imbalance_class_undersampling_oversampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import make_pipeline\n",
    "from pylab import rcParams\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import f1_score, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_model_report(y_actual, y_predicted):\n",
    "    print(\"Accuracy = \" , accuracy_score(y_actual, y_predicted))\n",
    "    print(\"Precision = \" ,precision_score(y_actual, y_predicted))\n",
    "    print(\"Recall = \" ,recall_score(y_actual, y_predicted))\n",
    "    print(\"F1 Score = \" ,f1_score(y_actual, y_predicted))\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_auc_roc_curve(clf, X_test):\n",
    "    y_pred_proba = clf.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test,  y_pred_proba)\n",
    "    auc = roc_auc_score(Y_test, y_pred_proba)\n",
    "    plt.plot(fpr,tpr,label=\"AUC ROC Curve with Area Under the curve =\"+str(auc))\n",
    "    plt.legend(loc=4)\n",
    "    plt.show()\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns!=target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df.loc[:, df.columns==target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    284315\n",
      "1       492\n",
      "Name: Class, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=target, data=df)\n",
    "print(df[target].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1727485630620034"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100* (492/float(df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.82725143693798"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100* (284315/float(df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    190477\n",
       "1       343\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train[target].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression().fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Test_Pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Actual</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30752</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Actual         0   1\n",
       "Predicted           \n",
       "0          30752  63\n",
       "1             34   0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(pd.Series(Y_Test_Pred, name = 'Predicted'), \n",
    "            pd.Series(Y_test[target], name = 'Actual'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.9987657867577431\n",
      "Precision =  0.6513761467889908\n",
      "Recall =  0.47651006711409394\n",
      "F1 Score =  0.5503875968992248\n"
     ]
    }
   ],
   "source": [
    "generate_model_report(Y_test, Y_Test_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_auc_roc_curve(clf, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# class_weight='balanced'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./class_weight_inp_image.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_classes = list(df[target].unique())\n",
    "unique_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dict = {}\n",
    "for classes in unique_classes:\n",
    "    out_dict[classes] = df.shape[0]/((df.loc[df[target] == classes].shape[0])\n",
    "                                     *len(unique_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.5008652375006595, 1: 289.4380081300813}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(190820, 30) (190820, 1)\n"
     ]
    }
   ],
   "source": [
    "print (X_train.shape, Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(class_weight='balanced').fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.50090037, 278.16326531])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weight.compute_class_weight('balanced', np.unique(Y_train), Y_train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Test_Pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Actual</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29954</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>832</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Actual         0   1\n",
       "Predicted           \n",
       "0          29954  62\n",
       "1            832   1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(pd.Series(Y_Test_Pred, name = 'Predicted'), \n",
    "            pd.Series(Y_test[target], name = 'Actual'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.9754966112334684\n",
      "Precision =  0.05605935696619951\n",
      "Recall =  0.912751677852349\n",
      "F1 Score =  0.10563106796116506\n"
     ]
    }
   ],
   "source": [
    "generate_model_report(Y_test, Y_Test_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_auc_roc_curve(clf, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters : {'class_weight': {0: 0.09736842105263158, 1: 0.9026315789473685}}\n"
     ]
    }
   ],
   "source": [
    "weights = np.linspace(0.05, 0.95, 20)\n",
    "gsc = GridSearchCV(\n",
    "    estimator=LogisticRegression(),\n",
    "    param_grid={\n",
    "        'class_weight': [{0: x, 1: 1.0-x} for x in weights]\n",
    "    },\n",
    "    scoring='f1',\n",
    "    cv=5\n",
    ")\n",
    "\n",
    "grid_result = gsc.fit(X_train, Y_train)\n",
    "print(\"Best parameters : %s\" % grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a570ac8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_out = pd.DataFrame({'score': grid_result.cv_results_['mean_test_score'],\n",
    "                       'weight': weights })\n",
    "data_out.plot(x='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>score</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.703298</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.770741</td>\n",
       "      <td>0.097368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.763401</td>\n",
       "      <td>0.144737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.753784</td>\n",
       "      <td>0.192105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.764725</td>\n",
       "      <td>0.239474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.759793</td>\n",
       "      <td>0.286842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.750428</td>\n",
       "      <td>0.334211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.708962</td>\n",
       "      <td>0.381579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.730055</td>\n",
       "      <td>0.428947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.674311</td>\n",
       "      <td>0.476316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.681596</td>\n",
       "      <td>0.523684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.601097</td>\n",
       "      <td>0.571053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.599889</td>\n",
       "      <td>0.618421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.538431</td>\n",
       "      <td>0.665789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.474159</td>\n",
       "      <td>0.713158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.464639</td>\n",
       "      <td>0.760526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.372763</td>\n",
       "      <td>0.807895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.350569</td>\n",
       "      <td>0.855263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.063391</td>\n",
       "      <td>0.902632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.047618</td>\n",
       "      <td>0.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       score    weight\n",
       "0   0.703298  0.050000\n",
       "1   0.770741  0.097368\n",
       "2   0.763401  0.144737\n",
       "3   0.753784  0.192105\n",
       "4   0.764725  0.239474\n",
       "5   0.759793  0.286842\n",
       "6   0.750428  0.334211\n",
       "7   0.708962  0.381579\n",
       "8   0.730055  0.428947\n",
       "9   0.674311  0.476316\n",
       "10  0.681596  0.523684\n",
       "11  0.601097  0.571053\n",
       "12  0.599889  0.618421\n",
       "13  0.538431  0.665789\n",
       "14  0.474159  0.713158\n",
       "15  0.464639  0.760526\n",
       "16  0.372763  0.807895\n",
       "17  0.350569  0.855263\n",
       "18  0.063391  0.902632\n",
       "19  0.047618  0.950000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(**grid_result.best_params_).fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Test_Pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Actual</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30722</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Actual         0   1\n",
       "Predicted           \n",
       "0          30722  63\n",
       "1             64   0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(pd.Series(Y_Test_Pred, name = 'Predicted'), \n",
    "            pd.Series(Y_test[target], name = 'Actual'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.9991807377616053\n",
      "Precision =  0.7168674698795181\n",
      "Recall =  0.7986577181208053\n",
      "F1 Score =  0.7555555555555555\n"
     ]
    }
   ],
   "source": [
    "generate_model_report(Y_test, Y_Test_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_auc_roc_curve(clf, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 190477, 1: 343}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, count = np.unique(Y_train, return_counts=True)\n",
    "Y_train_dict_value_count = { k:v for (k,v) in zip(unique, count)}\n",
    "Y_train_dict_value_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=12, ratio = 1.0)\n",
    "x_train_res, y_train_res = sm.fit_sample(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 190477, 1: 190477}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, count = np.unique(y_train_res, return_counts=True)\n",
    "y_train_smote_value_count = { k:v for (k,v) in zip(unique, count)}\n",
    "y_train_smote_value_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression().fit(x_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Test_Pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Actual</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30184</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Actual         0   1\n",
       "Predicted           \n",
       "0          30184  63\n",
       "1            602   0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(pd.Series(Y_Test_Pred, name = 'Predicted'), \n",
    "            pd.Series(Y_test[target], name = 'Actual'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.9835189973081384\n",
      "Precision =  0.08083832335329341\n",
      "Recall =  0.9060402684563759\n",
      "F1 Score =  0.148433205057724\n"
     ]
    }
   ],
   "source": [
    "generate_model_report(Y_test, Y_Test_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AABiA0AUAAGAAQhcAAIABCF0AAAAGIHQBAAAYgNAFAABgAEIXAACAAapM6Pr8p9+0dXdOeZcBAACqqCoTujb8nCFJurpNcDlXAgAAqqIqE7okqW2LAPXq2Ki8ywAAAFVQlQpdAAAA5YXQBQAAYABCFwAAgAEIXQAAAAYgdAEAABiA0AUAAGAAQhcAAIABCF0AAAAGIHQBAAAYgNAFAABgAEIXAACAAQhdAAAABiB0AQAAGIDQBQAAYABCFwAAgAEIXQAAAAYgdAEAABiA0AUAAGAAQhcAAIABCF0AAAAGIHQBAAAYgNAFAABgAEIXAACAAQhdAAAABiB0AQAAGIDQBQAAYABCFwAAgAEIXQAAAAYgdAEAABiA0AUAAGAAQhcAAIABCF0AAAAGIHQBAAAYgNAFAABgAEIXAACAAQhdAAAABrB6smjv3r0aO3as8vLyVK9ePSUlJSksLMxtzaxZs5SamiqLxSKr1aoxY8aoZ8+eZVEzAABAheNR6EpISFBsbKyio6O1dOlSTZgwQW+99Zbbmvbt22vIkCGqWbOmtm3bpoEDB2r9+vWqUaNGmRQOAABQkRR7eDEnJ0dpaWmKjIyUJEVGRiotLU25ublu63r27KmaNWtKklq1aiWn06m8vLwyKBkAAKDiKTZ0paenKzg4WBaLRZJksVgUFBSk9PT0C95nyZIlatq0qRo2bFh6lQIAAFRgHh1eLIlvv/1W06dP1xtvvFHi+wYE+JZ2OS7VfApDY2CgX5ntA5eOvngfeuKd6Iv3oSfeyRv7UmzoCgkJUUZGhux2uywWi+x2uzIzMxUSElJk7caNG/XYY49p9uzZat68eYmLyck5LofDWeL7eaIg365qPhZlZR0rk+3j0gUG+tEXL0NPvBN98T70xDuVdV/MZtMlDYqKPbwYEBCg8PBwLV++XJK0fPlyhYeHy9/f323d5s2bNWbMGM2YMUNt2rQpcSEAAACVmUff0zVx4kQlJycrIiJCycnJSkxMlCTFx8dry5YtkqTExESdPn1aEyZMUHR0tKKjo7V9+/ayqxwAAKAC8eicrhYtWmjx4sVFrp83b57r/z/44IPSqwoAAKCS4RvpAQAADEDoAgAAMAChCwAAwACELgAAAAMQugAAAAxA6AIAADAAoQsAAMAAhC4AAAADELoAAAAMQOgCAAAwAKELAADAAIQuAAAAAxC6AAAADEDoAgAAMAChCwAAwACELgAAAAMQugAAAAxA6AIAADAAoQsAAMAAhC4AAAADELoAAAAMQOgCAAAwAKELAADAAIQuAAAAAxC6AAAADEDoAgAAMAChCwAAwACELgAAAAMQugAAAAxA6AIAADAAoQsAAMAAhC4AAAADELoAAAAMQOgCAAAwAKELAADAAIQuAAAAAxC6AAAADEDoAgAAMAChCwAAwACELgAAAAMQugAAAAxA6AIAADAAoQsAAMAAhC4AAAADELoAAAAMYC3vAoC/wm636fDhLNls+eVdSqWSmWmWw+Eo7zLwJ/TF+9AT71RafTGbLapZ01e+vnVlMpn+8vYIXajQDh/OUo0atVS7dsNS+YVAIavVLJuNFxJvQ1+8Dz3xTqXRF6fTKbvdpmPH8nT4cJb8/YP+cl0cXkSFZrPlq3btOgQuAECpMplMslqrqV69AOXnny6VbRK6UOERuAAAZcVkMktylsq2OLwIlLKjR4+qf/9+io6+XQ8++Ijr+tdfn6tTp07pgQcecl33wQfvatu2X/T00xMlSfv3/09z5szUrl07VaNGddWsWUsDB8bp2mt7ue0jPf2g/vGPW9WsWQs5nQ5ZrdX00EOPql27Dq4169Z9rgUL5un06dOy2Wzq2bOXhg8fKR8fH0mSzWbTggWvac2a1bJarXI6HerW7Rrdf/8oWa0+bvtzOp1avHiRPv74I0lOORwOdehwpUaMeFB+fn6l+wSWkkcfHa0xYx5Xo0aNlZq6TG3btlfTppdJklJTl+mrr77Q5MnPebQtu92u22+PVHj4FZo69V9lWbYkacqUiWrdOly33x7juu6VV15W7dq1dO+9w0q0rfP93F2KH3/8XjabTVdd1U1S4c/g0KGDlJLy6V/abkWzf///NGXKRB05ckR169ZVQsIkhYY2LrIuJydbzz//rNLTD8pms+mee4YoIuImSdKkSRO0e/cu19rdu3dq6tQX1KPHdbLb7Xr55Re0YcNXMplMGjgwTlFR/SUV9vKjj95XgwaBkqR27TrokUeekCSdPn1azz6bqO3bf5HFYtHIkQ/pmmt6SpLefPN1rV37icxms5xOadCgOPXpc2Oxt02ZMlHff/+t6tatJ0m6/vo+Gjz4PkmSw+HQ66/P1aeffqLq1X0UFBSs55+f7vYcrFixXFOmTFRS0kuuWl555WX9979rlZ5+UG+9tUjNm1/uWv/VV+v12mtzZLPZVKdOXT31VIJCQxsVe9vFtvnnfo0bl6gmTZpKkp588hEdPHhQZrNJNWvW0pgxj6lly1Yl+XEoMUIXUMo++WSF2rRppzVrVmnEiNGqVq2aR/fLzs7WAw8M04gRozV16guu67777pvzrvf19dWCBf+RJH300fuaNm2S3n77fUnSTz/9qJdeek4vvDBDLVpcrt9//11TpkzUiy8maezY8ZKkZ59N1O+/n9YbbyxUrVq1ZbPZlJLysfLz81Wjhnvomjdvjn766UfNmDFH/v4Bcjgc+uKLz3X06JEShS673S6LxeLx+r/ihRdmuP4/NXWZ6tat5wpdJfXNN1+pQYNAbdr0k3Jzc+TvH3DedUY+vrJks9lktbq/PGzc+INOnTrlCl3e4ny1lqUXXpiq224boIiIm7RqVaqSkiZr+vR/F1k3c+ZLat36Ck2b9qIOHz6s++4bqI4dOyk4uKHGj3/GtW7nzh168MH7ddVV3SVJq1ev0G+/HdCiRR/pyJEjGjLkbnXpcpVCQkIlSf363XzeAP3OOwtVq1YtvfvuEh04sF8jR8Zr0aKPVKtWLd1+e4wrLGVnZyk29g517dpNderUuehtkjRw4GC38H/We+/9R/v3/0/Jye/JarUqNzfH7fbMzAwtXfqh2rRp53Z9z569NGDAPzRyZLzb9UePHtWUKQmaM+cNNW16mVatStULL0zTiy/OvOhtF9vm+fr1/PPPasaMwn49/XSifH19JUlffPG5pk59Rm+88XaRbZQmjw4v7t27VzExMYqIiFBMTIz27dtXZI3dbldiYqL69u2rG264QYsXLy7tWoEKISXlYw0efJ+aN79c69f/1+P7ffjhe7ryys7q1+9m13UNGjTQ3/8eWex9r7yyszIyDrkuv/HGqxo8eIhatCh8x1e9enU9+uhYffrpah06lK4DB/Zr3brP9MQT41WrVm1JktVqVXT0bapVq5bbtk+ePKlFi97W2LHjXGHDbDbruut6u6ZI48Y97lp/7uXU1GV6+OFRmjRpvIYMGaitWzfr3ntj3bY/ZMhAbdz4g6TCd8bx8YM1ZMhAjR79T+3fv6/IY92w4Ws99tiDkqTDh3PVo0cXrV27RpL09ttvau7cWZKkO+6I0p49u5SS8rG2b/9FL7/8guLiYvXddxskSSdOnNCECU9q4MA7df/9Q5STk33B5zcl5WP173+brr22l1auTHV7rOc+vj17dik7O1vjxj2u+Ph7dM89MXrrrTdc61955WUNHXqPBg++Sw8+eL8OHUq/4D4v5vXX5yoh4Sk9+uhoxcberscee1CnTxeec3L8+HGNG/e4Bg4coIcfHqWDB3913a+goECzZk1XfPw9iouL1aRJ43Xy5ElJhVONl156Tg8/PErDhsW57W/37l1auvRDrVyZori4WC1cuMB129y5s3TvvbG6667btGnTT67rv/56ve6/f4iGDBmo4cPv1datW877WLKyMvX0049p8OB/aPDgf2jhwvmSpAceGKYvv/zCte7cyw88MExz587Sgw/eryeffERTpz6j9957x7V2z55dGjAgWk6nUydOHNe0aZMUH3+PBg/+h15++QXZ7fZLeNYLf9527Nimvn0jJEl9+0Zo+/ZtOnz4cJG1u3bt1NVXFwap+vXrq2XLv7l+Ts+VkrJUN97YzzWBXrv2E0VF9ZfZbFb9+vXVs+d1+uyzovf7s08//UT9+98uSWrSpKlatw7XN998JUmuYCEV/j6bTJLT6Sj2totZtOht/fOfD7gC75/fiDz33BSNGvVwkTedHTp0VHBwwyLb++23A6pfP8D1xqh792v07bdfKy8v76K3XWyb5+vXjh1/9Ovcx378+PEzhxHLlkdvDxISEhQbG6vo6GgtXbpUEyZM0FtvveW2ZtmyZdq/f79Wr16tvLw89e/fX927d1fjxkXHrkBltXPnDh09elSdO3dVbm6OUlI+1vXX9/Xovjt2bLvkKcK6dZ+5DglIhYcrRo0a47amTp26Cg1trN27d+n06dNq3Lip693sxezbt0c+PtXUtGnYJdW2ZctPWrDgHTVqVPhvwalTp7Rr105dfnlL7dmzS8ePH1PHjp20adNGrV37iWbNmicfHx99/fWXmjr1Gc2Z84bb9jp0uFKJieNks9n0/fffqm3b9vrhh2/Vu3df/fDDd4qNvcdt/c0336IVK5brrrsGuQ5xpKYu0y+/pOnNN99RcHBDJSVN1vvvv6vhw0cWqf/w4cP68cfvNW7cRDVtGqbnnpui2NhBF3x8Dz00QnFxQ9WxYycVFBTowQfvV3j4FeratZsGDoxzTSmWLVuiOXNmKDFx6iU9r9u3/6J5896Sr6+vHn74Aa1evUK33HKr5s+fp1q1ais5ebHy8vI0ZMjd6t37BkmFobR27dqaN6/w3+/Zs2do4cL5rse9desWvfLKq6pZs6bbvlq0uFzR0be5HaZMTz+oI0eOqG3b9ho+fKRWr16hf/97hubMeUO//farFix4XS++OFO1a/tqz57devTR0frww5Qij+OZZ8are/drNGXK85LkeiEtzp49u/Svf82U1WrVpk0bNX36C7rzzrskSSkpy3TTTZEymUyaOfMldezYSWPHjpfD4VBi4jilpHysW2651W17x44d06hRw8+7r2bNmishYbIyMjLUoEGQa5ppsVjUoEGgMjMzVL9+fbf7tGrVWmvWrFbr1lcoPf2gtm7d7JpWnVVQUKBPPlmpl1+e7bouI+OQGjYMcV0ODm6ozMwM1+VPP12t7777Rv7+AbrvvuFq27a9637BwX/cLyiooTIz/3gjtmTJ+3rvvXeUmZmhJ5+c4DpkWNxtixb9R0uXfqhGjRpr+PAHFBbWTCdOHNeRI3lau3aN1q37TGazWQMHDlbPnr0kFU7emzVroTZt2p73+TyfJk0uU25ujn755WeFh7fR6tUrXI/rYrfVq1fvgtv0pF/Tpk3St98WHk04dzpeVooNXTk5OUpLS9P8+YXvPiIjIzVp0iTl5ubK39/ftS41NVUDBgyQ2WyWv7+/+vbtq5UrV2ro0KFlVz1wji+3pGv95kubHBSnR/sQXdMupNh1KSlL1a/fzTKZTLruuuv10kvPKysrU4GBQRc84f/s9c4Snqd5/PhxxcXFKi/vsOx2u+bOne+6rfhteb6zktb1Z+3adXQFEqnw8MiKFcs0atTDZ14co2QymfTll+u0a9dO15TF6XTq2LGjRbZXo0YNNWvWXD//vFXff/+t4uKGavbs6SooKNC2bb+4ndd2Me3bd3C9O27Tpq1rAvZnq1al6JpreqpWrdpq376jbDa7tm7dorZt2xV5fKdOndLGjT+4BYeTJ09o37596tq1m7755kt9+OFinTp18qLTluJ+ViTpqqu6uQ7tXnFFW/32W+FEa+PG7/XQQ49JkurVq6frruvtus+XX67TiRMn9PnnayVJBQX5uvzylq7be/XqUyRwXUzNmrVcQbZNm3Z65ZWXJRVOI3/77VeNHPnH+Wd2u73IodmTJ09q69bNeumlWa7rLvYieq4bbujnmrJ06HClTp48qV27diosrJnWrFnl+n1Yv36dfvnlZy1aVHjY6PTp0woKCi6yPT8/P9fh+tLwwANjNHPmi4qLi1VwcEN16tRVFov7S+66dZ8rOLihx+cR9e9/uwYPvk9Wq1XfffeNxo59RG+/vdjA3wjdAAARiElEQVQtJF34vneof/87tHv3Lj3zzDh16XKV634Xum3YsBEKCGggs9msFSuW65FHRum995bKZrOpoKBATqdD8+a9qV9/PaARI4aqefPLZTKZtGzZR5oz5/USPV++vr5KTHxWM2a8qPz8fHXr9n/y9fWT1Wq96G1/1dnTLVauTNHs2dPLPHgVW3F6erqCg4PdkmJQUJDS09PdQld6erpCQ/9I8SEhITp06FCR7V1MQIBv8Ysu0U09mkmSAgO986Tfqu5S+5KZaZbVWjgStlhMKqsPMlosJtd+LqTwXesq+fj4aOXKwnf0drtNq1alKC7uPvn7+ysjI91tO0ePHpG/v7+sVrPCw8O1bVtasfsprMcsX18/JScvOnPi7b80ceLTeuONwglGy5Ytz7wrDHfd58iRIzp48Fe1bNlSNptNv/66XydPHr/gtOtsHS1bXq78/HwdPHjgvOdEVatmldPpdK232QrOfNTaLLPZpNq1a7k9psjIKN1332CNGDFKn366SvPmLZDVapbJJEVFRWvYsPuLffxdu16ljRu/U1raVo0d+7QWLQrQp5+uUsuWLVW79h+hwWIxn9m2ya2HZrNJ1atXd122Wq1yOBznfe5XrFiuw4cP6447oiRJJ04c14oVH6tjxw5FHp/ZXBiMFixYKKvV/bBKevpBzZz5kubPX6jQ0EbavHmTJkx46rz7rF+/vo4dO1rkZ6VJkyau57VGjRrn1G9RQUH+Ob8LZreazOY/Hvvjjz+pLl2uKrJPk8kkX99aF/z5M5tNbtuxWMzy8fFxXa5WzSq73X6mPql79/9TQsKk827rrD/qNxfZr9Vqlcn0x5qCgnxXDwtrre12n5tuitSqVSnq1KmzmjVrpsaNG525xannnnvRLfifz7FjxzRiRNFzgiSpWbMWeuaZKQoNDVF2dqZMJqcsFovsdruys7MUGhpSpP7AwAA988wU1+UxY0apefPmbutWrPhYUVHRbtc1bBiirKwMtWtXGOqzsjLUsGHh9oOD//ieqO7d/0/BwcH63//2qlOnzmrYsKGyszMUGBhw5n6H1LVr1yJ1tWr1NwUGBmrTpo3q3bvPRW8LCfnjkF1U1C2aOfMl5eZmKSQkVLVq1dJNN90sq9WssLDL1KpVa+3evUMFBQXKzs7W3XcPkCTl5uZo2rRJGjHiAdcHAs469+e08DF1V/fuhYdkc3Jy9M47C9W0aeHP/MVuu9A2S9KvyMgoPf/8szpx4uh5Q6zZbC6V/OBVJ9Ln5ByXw1E6H8v8s3aX1VdgoJ+yso6VyfZx6f5KXxwOh+sL8Lpd0VDdrih6XL+0FPdFe2vXrlXTppe5vcPbunWzJk9O0MCB96pjx86aP/81HTyYrqCgYB09ekRr1qzWmDGPy2ZzqH//O3TvvXcrNTVVN97YT1Lhia1ffbW+yKEQu90hyXmmJpP++c9Ruuuu2/TZZ2vVs2cvDR48VJMmTVCbNu1dJ9InJT2r66/vq6Cgwufommuu1dSpk/Xkk4Xnddntdn3wwXuKjIxWnTq+rsfr41NDd94Zq2efnaRJk6apfn1/OZ1OffLJSrVp004hIY20a9dOnTx5WiaTSZ9+ukZ+foX3dziccjqdbs9dgwbBCgtrphdeeE6XXdZcgYENZbM51L17T02enKDIyP4KCgqW3W7Xzp071Lp1uP7syiu7atKk8WraNEwmk0WdO3fVa6/NVVRUf7d92e2FPx+1atXS0aPHXLf9ua7z1SlJaWlbdezYMS1dutI1ZcrKytSgQTEaNeqRIverXr2mOnS4UgsWzFdcXOGUPyPjkKxWq44ePS6r1aq6desrP9+mDz5YfMGfqy5drtL06S/qtttiVKdOHWVkHNKGDV/pnnviXM+rw+Fe/9nLnTp11bJlS9WmTXsdOZKnzz//TNdf31c2m0PXXHOt/vOfZIWHt1X16jV08uQJZWZmKiysmZxO923+Wc2atZSZmem63f1n0P1y585X67XXXtWOHTvVvHkLSXIdGjqXj08NtW3bXv/5T7LrsHBeXp7q1aun0NBG+vnnrfq//+upvXv3aOfOHbLbC7df+KWV7rXeeOPNGj48TgcO7Nff/x7luu2aa67VggXz9eijY2WxWJSXl6eTJ0+4Pvn2x+OrrfnzLzzpstkcqlOnni6//G9auXLFmROzV+hvf2stP7+6RZ63I0fyVLu2r6xWq3744Tvt3r1LkyYludZlZmbop582asKEyW737dWrj5Ys+VA9evTSkSNH9N//fq5XXnlVNpvDNTWXpJ07tys9PV2NGjWRzeZQr1599OGH7+uJJ8bpwIH9SktLU0LCFNlsDu3bt1dhYYWDh4MHf9P27dvVtGlYsbedu78NG74+c55ZA9lsDvXpE6Evv/xS0dG36fDhXO3cuUOXXdZcl10Wpj59IlyP54EHhrkO7f/5OTr7+3lWTk62AgIayOFwaPbsmYqOvl3VqlWXzea46G3n26bVaj5vv1q2bCU/v7o6evS4jh076pp2r1+/Tn5+dVSrlt95fwccDofb65TZbLqkQVGxoSskJEQZGRmuT+XY7XZlZmYqJCSkyLqDBw+qffvC48t/nnwBlV1q6jLdeOPf3a5r27a9HA6HfvrpR3Xs2EmjRz+ssWMfkcNR+MJx++0xrqlDgwaBeuWVVzVnzgzNmzdHNWvWOPOVEYOL3Xf16tUVH3+/5s+fp549e6lTpy566KFHNXnyBP3+++8qKLCpZ89r3c5ZGjcuUW+88aqGDBnkmlZ163aN64Tecw0fPlLvvvu265wXp9Op9u2vVPfuPdSoUWN16XKV7rknRiEhoQoLC7voSemSdNNNUZo0aYLbp7g6duykYcNGaOzYh8/8w1mg66/ve97Q1aZNWx05kqcuXbpKkjp37qq5c2epc+eu593fLbfcplmzXtY77yzUiBEPFvt8npWaukx9+0a4HdYLDAzS3/7W6oInN0+YMEkzZryoe+4p/MRXrVq19eSTE9SixeW6/vq+GjgwRsHBwbryys7atGnjebfRtWs39e9/m0aNGi6TySSz2ayHHnpMYWHNig3/cXFDNXVqogYOHKCGDUPczhMcODBOr78+V0OH3iOz2SzJpCFD4l0vuhdz7bXX6+mnH1NcXKz69LlRffveeMG1TZo01YQJkzRt2iT9/vvvstkK1K5dhyKh6+zz9eKLSRo06E6ZzRbdcEOEBg6M0913D9b48WP1zTdfqUWLy4s9BNewYUOFhTXXxo0/aOLEZ13XP/jgI5o9e4bi4u6SyWRStWo+Gj36kSKhy1OPPfaUJk9O0Pz5r8nPz08TJ/4xzXv00dEaOvSfat36CqWl/azp01+Q2WxW3br1lJT0omrUqOFau2LFcl1zTU/VqVPXbfsRETcpLW2r/vGPwjdacXFDXVO6uXNnafv2X2Q2W1StWjWNH5+ogIAGkqTY2Hs0ZcpExcQUnoT/+ONPuT4k8/rrc7V37x5ZrdYzP0uPunp+sdsmT56ow4dzZDKZVbt2bU2b9qLrkN7w4SP17LOJev/9RTKZTBo+fKQuuyys2Ofv5Zef13//+5lyc3P00EMjVadOXSUnvyep8FPSW7ZsUkFBga66qpv++c8HXPe72G3n2+aiRe+ft1/jxydKkk6fPqXx48fq9OlTMpstqlOnjpKSXirz7300OZ3Fn7ExaNAg3XHHHa4T6d9//30tXLjQbc2HH36olJQUzZs3z3Ui/dtvv60mTZp4XExZTrqkvzZRQdn5K305dOh/atjw0r4GABfGnzbxTvTF+9AT71Taffnza82lTro8+nzkxIkTlZycrIiICCUnJysxsTApxsfHa8uWwo8BR0dHq3Hjxrrxxht15513auTIkSUKXAAAAJWZR5MuozDpqpqYdHkf3r17J/rifeiJd6rQky4AAAD8NYQuAAAAAxC6UOF50RFyAEAlU/hnkUrnU42ELlRoVquPTpw4SvACAJSqwu/gK1BeXrZ8fGoUfwcPeNWXowIlVb9+oA4fztLx4579vTZ4xmw2y+Hg5GBvQ1+8Dz3xTqXVF7PZopo1feXrW7f4xR4gdKFCs1isatCg+L+JiJLhk77eib54H3rinby1LxxeBAAAMAChCwAAwABedXjRbC7bv3lk1D5QcvTF+9AT70RfvA898U5l2ZdL3bZXfSM9AABAZcXhRQAAAAMQugAAAAxA6AIAADAAoQsAAMAAhC4AAAADELoAAAAMQOgCAAAwAKELAADAAIQuAAAAA1TK0LV3717FxMQoIiJCMTEx2rdvX5E1drtdiYmJ6tu3r2644QYtXrzY+EKrEE96MmvWLN1888265ZZbdNttt+mLL74wvtAqxJOenLVnzx516NBBSUlJxhVYRXnal9TUVEVFRSkyMlJRUVHKzs42ttAqxJOe5OTkaNiwYYqKilK/fv00ceJE2Ww244utIpKSktS7d2+1atVKO3bsOO8ar3ydd1ZCgwYNci5ZssTpdDqdS5YscQ4aNKjImo8++sg5ZMgQp91ud+bk5Dh79uzpPHDggNGlVhme9GTdunXOkydPOp1Op/OXX35xdu7c2Xnq1ClD66xKPOmJ0+l02mw258CBA50PP/ywc9q0aUaWWCV50pfNmzc7//73vzszMzOdTqfTefToUefp06cNrbMq8aQnkydPdv1+5OfnO++44w5nSkqKoXVWJd99953z4MGDzuuvv965ffv2867xxtf5SjfpysnJUVpamiIjIyVJkZGRSktLU25urtu61NRUDRgwQGazWf7+/urbt69WrlxZHiVXep72pGfPnqpZs6YkqVWrVnI6ncrLyzO83qrA055I0quvvqpevXopLCzM4CqrHk/7smDBAg0ZMkSBgYGSJD8/P1WvXt3weqsCT3tiMpl04sQJORwO5efnq6CgQMHBweVRcpXQpUsXhYSEXHSNN77OV7rQlZ6eruDgYFksFkmSxWJRUFCQ0tPTi6wLDQ11XQ4JCdGhQ4cMrbWq8LQn51qyZImaNm2qhg0bGlVmleJpT7Zt26b169crLi6uHKqsejzty+7du3XgwAHdfffduvXWWzV79mw5nc7yKLnS87QnI0aM0N69e9WjRw/Xf507dy6PknGGN77OV7rQhYrv22+/1fTp0/Wvf/2rvEup0goKCjR+/HglJia6XnDgHex2u7Zv36758+dr4cKFWrdunZYuXVreZVVpK1euVKtWrbR+/XqtW7dO33//fblPVeB9Kl3oCgkJUUZGhux2u6TCf5wyMzOLjCFDQkJ08OBB1+X09HSmKmXE055I0saNG/XYY49p1qxZat68udGlVhme9CQrK0v79+/XsGHD1Lt3b7355pt67733NH78+PIqu9Lz9HclNDRU/fr1k4+Pj3x9fdWnTx9t3ry5PEqu9DztSXJysm655RaZzWb5+fmpd+/e2rBhQ3mUjDO88XW+0oWugIAAhYeHa/ny5ZKk5cuXKzw8XP7+/m7r+vXrp8WLF8vhcCg3N1dr1qxRREREeZRc6Xnak82bN2vMmDGaMWOG2rRpUx6lVhme9CQ0NFQbNmzQ2rVrtXbtWg0ePFh33nmnJk2aVF5lV3qe/q5ERkZq/fr1cjqdKigo0DfffKPWrVuXR8mVnqc9ady4sdatWydJys/P19dff62WLVsaXi/+4I2v8yZnJTwRYPfu3Ro7dqyOHj2qOnXqKCkpSc2bN1d8fLxGjx6tdu3ayW6365lnntGXX34pSYqPj1dMTEw5V155edKT22+/Xb/99pvbyafPPfecWrVqVY6VV16e9ORcM2fO1MmTJ/XEE0+UU8VVgyd9cTgcSkpK0rp162Q2m9WjRw898cQTMpsr3ftor+BJT/bv36+EhARlZ2fLbrfr6quv1tNPPy2r1Vre5VdKkydP1urVq5Wdna369eurXr16SklJ8frX+UoZugAAALwNb4sAAAAMQOgCAAAwAKELAADAAIQuAAAAAxC6AAAADEDoAgAAMAChCwAAwACELgAAAAP8PwtL492J6cxnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_auc_roc_curve(clf, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.005     , 0.03222222, 0.05944444, 0.08666667, 0.11388889,\n",
       "       0.14111111, 0.16833333, 0.19555556, 0.22277778, 0.25      ])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.linspace(0.005, 0.25, 10)\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters : {'smote__ratio': 0.005}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a8499b0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe = make_pipeline(\n",
    "    SMOTE(),\n",
    "    LogisticRegression()\n",
    ")\n",
    "\n",
    "weights = np.linspace(0.005, 0.25, 10)\n",
    "\n",
    "gsc = GridSearchCV(\n",
    "    estimator=pipe,\n",
    "    param_grid={\n",
    "        'smote__ratio': weights\n",
    "    },\n",
    "    scoring='f1',\n",
    "    cv=3\n",
    ")\n",
    "grid_result = gsc.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Best parameters : %s\" % grid_result.best_params_)\n",
    "weight_f1_score_df = pd.DataFrame({ 'score': grid_result.cv_results_['mean_test_score'],\n",
    "                                   'weight': weights })\n",
    "weight_f1_score_df.plot(x='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(\n",
    "    SMOTE(ratio=0.005),\n",
    "    LogisticRegression()\n",
    ")\n",
    "\n",
    "pipe.fit(X_train, Y_train)\n",
    "\n",
    "Y_Test_Pred = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Actual</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30730</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Actual         0   1\n",
       "Predicted           \n",
       "0          30730  63\n",
       "1             56   0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(pd.Series(Y_Test_Pred, name = 'Predicted'), \n",
    "            pd.Series(Y_test[target], name = 'Actual'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.9991275389149563\n",
      "Precision =  0.7161290322580646\n",
      "Recall =  0.7449664429530202\n",
      "F1 Score =  0.7302631578947368\n"
     ]
    }
   ],
   "source": [
    "generate_model_report(Y_test, Y_Test_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_auc_roc_curve(clf, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UNDERSAMPLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n"
     ]
    }
   ],
   "source": [
    "minority_class_len = len(df[df[target] == 1])\n",
    "print(minority_class_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
      "                 8,      9,\n",
      "            ...\n",
      "            284797, 284798, 284799, 284800, 284801, 284802, 284803, 284804,\n",
      "            284805, 284806],\n",
      "           dtype='int64', length=284315)\n"
     ]
    }
   ],
   "source": [
    "majority_class_indices = df[df[target] == 0].index\n",
    "print(majority_class_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n"
     ]
    }
   ],
   "source": [
    "random_majority_indices = np.random.choice(majority_class_indices,\n",
    "                                           minority_class_len, \n",
    "                                           replace=False)\n",
    "print(len(random_majority_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([   541,    623,   4920,   6108,   6329,   6331,   6334,   6336,\n",
      "              6338,   6427,\n",
      "            ...\n",
      "            274382, 274475, 275992, 276071, 276864, 279863, 280143, 280149,\n",
      "            281144, 281674],\n",
      "           dtype='int64', length=492)\n"
     ]
    }
   ],
   "source": [
    "minority_class_indices = df[df[target] == 1].index\n",
    "print(minority_class_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "under_sample_indices = np.concatenate([minority_class_indices,random_majority_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "under_sample = df.loc[under_sample_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b4597f0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=target, data=under_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = under_sample.loc[:, df.columns!=target]\n",
    "Y = under_sample.loc[:, df.columns==target]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n",
    "clf = LogisticRegression().fit(X_train, Y_train)\n",
    "Y_Test_Pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.916923076923077\n",
      "Precision =  0.9419354838709677\n",
      "Recall =  0.8902439024390244\n",
      "F1 Score =  0.9153605015673981\n"
     ]
    }
   ],
   "source": [
    "generate_model_report(Y_test, Y_Test_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_auc_roc_curve(clf, X_test)"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}