credit_card_fraud_detection_555 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, auc, roc_curve\n",
    "from sklearn.metrics import plot_confusion_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/cxldata/projects/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "    <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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "    <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",
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       "      <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",
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       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
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       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
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       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
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       "   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",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \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",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
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     "execution_count": 12,
     "metadata": {},
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  {
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   "execution_count": 13,
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     "execution_count": 13,
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   "source": [
    "data.shape"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.loc[:, data.columns != 'Class']\n",
    "y = data.loc[:, data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    284315\n",
      "1       492\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['Class'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid Transactions:  99.83 % of the dataset\n",
      "Fraudulent Transactions:  0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "print('Valid Transactions: ', round(data['Class'].value_counts()[0]/len(data) * 100,2), '% of the dataset')\n",
    "\n",
    "print('Fraudulent Transactions: ', round(data['Class'].value_counts()[1]/len(data) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['blue','red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Class', data=data, palette=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =  train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transactions in X_train dataset:  (199364, 30)\n",
      "Transaction classes in y_train dataset:  (199364, 1)\n",
      "Transactions in X_test dataset:  (85443, 30)\n",
      "Transaction classes in y_test dataset:  (85443, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Transactions in X_train dataset: \", X_train.shape)\n",
    "print(\"Transaction classes in y_train dataset: \", y_train.shape)\n",
    "\n",
    "print(\"Transactions in X_test dataset: \", X_test.shape)\n",
    "print(\"Transaction classes in y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_amount = StandardScaler()\n",
    "scaler_time = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['normAmount'] = scaler_amount.fit_transform(X_train['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_test['normAmount'] = scaler_amount.transform(X_test['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['normTime'] = scaler_time.fit_transform(X_train['Time'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_test['normTime'] = scaler_time.transform(X_test['Time'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.drop(['Time', 'Amount'], axis=1)\n",
    "X_test = X_test.drop(['Time', 'Amount'], axis=1)"
   ]
  },
  {
   "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></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>V10</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>normAmount</th>\n",
       "      <th>normTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>161145</th>\n",
       "      <td>-0.132066</td>\n",
       "      <td>0.107044</td>\n",
       "      <td>-0.650588</td>\n",
       "      <td>-0.996032</td>\n",
       "      <td>1.814333</td>\n",
       "      <td>1.740740</td>\n",
       "      <td>0.496852</td>\n",
       "      <td>0.633016</td>\n",
       "      <td>0.017181</td>\n",
       "      <td>-0.362707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062489</td>\n",
       "      <td>0.005292</td>\n",
       "      <td>0.251519</td>\n",
       "      <td>-2.466810</td>\n",
       "      <td>-0.889690</td>\n",
       "      <td>0.337462</td>\n",
       "      <td>0.306395</td>\n",
       "      <td>0.074817</td>\n",
       "      <td>-0.165692</td>\n",
       "      <td>0.402560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204520</th>\n",
       "      <td>2.125994</td>\n",
       "      <td>0.014207</td>\n",
       "      <td>-1.514760</td>\n",
       "      <td>0.115021</td>\n",
       "      <td>0.598510</td>\n",
       "      <td>-0.333235</td>\n",
       "      <td>0.199289</td>\n",
       "      <td>-0.264353</td>\n",
       "      <td>0.384111</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.329368</td>\n",
       "      <td>-0.788150</td>\n",
       "      <td>0.267730</td>\n",
       "      <td>0.066122</td>\n",
       "      <td>-0.135785</td>\n",
       "      <td>0.203841</td>\n",
       "      <td>-0.068267</td>\n",
       "      <td>-0.057678</td>\n",
       "      <td>-0.352239</td>\n",
       "      <td>0.853047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182659</th>\n",
       "      <td>-0.086694</td>\n",
       "      <td>0.166240</td>\n",
       "      <td>1.573127</td>\n",
       "      <td>0.687266</td>\n",
       "      <td>0.222359</td>\n",
       "      <td>1.102606</td>\n",
       "      <td>1.575093</td>\n",
       "      <td>-1.098608</td>\n",
       "      <td>0.763887</td>\n",
       "      <td>1.404677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015324</td>\n",
       "      <td>1.063663</td>\n",
       "      <td>-0.410841</td>\n",
       "      <td>0.722723</td>\n",
       "      <td>-0.171733</td>\n",
       "      <td>-0.613543</td>\n",
       "      <td>-1.201571</td>\n",
       "      <td>-1.139931</td>\n",
       "      <td>0.330886</td>\n",
       "      <td>0.646288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25117</th>\n",
       "      <td>1.352339</td>\n",
       "      <td>-0.534984</td>\n",
       "      <td>0.555143</td>\n",
       "      <td>-0.629355</td>\n",
       "      <td>-1.144170</td>\n",
       "      <td>-0.852967</td>\n",
       "      <td>-0.642128</td>\n",
       "      <td>-0.032659</td>\n",
       "      <td>-0.654482</td>\n",
       "      <td>0.619206</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014814</td>\n",
       "      <td>-0.180379</td>\n",
       "      <td>0.178112</td>\n",
       "      <td>0.347720</td>\n",
       "      <td>0.151810</td>\n",
       "      <td>-0.404361</td>\n",
       "      <td>0.013746</td>\n",
       "      <td>0.016152</td>\n",
       "      <td>-0.336067</td>\n",
       "      <td>-1.290102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227642</th>\n",
       "      <td>-1.526760</td>\n",
       "      <td>0.647782</td>\n",
       "      <td>0.615391</td>\n",
       "      <td>-0.561114</td>\n",
       "      <td>0.836950</td>\n",
       "      <td>-0.514251</td>\n",
       "      <td>0.984325</td>\n",
       "      <td>-0.097430</td>\n",
       "      <td>-0.062634</td>\n",
       "      <td>-1.033567</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.221533</td>\n",
       "      <td>-0.393158</td>\n",
       "      <td>-0.214990</td>\n",
       "      <td>0.588447</td>\n",
       "      <td>0.679496</td>\n",
       "      <td>0.518434</td>\n",
       "      <td>0.065022</td>\n",
       "      <td>0.147294</td>\n",
       "      <td>0.005211</td>\n",
       "      <td>1.060059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              V1        V2        V3        V4        V5        V6        V7  \\\n",
       "161145 -0.132066  0.107044 -0.650588 -0.996032  1.814333  1.740740  0.496852   \n",
       "204520  2.125994  0.014207 -1.514760  0.115021  0.598510 -0.333235  0.199289   \n",
       "182659 -0.086694  0.166240  1.573127  0.687266  0.222359  1.102606  1.575093   \n",
       "25117   1.352339 -0.534984  0.555143 -0.629355 -1.144170 -0.852967 -0.642128   \n",
       "227642 -1.526760  0.647782  0.615391 -0.561114  0.836950 -0.514251  0.984325   \n",
       "\n",
       "              V8        V9       V10  ...       V21       V22       V23  \\\n",
       "161145  0.633016  0.017181 -0.362707  ... -0.062489  0.005292  0.251519   \n",
       "204520 -0.264353  0.384111  0.028747  ... -0.329368 -0.788150  0.267730   \n",
       "182659 -1.098608  0.763887  1.404677  ...  0.015324  1.063663 -0.410841   \n",
       "25117  -0.032659 -0.654482  0.619206  ... -0.014814 -0.180379  0.178112   \n",
       "227642 -0.097430 -0.062634 -1.033567  ... -0.221533 -0.393158 -0.214990   \n",
       "\n",
       "             V24       V25       V26       V27       V28  normAmount  normTime  \n",
       "161145 -2.466810 -0.889690  0.337462  0.306395  0.074817   -0.165692  0.402560  \n",
       "204520  0.066122 -0.135785  0.203841 -0.068267 -0.057678   -0.352239  0.853047  \n",
       "182659  0.722723 -0.171733 -0.613543 -1.201571 -1.139931    0.330886  0.646288  \n",
       "25117   0.347720  0.151810 -0.404361  0.013746  0.016152   -0.336067 -1.290102  \n",
       "227642  0.588447  0.679496  0.518434  0.065022  0.147294    0.005211  1.060059  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before over-sampling:\n",
      " <bound method IndexOpsMixin.value_counts of 161145    0\n",
      "204520    0\n",
      "182659    0\n",
      "25117     0\n",
      "227642    0\n",
      "         ..\n",
      "211543    0\n",
      "86293     0\n",
      "122579    0\n",
      "152315    0\n",
      "117952    0\n",
      "Name: Class, Length: 199364, dtype: int64>\n"
     ]
    }
   ],
   "source": [
    "print(\"Before over-sampling:\\n\", y_train['Class'].value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After over-sampling:\n",
      " <bound method IndexOpsMixin.value_counts of 0         0\n",
      "1         0\n",
      "2         0\n",
      "3         0\n",
      "4         0\n",
      "         ..\n",
      "398033    1\n",
      "398034    1\n",
      "398035    1\n",
      "398036    1\n",
      "398037    1\n",
      "Name: Class, Length: 398038, dtype: int64>\n"
     ]
    }
   ],
   "source": [
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train['Class'])\n",
    "print(\"After over-sampling:\\n\", y_train_res.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 14 candidates, totalling 70 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
      "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:   16.4s\n",
      "[Parallel(n_jobs=3)]: Done  70 out of  70 | elapsed:  3.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)\n",
    "k = clf.fit(X_train_res, y_train_res)\n",
    "print(k.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_gridcv_best = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pre = lr_gridcv_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix_test = confusion_matrix(y_test, y_test_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the test dataset: 0.9183673469387755\n"
     ]
    }
   ],
   "source": [
    "print(\"Recall metric in the test dataset:\", (cnf_matrix_test[1,1]/(cnf_matrix_test[1,0]+cnf_matrix_test[1,1] )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pre = lr_gridcv_best.predict(X_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix_train = confusion_matrix(y_train_res, y_train_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall metric in the train dataset: 0.9177163989367849\n"
     ]
    }
   ],
   "source": [
    "print(\"Recall metric in the train dataset:\", (cnf_matrix_train[1,1]/(cnf_matrix_train[1,0]+cnf_matrix_train[1,1] )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['Not Fraud', 'Fraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(k, X_test, y_test,  values_format = '.5g', display_labels=class_names)\n",
    "plt.title(\"Test data Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(k, X_train_res, y_train_res,  values_format = '.5g', display_labels=class_names) \n",
    "plt.title(\"Oversampled Train data Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_k =  k.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC: 0.9838284638559982\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_k)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"ROC-AUC:\", roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}