OVER03. Testing codes for sampling.ipynb

{
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  {
   "cell_type": "code",
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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>176518</td>\n",
       "      <td>122804.0</td>\n",
       "      <td>1.867633</td>\n",
       "      <td>0.067490</td>\n",
       "      <td>-2.083760</td>\n",
       "      <td>2.949168</td>\n",
       "      <td>2.798819</td>\n",
       "      <td>4.513581</td>\n",
       "      <td>-0.459531</td>\n",
       "      <td>1.013802</td>\n",
       "      <td>-0.603922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.346401</td>\n",
       "      <td>0.037249</td>\n",
       "      <td>0.720629</td>\n",
       "      <td>0.339727</td>\n",
       "      <td>0.209881</td>\n",
       "      <td>-0.010506</td>\n",
       "      <td>-0.061317</td>\n",
       "      <td>19.32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>283094</td>\n",
       "      <td>171370.0</td>\n",
       "      <td>-10.243984</td>\n",
       "      <td>8.517532</td>\n",
       "      <td>-6.591644</td>\n",
       "      <td>-2.460064</td>\n",
       "      <td>-3.135762</td>\n",
       "      <td>-1.112216</td>\n",
       "      <td>-2.164932</td>\n",
       "      <td>3.746472</td>\n",
       "      <td>4.803544</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.996068</td>\n",
       "      <td>-1.425127</td>\n",
       "      <td>0.660345</td>\n",
       "      <td>-1.235856</td>\n",
       "      <td>1.492283</td>\n",
       "      <td>0.295328</td>\n",
       "      <td>2.177236</td>\n",
       "      <td>1.240884</td>\n",
       "      <td>10.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45955</td>\n",
       "      <td>42579.0</td>\n",
       "      <td>-3.529237</td>\n",
       "      <td>-1.085031</td>\n",
       "      <td>0.599323</td>\n",
       "      <td>-1.531427</td>\n",
       "      <td>-1.084952</td>\n",
       "      <td>-0.020062</td>\n",
       "      <td>1.592441</td>\n",
       "      <td>-0.500454</td>\n",
       "      <td>-0.928957</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.538717</td>\n",
       "      <td>0.445493</td>\n",
       "      <td>0.360510</td>\n",
       "      <td>0.307602</td>\n",
       "      <td>0.745300</td>\n",
       "      <td>-0.319171</td>\n",
       "      <td>0.674855</td>\n",
       "      <td>0.267995</td>\n",
       "      <td>372.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1582</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>-0.956865</td>\n",
       "      <td>1.483801</td>\n",
       "      <td>1.601873</td>\n",
       "      <td>0.884115</td>\n",
       "      <td>-0.094897</td>\n",
       "      <td>-0.015144</td>\n",
       "      <td>0.305496</td>\n",
       "      <td>0.443680</td>\n",
       "      <td>-0.567253</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.238321</td>\n",
       "      <td>-0.462552</td>\n",
       "      <td>-0.268929</td>\n",
       "      <td>-0.094198</td>\n",
       "      <td>0.524294</td>\n",
       "      <td>-0.364814</td>\n",
       "      <td>0.227555</td>\n",
       "      <td>0.078699</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216856</td>\n",
       "      <td>140662.0</td>\n",
       "      <td>-0.348561</td>\n",
       "      <td>0.813080</td>\n",
       "      <td>0.801079</td>\n",
       "      <td>-0.417471</td>\n",
       "      <td>0.132750</td>\n",
       "      <td>0.186855</td>\n",
       "      <td>0.167475</td>\n",
       "      <td>0.046369</td>\n",
       "      <td>0.260304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088899</td>\n",
       "      <td>0.104370</td>\n",
       "      <td>0.302994</td>\n",
       "      <td>0.753871</td>\n",
       "      <td>-1.752069</td>\n",
       "      <td>-0.259744</td>\n",
       "      <td>-0.274304</td>\n",
       "      <td>0.266956</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "            Time         V1        V2        V3        V4        V5        V6  \\\n",
       "176518  122804.0   1.867633  0.067490 -2.083760  2.949168  2.798819  4.513581   \n",
       "283094  171370.0 -10.243984  8.517532 -6.591644 -2.460064 -3.135762 -1.112216   \n",
       "45955    42579.0  -3.529237 -1.085031  0.599323 -1.531427 -1.084952 -0.020062   \n",
       "1582      1233.0  -0.956865  1.483801  1.601873  0.884115 -0.094897 -0.015144   \n",
       "216856  140662.0  -0.348561  0.813080  0.801079 -0.417471  0.132750  0.186855   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "176518 -0.459531  1.013802 -0.603922  ...  0.064481  0.346401  0.037249   \n",
       "283094 -2.164932  3.746472  4.803544  ... -0.996068 -1.425127  0.660345   \n",
       "45955   1.592441 -0.500454 -0.928957  ... -0.538717  0.445493  0.360510   \n",
       "1582    0.305496  0.443680 -0.567253  ... -0.238321 -0.462552 -0.268929   \n",
       "216856  0.167475  0.046369  0.260304  ...  0.088899  0.104370  0.302994   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "176518  0.720629  0.339727  0.209881 -0.010506 -0.061317   19.32      0  \n",
       "283094 -1.235856  1.492283  0.295328  2.177236  1.240884   10.76      0  \n",
       "45955   0.307602  0.745300 -0.319171  0.674855  0.267995  372.62      0  \n",
       "1582   -0.094198  0.524294 -0.364814  0.227555  0.078699    1.49      0  \n",
       "216856  0.753871 -1.752069 -0.259744 -0.274304  0.266956    4.99      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,
   "metadata": {},
   "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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\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": [],
   "source": [
    "x = df.drop(['Class'], axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum()\n",
    "\n",
    "# Only 492 positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 284315), (1, 284315)]\n"
     ]
    }
   ],
   "source": [
    "# imports \n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# random oversampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_01, y_01 = ros.fit_resample(x, y)\n",
    "\n",
    "# using Counter to display results of naive oversampling\n",
    "from collections import Counter\n",
    "print(sorted(Counter(y_01).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_01[0:50,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284315"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_01.sum()\n",
    "\n",
    "# There are indeed 284315 values after resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</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",
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       "    <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",
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       "    <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",
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       "  </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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Time</th>\n",
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       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>V8</th>\n",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
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       "      <td>1.378155</td>\n",
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       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "    <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",
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       "  </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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to get square root of number of feature for max depth of XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Xs and Ys for ADASYN:\n",
      "[(0, 284315), (1, 284298)]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import ADASYN\n",
    "X = df.drop(['Class'], axis=1)\n",
    "y = df['Class']\n",
    "X_adasyn, y_adasyn = ADASYN().fit_resample(X, y)\n",
    "print(\"Number of Xs and Ys for ADASYN:\")\n",
    "print(sorted(Counter(y_adasyn).items()))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_adasyn, y_adasyn, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "a = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test standardization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Time', '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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    2.848070e+05\n",
       "mean    -2.316949e-17\n",
       "std      1.000000e+00\n",
       "min     -2.879850e+01\n",
       "25%     -4.698909e-01\n",
       "50%      9.245335e-03\n",
       "75%      6.716927e-01\n",
       "max      1.253349e+00\n",
       "Name: V1, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns.values.tolist()\n",
    "columns.remove('Class')\n",
    "print(columns)\n",
    "\n",
    "test = df['V1']\n",
    "\n",
    "test = (test - test.mean())/test.std()\n",
    "\n",
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>-1.996580</td>\n",
       "      <td>-0.694241</td>\n",
       "      <td>-0.044075</td>\n",
       "      <td>1.672771</td>\n",
       "      <td>0.973364</td>\n",
       "      <td>-0.245116</td>\n",
       "      <td>0.347067</td>\n",
       "      <td>0.193679</td>\n",
       "      <td>0.082637</td>\n",
       "      <td>0.331127</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024923</td>\n",
       "      <td>0.382854</td>\n",
       "      <td>-0.176911</td>\n",
       "      <td>0.110507</td>\n",
       "      <td>0.246585</td>\n",
       "      <td>-0.392170</td>\n",
       "      <td>0.330891</td>\n",
       "      <td>-0.063781</td>\n",
       "      <td>0.244964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.996580</td>\n",
       "      <td>0.608495</td>\n",
       "      <td>0.161176</td>\n",
       "      <td>0.109797</td>\n",
       "      <td>0.316522</td>\n",
       "      <td>0.043483</td>\n",
       "      <td>-0.061820</td>\n",
       "      <td>-0.063700</td>\n",
       "      <td>0.071253</td>\n",
       "      <td>-0.232494</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.307376</td>\n",
       "      <td>-0.880075</td>\n",
       "      <td>0.162201</td>\n",
       "      <td>-0.561130</td>\n",
       "      <td>0.320693</td>\n",
       "      <td>0.261069</td>\n",
       "      <td>-0.022256</td>\n",
       "      <td>0.044607</td>\n",
       "      <td>-0.342474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.996558</td>\n",
       "      <td>-0.693499</td>\n",
       "      <td>-0.811576</td>\n",
       "      <td>1.169466</td>\n",
       "      <td>0.268231</td>\n",
       "      <td>-0.364571</td>\n",
       "      <td>1.351451</td>\n",
       "      <td>0.639775</td>\n",
       "      <td>0.207372</td>\n",
       "      <td>-1.378673</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337631</td>\n",
       "      <td>1.063356</td>\n",
       "      <td>1.456317</td>\n",
       "      <td>-1.138090</td>\n",
       "      <td>-0.628536</td>\n",
       "      <td>-0.288446</td>\n",
       "      <td>-0.137137</td>\n",
       "      <td>-0.181021</td>\n",
       "      <td>1.160684</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-1.996558</td>\n",
       "      <td>-0.493324</td>\n",
       "      <td>-0.112169</td>\n",
       "      <td>1.182514</td>\n",
       "      <td>-0.609726</td>\n",
       "      <td>-0.007469</td>\n",
       "      <td>0.936148</td>\n",
       "      <td>0.192070</td>\n",
       "      <td>0.316017</td>\n",
       "      <td>-1.262501</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.147443</td>\n",
       "      <td>0.007267</td>\n",
       "      <td>-0.304776</td>\n",
       "      <td>-1.941024</td>\n",
       "      <td>1.241902</td>\n",
       "      <td>-0.460217</td>\n",
       "      <td>0.155396</td>\n",
       "      <td>0.186188</td>\n",
       "      <td>0.140534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.996537</td>\n",
       "      <td>-0.591329</td>\n",
       "      <td>0.531540</td>\n",
       "      <td>1.021410</td>\n",
       "      <td>0.284655</td>\n",
       "      <td>-0.295015</td>\n",
       "      <td>0.071998</td>\n",
       "      <td>0.479301</td>\n",
       "      <td>-0.226510</td>\n",
       "      <td>0.744325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012839</td>\n",
       "      <td>1.100009</td>\n",
       "      <td>-0.220123</td>\n",
       "      <td>0.233250</td>\n",
       "      <td>-0.395201</td>\n",
       "      <td>1.041609</td>\n",
       "      <td>0.543619</td>\n",
       "      <td>0.651815</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-1.996537</td>\n",
       "      <td>-0.217474</td>\n",
       "      <td>0.581674</td>\n",
       "      <td>0.752584</td>\n",
       "      <td>-0.118833</td>\n",
       "      <td>0.305008</td>\n",
       "      <td>-0.022313</td>\n",
       "      <td>0.384935</td>\n",
       "      <td>0.217954</td>\n",
       "      <td>-0.517618</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.283522</td>\n",
       "      <td>-0.771426</td>\n",
       "      <td>-0.042273</td>\n",
       "      <td>-0.613272</td>\n",
       "      <td>-0.446583</td>\n",
       "      <td>0.219637</td>\n",
       "      <td>0.628899</td>\n",
       "      <td>0.245636</td>\n",
       "      <td>-0.338556</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-1.996495</td>\n",
       "      <td>0.627794</td>\n",
       "      <td>0.085389</td>\n",
       "      <td>0.029923</td>\n",
       "      <td>0.849382</td>\n",
       "      <td>0.139019</td>\n",
       "      <td>0.204694</td>\n",
       "      <td>-0.004170</td>\n",
       "      <td>0.067997</td>\n",
       "      <td>0.423217</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228333</td>\n",
       "      <td>-0.373032</td>\n",
       "      <td>-0.246779</td>\n",
       "      <td>-1.287970</td>\n",
       "      <td>1.439034</td>\n",
       "      <td>-0.533435</td>\n",
       "      <td>0.085492</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>-0.333278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-1.996432</td>\n",
       "      <td>-0.328928</td>\n",
       "      <td>0.858691</td>\n",
       "      <td>0.708575</td>\n",
       "      <td>-0.347630</td>\n",
       "      <td>0.687510</td>\n",
       "      <td>0.321345</td>\n",
       "      <td>0.905858</td>\n",
       "      <td>-3.188224</td>\n",
       "      <td>0.560128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.645884</td>\n",
       "      <td>-1.399273</td>\n",
       "      <td>0.092085</td>\n",
       "      <td>-1.072752</td>\n",
       "      <td>-0.796632</td>\n",
       "      <td>-0.107075</td>\n",
       "      <td>-2.990148</td>\n",
       "      <td>-3.288077</td>\n",
       "      <td>-0.190107</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-1.996432</td>\n",
       "      <td>-0.456572</td>\n",
       "      <td>0.173291</td>\n",
       "      <td>-0.074652</td>\n",
       "      <td>-0.191774</td>\n",
       "      <td>1.934146</td>\n",
       "      <td>2.793589</td>\n",
       "      <td>0.299205</td>\n",
       "      <td>0.712590</td>\n",
       "      <td>-0.356851</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099963</td>\n",
       "      <td>-0.369424</td>\n",
       "      <td>-0.327055</td>\n",
       "      <td>1.670266</td>\n",
       "      <td>0.715942</td>\n",
       "      <td>-0.796632</td>\n",
       "      <td>0.029104</td>\n",
       "      <td>0.431419</td>\n",
       "      <td>0.019392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-1.996390</td>\n",
       "      <td>-0.172697</td>\n",
       "      <td>0.678004</td>\n",
       "      <td>0.688780</td>\n",
       "      <td>-0.156926</td>\n",
       "      <td>0.361791</td>\n",
       "      <td>-0.185218</td>\n",
       "      <td>0.526705</td>\n",
       "      <td>0.058223</td>\n",
       "      <td>-0.670586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.336155</td>\n",
       "      <td>-0.873297</td>\n",
       "      <td>-0.193438</td>\n",
       "      <td>-0.635766</td>\n",
       "      <td>-0.133773</td>\n",
       "      <td>0.195341</td>\n",
       "      <td>0.610009</td>\n",
       "      <td>0.251681</td>\n",
       "      <td>-0.338516</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0 -1.996580 -0.694241 -0.044075  1.672771  0.973364 -0.245116  0.347067   \n",
       "1 -1.996580  0.608495  0.161176  0.109797  0.316522  0.043483 -0.061820   \n",
       "2 -1.996558 -0.693499 -0.811576  1.169466  0.268231 -0.364571  1.351451   \n",
       "3 -1.996558 -0.493324 -0.112169  1.182514 -0.609726 -0.007469  0.936148   \n",
       "4 -1.996537 -0.591329  0.531540  1.021410  0.284655 -0.295015  0.071998   \n",
       "5 -1.996537 -0.217474  0.581674  0.752584 -0.118833  0.305008 -0.022313   \n",
       "6 -1.996495  0.627794  0.085389  0.029923  0.849382  0.139019  0.204694   \n",
       "7 -1.996432 -0.328928  0.858691  0.708575 -0.347630  0.687510  0.321345   \n",
       "8 -1.996432 -0.456572  0.173291 -0.074652 -0.191774  1.934146  2.793589   \n",
       "9 -1.996390 -0.172697  0.678004  0.688780 -0.156926  0.361791 -0.185218   \n",
       "\n",
       "         V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
       "0  0.193679  0.082637  0.331127  ... -0.024923  0.382854 -0.176911  0.110507   \n",
       "1 -0.063700  0.071253 -0.232494  ... -0.307376 -0.880075  0.162201 -0.561130   \n",
       "2  0.639775  0.207372 -1.378673  ...  0.337631  1.063356  1.456317 -1.138090   \n",
       "3  0.192070  0.316017 -1.262501  ... -0.147443  0.007267 -0.304776 -1.941024   \n",
       "4  0.479301 -0.226510  0.744325  ... -0.012839  1.100009 -0.220123  0.233250   \n",
       "5  0.384935  0.217954 -0.517618  ... -0.283522 -0.771426 -0.042273 -0.613272   \n",
       "6 -0.004170  0.067997  0.423217  ... -0.228333 -0.373032 -0.246779 -1.287970   \n",
       "7  0.905858 -3.188224  0.560128  ...  2.645884 -1.399273  0.092085 -1.072752   \n",
       "8  0.299205  0.712590 -0.356851  ... -0.099963 -0.369424 -0.327055  1.670266   \n",
       "9  0.526705  0.058223 -0.670586  ... -0.336155 -0.873297 -0.193438 -0.635766   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.246585 -0.392170  0.330891 -0.063781  0.244964      0  \n",
       "1  0.320693  0.261069 -0.022256  0.044607 -0.342474      0  \n",
       "2 -0.628536 -0.288446 -0.137137 -0.181021  1.160684      0  \n",
       "3  1.241902 -0.460217  0.155396  0.186188  0.140534      0  \n",
       "4 -0.395201  1.041609  0.543619  0.651815 -0.073403      0  \n",
       "5 -0.446583  0.219637  0.628899  0.245636 -0.338556      0  \n",
       "6  1.439034 -0.533435  0.085492  0.015656 -0.333278      0  \n",
       "7 -0.796632 -0.107075 -2.990148 -3.288077 -0.190107      0  \n",
       "8  0.715942 -0.796632  0.029104  0.431419  0.019392      0  \n",
       "9 -0.133773  0.195341  0.610009  0.251681 -0.338516      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test if finding actual accuracy with all fraud points work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "allpositive = df[df['Class'] == 1].copy()\n",
    "x_positive = allpositive.drop(['Class'], axis=1)\n",
    "y_positive = allpositive['Class']\n",
    "y_pospredict = store[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",
    "store['actual_accuracy'] = accuracy_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 284315), (1, 284315)]\n"
     ]
    }
   ],
   "source": [
    "# importing SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# applying SMOTE to our data and checking the class counts\n",
    "X_resampled2, y_resampled2 = SMOTE().fit_resample(x, y)\n",
    "print(sorted(Counter(y_resampled2).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing ADASYN\n",
    "from imblearn.over_sampling import ADASYN\n",
    "\n",
    "# applying ADASYN\n",
    "X_resampled3, y_resampled3 = ADASYN().fit_resample(x, y)\n",
    "print(sorted(Counter(y_resampled3).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BorderlineSMOTE\n",
    "from imblearn.over_sampling import BorderlineSMOTE\n",
    "\n",
    "X_resampled4, y_resampled4 = BorderlineSMOTE().fit_resample(x, y)\n",
    "print(sorted(Counter(y_resampled4).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def model_train(X, Y):\n",
    "#     # Define search parameter values\n",
    "#     depth = [5,10,15,20]\n",
    "#     tree_amt = [50,100,150]\n",
    "#     RFC = RandomForestClassifier(max_depth=5, random_state=42, n_estimators=50)\n",
    "\n",
    "#     # Create a parameter grid: map the parameter names to search values\n",
    "#     param_grid = dict(max_depth=depth, n_estimators=tree_amt)\n",
    "#     print(param_grid)\n",
    "\n",
    "#     # Instantiate and fit the grid\n",
    "#     grid = GridSearchCV(RFC, param_grid, cv=10, scoring='accuracy')\n",
    "#     grid.fit(X,Y)\n",
    "    \n",
    "#     return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# naive_sampling_model = model_train(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#     def model_normalnaive(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         return self.initialize_model(df, testratio, text, \"naive\", modelname)\n",
    "\n",
    "#     def model_w_standardize(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         df = self.standardize(df)\n",
    "#         return self.initialize_model(df, testratio, text, \"naive\", modelname)\n",
    "\n",
    "#     def model_smote(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         df = self.standardize(df)\n",
    "#         return self.initialize_model(df, testratio, text, \"smote\", modelname)\n",
    "    \n",
    "#     def model_adasyn(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         df = self.standardize(df)\n",
    "#         return self.initialize_model(df, testratio, text, \"adasyn\", modelname)"
   ]
  },
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   "execution_count": null,
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   "execution_count": null,
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