuAPqULG8GvLMS25iZWYrSDIKpQH9J/SStARwGjC/bZjxwVPHqoV2BBfU4P2BmZl9L7dBQRCySNAKYBHQDro+I5yUNL64fA0wAhgGzgU+BY9Kqx8zMKkv1hrKImEDhw750bEzJ1wGcmGYNZmbWuVRvKDMzs8bnIDAzyzkHgZlZzjkIzMxyToXztc1D0jzgjZV8eU9gfg3LaQbe53zwPufDquzztyOiV6UVTRcEq0LStIhoz7qOevI+54P3OR/S2mcfGjIzyzkHgZlZzuUtCMZmXUAGvM/54H3Oh1T2OVfnCMzMbFl56wjMzKyMg8DMLOdaMggkDZE0S9JsSSMrrJeky4vrZ0jaKYs6a6mKff5RcV9nSHpM0oAs6qylrva5ZLuBkhZL+vt61peGavZZ0mBJz0p6XtLD9a6x1qr4t72epDslTS/uc1PPYizpeklzJc1czvraf35FREv9oTDl9avA5sAawHRg27JthgH3UHhC2q7AE1nXXYd9/i7wreLXQ/OwzyXbPUBhFty/z7ruOvyc16fwXPC+xeWNsq67Dvv8C+DC4te9gPeBNbKufRX2eU9gJ2DmctbX/POrFTuCQcDsiJgTEQuBccBBZdscBNwcBVOA9SVtWu9Ca6jLfY6IxyLiL8XFKRSeBtfMqvk5A5wE/AGYW8/iUlLNPh8B3B4RbwJERLPvdzX7HEAPSQLWpRAEi+pbZu1ExGQK+7A8Nf/8asUg6A28VbLcURxb0W2ayYruz3EUfqNoZl3us6TewMHAGFpDNT/nrYBvSXpI0lOSjqpbdemoZp+vBP6awmNunwP+JSKW1Ke8TNT88yvVB9NkRBXGyq+RrWabZlL1/kjam0IQ7JFqRemrZp8vBU6LiMWFXxabXjX7vDqwM7AvsBbwuKQpEfFy2sWlpJp9PhB4FtgH2AK4T9IjEfFh2sVlpOafX60YBB1An5LlzSj8prCi2zSTqvZH0g7AdcDQiHivTrWlpZp9bgfGFUOgJzBM0qKI+GN9Sqy5av9tz4+IT4BPJE0GBgDNGgTV7PMxwOgoHECfLek1YBvgyfqUWHc1//xqxUNDU4H+kvpJWgM4DBhfts144Kji2fddgQUR8W69C62hLvdZUl/gduDIJv7tsFSX+xwR/SKiLSLagNuAE5o4BKC6f9t3AN+TtLqktYFdgBfrXGctVbPPb1LogJC0MbA1MKeuVdZXzT+/Wq4jiIhFkkYAkyhccXB9RDwvaXhx/RgKV5AMA2YDn1L4jaJpVbnPZwEbAlcXf0NeFE08c2OV+9xSqtnniHhR0kRgBrAEuC4iKl6G2Ayq/DmfC9wo6TkKh01Oi4imnZ5a0i3AYKCnpA5gFNAd0vv88hQTZmY514qHhszMbAU4CMzMcs5BYGaWcw4CM7OccxCYmeVcy10+arYqJG0I3F9c3ARYDMwrLg8qzndTq++1PnBERFxdq/c0Wxm+fNRsOST9Evg4Ii6uYtvVI2KFJjqT1AbcFRHbr1SBZjXiQ0NmXZD0E0lTi/Pd/6F4xy6SbpR0iaQHgQslbSFpSnHbcyR9XPIepxbHZ0g6uzg8Gtii+OyAizLYNTPAQWBWjdsjYmBEDKAwXcNxJeu2AvaLiJ8DlwGXRcRASuZ+kXQA0J/ClMo7AjtL2hMYCbwaETtGxKl12hezZTgIzLq2vaRHilMY/AjYrmTdrRGxuPj1bsCtxa9/V7LNAcU/zwBPU5gQrX+6JZtVzyeLzbp2I/CDiJgu6WgK88As9UkVrxdwQURcmxgsnCMwy5w7ArOu9QDeldSdQkewPFOAvyt+fVjJ+CTgWEnrQuGBOZI2Aj4qvrdZphwEZl07E3gCuA94qZPt/i9wiqQngU2BBQARcS+FQ0WPFw8v3Qb0KD4T4lFJM32y2LLky0fNaqR4NdFnERGSDgMOj4hKz1E2ayg+R2BWOzsDVxYfov4BcGzG9ZhVxR2BmVnO+RyBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnl3P8Hlhh7Ss+iqP0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod=GaussianNB()\n",
    "mod.fit(train_X,train_y)\n",
    "pred=mod.predict(test_X)\n",
    "Acc=classification_report(test_y,pred)\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "svc_disp=plot_roc_curve(mod,test_X,test_y)\n",
    "print(\"Accuracy:\",accuracy_score(test_y,pred)*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "print(Acc)\n",
    "print(matrix)\n",
    "plot_confusion_matrix(cm = np.array([[ 47,  10],\n",
    " [  5, 156]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "plt.figure()\n",
    "plt.plot(test_y,pred)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('predicted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(4, 3), random_state=1,max_iter=800)\n",
    "clf.fit(train_X,train_y)\n",
    "\n",
    "pred=clf.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf.predict_proba(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.11926605504587\n",
      "Mean_Squared_Error: 0.06880733944954129\n",
      "[[ 47  10]\n",
      " [  5 156]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.82      0.86        57\n",
      "           1       0.94      0.97      0.95       161\n",
      "\n",
      "    accuracy                           0.93       218\n",
      "   macro avg       0.92      0.90      0.91       218\n",
      "weighted avg       0.93      0.93      0.93       218\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_correct = sum(pred == test_y)\n",
    "acc=n_correct / len(pred)\n",
    "print(\"Accuracy:\",acc*100)\n",
    "print(\"Mean_Squared_Error:\",mean_squared_error(test_y,pred))\n",
    "matrix=confusion_matrix(test_y,pred)\n",
    "print(matrix)\n",
    "plot_confusion_matrix(cm = np.array([[ 46, 11],\n",
    " [  1, 160]]), \n",
    "                      normalize    = False,\n",
    "                      target_names = ['0', '1'],\n",
    "                      title        = \"Confusion Matrix\")\n",
    "rep=classification_report(test_y,pred)\n",
    "print(rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x14716ce5448>,\n",
       "  <matplotlib.lines.Line2D at 0x14716d96048>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x14716d8da48>,\n",
       "  <matplotlib.lines.Line2D at 0x14716d6e588>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x14716ce5a08>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x14716d4d288>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x14716d4de88>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig=plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax=fig.add_subplot(111)\n",
    "plt.boxplot(acc)\n"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}