OVER04. Variable correlation and selection.ipynb

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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",
       "      <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",
       "      <td>190066</td>\n",
       "      <td>128675.0</td>\n",
       "      <td>1.908301</td>\n",
       "      <td>-0.401552</td>\n",
       "      <td>-3.097451</td>\n",
       "      <td>-0.622032</td>\n",
       "      <td>2.486640</td>\n",
       "      <td>3.233742</td>\n",
       "      <td>-0.464173</td>\n",
       "      <td>0.819237</td>\n",
       "      <td>0.874966</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.218154</td>\n",
       "      <td>-0.599959</td>\n",
       "      <td>0.181458</td>\n",
       "      <td>0.586912</td>\n",
       "      <td>-0.048146</td>\n",
       "      <td>-0.073084</td>\n",
       "      <td>0.002446</td>\n",
       "      <td>-0.018991</td>\n",
       "      <td>61.16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6472</td>\n",
       "      <td>7740.0</td>\n",
       "      <td>1.023874</td>\n",
       "      <td>2.001485</td>\n",
       "      <td>-4.769752</td>\n",
       "      <td>3.819195</td>\n",
       "      <td>-1.271754</td>\n",
       "      <td>-1.734662</td>\n",
       "      <td>-3.059245</td>\n",
       "      <td>0.889805</td>\n",
       "      <td>0.415382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.343283</td>\n",
       "      <td>-0.054196</td>\n",
       "      <td>0.709654</td>\n",
       "      <td>-0.372216</td>\n",
       "      <td>-2.032068</td>\n",
       "      <td>0.366778</td>\n",
       "      <td>0.395171</td>\n",
       "      <td>0.020206</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>169925</td>\n",
       "      <td>119933.0</td>\n",
       "      <td>-0.411177</td>\n",
       "      <td>1.247542</td>\n",
       "      <td>-0.574801</td>\n",
       "      <td>-0.136464</td>\n",
       "      <td>0.575551</td>\n",
       "      <td>-1.320732</td>\n",
       "      <td>0.804512</td>\n",
       "      <td>0.082755</td>\n",
       "      <td>-0.526066</td>\n",
       "      <td>...</td>\n",
       "      <td>0.410620</td>\n",
       "      <td>1.170738</td>\n",
       "      <td>-0.171397</td>\n",
       "      <td>-0.050942</td>\n",
       "      <td>-0.445156</td>\n",
       "      <td>-0.160102</td>\n",
       "      <td>0.112786</td>\n",
       "      <td>0.164709</td>\n",
       "      <td>20.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31361</td>\n",
       "      <td>36337.0</td>\n",
       "      <td>-1.039504</td>\n",
       "      <td>-0.063318</td>\n",
       "      <td>3.672998</td>\n",
       "      <td>3.480074</td>\n",
       "      <td>-1.066632</td>\n",
       "      <td>1.419329</td>\n",
       "      <td>-0.426282</td>\n",
       "      <td>0.284342</td>\n",
       "      <td>0.546964</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086411</td>\n",
       "      <td>0.832867</td>\n",
       "      <td>-0.174056</td>\n",
       "      <td>0.442634</td>\n",
       "      <td>0.168808</td>\n",
       "      <td>0.501599</td>\n",
       "      <td>-0.102883</td>\n",
       "      <td>-0.080087</td>\n",
       "      <td>107.17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185293</td>\n",
       "      <td>126610.0</td>\n",
       "      <td>0.271531</td>\n",
       "      <td>0.276062</td>\n",
       "      <td>0.195402</td>\n",
       "      <td>-1.573613</td>\n",
       "      <td>-0.107662</td>\n",
       "      <td>-1.155381</td>\n",
       "      <td>0.292537</td>\n",
       "      <td>-0.222503</td>\n",
       "      <td>-0.951144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182163</td>\n",
       "      <td>0.563054</td>\n",
       "      <td>-0.089366</td>\n",
       "      <td>-0.029637</td>\n",
       "      <td>-0.357233</td>\n",
       "      <td>-0.281287</td>\n",
       "      <td>0.081976</td>\n",
       "      <td>0.069527</td>\n",
       "      <td>12.05</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "190066  128675.0  1.908301 -0.401552 -3.097451 -0.622032  2.486640  3.233742   \n",
       "6472      7740.0  1.023874  2.001485 -4.769752  3.819195 -1.271754 -1.734662   \n",
       "169925  119933.0 -0.411177  1.247542 -0.574801 -0.136464  0.575551 -1.320732   \n",
       "31361    36337.0 -1.039504 -0.063318  3.672998  3.480074 -1.066632  1.419329   \n",
       "185293  126610.0  0.271531  0.276062  0.195402 -1.573613 -0.107662 -1.155381   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "190066 -0.464173  0.819237  0.874966  ... -0.218154 -0.599959  0.181458   \n",
       "6472   -3.059245  0.889805  0.415382  ...  0.343283 -0.054196  0.709654   \n",
       "169925  0.804512  0.082755 -0.526066  ...  0.410620  1.170738 -0.171397   \n",
       "31361  -0.426282  0.284342  0.546964  ...  0.086411  0.832867 -0.174056   \n",
       "185293  0.292537 -0.222503 -0.951144  ...  0.182163  0.563054 -0.089366   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "190066  0.586912 -0.048146 -0.073084  0.002446 -0.018991   61.16      0  \n",
       "6472   -0.372216 -2.032068  0.366778  0.395171  0.020206    1.00      1  \n",
       "169925 -0.050942 -0.445156 -0.160102  0.112786  0.164709   20.75      0  \n",
       "31361   0.442634  0.168808  0.501599 -0.102883 -0.080087  107.17      0  \n",
       "185293 -0.029637 -0.357233 -0.281287  0.081976  0.069527   12.05      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import sklearn\n",
    "import imblearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# reading in CSV\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
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       "<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",
       "      <td>count</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No Fraud (0) vs. Fraud (1)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vq8sup5/afDP99ObjATJzY0ScBlxb6705MzfW56/mx9ObP1ofTLIPSdIIlK6b0aWM7UU3NjY2da1JrD/xhFlqiuaTRWeeN9dNkJqp12g2u2bunQEkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU1NO2gi4g+3UP762WuOJGm+mUmP5k+2UH7ybDREkjQ/7TBVhYh4Tn26ICKezYN/D/pJwF0tGiZJmh+mDBrg/Pp3J+CCgfIO+CbwP2e7UZKk+WPKoMnMfQEiYk1mHte+SZKk+WQ6PRoABkMmIh4xYdn9s9koSdL8Me2giYhfAN4N/Bz9MBr012s6YMHsN02SNB9MO2iA1cCHgd8Cvt+mOZKk+WYmQfNE4H9lZteqMZKk+Wcm36O5BDisVUMkSfPTTHo0OwGXRMQn6ac1P2Cq2WgRcQHwQuD2zNyvlp0K/DbwrVrtTZl5eV32RuCVwH3A72Xm2lq+DDib/prQeZl5Ri3fF7gY2B24Djg2M++JiB2BNcABwLeB38zMW2ZwzJKkbTSTHs2NwNuAfwW+OuExlQuBZUPKz8rM/etjPGSWAkcCT6/rvCciFkTEAvrJCC8AlgJH1brUdp2VmUuATfQhRf27KTOfDJxV60mSRmgm05v/dGt3kpmfiIh9pll9OXBxZt4N/GdE3AwcWJfdnJlfA4iIi4HlEXET8Bzg6FpnNXAqcE7d1qm1/APAX0VE8TqTJI3OTKY3P2dLyzLz41u5/9dGxHHAZ4E3ZOYmYE/gmoE662oZwK0Tyg8CHgd8JzPvHVJ/z/F1MvPeiLij1t+wle2VJM3QTK7RnD/h9U8Bj6L/h/1JW7Hvc4DT6L+Hcxrwdvqp02VI3Y7hw3zdJPWZYtmDRMRKYCVAZrJw4cLJ2j6l9du0tuarbX1fSQ9HMxk623fwdb1mcjJbeVPNzLxtYFvnAh+pL9cBew9U3QsYq8+HlW8Ado2IHWqvZrD++LbWRcQOwC7Axi20ZxWwqr7sNmyw06PZ5/tK89nixYuHlm/1D59l5n3AW4A/2pr1I2LRwMuXAF+ozy8DjoyIHetssiXAZ4BrgSURsW9EPIp+wsBl9XrLPwIvreuvAC4d2NaK+vylwMe9PiNJozWTobNhngdMeZ+ziPhb4FnAwohYB5wCPCsi9qcfyroF+B2AzLwhIpJ+ltu9wGtqqBERrwXW0k9vviAzb6i7+GPg4og4Hfg8Px7mOx94b51QsJE+nCRJI1S6bnof8CPiVh58fePR9N+t+d3MXNOgbXOpGxsbm7rWJNafeMIsNUXzyaIzz5vrJkjN1KGzza6Nz6RHc8yE198DvpyZd25DuyRJ89xMJgP8MzzwEwF7ALf58wCSpKnM5Hs0j6H/Zv5vAo8EflS/NPl7mXlHo/ZJkh7mZjLr7C+BnwB+Fti5/n008K4G7ZIkzRMzuUazDHhSZo7/Fs2XI+J4pnevM0nSdmomPZof0t8NYNBC4O7Za44kab6ZSY/mPODKiHgH8HX6H0L7A+DcFg2TJM0PMwmatwDfAF4OLKa/zcufZ+bEe6BJkvSAmQydnQ18KTOfm5lLM/O5wE0R8c5GbZMkzQMzCZqj6G/nP+hz/Ph3YCRJ2sxMgqajv8fYoAUz3IYkaTszk5D4F+C0emeA8TsEnFrLJUkaaiaTAV5H/5sx6yPi68BP0/++14taNEySND9Mu0eTmeuAXwCWA2cCRwAH1HJJkoaa0e/R1JtoXlMfkiRNyQv5kqSmDBpJUlMGjSSpKYNGktSUQSNJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktSUQSNJampGd2/eWhFxAfBC4PbM3K+W7Q68H9gHuAWIzNwUEQU4Gzgc+D7wisy8rq6zAji5bvb0zFxdyw8ALgR2Bi4HXpeZ3Zb20fhwJUkDRtWjuRBYNqHsJOCqzFwCXFVfA7wAWFIfK4Fz4IFgOgU4CDgQOCUidqvrnFPrjq+3bIp9SJJGZCRBk5mfADZOKF4OrK7PV9P/kNp4+ZrM7DLzGmDXiFgEPB+4MjM31l7JlcCyuuyxmXl1ZnbAmgnbGrYPSdKIjGTobAv2yMz1AJm5PiIeX8v3BG4dqLeulk1Wvm5I+WT72ExErKTvFZGZLFy4cGuPC+h/41qaaFvfV9LD0VwGzZaUIWXdVpTPSGauAlaNr79hw4aZbkKaku8rzWeLFy8eWj6Xs85uq8Ne1L+31/J1wN4D9fYCxqYo32tI+WT7kCSNyFwGzWXAivp8BXDpQPlxEVEi4mDgjjr8tRY4LCJ2q5MADgPW1mV3RcTBdcbacRO2NWwfkqQRGdX05r8FngUsjIh19LPHzgAyIl4J/Bfwslr9cvqpzTfTT28+HiAzN0bEacC1td6bM3N8gsGr+fH05o/WB5PsQ5I0IqXrZnw5Y3vQjY2NTV1rEutPPGGWmqL5ZNGZ5811E6Rm6jWaza6be2cASVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLU1A5z3YCIuAW4C7gPuDcznxERuwPvB/YBbgEiMzdFRAHOBg4Hvg+8IjOvq9tZAZxcN3t6Zq6u5QcAFwI7A5cDr8vMbiQHJ0l6yPRonp2Z+2fmM+rrk4CrMnMJcFV9DfACYEl9rATOAajBdApwEHAgcEpE7FbXOafWHV9vWfvDkSSNe6gEzUTLgdX1+WrgiIHyNZnZZeY1wK4RsQh4PnBlZm7MzE3AlcCyuuyxmXl17cWsGdiWJGkE5nzoDOiAKyKiA/46M1cBe2TmeoDMXB8Rj6919wRuHVh3XS2brHzdkPLNRMRK+p4PmcnChQu36aDWb9Pamq+29X0lPRw9FILmkMwcq2FyZUR8cZK6ZUhZtxXlm6kBt2q8zoYNGyZphrR1fF9pPlu8ePHQ8jkfOsvMsfr3duAS+msst9VhL+rf22v1dcDeA6vvBYxNUb7XkHJJ0ojMadBExE9ExGPGnwOHAV8ALgNW1GorgEvr88uA4yKiRMTBwB11iG0tcFhE7FYnARwGrK3L7oqIg+uMteMGtiVJGoG57tHsAXwyIq4HPgP8fWb+A3AG8LyI+ArwvPoa+unJXwNuBs4FfhcgMzcCpwHX1sebaxnAq4Hz6jpfBT46guOSJFWl6/xKyRDd2Ni2jbCtP/GEWWqK5pNFZ543102QmqnXaDa7Nj7XPRpJ0jxn0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKZ2mOsGjEJELAPOBhYA52XmGXPcJEnabsz7Hk1ELADeDbwAWAocFRFL57ZVkrT92B56NAcCN2fm1wAi4mJgOXDjnLZKmiOvWH31XDdBD0EXrvilZtveHoJmT+DWgdfrgIMmVoqIlcBKgMxk8eLF27TTxRddvk3rS61c8cbfmOsmaDuzPQRNGVLWTSzIzFXAqvbN2f5ExGcz8xlz3Q5pIt+bozHvr9HQ92D2Hni9FzA2R22RpO3O9tCjuRZYEhH7At8AjgSOntsmSdL2Y973aDLzXuC1wFrgpr4ob5jbVm13HJLUQ5XvzREoXbfZ5QpJkmbNvO/RSJLmlkEjSWpqe5gMoDnirX/0UBURFwAvBG7PzP3muj3znT0aNeGtf/QQdyGwbK4bsb0waNTKA7f+ycx7gPFb/0hzLjM/AWyc63ZsLwwatTLs1j97zlFbJM0hg0atTOvWP5LmP4NGrXjrH0mAs87Ujrf+kQR4ZwA1FBGHA++kn958QWa+ZY6bJAEQEX8LPAtYCNwGnJKZ589po+Yxg0aS1JTXaCRJTRk0kqSmDBpJUlMGjSSpKYNGktSUQSM9xETEqRHxN3PdDmm2+IVNaY5ExNHA64GnAncB/wb4XSPNOwaNNAci4vXAScCrgLXAPfS3rV8OfG8OmybNOoNGGrGI2AV4M3B8Zn5oYNGHgQ9HxKkT6v8/4JnAzsD1wKsz84a67HDgL+jvK3cncFZm/kVELKT/zZVfAe4HbgB+LTPvb3ho0lBeo5FG75eAnYBLpln/o8AS4PHAdcBFA8vOB34nMx8D7Ad8vJa/gf7Gpj8F7AG8Ce+erTlij0YavccBGzLz3ulUzswLxp/X3s6miNglM+8AfgQsjYjrM3MTsKlW/RGwCHhiZt4M/MtsHoA0EwaNNHrfBhZGxA5ThU39Sey3AC+j752MD30tBO4AfgM4GTgjIv4dOCkzrwbOBE4FrogIgFWZeUaDY5Gm5NCZNHpXAz8EjphG3aPpJwg8F9gF2KeWF4DMvDYzl9MPq/0dkLX8rsx8Q2Y+CXgR8PqIOHQ2D0KaLns00ohl5h0R8SfAuyPiXuAK+qGu5wLPBr4/UP0xwN30vaBHA28dXxARj6Lv6XykbvNO4L667IXAF4Gv0k8SuG98mTRq9mikOZCZ76D/Ds3JwLeAW4HX0vdKBq0Bvk7/43E3AtdMWH4scEsNmVcBx9TyJcDHgO/S96Dek5n/NOsHIk2Dv0cjSWrKHo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJT/x9fgzh3m4SvVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# taking a closer look at the class variable\n",
    "sns.countplot('Class', data = df)\n",
    "plt.title('No Fraud (0) vs. Fraud (1)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>Time</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.173963e-01</td>\n",
       "      <td>-1.059333e-02</td>\n",
       "      <td>-4.196182e-01</td>\n",
       "      <td>-1.052602e-01</td>\n",
       "      <td>1.730721e-01</td>\n",
       "      <td>-6.301647e-02</td>\n",
       "      <td>8.471437e-02</td>\n",
       "      <td>-3.694943e-02</td>\n",
       "      <td>-8.660434e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>4.473573e-02</td>\n",
       "      <td>1.440591e-01</td>\n",
       "      <td>5.114236e-02</td>\n",
       "      <td>-1.618187e-02</td>\n",
       "      <td>-2.330828e-01</td>\n",
       "      <td>-4.140710e-02</td>\n",
       "      <td>-5.134591e-03</td>\n",
       "      <td>-9.412688e-03</td>\n",
       "      <td>-0.010596</td>\n",
       "      <td>-0.012323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V1</td>\n",
       "      <td>0.117396</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.697350e-17</td>\n",
       "      <td>-1.424390e-15</td>\n",
       "      <td>1.755316e-17</td>\n",
       "      <td>6.391162e-17</td>\n",
       "      <td>2.398071e-16</td>\n",
       "      <td>1.991550e-15</td>\n",
       "      <td>-9.490675e-17</td>\n",
       "      <td>2.169581e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.755072e-16</td>\n",
       "      <td>7.477367e-17</td>\n",
       "      <td>9.808705e-16</td>\n",
       "      <td>7.354269e-17</td>\n",
       "      <td>-9.805358e-16</td>\n",
       "      <td>-8.621897e-17</td>\n",
       "      <td>3.208233e-17</td>\n",
       "      <td>9.820892e-16</td>\n",
       "      <td>-0.227709</td>\n",
       "      <td>-0.101347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V2</td>\n",
       "      <td>-0.010593</td>\n",
       "      <td>4.697350e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.512175e-16</td>\n",
       "      <td>-1.126388e-16</td>\n",
       "      <td>-2.039868e-16</td>\n",
       "      <td>5.024680e-16</td>\n",
       "      <td>3.966486e-16</td>\n",
       "      <td>-4.413984e-17</td>\n",
       "      <td>-5.728718e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>8.444409e-17</td>\n",
       "      <td>2.500830e-16</td>\n",
       "      <td>1.059562e-16</td>\n",
       "      <td>-8.142354e-18</td>\n",
       "      <td>-4.261894e-17</td>\n",
       "      <td>2.601622e-16</td>\n",
       "      <td>-4.478472e-16</td>\n",
       "      <td>-3.676415e-16</td>\n",
       "      <td>-0.531409</td>\n",
       "      <td>0.091289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V3</td>\n",
       "      <td>-0.419618</td>\n",
       "      <td>-1.424390e-15</td>\n",
       "      <td>2.512175e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-3.416910e-16</td>\n",
       "      <td>-1.436514e-15</td>\n",
       "      <td>1.431581e-15</td>\n",
       "      <td>2.168574e-15</td>\n",
       "      <td>3.433113e-16</td>\n",
       "      <td>-4.233770e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.971969e-17</td>\n",
       "      <td>4.648259e-16</td>\n",
       "      <td>2.115206e-17</td>\n",
       "      <td>-9.351637e-17</td>\n",
       "      <td>4.771164e-16</td>\n",
       "      <td>6.521501e-16</td>\n",
       "      <td>6.239832e-16</td>\n",
       "      <td>7.726948e-16</td>\n",
       "      <td>-0.210880</td>\n",
       "      <td>-0.192961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V4</td>\n",
       "      <td>-0.105260</td>\n",
       "      <td>1.755316e-17</td>\n",
       "      <td>-1.126388e-16</td>\n",
       "      <td>-3.416910e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.940929e-15</td>\n",
       "      <td>-2.712659e-16</td>\n",
       "      <td>1.556330e-16</td>\n",
       "      <td>5.195643e-16</td>\n",
       "      <td>3.859585e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.976950e-17</td>\n",
       "      <td>2.099922e-16</td>\n",
       "      <td>6.002528e-17</td>\n",
       "      <td>2.229738e-16</td>\n",
       "      <td>5.394585e-16</td>\n",
       "      <td>-6.179751e-16</td>\n",
       "      <td>-6.403423e-17</td>\n",
       "      <td>-5.863664e-17</td>\n",
       "      <td>0.098732</td>\n",
       "      <td>0.133447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Time            V1            V2            V3            V4  \\\n",
       "Time  1.000000  1.173963e-01 -1.059333e-02 -4.196182e-01 -1.052602e-01   \n",
       "V1    0.117396  1.000000e+00  4.697350e-17 -1.424390e-15  1.755316e-17   \n",
       "V2   -0.010593  4.697350e-17  1.000000e+00  2.512175e-16 -1.126388e-16   \n",
       "V3   -0.419618 -1.424390e-15  2.512175e-16  1.000000e+00 -3.416910e-16   \n",
       "V4   -0.105260  1.755316e-17 -1.126388e-16 -3.416910e-16  1.000000e+00   \n",
       "\n",
       "                V5            V6            V7            V8            V9  \\\n",
       "Time  1.730721e-01 -6.301647e-02  8.471437e-02 -3.694943e-02 -8.660434e-03   \n",
       "V1    6.391162e-17  2.398071e-16  1.991550e-15 -9.490675e-17  2.169581e-16   \n",
       "V2   -2.039868e-16  5.024680e-16  3.966486e-16 -4.413984e-17 -5.728718e-17   \n",
       "V3   -1.436514e-15  1.431581e-15  2.168574e-15  3.433113e-16 -4.233770e-16   \n",
       "V4   -1.940929e-15 -2.712659e-16  1.556330e-16  5.195643e-16  3.859585e-16   \n",
       "\n",
       "      ...           V21           V22           V23           V24  \\\n",
       "Time  ...  4.473573e-02  1.440591e-01  5.114236e-02 -1.618187e-02   \n",
       "V1    ... -1.755072e-16  7.477367e-17  9.808705e-16  7.354269e-17   \n",
       "V2    ...  8.444409e-17  2.500830e-16  1.059562e-16 -8.142354e-18   \n",
       "V3    ... -2.971969e-17  4.648259e-16  2.115206e-17 -9.351637e-17   \n",
       "V4    ... -9.976950e-17  2.099922e-16  6.002528e-17  2.229738e-16   \n",
       "\n",
       "               V25           V26           V27           V28    Amount  \\\n",
       "Time -2.330828e-01 -4.140710e-02 -5.134591e-03 -9.412688e-03 -0.010596   \n",
       "V1   -9.805358e-16 -8.621897e-17  3.208233e-17  9.820892e-16 -0.227709   \n",
       "V2   -4.261894e-17  2.601622e-16 -4.478472e-16 -3.676415e-16 -0.531409   \n",
       "V3    4.771164e-16  6.521501e-16  6.239832e-16  7.726948e-16 -0.210880   \n",
       "V4    5.394585e-16 -6.179751e-16 -6.403423e-17 -5.863664e-17  0.098732   \n",
       "\n",
       "         Class  \n",
       "Time -0.012323  \n",
       "V1   -0.101347  \n",
       "V2    0.091289  \n",
       "V3   -0.192961  \n",
       "V4    0.133447  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get correlation between fields and target\n",
    "corr = df.corr()\n",
    "corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2322c877a08>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "sns.heatmap(corr)\n",
    "\n",
    "# As data already has been converted to few features via PCA, the features will have miniminal correlation to each other\n",
    "# Only time has some interesting small correlation with other variables\n",
    "# Amount is also highly correlated to a few variables but not the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V1 is correlated with Time with value of 0.11739630597839038\n",
      "V5 is correlated with Time with value of 0.17307212331039132\n",
      "V12 is correlated with Time with value of 0.12434806837193435\n",
      "V22 is correlated with Time with value of 0.14405905486146361\n",
      "Class is correlated with V4 with value of 0.1334474862385853\n",
      "Amount is correlated with V6 with value of 0.21598118022524546\n",
      "Amount is correlated with V7 with value of 0.3973112781816456\n",
      "Class is correlated with V11 with value of 0.15487564474346413\n",
      "Amount is correlated with V20 with value of 0.3394034045461327\n",
      "Amount is correlated with V21 with value of 0.10599892763770487\n"
     ]
    }
   ],
   "source": [
    "# i is the row number from 0 to number of columns - 1\n",
    "# j is column number from i+1 to number of columns - 1\n",
    "# The two for loops traverse the upper right of matrix since the correlation value of corr.iloc[i,j] = corr.iloc[j,i]\n",
    "# We can skip out the lower left triangle matrix as it is repeat of top right triangle\n",
    "# Note the j starts from i+1 as i=j is just the white diagonal of feature having perfect corr with itself and is ignored\n",
    "for i in range(corr.shape[0]):\n",
    "    for j in range(i+1, corr.shape[0]):\n",
    "        if corr.iloc[i,j] >= 0.1:\n",
    "            print(str(df.columns[j]) + ' is correlated with ' + str(df.columns[i]) + ' with value of ' + str(corr.iloc[i,j]))\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount is correlated with V6 with value of 0.21598118022524546\n",
      "Amount is correlated with V7 with value of 0.3973112781816456\n",
      "Amount is correlated with V20 with value of 0.3394034045461327\n"
     ]
    }
   ],
   "source": [
    "for i in range(corr.shape[0]):\n",
    "    for j in range(i+1, corr.shape[0]):\n",
    "        if corr.iloc[i,j] >= 0.2:\n",
    "            print(str(df.columns[j]) + ' is correlated with ' + str(df.columns[i]) + ' with value of ' + str(corr.iloc[i,j]))\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "dfplot = df[['Amount','V6','V7','V20','Class']]\n",
    "dfplot['Amount'] = (dfplot['Amount'] - dfplot['Amount'].mean()) / dfplot['Amount'].std()\n",
    "dfplot['Amt_V7'] = (dfplot['Amount'] ** 2 + dfplot['V7'] ** 2) ** 0.5\n",
    "dfplot['Amt_V20'] = (dfplot['Amount'] ** 2 + dfplot['V20'] ** 2) ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Amount</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V20</th>\n",
       "      <th>Class</th>\n",
       "      <th>Amt_V7</th>\n",
       "      <th>Amt_V20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>0</td>\n",
       "      <td>0.342658</td>\n",
       "      <td>0.351020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.342474</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>0</td>\n",
       "      <td>0.351423</td>\n",
       "      <td>0.349372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.160684</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0</td>\n",
       "      <td>1.404848</td>\n",
       "      <td>1.273888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.140534</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>0</td>\n",
       "      <td>0.276058</td>\n",
       "      <td>0.251057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>0</td>\n",
       "      <td>0.597467</td>\n",
       "      <td>0.415084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Amount        V6        V7       V20  Class    Amt_V7   Amt_V20\n",
       "0  0.244964  0.462388  0.239599  0.251412      0  0.342658  0.351020\n",
       "1 -0.342474 -0.082361 -0.078803 -0.069083      0  0.351423  0.349372\n",
       "2  1.160684  1.800499  0.791461  0.524980      0  1.404848  1.273888\n",
       "3  0.140534  1.247203  0.237609 -0.208038      0  0.276058  0.251057\n",
       "4 -0.073403  0.095921  0.592941  0.408542      0  0.597467  0.415084"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfplot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1122.38x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlatedvarplots = sns.PairGrid(dfplot, vars=['Amount','V6','V7','V20','Amt_V7','Amt_V20'], hue='Class', palette='GnBu') \n",
    "# Set up diagonals to be histogram\n",
    "correlatedvarplots = correlatedvarplots.map_diag(plt.hist)\n",
    "# Set up non-diagonals to be scatter plot\n",
    "correlatedvarplots = correlatedvarplots.map_offdiag(plt.scatter)\n",
    "# Add legends on RHS\n",
    "correlatedvarplots = correlatedvarplots.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.discrete.discrete_model as sm\n",
    "def backwardElimination(x, Y, sl, columns):\n",
    "    numVars = len(x[0]) # Get length of a row for num of vars\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.Logit(Y, x, maxiter=200).fit() \n",
    "        # for loop to fit current set of all vars in x and response Y\n",
    "        # As loop goes on, x gets smaller as columns are deleted, and columns keeping track of col names also edited\n",
    "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
    "        print('Regression model retrained with ' + str(numVars) + ' variables.')\n",
    "        print('Max p value for a feature is: ' + str(maxVar))\n",
    "        # get max p value and if its more than sig level, start deleting jth column\n",
    "        # Since columns are getting deleted and x gets smaller, we need to update columns keeping track of column names\n",
    "        # Hence the only way to ensure the deletion of right column\n",
    "        # is to check the max p value with the current pvalues[j] in regression model\n",
    "        # if they are same, then jth column safe to delete\n",
    "        if maxVar > sl:\n",
    "            print('Max p value > ' + str(sl) + ', feature will be removed.')\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    print(str(j) + 'th column deleted: ' + str(columns[j]))\n",
    "                    x = np.delete(x, j, 1)\n",
    "                    columns = np.delete(columns, j)\n",
    "                    numVars -= 1\n",
    "        else:\n",
    "            print('All p values are above ' + str(sl) + '. Terminating model training')\n",
    "            print('p values list: ' + str(regressor_OLS.pvalues))\n",
    "            break\n",
    "                    \n",
    "    regressor_OLS.summary()\n",
    "    return x, columns # Return x data and list of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['V1' 'V2' 'V3' 'V4' 'V5' 'V6' 'V7' 'V8' 'V9' 'V10' 'V11' 'V12' 'V13'\n",
      " 'V14' 'V15' 'V16' 'V17' 'V18' 'V19' 'V20' 'V21' 'V22' 'V23' 'V24' 'V25'\n",
      " 'V26' 'V27' 'V28' 'Amount']\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.021744\n",
      "         Iterations 13\n",
      "Regression model retrained with 29 variables.\n",
      "Max p value for a feature is: 0.0008223777240315293\n",
      "All p values are above 0.05. Terminating model training\n",
      "p values list: [1.22626063e-011 0.00000000e+000 0.00000000e+000 2.31496716e-283\n",
      " 0.00000000e+000 0.00000000e+000 0.00000000e+000 3.48971140e-077\n",
      " 0.00000000e+000 0.00000000e+000 8.22377724e-004 8.50653594e-234\n",
      " 3.56590712e-018 2.07620593e-013 3.96097221e-113 1.51796297e-273\n",
      " 2.62253051e-029 0.00000000e+000 0.00000000e+000 0.00000000e+000\n",
      " 0.00000000e+000 3.41661137e-198 9.20480965e-068 1.11124175e-037\n",
      " 5.08786840e-032 2.29908502e-014 0.00000000e+000 0.00000000e+000\n",
      " 0.00000000e+000]\n",
      "New variable list:\n",
      "['V1' 'V2' 'V3' 'V4' 'V5' 'V6' 'V7' 'V8' 'V9' 'V10' 'V11' 'V12' 'V13'\n",
      " 'V14' 'V15' 'V16' 'V17' 'V18' 'V19' 'V20' 'V21' 'V22' 'V23' 'V24' 'V25'\n",
      " 'V26' 'V27' 'V28' 'Amount']\n"
     ]
    }
   ],
   "source": [
    "# .values convert dataframe to np array\n",
    "x = df.drop(['Class','Time'], axis=1)\n",
    "varlist = x.columns.values\n",
    "x = x.values\n",
    "Y = df['Class'].values\n",
    "print(varlist)\n",
    "\n",
    "new_x, newvarlist = backwardElimination(x, Y, 0.05, varlist)\n",
    "\n",
    "print('New variable list:')\n",
    "print(newvarlist)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the time\n",
    "df = df.drop(['Time'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "from collections import Counter\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb \n",
    "from sklearn import metrics\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.over_sampling import ADASYN\n",
    "import math\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "class modelpipeline:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def run_model(self, df, testratio, standardize, sampletype, modelname, text, CV):\n",
    "        # df = df.drop('Time', axis=1)\n",
    "        if standardize == True:\n",
    "            df = self.standardize(df)\n",
    "        if sampletype == 'smote':\n",
    "            X_train, X_test, y_train, y_test = sampling.smote_oversample(df, testratio)\n",
    "        elif sampletype == 'adasyn':\n",
    "            X_train, X_test, y_train, y_test = sampling.adasyn_oversample(df, testratio)\n",
    "        else:\n",
    "            X_train, X_test, y_train, y_test = sampling.naive_oversample(df, testratio)\n",
    "        store = self.build_model(X_train, X_test, y_train, y_test, text, modelname, CV)\n",
    "        # test model with all actual fraud results\n",
    "        store['actual_accuracy'] = evaluate.actual_acc(df, store['model'])\n",
    "        return store\n",
    "    \n",
    "    def build_model(self, X_train, X_test, y_train, y_test, text, modelname, CV):\n",
    "        if modelname == 'LogisticRegression':\n",
    "            if CV == True:\n",
    "                param_grid = dict(C=[0.8,1,1.2], max_iter=[300], solver=['liblinear'])\n",
    "                LogRegression = LogisticRegression()\n",
    "                model = GridSearchCV(LogRegression, param_grid, cv=5, scoring='f1', verbose=10)\n",
    "                model.fit(X_train,y_train)\n",
    "                print(\"Best f1 score: \" + str(model.best_score_))\n",
    "                print(\"Best parameters: \" + str(model.best_params_))\n",
    "            else:\n",
    "                model = LogisticRegression(max_iter=300, C=0.8, solver='liblinear')\n",
    "                model.fit(X_train,y_train)\n",
    "        elif modelname == 'XGBoost':\n",
    "            if CV == True:\n",
    "                end_value = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "                start_value = end_value - 2       \n",
    "                # treedepth = list(range(start_value, end_value+1, 2))\n",
    "                param_grid = dict(n_estimators=[100], max_depth=[end_value])\n",
    "                GradientBoost = GradientBoostingClassifier()\n",
    "                model = GridSearchCV(GradientBoost, param_grid, cv=5, scoring='f1', verbose=10)\n",
    "                model.fit(X_train,y_train)\n",
    "                print(\"Best f1 score: \" + str(model.best_score_))\n",
    "                print(\"Best parameters: \" + str(model.best_params_))\n",
    "            else:\n",
    "                model = xgb.XGBClassifier(seed=42, nthread=1, max_depth=math.ceil(math.sqrt(X_train.shape[1])),\n",
    "                                          n_estimators=100, random_state=42)\n",
    "                model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=5)\n",
    "        elif modelname == 'RandomForest':\n",
    "            if CV == True:\n",
    "                start_value = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "                end_value = start_value + 11         \n",
    "                treedepth = list(range(start_value, end_value, 5))\n",
    "                param_grid = dict(random_state=[42], max_depth=treedepth, n_estimators=[100,150])\n",
    "                RFC = RandomForestClassifier()\n",
    "                model = GridSearchCV(RFC, param_grid, cv=5, scoring='f1', verbose=10)\n",
    "                model.fit(X_train,y_train)\n",
    "                print(\"Best f1 score: \" + str(model.best_score_))\n",
    "                print(\"Best parameters: \" + str(model.best_params_))\n",
    "            else:\n",
    "                treedepth = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "                model = RandomForestClassifier(random_state=42, max_depth=treedepth, n_estimators=100)\n",
    "                model.fit(X_train,y_train)\n",
    "        else:\n",
    "            # Parameters based on gridsearchcv of modelname = logistic regresion\n",
    "            # Leave parameter blank for modelname to run this instance of logistic regression\n",
    "            model = LogisticRegression(C=0.8, max_iter=300, solver='liblinear')\n",
    "            model.fit(X_train,y_train)\n",
    "        \n",
    "        y_predict = model.predict(X_test)\n",
    "        results = evaluate.model_results(y_test, y_predict, text)\n",
    "        store = {\"model\": model, \"X_train\": X_train, \"X_test\": X_test, \"y_train\": y_train, \n",
    "                 \"y_test\": y_test, \"results\": results}\n",
    "        print(\"Model fitting and results are complete!\")\n",
    "        return store\n",
    "    \n",
    "    def standardize(self, df):\n",
    "        # Variables already standardized except for Amount\n",
    "        # columns = df.columns.values.tolist()\n",
    "        # columns.remove('Class')\n",
    "        for column in ['Amount']:\n",
    "            df[column] = (df[column] - df[column].mean()) / df[column].std()\n",
    "        return df\n",
    "\n",
    "class sampling:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    @staticmethod\n",
    "    def naive_oversample(df, testratio):\n",
    "        X = df.drop(['Class'], axis=1)\n",
    "        y = df['Class']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testratio, random_state=42)\n",
    "        ros = RandomOverSampler(random_state=42)\n",
    "        X_train, y_train = ros.fit_resample(X_train, y_train)\n",
    "        # train test split keeps X_test and y_test as pd series, oversampler converts X_train, y_train to numpy\n",
    "        # Convert all to numpy array for XGBoost to not have bugs\n",
    "        X_test = X_test.values\n",
    "        y_test = y_test.values\n",
    "        print(\"Oversampling is complete!\")\n",
    "        return X_train, X_test, y_train, y_test\n",
    "    \n",
    "    @staticmethod\n",
    "    def smote_oversample(df, testratio):\n",
    "        X = df.drop(['Class'], axis=1)\n",
    "        y = df['Class']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testratio, random_state=42)\n",
    "        X_train, y_train = SMOTE().fit_resample(X_train, y_train)\n",
    "        # train test split keeps X_test and y_test as pd series, oversampler converts X_train, y_train to numpy\n",
    "        # Convert all to numpy array for XGBoost to not have bugs\n",
    "        X_test = X_test.values\n",
    "        y_test = y_test.values\n",
    "        print(\"Number of Xs and Ys for SMOTE:\")\n",
    "        print(sorted(Counter(y_train).items()))\n",
    "        print(\"Oversampling is complete!\")\n",
    "        return X_train, X_test, y_train, y_test\n",
    "    \n",
    "    @staticmethod\n",
    "    def adasyn_oversample(df, testratio):\n",
    "        X = df.drop(['Class'], axis=1)\n",
    "        y = df['Class']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testratio, random_state=42)\n",
    "        X_train, y_train = ADASYN().fit_resample(X_train, y_train)\n",
    "        # train test split keeps X_test and y_test as pd series, oversampler converts X_train, y_train to numpy\n",
    "        # Convert all to numpy array for XGBoost to not have bugs\n",
    "        X_test = X_test.values\n",
    "        y_test = y_test.values\n",
    "        print(\"Number of Xs and Ys for ADASYN:\")\n",
    "        print(sorted(Counter(y_train).items()))\n",
    "        print(\"Oversampling is complete!\")\n",
    "        return X_train, X_test, y_train, y_test\n",
    "\n",
    "\n",
    "\n",
    "class evaluate:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    @staticmethod\n",
    "    def model_results(y_test, y_predict, text):\n",
    "        cm = metrics.confusion_matrix(y_test, y_predict)\n",
    "        print(cm)\n",
    "        RFC_CM = pd.DataFrame(cm, ['Actual 0', 'Actual 1'], ['Predict 0', 'Predict 1'])\n",
    "        sns.heatmap(RFC_CM, annot=True, annot_kws={\"size\": 16}, cmap='Greens', linewidths=1, fmt='g')# font size\n",
    "        sns.set(font_scale=1.4)#for label size\n",
    "        plt.title(\"Confusion Matrix for \" + text)\n",
    "\n",
    "        # fix for mpl bug that cuts off top/bottom of seaborn viz\n",
    "        b, t = plt.ylim() \n",
    "        b += 0.5 \n",
    "        t -= 0.5 \n",
    "        plt.ylim(b, t) \n",
    "        plt.show() \n",
    "\n",
    "        accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "        print('Accuracy: ' + str(accuracy))\n",
    "        sensitivity = cm[1][1] / (cm[1][1] + cm[1][0])\n",
    "        recall = sensitivity\n",
    "        print('Sensitivity: ' + str(sensitivity))\n",
    "        specificity = cm[0][0] / (cm[0][0] + cm[0][1])\n",
    "        print('Specificity: ' + str(specificity))\n",
    "        precision = cm[1][1] / (cm[1][1] + cm[0][1])\n",
    "        print('Precision: ' + str(precision))\n",
    "        f1 = 2 * (recall * precision)/(recall + precision)\n",
    "        print('f1 score: ' + str(f1))\n",
    "        auc = evaluate.ROC(y_test, y_predict, text)\n",
    "        results = {\"accuracy\": accuracy, \"sensitivity\": sensitivity, \"specificity\": specificity, \n",
    "                   \"precision\": precision, \"f1\": f1, \"auc\": auc}\n",
    "        print(\"Model classification metrics have finished calculating!\")\n",
    "        return results\n",
    "    \n",
    "    @staticmethod\n",
    "    def ROC(y_test, y_predict, text):\n",
    "        # IMPORTANT: first argument is true values, second argument is predicted probabilities\n",
    "        auc = metrics.roc_auc_score(y_test, y_predict)\n",
    "        print(\"AUC value is: \" + str(auc))\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_test, y_predict)\n",
    "        plt.plot(fpr, tpr)\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.0])\n",
    "        plt.title('ROC curve for ' + text)\n",
    "        plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "        plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "        plt.grid(True)\n",
    "        return auc\n",
    "\n",
    "    @staticmethod\n",
    "    def actual_acc(df, model):\n",
    "        allpositive = df[df['Class'] == 1].copy()\n",
    "        x_positive = allpositive.drop(['Class'], axis=1)\n",
    "        y_positive = allpositive['Class']\n",
    "        y_pospredict = model.predict(x_positive)\n",
    "        accuracy_positive = metrics.accuracy_score(y_positive, y_pospredict)\n",
    "        print(\"Accuracy with all fraud results is \" + str(accuracy_positive * 100) + \"%\")\n",
    "        return accuracy_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelpipeline = modelpipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 30)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56743   121]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9976826656367402\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9978721159257175\n",
      "Precision: 0.4182692307692308\n",
      "f1 score: 0.5686274509803921\n",
      "AUC value is: 0.9428136089832668\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.08943089430895%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranforest_29 = modelpipeline.run_model(df, 0.2, True, \"naive\", \"RandomForest\", \"Random Forest with 29 variables\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56736   128]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.997559776693234\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9977490151941475\n",
      "Precision: 0.4046511627906977\n",
      "f1 score: 0.5559105431309904\n",
      "AUC value is: 0.9427520586174819\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.69918699186992%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop V7 and rerun\n",
    "df2 = df.drop(columns=['V7'], axis=1)\n",
    "ranforest_v7 = modelpipeline.run_model(df2, 0.2, True, \"naive\", \"RandomForest\", \"Random Forest with 28 variables no V7\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56745   119]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9977177767634563\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9979072875633089\n",
      "Precision: 0.4223300970873786\n",
      "f1 score: 0.5723684210526315\n",
      "AUC value is: 0.9428311948020626\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.69918699186992%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop V20 and rerun\n",
    "df2 = df.drop(columns=['V20'], axis=1)\n",
    "ranforest_v20 = modelpipeline.run_model(df2, 0.2, True, \"naive\", \"RandomForest\", \"Random Forest with 28 variables no V20\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56741   123]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9976475545100242\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9978369442881261\n",
      "Precision: 0.4142857142857143\n",
      "f1 score: 0.564935064935065\n",
      "AUC value is: 0.9427960231644712\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.4959349593496%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop Amount and rerun\n",
    "df2 = df.drop(columns=['Amount'], axis=1)\n",
    "ranforest_v20 = modelpipeline.run_model(df2, 0.2, False, \"naive\", \"RandomForest\", \"Random Forest with 28 variables no Amount\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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",
       "      <td>count</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation of dataset for variable selection and correlation measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_notime = df.drop('Time', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurenames = list(df_notime.columns.values)\n",
    "featurenames.remove('Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount']\n"
     ]
    }
   ],
   "source": [
    "print(featurenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n"
     ]
    }
   ],
   "source": [
    "# Run the class sampling above to be able to do naive oversampling\n",
    "\n",
    "# Sampling data before looking at correlation matrix\n",
    "X_train, X_test, y_train, y_test = sampling.naive_oversample(df_notime, 0.2)\n",
    "X_train = pd.DataFrame(X_train, columns=featurenames)\n",
    "X_test = pd.DataFrame(X_train, columns=featurenames)\n",
    "y_train = pd.DataFrame(y_train, columns=['Class'])\n",
    "y_test = pd.DataFrame(y_test, columns=['Class'])\n",
    "all_train = pd.concat([X_train, y_train], axis=1)\n",
    "all_test = pd.concat([X_test, y_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>V20</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.955041</td>\n",
       "      <td>-0.380783</td>\n",
       "      <td>-0.315013</td>\n",
       "      <td>0.330155</td>\n",
       "      <td>-0.509374</td>\n",
       "      <td>-0.086197</td>\n",
       "      <td>-0.627978</td>\n",
       "      <td>0.035994</td>\n",
       "      <td>1.054560</td>\n",
       "      <td>-0.030441</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125390</td>\n",
       "      <td>0.238197</td>\n",
       "      <td>0.968305</td>\n",
       "      <td>0.053208</td>\n",
       "      <td>-0.278602</td>\n",
       "      <td>-0.044999</td>\n",
       "      <td>-0.216780</td>\n",
       "      <td>0.045168</td>\n",
       "      <td>-0.047145</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.400975</td>\n",
       "      <td>-0.626943</td>\n",
       "      <td>1.555339</td>\n",
       "      <td>-2.017772</td>\n",
       "      <td>-0.107769</td>\n",
       "      <td>0.168310</td>\n",
       "      <td>0.017959</td>\n",
       "      <td>-0.401619</td>\n",
       "      <td>0.040378</td>\n",
       "      <td>0.611115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.470372</td>\n",
       "      <td>-0.153485</td>\n",
       "      <td>0.421703</td>\n",
       "      <td>0.113442</td>\n",
       "      <td>-1.004095</td>\n",
       "      <td>-1.176695</td>\n",
       "      <td>0.361924</td>\n",
       "      <td>-0.370469</td>\n",
       "      <td>-0.144792</td>\n",
       "      <td>45.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.072509</td>\n",
       "      <td>0.820566</td>\n",
       "      <td>-0.561351</td>\n",
       "      <td>-0.709897</td>\n",
       "      <td>1.080399</td>\n",
       "      <td>-0.359429</td>\n",
       "      <td>0.787858</td>\n",
       "      <td>0.117276</td>\n",
       "      <td>-0.131275</td>\n",
       "      <td>-0.638222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012227</td>\n",
       "      <td>-0.314638</td>\n",
       "      <td>-0.872959</td>\n",
       "      <td>0.083391</td>\n",
       "      <td>0.148178</td>\n",
       "      <td>-0.431459</td>\n",
       "      <td>0.119690</td>\n",
       "      <td>0.206395</td>\n",
       "      <td>0.070288</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.535045</td>\n",
       "      <td>1.014587</td>\n",
       "      <td>1.750679</td>\n",
       "      <td>2.769390</td>\n",
       "      <td>0.500089</td>\n",
       "      <td>1.002270</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>-0.081323</td>\n",
       "      <td>0.371579</td>\n",
       "      <td>0.560595</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.253757</td>\n",
       "      <td>0.063525</td>\n",
       "      <td>0.443431</td>\n",
       "      <td>-0.072754</td>\n",
       "      <td>0.448192</td>\n",
       "      <td>-0.655203</td>\n",
       "      <td>-0.181038</td>\n",
       "      <td>-0.093013</td>\n",
       "      <td>-0.064931</td>\n",
       "      <td>117.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-4.026938</td>\n",
       "      <td>1.897371</td>\n",
       "      <td>-0.429786</td>\n",
       "      <td>-0.029571</td>\n",
       "      <td>-0.855751</td>\n",
       "      <td>-0.480406</td>\n",
       "      <td>-0.435632</td>\n",
       "      <td>1.313760</td>\n",
       "      <td>0.536044</td>\n",
       "      <td>1.221746</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012320</td>\n",
       "      <td>-0.480691</td>\n",
       "      <td>-0.230369</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.066399</td>\n",
       "      <td>0.470787</td>\n",
       "      <td>0.245335</td>\n",
       "      <td>0.286904</td>\n",
       "      <td>-0.322672</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
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       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0  1.955041 -0.380783 -0.315013  0.330155 -0.509374 -0.086197 -0.627978   \n",
       "1 -0.400975 -0.626943  1.555339 -2.017772 -0.107769  0.168310  0.017959   \n",
       "2  0.072509  0.820566 -0.561351 -0.709897  1.080399 -0.359429  0.787858   \n",
       "3 -0.535045  1.014587  1.750679  2.769390  0.500089  1.002270  0.847902   \n",
       "4 -4.026938  1.897371 -0.429786 -0.029571 -0.855751 -0.480406 -0.435632   \n",
       "\n",
       "         V8        V9       V10  ...       V20       V21       V22       V23  \\\n",
       "0  0.035994  1.054560 -0.030441  ... -0.125390  0.238197  0.968305  0.053208   \n",
       "1 -0.401619  0.040378  0.611115  ... -0.470372 -0.153485  0.421703  0.113442   \n",
       "2  0.117276 -0.131275 -0.638222  ...  0.012227 -0.314638 -0.872959  0.083391   \n",
       "3 -0.081323  0.371579  0.560595  ... -0.253757  0.063525  0.443431 -0.072754   \n",
       "4  1.313760  0.536044  1.221746  ... -0.012320 -0.480691 -0.230369  0.250717   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Amount  \n",
       "0 -0.278602 -0.044999 -0.216780  0.045168 -0.047145    9.99  \n",
       "1 -1.004095 -1.176695  0.361924 -0.370469 -0.144792   45.90  \n",
       "2  0.148178 -0.431459  0.119690  0.206395  0.070288   11.99  \n",
       "3  0.448192 -0.655203 -0.181038 -0.093013 -0.064931  117.44  \n",
       "4  0.066399  0.470787  0.245335  0.286904 -0.322672   25.76  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <td>-0.131275</td>\n",
       "      <td>-0.638222</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.314638</td>\n",
       "      <td>-0.872959</td>\n",
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       "      <td>0.371579</td>\n",
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       "      <td>-0.093013</td>\n",
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       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
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       "\n",
       "[5 rows x 30 columns]"
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     "execution_count": 13,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <td>-0.638222</td>\n",
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       "      <td>3</td>\n",
       "      <td>-0.535045</td>\n",
       "      <td>1.014587</td>\n",
       "      <td>1.750679</td>\n",
       "      <td>2.769390</td>\n",
       "      <td>0.500089</td>\n",
       "      <td>1.002270</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>-0.081323</td>\n",
       "      <td>0.371579</td>\n",
       "      <td>0.560595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063525</td>\n",
       "      <td>0.443431</td>\n",
       "      <td>-0.072754</td>\n",
       "      <td>0.448192</td>\n",
       "      <td>-0.655203</td>\n",
       "      <td>-0.181038</td>\n",
       "      <td>-0.093013</td>\n",
       "      <td>-0.064931</td>\n",
       "      <td>117.44</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-4.026938</td>\n",
       "      <td>1.897371</td>\n",
       "      <td>-0.429786</td>\n",
       "      <td>-0.029571</td>\n",
       "      <td>-0.855751</td>\n",
       "      <td>-0.480406</td>\n",
       "      <td>-0.435632</td>\n",
       "      <td>1.313760</td>\n",
       "      <td>0.536044</td>\n",
       "      <td>1.221746</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.480691</td>\n",
       "      <td>-0.230369</td>\n",
       "      <td>0.250717</td>\n",
       "      <td>0.066399</td>\n",
       "      <td>0.470787</td>\n",
       "      <td>0.245335</td>\n",
       "      <td>0.286904</td>\n",
       "      <td>-0.322672</td>\n",
       "      <td>25.76</td>\n",
       "      <td>0.0</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",
       "0  1.955041 -0.380783 -0.315013  0.330155 -0.509374 -0.086197 -0.627978   \n",
       "1 -0.400975 -0.626943  1.555339 -2.017772 -0.107769  0.168310  0.017959   \n",
       "2  0.072509  0.820566 -0.561351 -0.709897  1.080399 -0.359429  0.787858   \n",
       "3 -0.535045  1.014587  1.750679  2.769390  0.500089  1.002270  0.847902   \n",
       "4 -4.026938  1.897371 -0.429786 -0.029571 -0.855751 -0.480406 -0.435632   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.035994  1.054560 -0.030441  ...  0.238197  0.968305  0.053208 -0.278602   \n",
       "1 -0.401619  0.040378  0.611115  ... -0.153485  0.421703  0.113442 -1.004095   \n",
       "2  0.117276 -0.131275 -0.638222  ... -0.314638 -0.872959  0.083391  0.148178   \n",
       "3 -0.081323  0.371579  0.560595  ...  0.063525  0.443431 -0.072754  0.448192   \n",
       "4  1.313760  0.536044  1.221746  ... -0.480691 -0.230369  0.250717  0.066399   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0 -0.044999 -0.216780  0.045168 -0.047145    9.99    1.0  \n",
       "1 -1.176695  0.361924 -0.370469 -0.144792   45.90    0.0  \n",
       "2 -0.431459  0.119690  0.206395  0.070288   11.99    0.0  \n",
       "3 -0.655203 -0.181038 -0.093013 -0.064931  117.44    0.0  \n",
       "4  0.470787  0.245335  0.286904 -0.322672   25.76    0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation matrix for naive oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28ce627f048>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dhwAAADC+uWJSW1JSovLycsViMQUCAdm2rVAoJJ/Pp1QqpYaGBhUWFupv//ZvFQwGx8yIRCKKRCKSpKa/D+WzfAAAgPzgQrGsXNHUSvuXIESjUQUCAUWjUa1evVqS9I1vfEOlpaXq6+vT+vXrdfTRR2v27NkHPD8cDiscDkuS9v3up3mtHQAAAM5yxfIDSQoGg+rs7FR3d7eSyaTKysokSaWlpZKkWbNmqbKyUq+//rqDVQIAAMCNXNPU+v1+VVZWqrW1VZZlSZL27Nkzss52165devXVVzVv3jwnywQAAHBOOu38i0u5ZvmBJNXU1KilpUVr166VJG3btk3f/OY3VVBQoHQ6rdraWppaAAAAHMBVTW0wGFRbW9vI6x/5yEd0yy23OFgRAACAi7BPbVauWX4AAAAAfFA0tQAAAPA8Vy0/AAAAwEG4+EItpzGpBQAAgOcxqQUAAPAKLhTLalw2tftuud1IzsWHVxnJGX5kQ84ZRxQUG6hE2v1PFxnJKfnWd4zk7F3zj0ZyfpP2G8m5YlPCSI6v5Pc5ZxQZ+kNKenufkZzDhs18Iy2YfYSRnL6B3UZyyjY9YyRHw8M5R6R39hsoRFqanmUkZ0bu75IkqdCXMZIzLW2moBOrtxvJ6dh04N0t3693Csz8GO4dNPMzYnNfiZGco4ZSRnKsOWa+f/36nWlGcuBuLD8AAACA543LSS0AAMC4xIViWTGpBQAAgOcxqQUAAPAKLhTLikktAAAAPI+mFgAAAJ7H8gMAAACv4EKxrJjUAgAAwPNcMaltampSbW2tqqr+fLOD9vZ29fb2qra2Vvfcc4927NghSbrmmmt05JFHOlUqAACAc5jUZuWKptayLNm2PaqptW1bdXV1uuuuu7Ry5UotXbpU+/btk8/nc7BSAAAAuJErlh9UV1ero6NDqdT+2+rF43ElEgkddthhGh4e1tKlSyVJfr9fkyZNcrJUAAAAuJArJrUlJSUqLy9XLBZTIBCQbdsKhULq7e3VlClT1NLSong8riVLlmjVqlUqKDiwF49EIopEIpKkhny/AwAAAPmQyThdgWu5YlIr7V+CEI1GJUnRaFSWZSmdTuuVV17RhRdeqJtvvll9fX3auHHjmM8Ph8Nqbm5Wc3NzHqsGAACAG7imqQ0Gg+rs7FR3d7eSyaTKyspUWlqqBQsWaNasWSosLFQwGFR3d7fTpQIAAMBlXLH8QNq/XrayslKtra2yLEuSVF5err1792rXrl2aOnWqOjs7VVZW5nClAAAADmH3g6xcM6mVpJqaGvX09Iw0tQUFBbrwwgu1fv16felLX1Imk1E4HHa4SgAAALiNaya10v4lCG1tbaOOLV26VC0tLQ5VBAAA4CJMarNy1aQWAAAA+CBoagEAAOB5rlp+AAAAgIPIsPwgGya1AAAA8DwmtQAAAF7BhWJZjcumtvjqq43k3BW6zEhO4/nfzDnj+XvM3Px38jVXGcnZu+YfjeRMufN+IznXn2Dm/ZpwzmlGcnyzjs0544/f/bfcC5FUsNTMNniTM5uM5KjAZySmfHbCSE7hJ/7OSI4mTMw5onB4yEAh0tZf/cpIzvYxbkn+QRw9YCZnZ4GZH1nPbZpjJCel3P8vL5ywx0AlUnK40EjOsqPjRnL27S4ykjO4z8zn/J1M0kgO3I3lBwAAAPC8cTmpBQAAGJcyGacrcC0mtQAAAPA8JrUAAABewYViWTGpBQAAgOfR1AIAAMDzWH4AAADgFSw/yMoVTW1TU5Nqa2tVVVU1cqy9vV2RSEQTJvy5xLfeektXXnmlgsGgE2UCAADApVzR1FqWJdu2RzW1tm3rkksu0eLFiyVJe/bs0Zo1a7Rs2TKnygQAAHBWhkltNq5YU1tdXa2Ojg6lUilJUjweVyKRUEVFxcg5mzZt0oknnqhJkyY5VSYAAABcyhWT2pKSEpWXlysWiykQCMi2bYVCIfl8f74FYTQa1ac+9amsGZFIRJFIRJJ0wwWnfug1AwAAwD1c0dRK+5cgRKNRBQIBRaNRrV69euSxnTt36o033jjo0oNwOKxweP897gdf/MWHXi8AAEC+ZdLcUSwbVyw/kKRgMKjOzk51d3crmUyqrKxs5LGnnnpKwWBw1EVjAAAAwP9wTZfo9/tVWVmp1tZWWZY16rFoNKrPfvazDlUGAADgEmzplZVrJrWSVFNTo56enlFNbTwe19tvv63KykoHKwMAAICbuWZSK+1fgtDW1jbq2JFHHql7773XoYoAAADgBa5qagEAAHAQ7FOblauWHwAAAAAfBE0tAAAAPI/lBwAAAF7BPrVZMakFAACA5/kymcy4a/l/MLfOSE65+o3kbJowOeeMT039k4FKpLbdRxjJ+U16h5Gc61NTjOQEO79mJOeHyxqN5BQZ+LKqOWq7gUqkn2yfYyTn/OP+aCTnoT/MN5Jz+sSdRnJ+mZpuJKffd+hzDsVv6LvxZ47eZiTnta2lRnK2FBQbyVlSuNtIzgOG6vl8JvefEUceZeZ9euuNaUZy5i8y83X10suzjORM8pm5KGrKpKSRnKqenxrJyUX/nZc5XYImr/mG0yWMiUktAAAAPI+mFgAAAJ7HhWIAAABewW1ys2JSCwAAAM9jUgsAAOAV4+/6fmOY1AIAAMDzaGoBAADgeSw/AAAA8AouFMvKFU1tU1OTamtrVVVVNXKsvb1dvb298vv9+t3vfqdMJqMlS5booosuks9nYJdzAAAAjBuuWH5gWZZs2x51zLZthUIhvfrqq2ppadEtt9yirVu36uWXX3aoSgAAAIelM86/uJQrmtrq6mp1dHQolUpJkuLxuBKJhCZMmKBkMqmhoSGlUikNDw9r2jQztwIEAADA+OGK5QclJSUqLy9XLBZTIBAYmdIed9xxOv7443XJJZcok8nok5/8pObNmzdmRiQSUSQSkSQtyWfxAAAAcJwrJrXS/iUI0WhUkhSNRmVZlrZv365t27bpnnvu0b333qvOzs6syw/C4bCam5vV3Nycz7IBAADyJ5N2/sWlXNPUBoNBdXZ2qru7W8lkUmVlZXrmmWe0aNEi+f1++f1+nXjiierq6nK6VAAAALiMa5pav9+vyspKtba2yrIsSdLMmTP1yiuvaHh4WENDQ3r55Zd11FFHOVwpAACAQ5y+SIwLxd6bmpoa9fT0jDS11dXVmjVrlr785S9r3bp1OuaYY7RixQqHqwQAAIDbuOJCsf8RDAbV1tY28npBQYEuueQSBysCAACAF7iqqQUAAEB2Ge4olpWrlh8AAAAAHwSTWgAAAK9w8YVaTmNSCwAAAM+jqQUAAIDnjcvlByub5xvJWbHuCSM5zz1wUc4ZC879dwOVSK/dZWZLtCs2JYzkTDjnNCM5P1zWaCTnsy+sN5Iz3PNizhlVZ95koBLp+R+ebyTniQt+bSTnC62VRnJ6Gv7bSM4ldxjaJnCSP/eMvbtyz5B03ZVDRnI0yUzMFwp3Gsn5VXK6kZyzBlJGcnb5cv8A7dhabKASaXthkZGcSa8PG8mZO22PkZzEbjMfny+nB43kRIyk5MjFd/RyGpNaAAAAeB5NLQAAADxvXC4/AAAAGJfY/SArJrUAAADwPCa1AAAAXsEdxbJiUgsAAADPo6kFAACA57li+UFTU5Nqa2tVVVU1cqy9vV29vb3y+/16/vnnJUl/93d/p1Ao5FSZAAAAzuJCsaxcMam1LEu2bY86Ztu2pk2bptdee01f+9rXdNNNN+mnP/2p+vv7HaoSAAAAbuWKpra6ulodHR1Kpfbf5SUejyuRSGjSpEmqrKxUYWGh/H6/jjnmGMViMYerBQAAcEgm7fyLS7miqS0pKVF5eflIw2rbtkKh0EgTOzg4qF27dumll17Sjh07xsyIRCJqaGhQQ0NDPksHAACAC7hiTa20fwlCNBpVIBBQNBrV6tWrVVZWpq1bt+r666/X1KlTddxxx6mwsHDM54fDYYXDYUnSwAPX5LN0AAAAOMw1TW0wGNQDDzyg7u5uJZNJlZWVSZJWrlyplStXSpL+/d//XbNnz3ayTAAAAOdwoVhWrlh+IEl+v1+VlZVqbW2VZVmSpHQ6rd27d0uSenp69MYbb2jZsmVOlgkAAAAXcs2kVpJqamrU0tKitWvXSpKGhobU2NgoSZo8ebLWrFmTdfkBAADAeJfhjmJZuaqpDQaDamtrG3l94sSJuu222xysCAAAAF7gmuUHAAAAwAflqkktAAAADoILxbJiUgsAAADPY1ILAADgFUxqs2JSCwAAAM8bn5PaGbOMxLyb2mskx3fE0TlnZDKGfjObPd9IjK/k92ZyZh1rJKcoEzWSM9zzopGcwmOW5pwRH3jHQCVSwdxFRnLSvl8bydG0mUZiCgrNbGvjm3eckRwjincaidmhp43kHKGJRnJ2D0wykuO2HR2HMr6cM/y+YQOVSEcM516LJE2cNGQkJ5M2U08ybWb2tmt4n5EcuNv4bGoBAADGowz71GbD8gMAAAB4Hk0tAAAAPI/lBwAAAF7B7gdZMakFAACA5zGpBQAA8IgMk9qsmNQCAADA82hqAQAA4Hl5XX7Q1NSk2tpaVVVVjRxrb29Xb2+v+vr61NXVpYqKCjU0NIw8Ho/Hdfvtt2vPnj1asGCB1qxZowkTWDUBAAD+CrH8IKu8Tmoty5Jt26OO2bYty7J0zjnn6IorrjjgOQ8++KDOPvts3XHHHZoyZYqeeOKJfJULAAAAj8hrU1tdXa2Ojg6lUilJ+6ewiURCFRUVWrJkiYqLi0edn8lk9NJLL6m6ulqSdMopp+jZZ5/NZ8kAAADukU47/+JSeW1qS0pKVF5erlgsJmn/lDYUCsnnG/se0bt379bkyZNV+P9v+F1aWqpEIjHmuZFIRA0NDaOWLgAAACD/YrGYrrzySq1Zs0Y/+clPsp63adMm/f3f/722bt2a89vM+4VilmUpGo1KkqLRqCzLMpIbDofV3Nys5uZmI3kAAAB4/9LptO6//35de+21uu222xSNRvXmm28ecN7AwIB+/vOfa9GiRUbebt6b2mAwqM7OTnV3dyuZTKqsrCzruSUlJerv79fw8LAkKZFIqLS0NF+lAgAAuEs64/zLIWzZskWzZ8/WrFmzNGHCBIVCoTGXjz700EM655xzVFRUZORDk/dtBPx+vyorK9Xa2nrIKa3P59Pxxx+vTZs2ybIsbdy4UStWrMhTpQAAAPhLkUhEkUhk5PVwOKxwODzyeiKR0IwZM0ZenzFjhrq6ukZlvPbaa3r77bd10kkn6dFHHzVSlyN7Y9XU1KilpUVr164dOdbY2Kht27Zp3759qq+vV319vaqqqrRq1Srdfvvt2rBhgxYsWKDTTjvNiZIBAACc54Itvf6yif1LmcyBNf7v66fS6bS+973v6bLLLjNalyNNbTAYVFtb26hj69evH/PcWbNm6eabb85HWQAAAMjRjBkztGPHjpHXd+zYoenTp4+8vm/fPv3xj3/UDTfcIEl655139LWvfU1XXXWVFi5c+IHfLncxAAAAgDELFy5Ub2+v4vG4SktLZdu2vvjFL448PnnyZN1///0jrzc1NenCCy/MqaGVaGoBAAA8Y6w/7btNYWGhLr74Yt10001Kp9M69dRTNX/+fD300ENauHDhh3Z9FE0tAAAAjFq+fLmWL18+6tj5558/5rlNTU1G3iZNLQAAgFe44EIxt8r7PrUAAACAaeNyUnve6l8ayem67VNGco4//fqcM3p+dp2BSqSVq35oJKfI0O9Df/zuvxnJeWTeJCM5VWfeZCQnPvBOzhl9r/3CQCVS8dyPG8l5bs5JRnKmnnmDkZz/LD3ZSE7o42sPfdJ7MKkw983DZ0yaaqASKXru9EOf9B68++xOIznJfjM/apb9ycz3nd2GfvQlCnLPmZcZNFCJtGj2jkOf9B4UFJqZAsb7SozkpAz9rHnyjk8YyYG7jcumFgAAYFxi+UFWLD8AAACA5zGpBQAA8IgMk9qsmNQCAADA82hqAQAA4HksPwAAAPAKlh9kxaQWAAAAnpfXSW1TU5Nqa2tVVVU1cqy9vV29vb3q6+tTV1eXKioq1NDQMPL4Y489pvb2dvX19em+++7T1Klm9nEEAADA+JHXptayLNm2PaqptW1bdXV1GjPwIIUAACAASURBVBoa0uDgoCKRyKjnfOQjH9Hy5ct1ww1mNmwHAADwrLTTBbhXXpcfVFdXq6OjQ6lUSpIUj8eVSCRUUVGhJUuWqLi4+IDnLFiwQEceeWQ+ywQAAIDH5HVSW1JSovLycsViMQUCAdm2rVAoJJ/Pl3N2JBI5YMoLAAAwnrBPbXZ5v1DMsixFo1FJUjQalWVZRnLD4bCam5vV3NxsJA8AAADekfemNhgMqrOzU93d3UomkyorK8t3CQAAABhn8r5Prd/vV2VlpVpbW41NaQEAAP4qsPwgK0f2qa2pqVFPT8+opraxsVG33nqrNm/erPr6esViMUnSf/3Xf6m+vl47duzQunXrdM899zhRMgAAAFzMkTuKBYNBtbW1jTq2fv36Mc8966yzdNZZZ+WjLAAAAHdjS6+suKMYAAAAPI+mFgAAAJ7nyPIDAAAAvH/sU5sdk1oAAAB4HpNaAAAAr+BCsazGZVPbdm25kZyPXv2kkZzOH12Wc8aRn/yqgUqkvrvONZKT3t5nJKdgadhIzn31zxnJef6H5xvJKZi7KOeM4rkfN1CJNPDWb43kPLLkK0Zy9vzuu0Zy/vDpO4zkbNv4LSM5GkrmHJHZud1AIdLFdf9pJOejmVIjOXNThv5cmvsd1SVJR/n2GcmZqlTOGQUFZj429p9mGck5YnjISI7fN2wkZ7KhnBWXP2ok56VzrzeSgw8Hyw8AAADgeeNyUgsAADAecaFYdkxqAQAA4HlMagEAALyCC8WyYlILAAAAz6OpBQAAgOex/AAAAMAjMiw/yIpJLQAAADwvr5PapqYm1dbWqqqqauRYe3u7ent71dfXp66uLlVUVKihoWHk8TvuuENbt27VhAkTtHDhQl1yySWaMIEBMwAA+CvEpDarvE5qLcuSbdujjtm2LcuydM455+iKK6444Dk1NTW6/fbb1dLSomQyqSeeeCJf5QIAAMAj8trUVldXq6OjQ6nU/lsLxuNxJRIJVVRUaMmSJSouLj7gOcuXL5fP55PP51N5ebl27NiRz5IBAADgAXn9O35JSYnKy8sVi8UUCARk27ZCoZB8vkPf0HtoaEi//e1v9Q//8A9jPh6JRBSJRCRJjccaLBoAAMAluFAsu7xfKGZZlqLRqCQpGo3Ksqz39Lz77rtPixcv1uLFi8d8PBwOq7m5Wc3NzcZqBQAAgDfkvakNBoPq7OxUd3e3ksmkysrKDvmcH/3oR9q1a5c+97nP5aFCAAAAeE3etxHw+/2qrKxUa2vre5rS/upXv9ILL7ygxsZGFRSwAxkAAPgrxvKDrBzpEmtqatTT0zOqqW1sbNStt96qzZs3q76+XrFYTJL0rW99S++++66uu+46rVu3Tg8//LATJQMAAMDFHNnwNRgMqq2tbdSx9evXj3nuhg0b8lESAACA63GhWHb8PR8AAACeR1MLAAAAz+N+swAAAB7B8oPsmNQCAADA85jUAgAAeAST2ux8mUwm43QRpj06+7NGcmYX7jOS88fh4pwzyibtMVCJ9OLQVCM5hw2b+aqabOirc/kJvUZynu2cayQnfeg7Px/S/El7cw+RtCVZYiTnbzffaCTnN8dfYyRnWAY+yJJ2FBYayUm9h9t950tNadxIzhvxaUZy0oY+VyUTUkZy4kN+IzmzigZyztiVmmigEnMfm1TazB9w/ygzH+NjfLl/jCVp73CRkZzT+x4ykpOLvlP/xukSNOvXTzpdwphYfgAAAADPY/kBAACAV2Tc81cht2FSCwAAAM9jUgsAAOARXCiWHZNaAAAAeB5NLQAAADyP5QcAAAAekTGxZ+Q4ldemtqmpSbW1taqqqho51t7ert7eXvX19amrq0sVFRVqaGgYeby1tVXd3d3KZDKaM2eOLr/8cvn9Zva/AwAAwPiQ16bWsizZtj2qqbVtW3V1dRoaGtLg4KAikcio53z+85/X5MmTJUnf+9739Nhjj6m2tjafZQMAALgCF4pll9c1tdXV1ero6FAqtf/OJ/F4XIlEQhUVFVqyZImKiw+889b/NLSZTEbJZDKf5QIAAMAj8jqpLSkpUXl5uWKxmAKBgGzbVigUku8Qt5f8xje+oeeff17z5s3T5z73uTHPiUQiI1Ney3jlAAAAcLO8735gWZai0agkKRqNyrIO3YJedtlluvfee3XUUUfJtu0xzwmHw2publZzc7PRegEAANwik/E5/uJWeW9qg8GgOjs71d3drWQyqbKysvf0vIKCAoVCIT399NMfcoUAAADwmrw3tX6/X5WVlWptbT3klDaTyWj79u0j/37uuec0d+7cfJQJAAAAD3Fkn9qamhq1tLRo7dq1I8caGxu1bds27du3T/X19aqvr9fSpUt19913q7+/X5J0zDHH6Atf+IITJQMAADiO3Q+yc6SpDQaDamtrG3Vs/fr1Y55744035qMkAAAAeBh3FAMAAPAI7iiWXd7X1AIAAACm0dQCAADA81h+AAAA4BGZjNMVuBeTWgAAAHjeuJzUnlrXbyTnUxv2Gsn5+W1Lc844un6DgUqk1/6pwkhOwewjjOSowMyC9/tvM/Nf+QutlUZyNG1mzhFTz7zBQCHSnt9910jOb46/xkjOyS/dbCRn0wlXGcmp/UHYSI769+Sesf3N3DMkXX1jykjOiglFRnLihUZidFpm0EjO9IKkkZySw3Kv5813JhuoRPp9wUQjOSlDn6vpw2ZyfIamkhelXjaS84aRlNxwoVh2TGoBAADgeTS1AAAA8LxxufwAAABgPGL5QXZMagEAAOB5TGoBAAA8gi29smNSCwAAAM+jqQUAAIDnsfwAAADAI7hQLLu8NrVNTU2qra1VVVXVyLH29nb19vaqr69PXV1dqqioUENDwwHP/fa3v61f//rX+v73v5/PkgEAAOABeW1qLcuSbdujmlrbtlVXV6ehoSENDg4qEokc8LytW7dq714zd/cCAADwqkyGSW02eV1TW11drY6ODqVS+2/hGI/HlUgkVFFRoSVLlqi4uPiA56TTaT344IOqq6vLZ6kAAADwkLxOaktKSlReXq5YLKZAICDbthUKheTzZf+t47HHHtNJJ52k6dOnHzQ7EomMTHmvN1o1AAAA3C7vux9YlqVoNCpJikajsiwr67mJREJPPfWUzjzzzEPmhsNhNTc3q7m52VitAAAAbpJJO//iVnlvaoPBoDo7O9Xd3a1kMqmysrKs577++uvavn27vvjFL+ryyy9XMpnUmjVr8lgtAAAAvCDvW3r5/X5VVlaqtbX1oFNaSVq+fLm+9a1vjbx+4YUX6s477/ywSwQAAHClNBeKZeXIzRdqamrU09MzqqltbGzUrbfeqs2bN6u+vl6xWMyJ0gAAAOBBjtx8IRgMqq2tbdSx9evXH/J57FELAACAsXBHMQAAAI9gn9rsHFl+AAAAAJhEUwsAAADPY/kBAACAR2TSLD/IhkktAAAAPM+XyWQyThdh2ssLzzaS8/S+g9+a9736+LQ/5Zyx6Z0jDFQiLZ34rpGcvoHJRnLKZyeM5AzsmWgkp6DQzJdDQWHut1x5daeZ/3/HFu8xktPbP8VITrFvyEhOdefXjORsOuEqIzkTC3L/nJv6Znz0sTuN5Pz2tblGcpYeZqae7l3TjOQUGvpIlxj4v5wydNHPtElJIzlTp+4zkpMcLDSSs3fAzPf2eMpvJOesvg1GcnLxyqKznC5Bi7v+y+kSxsSkFgAAAJ5HUwsAAADP40IxAAAAj+BCseyY1AIAAMDzmNQCAAB4RJo7imXFpBYAAACeR1MLAAAAz8vr8oOmpibV1taqqqpq5Fh7e7t6e3vV19enrq4uVVRUqKGhYeTxu+++Wy+//LImT96/L+rll1+uY489Np9lAwAAuEKG5QdZ5bWptSxLtm2Pampt21ZdXZ2GhoY0ODioSCRywPMuvPBCVVdX57NUAAAAeEhelx9UV1ero6NDqVRKkhSPx5VIJFRRUaElS5aouLg4n+UAAAB4Sibj/Itb5XVSW1JSovLycsViMQUCAdm2rVAoJJ/v4KP0H/7wh3r44Yd1wgknaNWqVSoqKjrgnEgkMjLl/dyHUj0AAADcKu9belmWpWg0qkAgoGg0qtWrVx/0/AsuuECHH364hoaGdO+99+qRRx7Rueeee8B54XBY4XBYkvTyj87+UGoHAACAO+V994NgMKjOzk51d3crmUyqrKzsoOdPnz5dPp9PRUVFOvXUU7Vly5Y8VQoAAOAu6YzP8Re3yntT6/f7VVlZqdbWVlmWdcjzd+7cKUnKZDJ69tlnNX/+/A+7RAAAAHiMI3cUq6mpUUtLi9auXTtyrLGxUdu2bdO+fftUX1+v+vp6VVVV6Y477tCuXbskScccc4wuueQSJ0oGAABwHFt6ZedIUxsMBtXW1jbq2Pr168c896tf/Wo+SgIAAICHcUcxAAAAeJ4jk1oAAAC8f27eJ9ZpTGoBAADgeUxqAQAAPMLNW2o5jUktAAAAPG9cTmoXfHWpkZzPN0SN5Hzm6ytzzrjhH75voBLpxbvPM5JTtukZIzmFn/g7IznfvPi/jeRccscKIzm+ecflnBH6+NpDn/QebNv4LSM5nad900hO7Q/CRnI2nXCVkZzqzq8ZyckM7HZFhiR9/JTrjORcPMHMROg3A6VGckoLzCwmnJsZNJKT0IG3bH+/jprYb6AS6a3ByUZy3v6T30jOZN+wkZzBjJnZW/OEPxnJOctICj4s47KpBQAAGI/YpzY7lh8AAADA82hqAQAA4HksPwAAAPAIdj/IjkktAAAAPI9JLQAAgEdwQ7HsmNQCAADA82hqAQAA4Hl5XX7Q1NSk2tpaVVVVjRxrb29Xb2+v+vr61NXVpYqKCjU0NIw8nslktGHDBm3atEkFBQX6xCc+obPOYvtjAADw14cLxbLLa1NrWZZs2x7V1Nq2rbq6Og0NDWlwcFCRSGTUczZu3KgdO3botttuU0FBgd599918lgwAAAAPyOvyg+rqanV0dCiVSkmS4vG4EomEKioqtGTJEhUXFx/wnMcff1znnnuuCgr2lzpt2rR8lgwAAOAamYzP8Re3yuuktqSkROXl5YrFYgoEArJtW6FQSD5f9g9QX1+fbNvWM888o6lTp+qiiy7SnDlzDjgvEomMTHm/WuneDzgAAADMy/uFYpZlKRqNSpKi0agsyzro+alUSkVFRWpubtbpp5+u1tbWMc8Lh8Nqbm5Wc3Oz8ZoBAADgbnlvaoPBoDo7O9Xd3a1kMqmysrKDnj9jxgx99KMfHXluT09PPsoEAABwnbQLXtwq7zdf8Pv9qqysVGtr6yGntJIUCATU2dmp0047TS+//LLmzp2bhyoBAADwQcViMX3nO99ROp3W6aefrtra2lGPp1Ip3XXXXeru7lZJSYnWrl2rI488Mqe36cg+tTU1Nerp6RnV1DY2NurWW2/V5s2bVV9fr1gsJkmqra3V008/rS996Uv6j//4D1166aVOlAwAAOC4jHyOvxxKOp3W/fffr2uvvVa33XabotGo3nzzzVHnPPHEE5oyZYruvPNOnX322frBD36Q88fGkdvkBoNBtbW1jTq2fv36Mc+dMmWKrrnmmnyUBQAAgBxt2bJFs2fP1qxZsyRJoVBIzz77rObNmzdyznPPPafzzjtP0v7dsb797W8rk8kcdPOAQ3GkqQUAAIA3/e8dp6T9F+uHw+GR1xOJhGbMmDHy+owZM9TV1TUq43+fU1hYqMmTJ2v37t2aOnXqB66LphYAAMAj0hmnKziwif1LmcyBRf7lBPa9nPN+ObKmFgAAAOPTjBkztGPHjpHXd+zYoenTp2c9Z3h4WP39/TrssMNyers0tQAAAB6Rls/xl0NZuHChent7FY/HNTQ0JNu2tWLFilHnnHTSSdq4caMkadOmTTr++ONzntSy/AAAAADGFBYW6uKLL9ZNN92kdDqtU089VfPnz9dDDz2khQsXasWKFTrttNN01113ac2aNTrssMO0du3anN/u+Gxq/cVGYsZa7/GBFOQ+EC/0GRqqT/ngC7BHGR42kzNhopGYflN3Rp7kNxSUu0mFRWaChpJGYlI5/gY9on+PkZiJBWa2AM8M7DaS4ysuyT1kOJV7hqRJPjPf2s1UI800tAgwaej/oE9m6hk08MfO5HChgUqkIkPvU5GhrfUnGPr6HBw287PPX2Do+ynes+XLl2v58uWjjp1//vkj/544caL+5V/+xejbHJ9NLQAAwDj0XvaJ/WvFmloAAAB4HpNaAAAAjzCzsGN8YlILAAAAz6OpBQAAgOex/AAAAMAjuFAsOya1AAAA8Ly8TmqbmppUW1urqqqqkWPt7e3q7e1VX1+furq6VFFRoYaGhpHHGxsbNTAwIEnatWuXFi5cqKuuuiqfZQMAAMDl8trUWpYl27ZHNbW2bauurk5DQ0MaHBxUJBIZ9Zz169eP/LulpUWBQCBv9QIAALgJux9kl9flB9XV1ero6FAqtf8+NfF4XIlEQhUVFVqyZImKi7PfCWxgYEAvvfQSTS0AAAAOkNdJbUlJicrLyxWLxRQIBGTbtkKhkHzv4daHzzzzjE444QRNnjx5zMcjkcjIlPery8c+BwAAwMuY1GaX9wvFLMtSNBqVJEWjUVmW9Z6ed6hzw+Gwmpub1dzcbKROAAAAeEfem9pgMKjOzk51d3crmUyqrKzskM/ZvXu3tmzZouXLl+ehQgAAAHhN3vep9fv9qqysVGtr63ue0j711FNavny5Jk6c+CFXBwAA4F7sU5udI/vU1tTUqKenZ1RT29jYqFtvvVWbN29WfX29YrHYyGO2baumpsaJUgEAAOABjtxRLBgMqq2tbdSx/711119qamr6kCsCAABwvzSD2qy4oxgAAAA8j6YWAAAAnufI8gMAAAC8f2kuFMuKSS0AAAA8j0ktAACAR2ScLsDFaGoPoqjA0IdnX3/OEVMnGLr1b8bMDfbSO3N/nySpcHjISI7f1Ff53l1mcop35hwxY9JUA4VImZ3bjeQYs/1NIzGmPuWZgd1mgoZTOUf4Dis1UIg0ydD3romGPsgTDOUM+cwEFRjKGTTwZ+CJhcMGKpEmDJv53j5lgpnvyZOKzOTsHTDzf3lmQbGRHLgbyw8AAADgeUxqAQAAPMLMTH58YlILAAAAz2NSCwAA4BFpH1t6ZcOkFgAAAJ5HUwsAAADPY/kBAACAR7BPbXZMagEAAOB5eZ3UNjU1qba2VlVVVSPH2tvb1dvbq76+PnV1damiokINDQ0jj2/evFkPPvig0um0/H6/Lr/8cs2ePTufZQMAAMDl8trUWpYl27ZHNbW2bauurk5DQ0MaHBxUJBIZ9Zz77rtP69at07x58/SLX/xCP/7xj3X55Zfns2wAAABXYJ/a7PK6/KC6ulodHR1KpfbfTjIejyuRSKiiokJLlixRcfHYt7EbGBiQJPX392v69Ol5qxcAAADekNdJbUlJicrLyxWLxRQIBGTbtkKhkHwH2XOtvr5eN998syZOnKji4mLddNNNY54XiURGprxfXT75Q6kfAADASWm2qc0q7xeKWZalaDQqSYpGo7Is66Dnt7e365prrtE999yjU089VQ888MCY54XDYTU3N6u5udl4zQAAAHC3vDe1wWBQnZ2d6u7uVjKZVFlZWdZzd+3apZ6eHi1atEiSFAqF9Oqrr+arVAAAAHhE3vep9fv9qqysVGtr6yGntFOmTFF/f7/eeustzZ07Vy+++KKOOuqoPFUKAADgLmmx/iAbR26+UFNTo5aWFq1du3bkWGNjo7Zt26Z9+/apvr5e9fX1qqqq0qWXXqpbbrlFBQUFmjJlilavXu1EyQAAAHAxR5raYDCotra2UcfWr1+f9dxgMJiPsgAAAFyNO4plxx3FAAAA4Hk0tQAAAPA8R5YfAAAA4P1jn9rsmNQCAADA88btpLbpqleM5PxkkZkl2V/55xdyzviFZebXs8su/XXOGf9+unT/L2flnFN/gbTuH3+Vc85CFekzR2/LOUdaquuu/F3OKf/2g6N06ar/zDkneq6Z20JfXJd7LZ/UFNWUxg1UM1tX3/jHnFMukHT0sTtzL0fSx0+5LueM/7Zv0enW1TllPPHCt3TmiWZ2ePn58605Z3y7qlEB3+6cc7bqMC0pyf1z9fye6QqWvp1zzp8SU5RMF+acowLppMMSOUX0DxTphXRJzqUco6QOnziYc85wukDpjJmfNW8NTMk5Y6LSmlqUNFCNT3dU5Pa5cou00wW4mC+TyYy7C+muPvazRnL+5dheIzlff31OzhnXBczU8i/PzjCSszRdbCRna0HKSM5180w0W9LX38y9UZekHcr9/brrUwMGKpEufXSSkZx/nbrXSM7tu8006g1zzXzOa1838yNiki/3GcGkAjNzBhMNrSRtPvGfjeRMLjbzdV40cdhIzra3pxrJOdLA18RTe0sNVCItK8j9lw9JSg0baPYl7RyeaCRnZtE+IznHHm/mF+CZv3jSSE4uvntUndMl6B+2Peh0CWNi+QEAAAA8b9wuPwAAABhvxt2f1w1iUgsAAADPY1ILAADgEWzplR2TWgAAAHgeTS0AAAA8j+UHAAAAHsE+tdnltaltampSbW2tqqqqRo61t7ert7dXfX196urqUkVFhRoaGkYe7+zs1Pe//30NDQ1pwYIFWr16tQoLzeyjBwAAgPEhr8sPLMuSbdujjtm2LcuydM455+iKK64Y9Vg6ndbdd9+tK6+8UrfccouOOOIIPfmk8xsfAwAAOCHtghe3ymtTW11drY6ODqVS++8uE4/HlUgkVFFRoSVLlqi4ePRdqvbs2aMJEyZo7ty5kqSlS5fq6aefzmfJAAAA8IC8Lj8oKSlReXm5YrGYAoGAbNtWKBSSzzf2/hQlJSUaHh7W1q1btXDhQm3atElvvz32fb8jkYgikciHWT4AAABcKu8XilmWpWg0qkAgoGg0qtWrV2c91+fzae3atfre976nVCqlZcuWZV1PGw6HFQ6HJUlXb/jsh1I7AACAkzLsU5tV3pvaYDCoBx54QN3d3UomkyorKzvo+ccdd5zWr18vSXrhhRf01ltv5aNMAAAAeEje96n1+/2qrKxUa2urLMs65PnvvvuuJCmVSumRRx7RGWec8WGXCAAAAI9xZJ/ampoatbS0aO3atSPHGhsbtW3bNu3bt0/19fWqr69XVVWVfvrTn+p3v/ud0um0zjjjDJ1wwglOlAwAAOA4N+8+4DRHmtpgMKi2trZRx/5nicFfuvDCC3XhhRfmoywAAAB4FHcUAwAA8AgmtdnlfU0tAAAAYBpNLQAAADyP5QcAAAAekXG6ABdjUgsAAADPG5eT2umZse869n4VFpv5fahIud/+Y+gdM7WUZfxGcmYMG4nR9gIzv1e9trXUSI4mmYk5QhNzznj32Z0GKpE+mjHzsXkjbubbxYoJRUZyfvvaXCM5F08wc3uelIGMiYZGMJtP/GcjOUuev81IzkUnfdlIzt0fe9dITuqpvUZy3nh3as4ZHykYMFCJNDBs5uuqz8D3Lkk6wpc0klM6vd9Izq63zHxzn2kkJTdp7iiWFZNaAAAAeB5NLQAAADxvXC4/AAAAGI/YpzY7JrUAAADwPCa1AAAAHsGkNjsmtQAAAPA8mloAAAB4HssPAAAAPII7imWX16a2qalJtbW1qqqqGjnW3t6uF198UXv27NHAwIAKCgq0cuVKhUIhSVI8Htftt9+uPXv2aMGCBVqzZo0mTKAXBwAAwJ/ltTu0LEu2bY9qam3b1qpVqzR9+nTNmTNHiURCDQ0NWrZsmaZMmaIHH3xQZ599tizL0je/+U098cQTOuOMM/JZNgAAgCtwR7Hs8rqmtrq6Wh0dHUql9t9QMh6PK5FIaPHixZozZ44kqbS0VNOmTdOuXbuUyWT00ksvqbq6WpJ0yimn6Nlnn81nyQAAAPCAvE5qS0pKVF5erlgspkAgINu2FQqF5PP9+deOLVu2aGhoSLNmzdLu3bs1efJkFRYWStrf8CYSiTGzI5GIIpGIJOnwD/9dAQAAgIvkffcDy7IUjUYlSdFoVJZljTy2c+dO3XnnnVq9erUKCt5faeFwWM3NzWpubjZaLwAAgFukXfDiVnlvaoPBoDo7O9Xd3a1kMqmysjJJUn9/v5qbm/WZz3xGxx13nKT9k93+/n4NDw9LkhKJhEpLS/NdMgAAAFwu702t3+9XZWWlWltbR6a0Q0NDamlp0cknn6yPfexjI+f6fD4df/zx2rRpkyRp48aNWrFiRb5LBgAAcIWMC17cypGbL9TU1Kinp2ekqbVtW6+88oo2btyodevWad26dXr99dclSatWrdLPfvYzrVmzRnv27NFpp53mRMkAAABwMUc2fA0Gg2praxt5/eSTT9bJJ5885rmzZs3SzTffnK/SAAAA4EHcxQAAAMAj0q5eAOAsR5YfAAAAACbR1AIAAMDzWH4AAADgEW7eJ9Zp47KpDexLGcl565WpRnL+z0Du9bz04pEq8uW+juYUDeacIUmFBmqRpKMHzPyxYMuEYiM5XyjcaSRn98CknDO2/fFwHTFjT845c1NmPldpmbnheLzQSIzOnmzmc/WbATN7X89Mm/k4TzAQM7nEzPfAi076spGc73S0GMk5fvHfG8n52cyZRnJMNBe70kU6Zuqu3HP2+g1UI5328V4jOUO7zLRerz5/hJGcWaW7jeTA3cZlUzsemWho4S0mGlp4i4mGFt5ioqHFXxe+TWTHmloAAAB4Hk0tAAAAPI/lBwAAAB7BhWLZMakFAACA5zGpBQAA8Ii0mY1oxiUmtQAAAPA8mloAAAB4Xl6XHzQ1Nam2tlZVVVUjx9rb2/Xiiy9qz549GhgYUEFBgVauXKlQKCRJeuyxx9Te3q6+vj7dd999mjrVzA0RAAAAvCbNTrVZ5bWptSxLtm2Pampt29aqVas0ffp0zZkzR4lEQg0NDVq2bJmmNeah/QAAIABJREFUTJmij3zkI1q+fLluuOGGfJYKAAAAD8lrU1tdXa0NGzYolUqpqKhI8XhciURCixf/v/buPizqMt8f+HuGp0EeB0XESgkQgzTUYkSGMz7Rk2fPuTheno5lWrb7M4kedAu17ZhY2zatZCfJ0NYe7GEzs9UeMKup0JzRldwIUbQAxUoRdMABBhyG+f7+4GI2VokZuZmZL71fXVxXDN95+5nhy/Dhnvt738lQKLpmPkdFRSEiIgIWiwUhISG4+uqrPVkiERERkc/iOG3vPNrUhoWFITExEWVlZUhLS4PJZEJGRoazoQWAqqoq2O12xMTEuJVtMBhgMBgAADcKrZqIiIiIfJ3HLxTTarUwGo0AAKPRCK1W6/xaY2MjCgsLkZOTA6XSvdKysrKg1+uh1+uF1ktEREREvs/jTa1Go0FFRQVqampgs9kQHx8PALBardDr9Zg7dy6SkpI8XRYRERGRz3P4wIev8nhTq1KpkJKSgqKiIucord1uR0FBAXQ6HaZMmeLpkoiIiIhI5ryyTm1mZiZqa2udTa3JZEJlZSVKSkqQl5eHvLw8nDhxAgCwc+dOLF68GOfOnUNeXh42bNjgjZKJiIiIvM4Byesfvsor2+RqNBps3brV+blOp4NOp7vksbNmzcKsWbM8VRoRERERyRB3FCMiIiIi2fPKSC0RERERuc933/z3Po7UEhEREZHssaklIiIiItnj9AMiIiIimfDldWK9bVA2taK+4Q1tQ4TktAsYEB8V3CKgEuD7tjAhORGOTiE5jUoxp+B4v2YhOZ/b1EJy/Pz6n5HaIOiNFEXfh7gizL9DSM4M6YKQnBpLhJCcKKWYGWo2Rf+faLtCTC0BgWJ+PtdPOS8k59rk24TkHK7c2vdBLtg/bpmQnJigtn5nGNqjBFQCTHC0C8n5499HCMkZLgl4EQQwURLzunPaLOZ3X6KQFBoog7KpJSIiIhqMfHmdWG/jnFoiIiIikj02tUREREQke5x+QERERCQTnHzQO47UEhEREZHscaSWiIiISCa4pFfvOFJLRERERLLHppaIiIiIZM+j0w/y8/ORnZ2NCRMmOG8rLi5GeXk5Wlpa0NbWBqVSidmzZyMjIwMAsG7dOlRXV8Pf3x8JCQlYtGgR/P05a4KIiIh+fSReKtYrj3aHWq0WJpOpR1NrMpkwb948qNVqxMbGwmw2Y8WKFUhNTUVISAgyMzPxwAMPAACef/55fPHFF7jppps8WTYRERER+TiPTj9IT0/HwYMH0dHRte1dfX09zGYzkpOTERsbCwCIiopCREQELBYLAGDSpElQKBRQKBRITEzEuXPnPFkyERERkc9w+MCHr/LoSG1YWBgSExNRVlaGtLQ0mEwmZGRkQPGzPdOrqqpgt9sRExPT4752ux1fffUV7r777ktmGwwGGAwGAMDMAXsEREREROSLPH6hmFarhdFoBAAYjUZotVrn1xobG1FYWIicnBwolT1L27RpE5KTk5GcnHzJ3KysLOj1euj1+oErnoiIiIh8ksebWo1Gg4qKCtTU1MBmsyE+Ph4AYLVaodfrMXfuXCQlJfW4z7vvvguLxYIFCxZ4ulwiIiIin+GA5PUPX+XxplalUiElJQVFRUXOUVq73Y6CggLodDpMmTKlx/Gff/45vv32WyxZsuSi0VsiIiIiIsBLO4plZmaioKAAS5YsAdC1AkJlZSWam5tRUlICAMjNzUVcXBz+8pe/IDo6Go899hgAYPLkyZgzZ443yiYiIiLyKt8dJ/U+rzS1Go0GW7dudX6u0+mg0+kueeyWLVs8VRYRERERDaCWlhY899xzaGhoQHR0NJYuXYrQ0NBLHmu1WrF06VJoNBr89re/7TOb7+cTERERkUfs2LED48ePx7p16zB+/Hjs2LGj12PfeecdpKSkuJzNppaIiIhIJrx9kVh/LxQrLS3F1KlTAQBTp05FaWnpJY+rqanB+fPnkZqa6nI295slIiIiIpf9fG8AoGtZ1aysLJfue/78eajVagCAWq12brb1cw6HA6+//jruv/9+VFRUuFwXm1oiIiIimfCFHb36amKffPJJNDU1XXT73LlzXcr/9NNPMXHiRAwbNsytugZlUytqTkVsaIuQnLqWkH5nNFiDoRRwzaNS0fcxrpiYXick5+v9sUJyXlcGC8mZ1dYhJEcEK/zQif5/w65QtAuoBqi3q4TkqJU2ITl+gq4BHildEJKjEFSPUtH/nJ/OhguoBOjY1yok5yM3fzH1Zv+4ZUJy0iv+LCSn5NpH+51xDTpwVURzv3PMdjGvgWPtfkJyJkhizp1K//7//gSAEXZBv/zIJStXruz1axEREWhsbIRarUZjYyPCwy9+vfruu+9QWVmJTz/9FO3t7bDb7VCpVJg3b94v/ruDsqkdjEQ0tCQvIhpakhcRDS3Ji4iGlkhObrjhBuzevRvZ2dnYvXs30tLSLjrmwQcfdP5/SUkJqqur+2xoAV4oRkRERCQbkg/81x/Z2dkoLy/Hgw8+iPLycmRnZwMAqqursWHDhn5lc6SWiIiIiDwiLCwMjz/++EW3JyQkICEh4aLbp02bhmnTprmUzZFaIiIiIpI9jtQSERERyYQvrH7gqzhSS0RERESyx5FaIiIiIpno74VagxlHaomIiIhI9jw6Upufn4/s7GxMmDDBeVtxcTHKy8vR0tKCtrY2KJVKzJ49GxkZGQCAoqIi1NTUQJIkxMbGIjc3FyqVmEXgiYiIiGhw8GhTq9VqYTKZejS1JpMJ8+bNg1qtRmxsLMxmM1asWIHU1FSEhITgrrvuwpAhQwAAmzdvxq5du5xrmhERERH9mvBCsd55dPpBeno6Dh48iI6Orq1I6+vrYTabkZycjNjYru1So6KiEBERAYvFAgDOhlaSJNhsYrbXJCIiIqLBxaMjtWFhYUhMTERZWRnS0tJgMpmQkZEBheKf24FWVVXBbrcjJibGeduLL76Ib775BldeeSUWLFhwyWyDwQCDwQAAuHFgHwYRERGRVzgkXijWG49fKKbVamE0GgEARqMRWq3W+bXGxkYUFhYiJycHSuU/S7vvvvuwceNGXHHFFTCZTJfMzcrKgl6vh16vH9gHQEREREQ+x+NNrUajQUVFBWpqamCz2RAfHw8AsFqt0Ov1mDt3LpKSki4uVKlERkYG/v73v3u6ZCIiIiLycR5valUqFVJSUlBUVOQcpbXb7SgoKIBOp8OUKVOcx0qShLq6Ouf/f/311xg5cqSnSyYiIiLyCZIPfPgqr2y+kJmZiYKCAixZsgRA1woIlZWVaG5uRklJCQAgNzcXo0aNwvr162G1WgEAo0ePxu9+9ztvlExEREREPswrTa1Go8HWrVudn+t0Ouh0ukse++STT3qqLCIiIiKf5vDpsVLv4o5iRERERCR7bGqJiIiISPa8Mv2AiIiIiNwncfpBrzhSS0RERESyx5FaIiIiIplweLsAHzYom9rzCj8hOQGtQ4Tk+Cv6/1bBeUHfqiY/MYPzB/ePEJLTAUXfB7ngLqlVSI5FESQkxy71/3GZlWK+5+HoEJITE9AmJCcs9IKQHKkpREiOGQFCci4IeOPrgqCfh+vDzUJyTp4PF5Ij6pdwTJCYc7Dk2keF5Ew7/HS/M3aNe0xAJYBVIea1vUPMKYiGDjGvpWP9rEJyflAGC8kh38bpB0REREQke4NypJaIiIhoMOI6tb3jSC0RERERyR5HaomIiIhkgkt69Y4jtUREREQke2xqiYiIiEj2OP2AiIiISCa4Tm3vOFJLRERERLLn0ZHa/Px8ZGdnY8KECc7biouLUV5ejpaWFrS1tUGpVGL27NnIyMjocd9XXnkFX375Jd544w1PlkxEREREMuDRplar1cJkMvVoak0mE+bNmwe1Wo3Y2FiYzWasWLECqampCAnp2jGouroara1idowiIiIikitJ4uoHvfHo9IP09HQcPHgQHR1d23bW19fDbDYjOTkZsbGxAICoqChERETAYrEAABwOB958803ceeedniyViIiIiGTEoyO1YWFhSExMRFlZGdLS0mAymZCRkQGF4p+bTVdVVcFutyMmJgYAsGvXLlx//fVQq9W/mG0wGGAwGAAAmoF7CERERERewx3FeufxC8W0Wi2MRiMAwGg0QqvVOr/W2NiIwsJC5OTkQKlUwmw2Y9++fbj11lv7zM3KyoJer4derx+w2omIiIjIN3m8qdVoNKioqEBNTQ1sNhvi4+MBAFarFXq9HnPnzkVSUhIA4MSJE6irq8ODDz6I3Nxc2Gw2PPDAA54umYiIiIh8nMfXqVWpVEhJSUFRUZFzlNZut6OgoAA6nQ5TpkxxHjtp0iT85S9/cX4+f/58FBYWerpkIiIiIp/AdWp755V1ajMzM1FbW+tsak0mEyorK1FSUoK8vDzk5eXhxIkT3iiNiIiIiGTIKzuKaTQabN261fm5TqeDTqfr835co5aIiIh+zSReKNYr7ihGRERERLLHppaIiIiIZM8r0w+IiIiIyH1cp7Z3HKklIiIiItkblCO1AYL+ihkWYhWS831rRL8zhkidAioBIjsVfR/kgialmFMnwb9FSM7wK5qF5JyrDhaSo1L0//t1pXRBQCWAUinm58HSESgk58emIUJyIqUOITlXBIr5Obd1+vU7I9BPzM/5vtYoITljlW1CctShYnIM7WIe140RZiE5u8Y91u+MWyqeElAJUHnDQ0JywiPbheQ4BP2u+cfZaCE5IyHmcfkCSeJIbW84UktEREREssemloiIiIhkb1BOPyAiIiIajLijWO84UktEREREsseRWiIiIiKZ4I5iveNILRERERHJHptaIiIiIpI9Tj8gIiIikgnuKNY7jza1+fn5yM7OxoQJE5y3FRcXo7y8HC0tLWhra4NSqcTs2bORkZEBAFi/fj2OHDmCIUO6FmzPzc1FXFycJ8smIiIiIh/n0aZWq9XCZDL1aGpNJhPmzZsHtVqN2NhYmM1mrFixAqmpqQgJCQEAzJ8/H+np6Z4slYiIiIhkxKNNbXp6OrZs2YKOjg4EBASgvr4eZrMZycnJUCi6ttSLiopCREQELBaLs6klIiIiIm6T+0s82tSGhYUhMTERZWVlSEtLg8lkQkZGhrOhBYCqqirY7XbExMQ4b3v77bexbds2jBs3DvPmzUNAQMBF2QaDAQaDAQCgHfiHQkREREQ+xOMXimm1WhiNRqSlpcFoNCInJ8f5tcbGRhQWFiI3NxdKZdfCDHfccQciIyNht9uxceNGvP/++5gzZ85FuVlZWcjKygIAfPja7Z55MEREREQexAvFeufxJb00Gg0qKipQU1MDm82G+Ph4AIDVaoVer8fcuXORlJTkPF6tVkOhUCAgIADTp09HVVWVp0smIiIiIh/n8aZWpVIhJSUFRUVF0Gq7JgrY7XYUFBRAp9NhypQpPY5vbGwE0DWHpLS0FFdddZWnSyYiIiIiH+eVdWozMzNRUFCAJUuWAOhaAaGyshLNzc0oKSkB8M+lu9atWweLxQIAGD16NBYtWuSNkomIiIi8jtvk9s4rTa1Go8HWrVudn+t0Ouh0ukseu2rVKk+VRUREREQyxR3FiIiIiGTCwSW9euXxObVERERERKKxqSUiIiIi2eP0AyIiIiKZ4OSD3nGkloiIiIhkb1CO1F4RYBWSc7Z1iJCcMMne74xRQ88LqARoblEJyTl9IVhIjq3TT0jOqZMRQnLq/C7egvlyRHcq+j6oD2NGnBNQCWBqiOn7IBck+bcKyTmqDBSSM1phE5Jz6oKYn/MAAeMn/p0OAZUAqYHNQnLaOsX8PFhaxbzuTHC0C8kx28W8flkV/R8XqrzhIQGVAMlfPy8kp+7W/yck5+zZMCE54Y5OITnBQf3/PewruKNY7zhSS0RERESyx6aWiIiIiGRvUE4/ICIiIhqMOP2gdxypJSIiIiLZ40gtERERkUxI3FGsVxypJSIiIiLZY1NLRERERLLH6QdEREREMsELxXrn0aY2Pz8f2dnZmDBhgvO24uJilJeXo6WlBW1tbVAqlZg9ezYyMjIAdM0d2bJlC/bv3w+lUokbb7wRs2bN8mTZREREROTjPNrUarVamEymHk2tyWTCvHnzoFarERsbC7PZjBUrViA1NRUhISEoKSnBuXPn8Nxzz0GpVOL8eTE7axERERHJjcSR2l55dE5teno6Dh48iI6ODgBAfX09zGYzkpOTERsbCwCIiopCREQELBYLAODTTz/FnDlzoFR2lRoRIWY7VCIiIiIaPDw6UhsWFobExESUlZUhLS0NJpMJGRkZUCgUzmOqqqpgt9sRE9O1X/2ZM2dgMplw4MABhIeHY+HChc4G+OcMBgMMBgMA4DbPPBwiIiIi8hEeX/1Aq9XCaDQCAIxGI7RarfNrjY2NKCwsRE5OjnNktqOjAwEBAdDr9Zg5cyaKiooumZuVlQW9Xg+9Xj/wD4KIiIjICyRJ8vqHr/J4U6vRaFBRUYGamhrYbDbEx8cDAKxWK/R6PebOnYukpCTn8UOHDsXkyZOd962trfV0yURERETk4zze1KpUKqSkpKCoqMg5Smu321FQUACdTocpU6b0OD4tLQ0VFRUAgCNHjmDkyJGeLpmIiIiIfJxX1qnNzMxEQUEBlixZAqBrBYTKyko0NzejpKQEAJCbm4u4uDhkZ2dj3bp1KC4uhkqlwr333uuNkomIiIi8juvU9s4rTa1Go8HWrVudn+t0Ouh0ukseGxISgkcffdRTpRERERGRDHFHMSIiIiKZ8OULtbzN43NqiYiIiIhEY1NLRERERLLH6QdEREREMsELxXo3KJvaYTEtQnIqTl+8c9nlmBh4vt8Zp81hAioBRo1sEpJz6IyYelJH1QvJ8Vd1CskJOiEmJzDI3u8MpZ+YF67ozv7XAgAdSjFv7HT4CYlBeHi7kJyzDSohOQFw9DsjxF/Q96pTzJN8BoFCcmb822khOX/8+wghOWPtYp6fDkXfx/QlPFLMeVx36/8TkjPi478IyVHdvlBITuD34UJyFAK+V+T7BmVTS0RERDQYSRyp7RXn1BIRERGR7LGpJSIiIiLZ4/QDIiIiIplwcJ3aXnGkloiIiIhkjyO1RERERDLBC8V6x5FaIiIiIpI9NrVEREREJHsenX6Qn5+P7OxsTJgwwXlbcXExysvL0dLSgra2NiiVSsyePRsZGRkAgMcffxxtbW0AAIvFgoSEBCxbtsyTZRMRERH5BF4o1juPNrVarRYmk6lHU2symTBv3jyo1WrExsbCbDZjxYoVSE1NRUhICJ544gnnsQUFBUhLS/NkyUREREQkAx6dfpCeno6DBw+io6MDAFBfXw+z2Yzk5GTExnZtSRsVFYWIiAhYLJYe921ra8Phw4fZ1BIREdGvluQD//kqj47UhoWFITExEWVlZUhLS4PJZEJGRgYUP9uUuaqqCna7HTExMT3ue+DAAYwbNw5Dhgy5ZLbBYIDBYAAA3DdwD4GIiIiIfJDHl/TSarUwGo1IS0uD0WhETk6O82uNjY0oLCxEbm4ulMqeg8hGoxEzZszoNTcrKwtZWVkAgJM3zByY4omIiIjIJ3l89QONRoOKigrU1NTAZrMhPj4eAGC1WqHX6zF37lwkJSX1uE9zczOqqqowadIkT5dLRERE5DMckuT1D1/l8aZWpVIhJSUFRUVF0Gq1AAC73Y6CggLodDpMmTLlovvs27cPkyZNQmBgoKfLJSIiIiIZ8Mo6tZmZmaitrXU2tSaTCZWVlSgpKUFeXh7y8vJw4sQJ5/EmkwmZmZneKJWIiIjIZ3j7IjFeKPYvNBoNtm7d6vxcp9NBp9P1enx+fr4HqiIiIiIiueKOYkREREQke14ZqSUiIiIi9/nyhVrexpFaIiIiIpI9NrVEREREJHucfkBEREQkE768+oC3Dcqm1mEXMwA9yn5BSE5gmL3fGRZrqIBKAEenou+DXHCFvUNITntzgJCc2pphQnJGRrQIyZEc/X+e68+ECagEUCk6heT8AJWQHLWYcmC74CckZ4ig58df6eh3RlBA/18rAOBUW4iQnGiFTUiO3dL/5wYAhktivucTpFYhOQ0dQf3OEPWafPasoNeL2xcKyYl8+1UhOQev/YOQnCtDxLy2k28blE0tERER0WAkSWL+SByMOKeWiIiIiGSPTS0RERERyR6nHxARERHJhIMXivWKI7VEREREJHscqSUiIiKSCYk7ivWKI7VEREREJHtsaomIiIhI9jw6/SA/Px/Z2dmYMGGC87bi4mKUl5ejpaUFbW1tUCqVmD17NjIyMgAAhw4dwptvvgmHwwGVSoXc3FyMGDHCk2UTERER+QReKNY7jza1Wq0WJpOpR1NrMpkwb948qNVqxMbGwmw2Y8WKFUhNTUVISAg2bdqEvLw8XHnllfjkk0/w3nvvITc315NlExEREZGP82hTm56eji1btqCjowMBAQGor6+H2WxGcnIyFIqurQKjoqIQEREBi8WCkJCurR7b2toAAFarFWq12pMlExEREfkMXijWO482tWFhYUhMTERZWRnS0tJgMpmQkZHhbGgBoKqqCna7HTExMQCAxYsX4+mnn0ZgYCCCg4Px1FNPXTLbYDDAYDB03WfgHwoRERER+RCPXyim1WphNBoBAEajEVqt1vm1xsZGFBYWIicnB0plV2nFxcV49NFHsWHDBkyfPh2vv/76JXOzsrKg1+uh1+sH/kEQERERkU/xeFOr0WhQUVGBmpoa2Gw2xMfHA+iaWqDX6zF37lwkJSUBACwWC2prazFmzBgAQEZGBo4dO+bpkomIiIh8gkOSvP7hqzze1KpUKqSkpKCoqMg5Smu321FQUACdTocpU6Y4jw0JCYHVasWpU6cAAOXl5bjiiis8XTIRERER+Tiv7CiWmZmJgoICLFmyBEDXCgiVlZVobm5GSUkJACA3NxdxcXG499578eyzz0KpVCIkJAQ5OTneKJmIiIjI6yQu6dUrrzS1Go0GW7dudX6u0+mg0+l6PVaj0XiqNCIiIiKSIe4oRkRERESy55WRWiIiIiJyn9zXqW1pacFzzz2HhoYGREdHY+nSpQgNDb3ouDfffBP/+Mc/IEkSxo8fj4ULF/ZYAvZSOFJLRERERB6xY8cOjB8/HuvWrcP48eOxY8eOi445duwYjh07hoKCAjz77LOorq7GkSNH+sxmU0tEREQkEw5IXv/oj9LSUkydOhUAMHXqVJSWll50jEKhgM1mg91uR0dHBzo7OxEREdFn9qCcfnDk9FAxOUFinp6Jjf3/2yFMYRdQCWBqiBGSo409IyTnQruY5zhI4RCSY24OFpJjc/T/e94h6G/OIYpOITmjFW1CchSC3jlrbQsUknNBEvM8X+jsf05rm5ifh2EB7UJyotRWITnHvokWkjNR6hCSU+kfIiRnrF//n59/nBXz3IQ7xPycB34fLiTn4LV/EJIz8/CfhOSUpT4sJIe6/HwXV6BrA6ysrCyX7nv+/Hmo1WoAgFqthsViueiYpKQkXHvttVi0aBEkScItt9yCK6+8ss/sQdnUEhEREdHA6KuJffLJJ9HU1HTR7XPnznUpv66uDj/99BM2bNjgzDty5AhSUlJ+8X5saomIiIhkQg4Xiq1cubLXr0VERKCxsRFqtRqNjY0ID7/43YEDBw5gzJgxUKlUAICJEyfi+++/77Op5ZxaIiIiIvKIG264Abt37wYA7N69G2lpaRcdM2zYMFRWVqKzsxN2ux1HjhxxaUdZNrVERERE5BHZ2dkoLy/Hgw8+iPLycmRnZwMAqqurndMN0tPTERMTg0ceeQR5eXkYPXo0brjhhj6zOf2AiIiISCYcMph+8EvCwsLw+OOPX3R7QkICEhISAABKpRKLFi1yO5sjtUREREQkexypJSIiIpIJOVwo5i0cqSUiIiIi2XOpqT1w4ABuu+02/PTTTwNdT6+Ki4tx4cIFr/37REREROS7XGpq9+7di2uuuQZGo3Gg6+nVzp072dQSERHRr5q3t8jt7za5A6nPObXt7e04duwYVq1ahT//+c+47bbbcPjwYWzduhURERGora2FRqPBqFGjsHPnTthsNuTl5WHEiBFoaGhAUVERLBYLwsPDcd9992HYsGFYv349rr/+eqSnpwMA5s+fjzfeeAOHDx/Gu+++i7CwMPzwww+Ij4/HAw88gI8//hhmsxmrV69GeHg4Vq1aNeBPDBERERHJR59N7YEDBzBhwgSMHDkSoaGhqKmpAQDU1tbiueeeQ2hoKO6//37MnDkTTz/9NHbu3Ildu3bh7rvvxssvvwydTodp06bhiy++wCuvvIJly5b94r93/PhxrF27Fmq1GitXrsSxY8cwa9YsFBcXY9WqVZfceQLouQ+xzt1ngYiIiEgGeKFY7/qcfmA0GqHVagEAGRkZzikICQkJUKvVCAgIwIgRI3DdddcBAEaNGoX6+noAwPfff4/MzEwAgE6nw7Fjx/osKDExEUOHDoVSqURcXJwzqy9ZWVnQ6/XQ6/UuHU9EREREg8cvjtQ2NzejoqICP/zwAxQKBRwOB4CuPXgDAgKcxykUCufnPz+uN35+fs5jJEmC3W53fu3nuUqlss8sIiIiIqJfbGr379+PqVOn9tjVYdWqVTh69KhL4UlJSTCZTNDpdM6LzQAgOjoaNTU1yMjIQGlpKTo7O/vMUqlUaG9v73X6AREREdFgJ/cdxQbSL04/MBqN0Gg0PW6bPHmyy6sgLFy4EF9++SUeeeQR7NmzBwsXLgQAzJw5E5WVlXj00UdRVVWFoKCgPrOysrLwpz/9CatXr3bp3yYiIiKiXw+FNAhnHO+MmSsk50iQmA3XJl7o6HdGgELMNIwflH3/AeEK7fAzQnIutIt5js82hQjJUSjE/DjYHP3f16RD0N4oQxR9vxPiCj+lmHNQISRFXD2WjkAhOb4kPMAmJCdKbRWSc7pBzDtsVslPSM4TYcIvAAAXqUlEQVRp/4C+D3LBWKn/z88PUrCASoBwh5if8xHBrUJyTrWJeU2eefhPQnLKUh8WkpP203YhOf0RMiTO2yWg1XrC2yVcEncUIyIiIiLZY1NLRERERLIn5r1fIiIiIhpwvFCsdxypJSIiIiLZ40gtERERkUwMwuv7hRmUqx/cEzdHSE7hPWJWCvjPlxr6nfHRizcJqARY/mCpkBwrxFxp2ySJuUr7f4WkAI84LgjJsXS29ztj97obBVQC3JD7oZCcdYgTkrOw44iQnA0B1wrJ0fv3/+cTAFTK/l9RP0wp5kr4ddeYheRYTol5DXR0ilnz4rQ5TEhOmyRmPMei7P9qDCPR/9cKAAgOsPd9kAv8/QStciJoJZm2C2JWqpjw7bNCcgKGxQvJ6Q+VapS3S0B7+0lvl3BJnH5ARERERLLH6QdEREREMiFh0L3BLgxHaomIiIhI9tjUEhEREZHscfoBERERkUwMwuv7heFILRERERHJHkdqiYiIiGSCI7W940gtEREREcnegIzUNjU14bXXXkN1dTX8/f0xfPhw3HXXXXj22Wfx7LNiFkAmIiIiIuomvKmVJAlr1qzB1KlTsWTJEgDAiRMncP78edH/FBEREdGvCicf9E54U3v48GH4+/vjppv+ua1rXFwc6uvrnZ/X19fjhRdewIULXVuS3nPPPRg7diwaGxvxf//3f7BarXA4HPjd736HsWPHoqioCDU1NQCA6dOn4ze/+Y3osomIiIhIziTBiouLpVdfffWi28+cOSP9/ve/lyRJktrb26ULFy5IkiRJp06dkpYvXy5JkiR98MEH0nvvvSdJkiR1dnZKVqtVqq6ulp544glnTktLyyX/3c8++0xavny5tHz5cumzzz7rs05XjnEFcwY+x5dqYY5ncnypFuZ4JseXamGOZ3J8qRaROeQ9XrlQrLOzExs3bsTDDz+MtWvX4scffwQAJCQk4Msvv8TWrVtx8uRJBAcHY/jw4aivr8crr7yCsrIyBAcHXzIzKysLer0eer0eWVlZfdZgMBiEPBbmDHyOL9XCHM/k+FItzPFMji/VwhzP5PhSLSJzyHuEN7VXXXUVjh8//ovHfPTRR4iIiMCaNWug1+tht9sBACkpKVi9ejWioqJQWFiI3bt3IzQ0FGvWrEFKSgp27dqFDRs2iC6ZiIiIiGROeFM7btw4dHR09PiLp6qqCmfPnnV+brVaoVaroVQqsWfPHjgcDgBAQ0MDIiIikJWVhRkzZuD48eOwWCxwOBxIT0/H3Llz+2yYiYiIiOjXxy8/Pz9fZKBCocDEiRPxySef4K9//SsMBgNOnDiBKVOmwGQy4eabb0ZMTAzefvtt7N69G8HBwTh+/Dhmz56N0tJSrF+/Hp9//jnOnDmDefPmwWKxoKCgAJ9++ilKS0txxx13IDY2Vkit8fHxzJFJji/VwhzP5PhSLczxTI4v1cIcz+T4Ui0ic8g7FJLErSmIiIiISN64oxgRERERyR6bWiIiIiKSPTa1RERERCR7bGqJiIiISPaEr34gJ+Xl5YiJiXH5eKvVinPnziE0NLTH7bW1tYiMjHQ5p6mpCe3t7VCpVLBYLCgrK4MkSYiIiHA541L++te/Yvz48f3KqK+vR0VFBRQKBcLDw12+39mzZ+Hv7w8/Pz9IkoSSkhJ8+eWXaGhoQFxcHJTKvv9++vrrrzFs2DD4+fn15yEAAI4cOYKOjg6EhYXh6NGjMJlMaG1tdXvljPb2dnz99dcoKytDdXU1WltbMXz4cCgUCpczOjs78fnnn2PLli344IMP8Nlnn+Hrr79GW1sbRo8e7dJz05eNGzfihhtucOlYh8MBg8GA0tJS+Pv7Y9iwYc6vvffee0hJSXEp58KFCyguLsZ3332Hq6++Gl999RXeeecdnDhxAklJSfD3v7xduB966CHceuutbt3n5z+Ddrsd27dvx4cffoiTJ0+6VcuuXbswfPhwBAUFoa6uDmvWrMHmzZtx4MABjBkzxuWf0YKCAtjtdsTExFz28wAAZ86cweuvv47q6mokJiZi06ZNePvtt3H48GEkJCQgJCTEpRyHw4Evv/zSeQ5+/vnnOHz4MMLCwjB8+HCX6xnoc3kwnccAz+Vuos7jbk888QSmTp3a522u2LlzJ6644gr4+/tjw4YN2LZtG4YPH44RI0a4nUXe96tuavPz8/Gb3/zGpWNNJhOeeeYZ/OMf/8CuXbuQkJCAqKgoAMDTTz+NG2+80aWczz77DC+++CK++OILBAQE4M0330Rrays+/PBDBAUF4eqrr3Yp55VXXsE333zT4+OLL76A2WzGN998g4kTJ7qU8+c//xlarRYAUFpaiueffx52ux0ffPABgoODERcX51LOypUrMXPmTPj7++Ott95CVVUVxo8fj6NHj+LgwYNIS0vrMyMvLw+ffPIJTp48iYCAAMTExLjVPHZ77bXXsHv3buzfvx9nz56FwWBAdHQ0TCYTTp48idTUVJdyTCYTXnrpJbS3t2Pfvn1QKBSora3Ftm3bcM0117j8S2HdunWwWq2YNWsWbrrpJmRmZuLqq6/Gt99+i71792LKlCku5bS0tMBms130ceHCBbz++usun8sbN25EXV0dYmNj8f777+PUqVOYMGECAGDz5s0un8vr1q2Dn58fLBYLPv74Y9jtdsycOROnTp2CyWTC5MmT+8xYsGABduzYgR07dmD79u3Yvn07zp8/jw8//BA7duzAf/3Xf7lUy89/Bt98802YzWbcfPPNOHnyJPbv3w+NRuNSzosvvoj//M//BAAUFhbi5ptvRm5uLkaMGIHXX38dM2bMcCln8+bNuHDhAl599VXU1NTAz88PMTExbjd9a9aswYQJEyBJEl599VVcd911uP3226FQKPDee+9h2rRpLuUUFRUBAKZNmwabzYZRo0ZBo9Fgx44dsFqtGDNmjEs5Is7lwXgeAzyXf4mo89hms6GtrQ2ffvopMjIynOdOU1MTPvvsM9xyyy0u19TtpZdewr//+7/j22+/xZEjR3D33Xfj1Vdfdfn8Id9y+X+CysQzzzxzydslSUJLS4vLOdu3b4der4darUZVVRVeeOEF3H777Zg8eTLcWRVt165dWLt2LWw2G+677z4UFhYiMjISLS0tWL16tcsvNAcOHEBKSgpSU1Od/77RaHR7jb2fb4rx/vvvY9WqVRg+fDgsFguefPJJl19sHA4HgoKCAACHDh3C008/DaVSCZ1Oh7y8PJcyrrjiCjz++OPYv38/PvroIxQVFSEtLQ2ZmZkuj7oAXSPwzz77LGw2GxYvXowNGzYgKCgIdrsdy5cvx/z5813K+dvf/oannnoKQUFBsFgsKCwsxGOPPYba2lq89NJL+OMf/+hSzvHjx/H888/3uG3o0KFISkrCQw895PLj+u1vf4vo6Oge55tCoYAkSTh//rzLOVVVVSgoKAAA3HLLLdi0aRMKCgrw0EMPuXUunz59Gr///e8hSRIWLVqElStXQqFQIDk52eXv+bRp02C1WnHnnXc6R6dyc3Oxfv16l+sA0KPu7vPP39/frVqArpHIbhaLxdlAXHvttWhra3M5JyIiAg8//DDa2tpQWlqKzz//HBs3bsT1118PrVbr8h9WbW1tuOmmmwAAn3zyCf7jP/4DADBjxgzs2rXL5Xpqampw3333AQCuueYaPPbYY/if//kfJCcnY9myZS6PJoo4lwfjeQzwXP4los5jg8GA4uJiNDY2YsWKFc7nasiQIbj55ptdzvm57oxvvvkG06dPR1xcnFvnD/mWQd/UHj16FA888ABUKlWP2yVJQnV1tcs5DocDarUaAJCYmIhVq1ZBr9fj3Llzbo0m+vn5ISgoCEFBQRgxYoTzxS80NNStnLVr12Lr1q0oKyvD/PnzERUVhW3btrnchHb7+b/Z2dnpfCsyPDzcrXqGDRuGiooKjBs3DtHR0Th37hyio6PR3NzsVi2hoaHIyspCVlYWmpqaYDKZ8NZbb8FsNjtHm1zJ6f74+WNUKpXO3etcIUkSAgMDAQAqlcr5C3f06NFu/VIIDQ3Fvn37MHnyZOfohsPhwP79+9162y0mJgaPP/54j7dZu+Xk5Lic070tNdB1Pt57773Ytm0bnnjiCbS3t7uc0617w5WfP9+unjv33HMPampq8PzzzyMtLQ233HLLZY3OW61WHDhwAA6HA3a73fk2qTu1AEB6ejrWr1+POXPmIC0tDcXFxZg8eTIOHTp0yee9N93/ZnBwMHQ6HXQ6HVpaWmAymbBjxw6Xm1qFQoFTp07BarXCZrOhuroaCQkJqKurc+tc9vPzQ11dHUaMGIGamhrn8xMQEOByBiDmXB6M5zHAc7mvDBHn8axZszBr1ix8/PHHbk/r6E18fDz++Mc/or6+HnfccQfa2tou6/tGvmHQN7VjxoxBYGDgJUf6Ro4c6XJOcHCw85cCAKjVauTn52PNmjX44YcfXM5RKpXOF6oVK1Y4b7fZbG79dThkyBDcfffdqKmpQWFhISZOnHhZf13W1tbirrvugiRJ6OjoQFNTEyIjI2G32916sbn33nuxfv16vPvuuwgODsayZcsQFxeH1tZWLFiwwO26ACAyMtL5ItbQ0ODy/SZOnIiVK1fCbrdjxowZWLt2LZKSknDkyBEkJye7nDNp0iT86U9/QnJyMsrKypCeng6g6+1Td57rhx56CG+99RY2bdrknI/d2tqKa6+9FkuWLHE5Z9asWWhpabnkL6TutxldER8fj7KyMudbtQAwZ84cqNVqbNq0yeWchIQE59zw7lFAAKirq7voj8i+6lm5ciV27dqF/Px8dHR0uHzfbikpKfj6668BdP3Md5/HTU1NCAsLcznn9ttvR0lJCZ5//nmcOXPGueV3WloaHnzwQZdzLvX4Q0NDcdNNNzlHrFxx55134plnnoFSqUReXh62b9+O2tpatLW14d5773U5Z/78+Vi9ejUCAwNht9ud553FYsH111/vco6Ic3mwnsfdNfFcvpio87jbrbfeimPHjqGhoaHHiPTlzKldvHgxTpw4gZiYGAQFBaGlpaXHeUDyMuh3FNu0aRMyMzNxzTXX9Cvn6aefRnZ29kVNkd1ux759+/Bv//ZvLuW8+OKLmDFjxkX1mM1m/Pjjj7juuutcynn55ZeRmZmJsWPHQpIkfPLJJ/juu+/cerECen9+Wltb8dNPPyEpKcnlerRaLUJDQ3H69Gl0dnZi6NChSEhIcHnu1dKlS7F48WKMHTvWrcfwrzZt2gStVgt/f3+MGTMGdXV1OHDgAIYNG4b09HSX69m0aRMiIyMRFBSE0aNHO783DocDnZ2dbo9yAUBzczMkSXLrIjw5kiTpskY7Ghsbcfz4cUyaNGkAqhocLBYLQkND3Z6fK0kSmpubhZ17v4Zz+XLPY4Dncl8u9zwGuuYInzlz5qKLkO+55x63s44ePYq4uDioVCrs2bMHx48fx6xZsxAdHe12FnnfoL9QrK6uDh999BH+9re/obGxEeHh4W6tVNCtubkZxcXFF+UolUqMHj3a5ZyGhoZL1hMcHOzWSgz/+rgmTZp0WXOKent+AgMDMXToULdzDAYDgoODkZKSgoSEBLd+ITgcDiHfq7q6OhQXF6OkpASNjY0YOXIkJk+ejKuuusqterqb4UOHDiEwMNBZj0KhuOwVGrqnnnRzdwWO3vhazqFDhy4rJzg42LlCha89Jl/JCQoKgkKhcDtHoVD0OPcut57uVWCGDh3aI8+dVWBErSQz0DknT5687Jyfn8u+9ri8kfOvGd3nsbu1AMCWLVvwzDPP4Prrr8fEiROdH5dDr9fj1ltvRW1tLd544w2MGzcO27dvx/Tp0y8rj7xr0I/UdmtoaIDRaITJZEJHRwe0Wi0yMjLcmoLQW45Wq3V7qaiBrEfuOb5Ui8icS8nJyXF5rvCvLceXamFOF5PJhM2bNyM8PBydnZ247777kJiYCABYvnx5rxfmis5gjrxyRNXSbe3atVi4cKHzOpf+6P73t23bhqioKMyYMeOyaiLfMOjn1HaLjo5GdnY2srOzcfz4cRQVFeHdd9/FO++8wxwfy/GlWkTkiFqBYzDm+FItzOmbiFVgRK0kwxz55IiqpVtzczN+//vfIzExscfaucuXL3c7S6VSYfv27fjqq6+wevVq5wV6JE+/mqbWbrejrKwMJpMJhw4dQkpKCubMmcMcH8zxpVpE5IhagWMw5vhSLczpm4hVYEStJMMc+eSIqqXbf//3f7t9n94sXboUe/fuxeLFixEZGYmzZ8+6dcEi+ZZB39SWl5dj7969+Oabb5CQkACtVotFixa5fVUrcwY+x5dqEZkjagWOwZjjS7Uwp28iVoERtZIMc+STI6qWbu6sW96XyMjIHht+DBs27LJWUSDfMOib2u3bt0Or1WLBggUXTXJnjm/l+FItInOGDx/e6/aSq1ev/lXn+FItzOlbSEgIGhsbe2whGhwcjD/84Q/Yt2+fxzKYI68cUbV0W7BggXOE1263w263Q6VSYfPmzW5nfffdd3j11Vfx448/OpeyvNws8r5fzYViRN6yc+dOGI1GNDU1YcqUKcjMzHR5++HBnuNLtTDHMzm+VAtzPJMjqpbeHDhwAFVVVbjjjjvcvu+KFSuwZMkSrF27Fnq9Hrt378bp06cvK4u8j00tkYf42moMvpTjS7Uw5/Jy3F0FZiBXkmGOb+aIquVSHnvsMTz11FNu32/FihXQ6/V45JFHnNsu/+///q/L26CTb2FTS+QF3aso1NbWur0aw2DP8aVamOOZHF+qhTmeyelPxt///nfn/3df7HjkyJHLampXrVqFlStXYsOGDYiMjERkZCR2796NNWvWuJ1F3jfo59QS+QpfWY3BF3N8qRbmeCbHl2phjmdyRNVy8OBB5/8rlUoMHz4cy5YtczsHAO6//344HA7cc889KC4uxrlz5/Dwww9fVhZ5H0dqiQbYpVZRSEtLE7Iag9xzfKkW5ngmx5dqYY5nckTVQtQXjtQSDTBfW43Bl3J8qRbmeCbHl2phjmdyRNXS7dy5c3jllVdw7NgxKBQKjB07FgsXLnRra/eHH374F9fI7Z5fS/LCkVoiIiKSjSeffBKZmZnQ6XQAgK+++gpfffUVVq5c6XLG6dOncf78+Ysa4YaGBkRFRfVYfozkQ+ntAoiIiIhcZbFYMH36dPj5+cHPzw/Tpk2DxWJxK2Pz5s0IDg5GdHR0j4+goCC89tprA1M4DTg2tURERCQb4eHh2LNnDxwOBxwOB/bs2YOwsDC3MhoaGjB69OiLbk9ISEBDQ4OoUsnDOKeWiIiIZCMnJwcvv/wyNm/eDIVCgaSkJOTk5LiVYbPZLutr5NvY1BIREZFsDBs2DMuXL+9XRkJCAgwGA7Kysnrc/sUXXyA+Pr5f2eQ9vFCMiIiIZKO+vh4ff/wxGhoa0NnZ6bzdnUa3qakJBQUF8Pf3dzax1dXVsNvtyMvLQ2RkpPC6aeCxqSUiIiLZyMvLw/Tp0zFq1Cgolf+8NCglJcXtrIqKCvzwww8AgKuuugrjxo0TVid5HqcfEBERkWwEBARg1qxZQrLGjRvHRnYQ4UgtERERycbevXtx+vRppKamwt//n2NznAtLHKklIiIi2Th58iT27NmDioqKHtMPVq1a5cWqyBewqSUiIiLZOHDgAF544YUeo7READdfICIiIhkZPXo0WltbvV0G+SD+mUNERESycf78eSxZsgSJiYnO0VqFQoFly5Z5uTLyNja1REREJBu33Xab8/8lScLRo0dhNBq9WBH5Cja1REREJBspKSk4ceIE9u7di3379mH48OG48cYbvV0W+QA2tUREROTzTp06BZPJBKPRiNDQUGRkZECSJK56QE5saomIiMjnLV26FNdccw2WL1+OESNGAACKi4u9XBX5Eq5+QERERD7v4YcfRmRkJFavXo0NGzbg0KFD4P5R9HPcUYyIiIhko729HaWlpTAajaioqMDUqVOh0WiQmprq7dLIy9jUEhERkSy1tLRg3759MJlMnFtLbGqJiIiISP44p5aIiIiIZI9NLRERERHJHptaIiIiIpI9NrVEREREJHv/H1iSvNF9SqxoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "sns.heatmap(all_train.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of corr matrix:\n",
      "(30, 30)\n",
      "V16 is correlated with V15 with value of 0.9527222779659853\n",
      "V17 is correlated with V15 with value of 0.9082742658105205\n",
      "V17 is correlated with V16 with value of 0.9404942043825227\n"
     ]
    }
   ],
   "source": [
    "corr = all_train.corr()\n",
    "\n",
    "print(\"Dimensions of corr matrix:\")\n",
    "print(corr.shape)\n",
    "\n",
    "for i in range(corr.shape[0]):\n",
    "    for j in range(i+1, corr.shape[0]):\n",
    "        if abs(corr.iloc[i,j]) >= 0.9:\n",
    "            print(str(df.columns[j]) + ' is correlated with ' + str(df.columns[i]) + ' with value of ' + str(corr.iloc[i,j]))\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables to take note\n",
    "# Every variable from V1-V17 except for 5, 7, 12, 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Class</td>\n",
       "      <td>-0.424655</td>\n",
       "      <td>0.498406</td>\n",
       "      <td>-0.570851</td>\n",
       "      <td>0.715211</td>\n",
       "      <td>-0.366131</td>\n",
       "      <td>-0.41734</td>\n",
       "      <td>-0.48452</td>\n",
       "      <td>0.077844</td>\n",
       "      <td>-0.561073</td>\n",
       "      <td>-0.631997</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144205</td>\n",
       "      <td>0.006675</td>\n",
       "      <td>-0.034963</td>\n",
       "      <td>-0.093598</td>\n",
       "      <td>0.020905</td>\n",
       "      <td>0.065959</td>\n",
       "      <td>0.093646</td>\n",
       "      <td>0.095103</td>\n",
       "      <td>0.046628</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V1        V2        V3        V4        V5       V6       V7  \\\n",
       "Class -0.424655  0.498406 -0.570851  0.715211 -0.366131 -0.41734 -0.48452   \n",
       "\n",
       "             V8        V9       V10  ...       V21       V22       V23  \\\n",
       "Class  0.077844 -0.561073 -0.631997  ...  0.144205  0.006675 -0.034963   \n",
       "\n",
       "            V24       V25       V26       V27       V28    Amount  Class  \n",
       "Class -0.093598  0.020905  0.065959  0.093646  0.095103  0.046628    1.0  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.iloc[29:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_corr = corr.iloc[29:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class   -0.424655\n",
       "Name: V1, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_corr['V1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column V13 has correlation of -0.022926994989805827 with Class\n",
      "Column V22 has correlation of 0.006674583686939916 with Class\n",
      "Column V23 has correlation of -0.03496329869951398 with Class\n",
      "Column V25 has correlation of 0.020904828675948076 with Class\n",
      "Column Amount has correlation of 0.046627852859380835 with Class\n"
     ]
    }
   ],
   "source": [
    "varlist = all_train.columns.values\n",
    "class_corr = corr.iloc[29:,:]\n",
    "\n",
    "for i in range(30):\n",
    "    if abs(class_corr[varlist[i]].values[0]) < 0.05:\n",
    "        print(\"Column \" + str(varlist[i]) + \" has correlation of \" + str(class_corr[varlist[i]].values[0]) +  \" with Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column V8 has correlation of 0.07784381635581406 with Class\n",
      "Column V13 has correlation of -0.022926994989805827 with Class\n",
      "Column V15 has correlation of -0.05002465025942525 with Class\n",
      "Column V22 has correlation of 0.006674583686939916 with Class\n",
      "Column V23 has correlation of -0.03496329869951398 with Class\n",
      "Column V24 has correlation of -0.0935976591848952 with Class\n",
      "Column V25 has correlation of 0.020904828675948076 with Class\n",
      "Column V26 has correlation of 0.06595914125343054 with Class\n",
      "Column V27 has correlation of 0.09364550069810622 with Class\n",
      "Column V28 has correlation of 0.09510332730685406 with Class\n",
      "Column Amount has correlation of 0.046627852859380835 with Class\n"
     ]
    }
   ],
   "source": [
    "for i in range(30):\n",
    "    if abs(class_corr[varlist[i]].values[0]) < 0.1:\n",
    "        print(\"Column \" + str(varlist[i]) + \" has correlation of \" + str(class_corr[varlist[i]].values[0]) +  \" with Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V15\n",
      "-0.05002465025942525\n",
      "V16\n",
      "-0.5919373895446429\n",
      "V17\n",
      "-0.5542168230845207\n"
     ]
    }
   ],
   "source": [
    "# Check for V15, 16 and 17\n",
    "for i in (14,15,16):\n",
    "    print(varlist[i])\n",
    "    print(class_corr[varlist[i]].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using train set that has been naively oversampled, try to do backward elimination to remove some features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.discrete.discrete_model as sm\n",
    "def backwardElimination(x, Y, sl, columns):\n",
    "    numVars = len(x[0]) # Get length of a row for num of vars\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.Logit(Y, x, maxiter=200).fit() \n",
    "        # for loop to fit current set of all vars in x and response Y\n",
    "        # As loop goes on, x gets smaller as columns are deleted, and columns keeping track of col names also edited\n",
    "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
    "        print('Regression model retrained with ' + str(numVars) + ' variables.')\n",
    "        print('Max p value for a feature is: ' + str(maxVar))\n",
    "        # get max p value and if its more than sig level, start deleting jth column\n",
    "        # Since columns are getting deleted and x gets smaller, we need to update columns keeping track of column names\n",
    "        # Hence the only way to ensure the deletion of right column\n",
    "        # is to check the max p value with the current pvalues[j] in regression model\n",
    "        # if they are same, then jth column safe to delete\n",
    "        if maxVar > sl:\n",
    "            print('Max p value > ' + str(sl) + ', feature will be removed.')\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    print(str(j) + 'th column deleted: ' + str(columns[j]))\n",
    "                    x = np.delete(x, j, 1)\n",
    "                    columns = np.delete(columns, j)\n",
    "                    numVars -= 1\n",
    "        else:\n",
    "            print('All p values are above ' + str(sl) + '. Terminating model training')\n",
    "            print('p values list: ' + str(regressor_OLS.pvalues))\n",
    "            break\n",
    "                    \n",
    "    regressor_OLS.summary()\n",
    "    return x, columns # Return x data and list of columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation matrix for smote oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run SMOTE sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Xs and Ys for SMOTE:\n",
      "[(0, 227451), (1, 227451)]\n",
      "Oversampling is complete!\n"
     ]
    }
   ],
   "source": [
    "# Run the class sampling above to be able to do naive oversampling\n",
    "\n",
    "# Sampling data before looking at correlation matrix\n",
    "X_train, X_test, y_train, y_test = sampling.smote_oversample(df_notime, 0.2)\n",
    "X_train = pd.DataFrame(X_train, columns=featurenames)\n",
    "X_test = pd.DataFrame(X_train, columns=featurenames)\n",
    "y_train = pd.DataFrame(y_train, columns=['Class'])\n",
    "y_test = pd.DataFrame(y_test, columns=['Class'])\n",
    "all_train = pd.concat([X_train, y_train], axis=1)\n",
    "all_test = pd.concat([X_test, y_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28c80f626c8>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "sns.heatmap(all_train.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of corr matrix:\n",
      "(30, 30)\n",
      "V11 is correlated with V9 with value of 0.9026790133459716\n",
      "V11 is correlated with V10 with value of -0.9057816767483187\n",
      "V15 is correlated with V11 with value of 0.9030624266783641\n",
      "V16 is correlated with V15 with value of 0.9568422360379054\n",
      "V17 is correlated with V15 with value of 0.9114344428728972\n",
      "V17 is correlated with V16 with value of 0.9404339751441819\n"
     ]
    }
   ],
   "source": [
    "corr = all_train.corr()\n",
    "\n",
    "print(\"Dimensions of corr matrix:\")\n",
    "print(corr.shape)\n",
    "\n",
    "for i in range(corr.shape[0]):\n",
    "    for j in range(i+1, corr.shape[0]):\n",
    "        if abs(corr.iloc[i,j]) >= 0.9:\n",
    "            print(str(df.columns[j]) + ' is correlated with ' + str(df.columns[i]) + ' with value of ' + str(corr.iloc[i,j]))\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables to take note\n",
    "# Every variable from V9, 10, 11, 15, 16, 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Class</td>\n",
       "      <td>-0.417259</td>\n",
       "      <td>0.518499</td>\n",
       "      <td>-0.573137</td>\n",
       "      <td>0.725496</td>\n",
       "      <td>-0.379316</td>\n",
       "      <td>-0.442266</td>\n",
       "      <td>-0.486703</td>\n",
       "      <td>0.109735</td>\n",
       "      <td>-0.586194</td>\n",
       "      <td>-0.650682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161099</td>\n",
       "      <td>0.003013</td>\n",
       "      <td>-0.03304</td>\n",
       "      <td>-0.113939</td>\n",
       "      <td>0.04002</td>\n",
       "      <td>0.073364</td>\n",
       "      <td>0.12251</td>\n",
       "      <td>0.123143</td>\n",
       "      <td>0.043603</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V1        V2        V3        V4        V5        V6        V7  \\\n",
       "Class -0.417259  0.518499 -0.573137  0.725496 -0.379316 -0.442266 -0.486703   \n",
       "\n",
       "             V8        V9       V10  ...       V21       V22      V23  \\\n",
       "Class  0.109735 -0.586194 -0.650682  ...  0.161099  0.003013 -0.03304   \n",
       "\n",
       "            V24      V25       V26      V27       V28    Amount  Class  \n",
       "Class -0.113939  0.04002  0.073364  0.12251  0.123143  0.043603    1.0  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.iloc[29:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_corr = corr.iloc[29:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class   -0.417259\n",
       "Name: V1, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_corr['V1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column V1 has correlation of -0.41725934205274595 with Class\n",
      "Column V2 has correlation of 0.518498737408424 with Class\n",
      "Column V3 has correlation of -0.5731371643170001 with Class\n",
      "Column V4 has correlation of 0.7254962000336809 with Class\n",
      "Column V5 has correlation of -0.379316244164841 with Class\n",
      "Column V6 has correlation of -0.44226580198475013 with Class\n",
      "Column V7 has correlation of -0.4867030811365793 with Class\n",
      "Column V8 has correlation of 0.10973465161919535 with Class\n",
      "Column V9 has correlation of -0.5861944685642274 with Class\n",
      "Column V10 has correlation of -0.6506818904894245 with Class\n",
      "Column V11 has correlation of 0.7074367489876603 with Class\n",
      "Column V12 has correlation of -0.7022472878401144 with Class\n",
      "Column V13 has correlation of -0.027657014246922223 with Class\n",
      "Column V14 has correlation of -0.7745628581978722 with Class\n",
      "Column V15 has correlation of -0.03888141131615159 with Class\n",
      "Column V16 has correlation of -0.6033205971925504 with Class\n",
      "Column V17 has correlation of -0.5711982021261706 with Class\n",
      "Column V18 has correlation of -0.4757782994524955 with Class\n",
      "Column V19 has correlation of 0.269598656475039 with Class\n",
      "Column V20 has correlation of 0.1886706542173731 with Class\n",
      "Column V21 has correlation of 0.16109862459182184 with Class\n",
      "Column V22 has correlation of 0.0030133872913561863 with Class\n",
      "Column V23 has correlation of -0.033040099794146766 with Class\n",
      "Column V24 has correlation of -0.11393932124114138 with Class\n",
      "Column V25 has correlation of 0.04001971117335157 with Class\n",
      "Column V26 has correlation of 0.07336386123167801 with Class\n",
      "Column V27 has correlation of 0.12251023180944035 with Class\n",
      "Column V28 has correlation of 0.12314293209128073 with Class\n",
      "Column Amount has correlation of 0.04360277486052886 with Class\n",
      "Column Class has correlation of 1.0 with Class\n"
     ]
    }
   ],
   "source": [
    "varlist = all_train.columns.values\n",
    "class_corr = corr.iloc[29:,:]\n",
    "\n",
    "for i in range(30):\n",
    "    print(\"Column \" + str(varlist[i]) + \" has correlation of \" + str(class_corr[varlist[i]].values[0]) +  \" with Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column V13 has correlation of -0.027657014246922223 with Class\n",
      "Column V15 has correlation of -0.03888141131615159 with Class\n",
      "Column V22 has correlation of 0.0030133872913561863 with Class\n",
      "Column V23 has correlation of -0.033040099794146766 with Class\n",
      "Column V25 has correlation of 0.04001971117335157 with Class\n",
      "Column Amount has correlation of 0.04360277486052886 with Class\n"
     ]
    }
   ],
   "source": [
    "varlist = all_train.columns.values\n",
    "class_corr = corr.iloc[29:,:]\n",
    "\n",
    "for i in range(30):\n",
    "    if abs(class_corr[varlist[i]].values[0]) < 0.05:\n",
    "        print(\"Column \" + str(varlist[i]) + \" has correlation of \" + str(class_corr[varlist[i]].values[0]) +  \" with Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column V13 has correlation of -0.027657014246922223 with Class\n",
      "Column V15 has correlation of -0.03888141131615159 with Class\n",
      "Column V22 has correlation of 0.0030133872913561863 with Class\n",
      "Column V23 has correlation of -0.033040099794146766 with Class\n",
      "Column V25 has correlation of 0.04001971117335157 with Class\n",
      "Column V26 has correlation of 0.07336386123167801 with Class\n",
      "Column Amount has correlation of 0.04360277486052886 with Class\n"
     ]
    }
   ],
   "source": [
    "for i in range(30):\n",
    "    if abs(class_corr[varlist[i]].values[0]) < 0.1:\n",
    "        print(\"Column \" + str(varlist[i]) + \" has correlation of \" + str(class_corr[varlist[i]].values[0]) +  \" with Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V15\n",
      "-0.03888141131615159\n",
      "V16\n",
      "-0.6033205971925504\n",
      "V17\n",
      "-0.5711982021261706\n"
     ]
    }
   ],
   "source": [
    "# Check for V15, 16 and 17\n",
    "for i in (14,15,16):\n",
    "    print(varlist[i])\n",
    "    print(class_corr[varlist[i]].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using train set that has been naively oversampled, try to do backward elimination to remove some features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.discrete.discrete_model as sm\n",
    "def backwardElimination(x, Y, sl, columns):\n",
    "    numVars = len(x[0]) # Get length of a row for num of vars\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.Logit(Y, x, maxiter=200).fit() \n",
    "        # for loop to fit current set of all vars in x and response Y\n",
    "        # As loop goes on, x gets smaller as columns are deleted, and columns keeping track of col names also edited\n",
    "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
    "        print('Regression model retrained with ' + str(numVars) + ' variables.')\n",
    "        print('Max p value for a feature is: ' + str(maxVar))\n",
    "        # get max p value and if its more than sig level, start deleting jth column\n",
    "        # Since columns are getting deleted and x gets smaller, we need to update columns keeping track of column names\n",
    "        # Hence the only way to ensure the deletion of right column\n",
    "        # is to check the max p value with the current pvalues[j] in regression model\n",
    "        # if they are same, then jth column safe to delete\n",
    "        if maxVar > sl:\n",
    "            print('Max p value > ' + str(sl) + ', feature will be removed.')\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    print(str(j) + 'th column deleted: ' + str(columns[j]))\n",
    "                    x = np.delete(x, j, 1)\n",
    "                    columns = np.delete(columns, j)\n",
    "                    numVars -= 1\n",
    "        else:\n",
    "            print('All p values are above ' + str(sl) + '. Terminating model training')\n",
    "            print('p values list: ' + str(regressor_OLS.pvalues))\n",
    "            break\n",
    "                    \n",
    "    regressor_OLS.summary()\n",
    "    return x, columns # Return x data and list of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old variable list:\n",
      "['V1' 'V2' 'V3' 'V4' 'V5' 'V6' 'V7' 'V8' 'V9' 'V10' 'V12' 'V13' 'V14'\n",
      " 'V16' 'V18' 'V19' 'V20' 'V21' 'V22' 'V23' 'V24' 'V25' 'V26' 'V27' 'V28'\n",
      " 'Amount' 'Class']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1789: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: inf\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "Singular matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-99-fa23ec2dd876>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvarlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mnew_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewvarlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbackwardElimination\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvarlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'New variable list:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-98-6e88598581bf>\u001b[0m in \u001b[0;36mbackwardElimination\u001b[1;34m(x, Y, sl, columns)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mnumVars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Get length of a row for num of vars\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumVars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[0mregressor_OLS\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLogit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m         \u001b[1;31m# for loop to fit current set of all vars in x and response Y\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;31m# As loop goes on, x gets smaller as columns are deleted, and columns keeping track of col names also edited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)\u001b[0m\n\u001b[0;32m   1900\u001b[0m         bnryfit = super(Logit, self).fit(start_params=start_params,\n\u001b[0;32m   1901\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1902\u001b[1;33m                 disp=disp, callback=callback, **kwargs)\n\u001b[0m\u001b[0;32m   1903\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1904\u001b[0m         \u001b[0mdiscretefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogitResults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbnryfit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)\u001b[0m\n\u001b[0;32m    215\u001b[0m         mlefit = super(DiscreteModel, self).fit(start_params=start_params,\n\u001b[0;32m    216\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 217\u001b[1;33m                 disp=disp, callback=callback, **kwargs)\n\u001b[0m\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmlefit\u001b[0m \u001b[1;31m# up to subclasses to wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)\u001b[0m\n\u001b[0;32m    475\u001b[0m             \u001b[0mHinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcov_params_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxopt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 477\u001b[1;33m             \u001b[0mHinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mretvals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Hessian'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    478\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mskip_hessian\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    479\u001b[0m             \u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhessian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxopt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\u001b[0m in \u001b[0;36minv\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m    549\u001b[0m     \u001b[0msignature\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'D->D'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'd->d'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    550\u001b[0m     \u001b[0mextobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m     \u001b[0mainv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    552\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[1;34m(err, flag)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Singular matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLinAlgError\u001b[0m: Singular matrix"
     ]
    }
   ],
   "source": [
    "# .values convert dataframe to np array\n",
    "# We drop V15 and V17 as they are highly correlated with other variables\n",
    "# While V17 has high correlation, V16 has even higher correlation. Since V16/V17 are correlated, one is removed\n",
    "x = all_train.drop(['V11','V15','V17'], axis=1)\n",
    "varlist = x.columns.values\n",
    "x = all_train.values\n",
    "Y = all_train['Class'].values\n",
    "\n",
    "print('Old variable list:')\n",
    "print(varlist)\n",
    "\n",
    "new_x, newvarlist = backwardElimination(x, Y, 0.05, varlist)\n",
    "\n",
    "print('New variable list:')\n",
    "print(newvarlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56738   126]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9975948878199501\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9977841868317389\n",
      "Precision: 0.4084507042253521\n",
      "f1 score: 0.5594855305466238\n",
      "AUC value is: 0.9427696444362775\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.08943089430895%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop V15 and rerun\n",
    "df15 = df.drop(columns=['V15'], axis=1)\n",
    "ranforest_v20 = modelpipeline.run_model(df15, 0.2, True, \"naive\", \"RandomForest\", \"Random Forest with 28 variables no V15\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56767    97]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9981039991573329\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9982941755768149\n",
      "Precision: 0.47282608695652173\n",
      "f1 score: 0.6170212765957447\n",
      "AUC value is: 0.9430246388088156\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.69918699186992%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop V15 and V17 and rerun\n",
    "df1517 = df.drop(columns=['V20'], axis=1)\n",
    "ranforest_v20 = modelpipeline.run_model(df1517, 0.2, True, \"naive\", \"RandomForest\", \"Random Forest with 27 variables no V15 and V17\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56740   124]\n",
      " [   12    86]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9976124433833081\n",
      "Sensitivity: 0.8775510204081632\n",
      "Specificity: 0.9978193584693303\n",
      "Precision: 0.4095238095238095\n",
      "f1 score: 0.5584415584415585\n",
      "AUC value is: 0.9376851894387468\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.29268292682927%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop V11, V15 and V17 and rerun\n",
    "df111517 = df.drop(columns=['V11','V15','V17'], axis=1)\n",
    "ranforest_v20 = modelpipeline.run_model(df111517, 0.2, True, \"naive\", \"RandomForest\", \"Random Forest with 26 variables no V11, V15 and V17\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}