projet_supervisé.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projet Apprentissage Supervisé\n",
    "\n",
    "\n",
    "<b>Réaslié par :</b>\n",
    "<li>FHIYIL Soufiane</li>\n",
    "<li>MOUHDA Mohammed Reda</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "columns = ['Time','Amount']\n",
    "sns.pairplot(data[columns],size = 2 ,kind ='scatter',diag_kind='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot data on the first principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy.random import rand\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.scatter(data.iloc[:, 2], data.iloc[:,3],\n",
    "            c=data.Class, edgecolor='none', alpha=0.5, s=50, cmap=plt.cm.get_cmap('jet'))\n",
    "\n",
    "\n",
    "plt.xlabel('component 1')\n",
    "plt.ylabel('component 2')\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a15221048>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxlLwAMOC7X0M9ta2w8YaASADiNETIAA8qnw9iWLVvOuh0AYo1ABmBAamw8oe9///uMjAGIC5yyBNAvuN052rfvnR5//9lGxHo6SjZ27FWqrq7t8b4A4IsQyAD0Cz0NSGdCV2PjCd3xwL9rY+l3u20DABMIZAAGJJdrqMbddK9crqmmSxnweju6eS6McMLKbOFwOGy6iJ44cqRZoZAlSu01bnAcH6L1odBTfCiYw1WW1nW+0EUP499A6J/dbtPw4am9/jkCWRTxgT5wDIQ3lYHgzClLWAfHXvzhs6+7SAMZpyyjKNL/QfhQAICeaWw8wQhnnOnNZx+B+txY9gKIwLneOAb6GwoQC42NJ9TYeEIF/29b178Bq2OE7Czu8VWrpbUjpvu844F/j9m+UgYlaq3HHbP99VdnPgQY4QSAnmGE89xiGsheeuklPfroowoGg7rttts0c+bMWO6+x1paO2L6ARvrSf2xDH9Ab8X6DyL+GAJiiz9mzy5mgezQoUNavXq1/H6/kpKSNGPGDOXk5GjMmDGxKgEDBCOc1hbLP4j4Yyi6OPasjf6ZFbNA9tprr+lb3/qW0tPTJUk33nijXn75Zc2dOzdWJWCAYITT2pLH1ejuf3/ZdBl9InlcqqT+OyLAsWdt9M+smAWyxsZGOZ3Orucul0v//d//3eOfj+QS0kj15w8E6fSHgtM5xXQZfYb+WVvbW7l66aH++ftN+udKOW9PM11Gn+HYszb6Z1bMAtnZljuz2Ww9/vlYrkPW9lZuv/8roT8vRNv2Vq7pEvpUyqDEft0/STH7/Uwsytyfe8d7p7XRv+iI+3XILr30Uu3du7freWNjo1wuV6x232vxPrR5IVIG9e+La2M9SZSJqdHXX4+//n7sSf23dxL9s7p471/MVuo/dOiQbr75Zj3//PMaPHiwZsyYoWXLlunrX/96j37eCiv1R4oPdGujf9ZF76yN/llbf+2fJUbI7r33Xv3gBz9QMBjU9OnTexzGgHj06bV0XP96+pG1dADg/HjvPLuYjt9NmjRJkyZNiuUugT5xrtt/uFxDeWMB+hgf6NbFe+e5ceskAIBlnO8DHbCy+J7hBsSQ252jffveueDX6ekHw9ixV0V8Q3pcGEZYgOjhvTM6Yjap/0L1x0n93M/Lus73xkEP4xu9iz/R+kDvqf76gW4FA+H4i/tJ/eiO8+gAcFpvwtFA+EDHwEQgA9AvRGOUZaCfMgFgDoEsijiPDpjT02OBERYA8YhAFkUMuwMAgEiw7AUAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABACzjqqu+2qvtgFUQyAAAlrFnz+8+F76uuuqr2rPnd4YqAqKDZS8AAJZyJnw5nWkKBE4argaIDkbIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGFRD2RvvvmmioqKNGXKFN16663av3+/JOnEiRO66667lJeXp5kzZyoQCER71wAAAJYU9UA2f/58rVixQpWVlZo0aZKWL18uSfL5fMrOzlZVVZWKi4u1YsWKaO8aAADAkqIayNrb2zVv3jyNHTtWkpSVlaWGhgZJ0u7duzVp0iRJUkFBgaqrqxUMBqO5ewAAAEtKjOaLJSUlacqUKZKkUCikdevWacKECZKkxsZGOZ3O0ztNTFRqaqqOHj2qSy+9tEevPXx4ajRLjWtOZ5rpEnAB6J910TvroWf9x0DvZcSBrKqqSitXruy2bfTo0dq0aZPa29tVWlqqjo4OzZkz55yvYbf3fIDuyJFmhULhSMu1lEDgpOkScAHon3XRO2txOtPoWT/SX3ppt9siGkSKOJDl5eUpLy/vc9tbWlr0T//0T0pPT9ejjz4qh8MhSXK5XDp8+LBGjBihjo4ONTc3Kz09PdLdAwAAi8rMHKtXX31FN9xwo+rr95kuJy70yaT+UaNGac2aNUpKSuraPn78eG3btk2StGPHDmVnZ3eFNQAAMHDU1+/TqFGjCGOfEtU5ZG+//bZ27dqlMWPGaOrUqZJOj4w98cQTmjdvnkpLS5Wfn6+0tDSVl5dHc9cAAACWFdVA9tWvflV1dXVn/Vp6eroee+yxaO4OAACgX2ClfgAAAMMIZAAAAIYRyAAAQExlZo7VBx98oMzMsaZLiRtRnUMGAABwPna7vesqyzPPQ6GQ4arMY4QMAADEzDe/ma2kpGRJUlJSsr75zWzDFcUHAhkAAIgJm82mvXvf0C23/F81NTXpllv+r/bufUM2m810acYRyAAAQEwMGzZMkrRp0walp6dr06YN3bYPZAQyAAPSsGHp3R4B9L2mpibZbDYlJCRIkhISEmSz2dTU1GS4MvMIZAAGpOPHm7o9Auh7NptNt956hxoajikcDquh4ZhuvfUOTlmKQAYAAGIkHA5r165XVVNTrWAwqJqaau3a9arC4bDp0owjkAEYkFJTU2W325Wammq6FGDASEpKVk7OtfJ652vQoEHyeucrJ+farqsuBzICGYABJyEhQc3NzQqFQmpubu6azwKgb82adateeGGrbr55lk6ePKmbb56lF17YqlmzbjVdmnG2sEXGCY8caVYoZIlSe8TlGnrOrzU2nohhJYgE/bMul2uoHA6HgsFg17Yzz+mdtTidaQoETpouA700fvy39M47b3c9v+qqr2rPnt8ZrCi67Habhg/v/cg7I2RABDIyvtSr7YgvwWBQ6enpstlsSk9P7xbOAPSd++4rUV3dPjmdLtlsNjmdLtXV7dN995WYLs04AhkQgQMH9vdqO+JPOHx6grE1zhEA/cNTT23UsGHD9PjjG9XW1qbHHz/9/KmnNpouzTgCGXAB7PaEbo+whiuvHK0TJ45Lkk6cOK4rrxxtuCJgYOjo6NDMmT/oNql/5swfqKOjw3RpxhHIgAsQCnV2e4Q1vP/+/8jpdMput8vpdOr99//HdEnAgPHLXz6lsrKfq7W1VWVlP9cvf/mU6ZLiQqLpAgY6m82mcDjc9Qigb5051tra2hUKhdTW1t61HUDfstvtOn78uObMuUOHDwd0ySVOHT9+XHY740P8FzDsTAgjjAGxceZY++xK/RyDQN8LhUKSpKNHjygcDuvo0SPdtg9kBDIAABATSUnJKir6nr7ylUzZ7XZ95SuZKir6HgvDikAGYIBasqRMLS0tWrKkzHQpwIARDLartvZ33eaQ1db+TsFgu+nSjGMOGYABx+FwaNmyxVq82KvERMfnFooF0Deyssbqb/7my5oxo0jt7W1KSkrW9dffoJSUIaZLM44RMgADzmcvseeSeyA2vv3t72jnzpe1YMFitbS0aMGCxdq582V9+9vfMV2acQQyAANOOBzW6NFf1gcffKDRo7/MhH4gRn772//QxIk3acWKJUpJSdGKFUs0ceJN+u1v/8N0acYRyIAInGuJBJZOsI76+n0aNWqU6uv3mS4FGDDq6vbprbf+qC1btqq9vV1btmzVW2/9UXV1HIcEMsPOfIDzQW4tZ0ZUUlJSuj0y0mINQ4cO0+WXXyGbzabLL79CQ4cOM10SMCA4HEmaPXuOcnPdcjgcys11a/bsOXI4kkyXZhyBzDDWIbOutLQ0tbS0SJJaWlqUlpZmuCL0RGJiYtecsTN/CHV0dCgxkWucgL4WDLZrw4bHVVNTrWAwqJqaam3Y8DhXWYqrLIGInTx5Una7XaFQSHa7XSdPnjRdEnqgo6NDnZ2dOnCgVaFQSAcO7FcoFOKPIiAGsrLGKi+vQF7vfE2fPlmZmVkqKvqeqqq2my7NOEbIDPvsKS9Yyw033KhAIKAbbrjRdCnooYSERA0ZMkQZGV+SzWZTRsaXNGTIECUk8Pcp0Nc8nhJt3VrRbR2yrVsr5PGUmC7NON6BDPv0KS9Yz69/vVNOp1MJCQmmS0EPdXZ2aMiQ4Vqz5hEVFEzU9u07NWfOnRyDQAwUFhZLUrcRMq93Ydf2gcwWtsg4/ZEjzQqFLFFqj7hcQ8/5tcbGEzGsBJFwuYZ+bjHRM8/pX3xzuYbqJz/5f9q5s0r19XXKzMzSxIl5evjhf6V3FuN0pikQYKqAVfXX/tntNg0fntr7n+uDWtAL2dn/oAMHDig7+x9Ml4Je+uzK7qz0bg0ZGRnasuWZbqdMtmx5RhkZGaZLAwYEv79CbneOEhIS5HbnyO+vMF1SXCCQGbZ37xvKyMjQ3r1vmC4FGBAWLVqmUKhT8+b9WMnJyZo378cKhTq1aNEy06UB/Z7fX6GysmXd/iAqK1tGKBOBDIjY8OGXnPc54lNhYbGWL39QKSkpstlsSklJ0fLlDzKHBYgBn69cPt+6buuQ+Xzr5POVmy7NOAKZYTfemKdAIKAbb8wzXQp66ciRw1qypEwtLS1asqRMR44cNl0SeqiwsFjV1bXq7OxUdXUtYQyIkfr6OuXkXNttW07OtaqvrzNUUfzgKkvDXnmlSk6n03QZiNCqVSt0//0LNGTIENOlAEDcy8zMUm3t68rNdXdtq619XZmZWQarig+MkAEXoKWlReFwmCUTAKAHPJ4SeTxzu63U7/HMZR0yEciM4ypLazq9oGhGt3uRfvo5AODzCguL5fUulNc7X4MGDZLXO591yP4X65AZwjpk1namf+np6Tpx4oSGDh2qpqYmSfTPSvrrOkgDBf2ztv7aP9Yhs6DBgwcrMdEhSUpMdGjw4MGGK0JPJSQkKjExUU1NTQqFQmpqalJiYiK33wEARIRAZlBCQoKee+4Ftbe367nnXuD2OxbS2dmhjo6ObttO37S64xw/AQDAufHnvCE2m00tLS2aM+cOHT4c0CWXONXS0sIcJIux2+0KhUJdjwAARIIRMkOyssbqppvydfz4cYXDYR0/flw33ZSvrKyxpktDLyxevFwtLS1avHi56VIAABZGIDPE4ynRn/70lrZs2ar29nZt2bJVf/rTW1z6ayFDhgzR+vWPKTU1VevXP8ZaZACAiPVZIHv77bc1bty4ruft7e2aP3++8vLyNG3aNL377rt9tWtLKCws1g03TNSMGUVKSkrSjBlFuuGGiVz6ayHt7UE1NDQoHA6roaFB7e3cXBwAEJk+CWSnTp3S0qVLFQx+8gG1efNmDR48WFVVVfJ6vSotLe2LXVuG31+hV1/d2W2E7NVXd3KDVYtISkpSR0dQyclJstlsSk4+/TwpKcl0aQAQ1/z+CrndOUpISJDbncPn3v/qk0D2wAMP6Lbbbuu2bffu3Zo8ebIk6ZprrtGxY8d04MCBvti9Jfh85SoqKu62OF5RUTE3WLWISy65RAkJCd1W6k9ISNAll3CDcQA4F7+/QmVly1RW9nO1traqrOznKitbRihTH1xluWvXLrW2tuqmm27qtr2xsbHbPRudTqcOHjyojIyMHr1uJIusxbO6un1qa2vVhg0blJubq5qaGt155516//335XSmmS4PX6ChoUGDBg3SqVOnurYlJSWpoaGB/lkM/bI2+mcta9f+qzZt2qjrrrtOkjRtWr7S04fonnvu0Zw5dxiuzqyIA1lVVZVWrlzZbdvo0aPV3NysTZs29eg17PaeD9D1t5X6HY4k3XbbDzVuXLYcDofGjcvWbbf9UCtWLOmXKxf3NzabTa2tbVqypEwlJfNUXr5G99//M9lsNvpnIf11pfCBgv5ZzzvvvKOsrKsVCJzs6l9W1tV65513+k0vI12pP+JAlpeXp7y8vG7bKioq9Pjjj2vmzJld26ZMmaJnnnlGLpdLgUBAo0aNkiQFAgG5XK5Id295wWC71qx5SOvXP6b9+z/Sl750mVpaWhQMtpsuDT0QCoWUnp6ur33t63I4HPra176uYcM+uX0SAODzMjOzVFv7unJz3V3bamtfV2ZmlsGq4kNUT1kWFxeruPiTqwSzsrJUWVkpSRo/frwqKyuVnZ2tvXv3Kjk5uceqq5YbAAAW3klEQVSnK/ujkSNHqrm5WZJ05naiHR1BjRw50mRZ6IV/+IdrNWNGkdrb25SUlKx//MfvaufOKtNlAUDc8nhKdNddt2nw4CH66KMPddlll+vUqY+1fPmDpkszLmbrkM2aNUvt7e3Kz8/XihUrtGrVqljtOm4NHjxYa9Y8ora2Nq1Z8wj3srQQu92uX//6FS1YsFgtLS1asGCxfv3rV3p1Gh4ABqL/HYPoujNNuP/MRrogtnDYGv8p+tscshEj0rV27WNau3a16uvrlJmZpXvuuVf33PMjHTzIaa94l5l5hY4fPy6n06nDhw/rkksuUSAQ0LBhw1Rf/1fT5aGHmINkbfTPetzuHJWV/Vy5ue6u/tXUVMvrna/q6lrT5UVFpHPI+HPekMzMLI0cmaHq6lp1dnaqurpWI0dmcB7dIk6cOKHbbrtDTU3HFQqF1NR0XLfddodOnDhhujQAiFv19XXKybm227acnGtVX19nqKL4QSAzxOMpkcczVzU11QoGg6qpqZbHM5dbJ1lEZmaWJk2apo8+CigcDuujjwKaNGkagRoAzuPMpP5PY1L/aQQyQwoLi+X1Luy2MKzXu5BbJ1kEgRoAeo/3znNjDlkcYB6ENfn9FfL5yrvmAHo8JQRqi+HYszb6Z039/b0z5uuQAQAA9FZhYbEKC4sJ1J9BIAMicOZ+bD7fOhUUTNT27Tvl8cyVpH71lx4AIDaYQwZEgJvDAwCiiREyIAJ1dfv08ccfy+f7xadGyO7Whx+yBhkAoPcYIQMi4HAk6c475yg31y2Hw6HcXLfuvHOOHI4k06UBACyIQAZEIBhs1/r1j3e7dHv9+se5OTwAICIEMoP8/gq53TlKSEiQ250jv7/CdEnooayssZo+/Xvd5pBNn/49ZWWNNV0aAMCCCGSGnLlKr6zs52ptbVVZ2c9VVraMUGYRHk+Jnn76SbW0tEiSWlpa9PTTT7K4IQAgIgQyQ3y+cvl867rNQfL51nGVnoXYbKcfz6ytfOY5AAC9RSAzhBusWpvPV65Zs25XSkqKbDabUlJSNGvW7QRqi2C6AIB4QyAzhBusWltd3T49//xz3U45P//8c6qr22e6NHwBpgsAiEcEMkO4waq1ORxJmj27+7IXs2ez7IUVMF0AQDxiYVhDztxe5/TVeZOVmZklr3cht92xiGCwXRs2PK6vfe3rKiiYqJqaam3YwLIXVsB0AQDxiEBmEDdYta6srLHKyyvoFqiLir6nqqrtpkvDFzgzXSA31921jekCAEzjlCUQAY+nRFu3VnSbh7R1awWnnC2A6QIA4hEjZEAECguL9fvf12rGjCK1t7cpKSlZs2bdyilnC2C6AIB4xAgZEAG/v0KvvrpTW7ZsVXt7u7Zs2apXX93JlXoWUVhYrOrqWnV2dqq6upYwBsA4AhkQAa7UAwBEE4EMiABX6gEAoolABkSAhX0BANFEIAMiwJV6AIBo4ipLIAJcqQcAiCYCGRAhFvYFAEQLpywBAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIDPL7K+R25yghIUFud478/grTJQEAAANYqd8Qv79CZWXL5POtU0HBRG3fvlMez1xJ4vY7AAAMMIyQGeLzlcvnW6fcXLccDodyc93y+dbJ5ys3XRoAAIgxApkh9fV1ysm5ttu2nJxrVV9fZ6giAABgCoHMkMzMLNXWvt5tW23t68rMzDJUEQAAMIVAZojHUyKPZ65qaqoVDAZVU1Mtj2euPJ4S06UBAIAYY1K/IWcm7nu98zV9+mRlZmbJ613IhH4AAAYgAplBhYXFKiwsltOZpkDgpOlyAACAIZyyBAAAMCzqgayxsVF33XWXpk6dqhkzZuijjz6SJJ04cUJ33XWX8vLyNHPmTAUCgWjvGgAAwJKiHsj+5V/+Rdddd522bdumKVOmqLz89LpaPp9P2dnZqqqqUnFxsVasWBHtXQMAAFhSVAPZ0aNHtW/fPs2YMUOSVFRUJI/HI0navXu3Jk2aJEkqKChQdfXpqwsBq+LWVwCAaInqpP4PP/xQGRkZKisrU21trTIyMrRw4UJJp09lOp3O0ztNTFRqaqqOHj2qSy+9tEevPXx4ajRLjTtOZ5rpEtALzz77rB58cLk2bNig3Nxc1dTU6M4779TQoYN18803my4PvcCxZ230z9ro3yciDmRVVVVauXJlt22jRo3S22+/rXvuuUcLFixQRUWFSktLtXnz5rO+ht3e8wG6I0eaFQqFIy03rnGVpfUsXbpMDz20VuPGZcvhcGjcuGw99NBaeb3zNWFCgeny0EMce9ZG/6ytv/bPbrdFNIgUcSDLy8tTXl5et21//etfNW3aNF133XWSTp+aXL58uSTJ5XLp8OHDGjFihDo6OtTc3Kz09PRIdw8Yxa2vAADRFNU5ZFdccYUuvfRS7dmzR5L0m9/8Rn/7t38rSRo/fry2bdsmSdqxY4eys0+PLABWxK2vAADRFPWrLNetW6f169eroKBATz/9tMrKyiRJ8+bN0x/+8Afl5+fr3/7t37Ro0aJo7xqIGW59BQCIJls4HLbExCzmkCHe+P0V8vnKVV9fp8zMLHk8Jdz6ymI49qyN/llbf+1fpHPIWKkfAADAMO5lCUTA769QWdky+XzrVFAwUdu375THM1eSGCUDAPQaI2RABHy+cvl865Sb65bD4VBurls+3zr5fOWmSwMAWBCBDIgAy14AAKKJQAZEgGUvAADRRCADIsCyFwCAaGJSPxCBMxP3vd75mj59sjIzs+T1LmRCPwAgIgQyIEKFhcUqLCzut2vpAABih1OWAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEciACPn9FXK7c5SQkCC3O0d+f4XpkgAAFpVougDAivz+CpWVLZPPt04FBRO1fftOeTxzJUmFhcWGqwMAWA0jZEAEfL5y+XzrlJvrlsPhUG6uWz7fOvl85aZLAwBYEIEMiEB9fZ1ycq7tti0n51rV19cZqggAYGUEMiACmZlZqq19vdu22trXlZmZZagiAICVEciACHg8JfJ45qqmplrBYFA1NdXyeObK4ykxXRoAwIKY1A9E4MzEfa93vqZPn6zMzCx5vQuZ0A8AiAiBDIhQYWGxCguL5XSmKRA4abocAICFccoSAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIZFPZB99NFHmjlzpqZMmaJZs2Zp//79kqT29nbNnz9feXl5mjZtmt59991o7xoAAMCSoh7I1qxZo/z8fFVWVmrixIlavXq1JGnz5s0aPHiwqqqq5PV6VVpaGu1dAwAAWFLUA1koFFJzc7Mk6dSpUxo0aJAkaffu3Zo8ebIk6ZprrtGxY8d04MCBaO8eAADAchKj/YLz5s3TjBkztHnzZgWDQf3qV7+SJDU2NsrpdHZ9n9Pp1MGDB5WRkdGj1x0+PDXapcYVpzPNdAm4APTPuuidtdE/a6N/n4g4kFVVVWnlypXdto0ePVptbW1aunSpJkyYoFdeeUVz587Viy++eNbXsNt7PkB35EizQqFwpOXGNaczTYHASdNlIEL0z7ronbXRP2vrr/2z220RDSJFHMjy8vKUl5fXbdvRo0eVl5enCRMmSJJuvPFGLV68WMeOHZPL5VIgENCoUaMkSYFAQC6XK9LdAwAA9BtRnUN20UUXKTk5WXv37pUkvfnmm0pJSdHFF1+s8ePHq7KyUpK0d+9eJScn9/h0JQAAQH8W1TlkNptN69at07Jly9Ta2qqUlBStXbtWkjRr1iwtWrRI+fn5SkpK0qpVq6K5awAAAMuyhcNhS0zMYg4Z4hX9sy56Z230z9r6a/8inUPGSv0AAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADDsggPZmjVrtHbt2q7nJ06c0F133aW8vDzNnDlTgUBAktTe3q758+crLy9P06ZN07vvvnuhuwYAAOgXIg5kJ0+elNfr1caNG7tt9/l8ys7OVlVVlYqLi7VixQpJ0ubNmzV48GBVVVXJ6/WqtLT0wioHAADoJyIOZLt27dKVV16p22+/vdv23bt3a9KkSZKkgoICVVdXKxgMavfu3Zo8ebIk6ZprrtGxY8d04MCBCygdAACgf0iM9AenTp0qSd1OV0pSY2OjnE7n6RdPTFRqaqqOHj3abbskOZ1OHTx4UBkZGT3a3/DhqZGWaglOZ5rpEnAB6J910Ttro3/WRv8+8YWBrKqqSitXruy2bfTo0dq0aVOPd2K3n30g7lzbz+bIkWaFQuEef7+VOJ1pCgROmi4DEaJ/1kXvrI3+WVt/7Z/dbotoEOkLA1leXp7y8vJ6/IIul0uHDx/WiBEj1NHRoebmZqWnp8vlcikQCGjUqFGSpEAgIJfL1euCAQAA+puoL3sxfvx4bdu2TZK0Y8cOZWdny+FwaPz48aqsrJQk7d27V8nJyT0+XQkAANCfRTyH7FzmzZun0tJS5efnKy0tTeXl5ZKkWbNmadGiRcrPz1dSUpJWrVoV7V0DAABYki0cDltiYhZzyBCv6J910Ttro3/W1l/7F+kcMlbqBwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAEDN+f4Xc7hwlJCTI7c6R319huqS4kGi6AAAAMDD4/RUqK1smn2+dCgomavv2nfJ45kqSCguLDVdnFiNkAAAgJny+cvl865Sb65bD4VBurls+3zr5fOWmSzOOQAYAAGKivr5OOTnXdtuWk3Ot6uvrDFUUPwhkAAAgJjIzs1Rb+3q3bbW1ryszM8tQRfGDQAYAAGLC4ymRxzNXNTXVCgaDqqmplsczVx5PienSjGNSPwAAiIkzE/e93vmaPn2yMjOz5PUuHPAT+iUCGQAAiKHCwmIVFhbL6UxTIHDSdDlxg1OWAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMuOJCtWbNGa9eu7Xr+7rvv6pZbbtGUKVP0/e9/X++8844kqb29XfPnz1deXp6mTZumd99990J3DQAA0C9EHMhOnjwpr9erjRs3dtv+s5/9TD/84Q9VWVkpj8ejn/70p5KkzZs3a/DgwaqqqpLX61VpaemFVQ4AANBPRBzIdu3apSuvvFK33357t+3FxcVyu92SpKysLDU0NEiSdu/ercmTJ0uSrrnmGh07dkwHDhyIdPcAAAD9RsT3spw6daokdTtdKUmFhYVd/3744Yc1YcIESVJjY6OcTmfX15xOpw4ePKiMjIwe7W/48NRIS7UEpzPNdAm4APTPuuidtdE/a6N/n/jCQFZVVaWVK1d22zZ69Ght2rTpnD8TDoe1atUq/dd//Zeefvrpc36f3d7zAbojR5oVCoV7/P1Wwg1WrY3+WRe9szb6Z239tX92uy2iQaQvDGR5eXnKy8vr8Qt2dHTopz/9qQ4dOqSnn35aaWmn06/L5VIgENCoUaMkSYFAQC6Xq8eva7fbevy9VtTff7/+jv5ZF72zNvpnbf2xf5H+ThGfsjyXBx98UM3Nzdq4caOSkpK6to8fP16VlZXKzs7W3r17lZyc3OPTlZJ00UUp0S41rvT3U7L9Hf2zLnpnbfTP2ujfJ6IayI4ePapnnnlGl112mYqLi7u2V1ZWatasWVq0aJHy8/OVlJSkVatWRXPXAAAAlmULh8P9c2IWAACARbBSPwAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAlkM3XLLLdq+fXu3bR9//LFycnJ09OhRSdKaNWu0du1aE+XhPM7XuzfeeENFRUWaMmWKbr31Vu3fv99QlTiX8/Vv7969Kiws1KRJk/SjH/1Ix48fN1QlzqYn75tvv/22xo0bZ6I8fIHz9W/btm3Kzc3VlClTNGXKFK1evdpQlfGBQBZDhYWFn/sfc+fOncrJyZHD4ZDX69XGjRsNVYfzOV/vSktLtWLFClVWVmrSpElavny5oSpxLufr33333adVq1bppZde0pgxY7RhwwZDVeJszte7iy++WKdOndLSpUsVDAYNVYjzOV///vjHP6q0tFSVlZWqrKzUvffea6jK+EAgi6G8vDz953/+p5qamrq2vfjiiyoqKtKuXbt05ZVX6vbbbzdYIc7lXL2bPHmy5s2bp7Fjx0qSsrKy1NDQYKpMnMP5jr0dO3ZozJgxCgaDOnTokIYOHWqwUnzW+XonSQ888IBuu+02Q9Xhi5yvf3/84x+1bds2TZ48WSUlJQN+dJpAFkMpKSm6/vrr9fLLL0uSDh06pPfee0/f+c53NHXqVN11111KSEgwXCXO5ly9++53v6spU6ZIkkKhkNatW6cJEyaYLBVncb5jz+FwqK6uTuPHj1dtba3y8/MNV4tPO1/vdu3apdbWVt10002Gq8S5nK9/TqdT99xzjyorKzVy5EgtXbrUcLVmEchirKioqGv49qWXXtLkyZNlt9MGKzhf79rb21VSUqKOjg7NmTPHZJk4h/P1LysrS6+99pp+/OMfD/jTJvHobL07cuSIHn30US1cuNBwdfgi5zr2fvGLX+jqq6+WzWbT7NmzVV1dbbhSs0gCMZadna1AIKCGhoZuw+6If+fqXUtLi2bPnq2Ojg49+uijcjgchivF2Zytf21tbfr1r3/d9T2TJ09WXV2dwSpxNmfr3e7du9XU1KSZM2d2jVJPmTJFzc3NhqvFZ52tfydPntSmTZu6viccDisxMdFckXFgYP/2hkybNk2PPvqohg0bpiuuuMJ0OeiFs/Vu/vz5GjVqlJYuXSqbzWa4QpzPZ/vX2dmpJUuWaMSIERo3bpyqqqr0zW9+03SZOIvP9u6KK65QcXFx19ezsrJUWVlpsEKcz9mOvfXr1+vv/u7vdPXVV+uXv/ylbrjhBtNlGkUgM2Dq1Km6/vrrtWLFCtOloJc+27u3335bu3bt0pgxYzR16lRJksvl0hNPPGGyTJzDZ/uXkJCg1atXa9GiRers7NSll17KcRmneN+0trMdez6fT/fff79aW1t15ZVXatWqVYarNMsWDofDposAAAAYyJhDBgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABj2/wHwYD67o/HOWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "data.boxplot(column=['V1', 'V2', 'V3', 'V4', 'V5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling (under-sampling & over-sampling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A widely adopted technique for dealing with highly unbalanced datasets is called resampling. It consists of removing samples from the majority class (under-sampling) and / or adding more examples from the minority class (over-sampling)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"image.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest implementation of over-sampling is to duplicate random records from the minority class, which can cause overfitting. In under-sampling, the simplest technique involves removing random records from the majority class, which can cause loss of information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b style=\"color:red\">---> to avoid this loss of information or overfitting, we'll use both methods and combine them in order ta balance our data</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine both methods (under-sampling & over-sampling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split the data (Training data 70% & Test data 30%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split our data\n",
    "x_train, x_test, y_train, y_test = train_test_split(data.ix[:,1:29],\n",
    "                                                          data['Class'],\n",
    "                                                          test_size=0.30,\n",
    "                                                          random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((199364, 28), (85443, 28), (199364,), (85443,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>---> we'll use combining method on training sample<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "smote_enn = SMOTEENN(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 198705), (1, 199019)]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(collections.Counter(y_resampled).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((397724, 28), (397724,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_resampled.shape, y_resampled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Bayesien Naïf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View class distributions\n",
    "data.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Initialize our classifier\n",
    "bayes = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train our classifier\n",
    "model_bayes = bayes.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict test data\n",
    "y_pred = model_bayes.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9751998408295589"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>---> On ne peut pas utiliser l'accuracy comme une métrique de performance, car les classes sont déséquilibres, on aura toujours une accuracy qui est très élevé et c'est à cause de la classe 0 qui représente 90% des données </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99     85296\n",
      "           1       0.06      0.86      0.11       147\n",
      "\n",
      "   micro avg       0.98      0.98      0.98     85443\n",
      "   macro avg       0.53      0.92      0.55     85443\n",
      "weighted avg       1.00      0.98      0.99     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9196685778982386"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation for Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "#init\n",
    "scores = cross_val_score(bayes, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95197932, 0.95017419, 0.95156574, 0.95081222, 0.95079121])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "lda.fit(X_resampled, y_resampled)\n",
    "\n",
    "#predict test data\n",
    "y_pred = lda.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-validation for LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97818286, 0.97929308, 0.97946947, 0.97951514, 0.97900331])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lda, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[84359   937]\n",
      " [   27   120]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99     85296\n",
      "           1       0.11      0.82      0.20       147\n",
      "\n",
      "   micro avg       0.99      0.99      0.99     85443\n",
      "   macro avg       0.56      0.90      0.60     85443\n",
      "weighted avg       1.00      0.99      0.99     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.9026706279022583\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quadratic Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
       "               store_covariance=False, store_covariances=None, tol=0.0001)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "#init\n",
    "qda = QuadraticDiscriminantAnalysis()\n",
    "\n",
    "#fit the data\n",
    "qda.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = qda.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-validation for Quadratic Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98278651, 0.98250811, 0.98270143, 0.9828983 , 0.98258829])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(qda, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[83035  2261]\n",
      " [   20   127]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99     85296\n",
      "           1       0.05      0.86      0.10       147\n",
      "\n",
      "   micro avg       0.97      0.97      0.97     85443\n",
      "   macro avg       0.53      0.92      0.54     85443\n",
      "weighted avg       1.00      0.97      0.98     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.9187189436832695\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "#init\n",
    "svm = svm.SVC(gamma='scale')\n",
    "\n",
    "#fit the data\n",
    "svm.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[84682   614]\n",
      " [   27   120]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     85296\n",
      "           1       0.16      0.82      0.27       147\n",
      "\n",
      "   micro avg       0.99      0.99      0.99     85443\n",
      "   macro avg       0.58      0.90      0.63     85443\n",
      "weighted avg       1.00      0.99      0.99     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.9045640343925978\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99970971, 0.9996671 , 0.99974394, 0.99966023, 0.99960437])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(svm, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "\n",
    "knn.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the .predict() method to make predictions from the X_test subset\n",
    "pred = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[84682   614]\n",
      " [   27   120]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     85296\n",
      "           1       0.16      0.82      0.27       147\n",
      "\n",
      "   micro avg       0.99      0.99      0.99     85443\n",
      "   macro avg       0.58      0.90      0.63     85443\n",
      "weighted avg       1.00      0.99      0.99     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.9045640343925978\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.99996226, 0.99994967, 0.99998742, 0.99997484])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(knn, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n"
     ]
    }
   ],
   "source": [
    "#init\n",
    "dtc = DecisionTreeClassifier()\n",
    "\n",
    "#fit the model\n",
    "dtc.fit(X_resampled, y_resampled)\n",
    "\n",
    "print(dtc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dtc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[85074   222]\n",
      " [   32   115]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.34      0.78      0.48       147\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     85443\n",
      "   macro avg       0.67      0.89      0.74     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.8898551119941505\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99844058, 0.99826463, 0.99792507, 0.99802565, 0.99856634])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(dtc, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# random forest model creation\n",
    "rfc = RandomForestClassifier()\n",
    "\n",
    "#fit the model\n",
    "rfc.fit(X_resampled, y_resampled)\n",
    "\n",
    "# predictions\n",
    "y_pred = rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[85267    29]\n",
      " [   29   118]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85296\n",
      "           1       0.80      0.80      0.80       147\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     85443\n",
      "   macro avg       0.90      0.90      0.90     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.9011905479693284\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99999976, 0.99996134, 0.99999972, 0.99999954, 0.99998687])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(rfc, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# random forest model creation\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# fit the model\n",
    "lr.fit(X_resampled, y_resampled)\n",
    "\n",
    "# predictions\n",
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[83101  2195]\n",
      " [   12   135]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99     85296\n",
      "           1       0.06      0.92      0.11       147\n",
      "\n",
      "   micro avg       0.97      0.97      0.97     85443\n",
      "   macro avg       0.53      0.95      0.55     85443\n",
      "weighted avg       1.00      0.97      0.99     85443\n",
      "\n",
      "\n",
      "\n",
      "=== AUC ===\n",
      "0.9463167160505169\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== AUC ===\")\n",
    "print(roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.98924042, 0.98939162, 0.9897195 , 0.9896437 , 0.98952616])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lr, X_resampled, y_resampled, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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