guide.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Guide to Classification with Regression\n",
    "\n",
    "We start by importing the Python libraries and our data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels as sm\n",
    "import sklearn as sl\n",
    "\n",
    "from statsmodels import tools, api\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor, OLSInfluence\n",
    "from sklearn import metrics, linear_model, preprocessing, model_selection, discriminant_analysis, neighbors, svm, ensemble\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn.model_selection._validation\")  # ignore future warning for Forward Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>id</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.260648</td>\n",
       "      <td>-0.469648</td>\n",
       "      <td>2.496266</td>\n",
       "      <td>-0.083724</td>\n",
       "      <td>0.129681</td>\n",
       "      <td>0.732898</td>\n",
       "      <td>0.519014</td>\n",
       "      <td>-0.130006</td>\n",
       "      <td>0.727159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110552</td>\n",
       "      <td>0.217606</td>\n",
       "      <td>-0.134794</td>\n",
       "      <td>0.165959</td>\n",
       "      <td>0.126280</td>\n",
       "      <td>-0.434824</td>\n",
       "      <td>-0.081230</td>\n",
       "      <td>-0.151045</td>\n",
       "      <td>17982.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.985100</td>\n",
       "      <td>-0.356045</td>\n",
       "      <td>0.558056</td>\n",
       "      <td>-0.429654</td>\n",
       "      <td>0.277140</td>\n",
       "      <td>0.428605</td>\n",
       "      <td>0.406466</td>\n",
       "      <td>-0.133118</td>\n",
       "      <td>0.347452</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194936</td>\n",
       "      <td>-0.605761</td>\n",
       "      <td>0.079469</td>\n",
       "      <td>-0.577395</td>\n",
       "      <td>0.190090</td>\n",
       "      <td>0.296503</td>\n",
       "      <td>-0.248052</td>\n",
       "      <td>-0.064512</td>\n",
       "      <td>6531.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.260272</td>\n",
       "      <td>-0.949385</td>\n",
       "      <td>1.728538</td>\n",
       "      <td>-0.457986</td>\n",
       "      <td>0.074062</td>\n",
       "      <td>1.419481</td>\n",
       "      <td>0.743511</td>\n",
       "      <td>-0.095576</td>\n",
       "      <td>-0.261297</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005020</td>\n",
       "      <td>0.702906</td>\n",
       "      <td>0.945045</td>\n",
       "      <td>-1.154666</td>\n",
       "      <td>-0.605564</td>\n",
       "      <td>-0.312895</td>\n",
       "      <td>-0.300258</td>\n",
       "      <td>-0.244718</td>\n",
       "      <td>2513.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.152152</td>\n",
       "      <td>-0.508959</td>\n",
       "      <td>1.746840</td>\n",
       "      <td>-1.090178</td>\n",
       "      <td>0.249486</td>\n",
       "      <td>1.143312</td>\n",
       "      <td>0.518269</td>\n",
       "      <td>-0.065130</td>\n",
       "      <td>-0.205698</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146927</td>\n",
       "      <td>-0.038212</td>\n",
       "      <td>-0.214048</td>\n",
       "      <td>-1.893131</td>\n",
       "      <td>1.003963</td>\n",
       "      <td>-0.515950</td>\n",
       "      <td>-0.165316</td>\n",
       "      <td>0.048424</td>\n",
       "      <td>5384.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.206820</td>\n",
       "      <td>-0.165280</td>\n",
       "      <td>1.527053</td>\n",
       "      <td>-0.448293</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>0.530549</td>\n",
       "      <td>0.658849</td>\n",
       "      <td>-0.212660</td>\n",
       "      <td>1.049921</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.106984</td>\n",
       "      <td>0.729727</td>\n",
       "      <td>-0.161666</td>\n",
       "      <td>0.312561</td>\n",
       "      <td>-0.414116</td>\n",
       "      <td>1.071126</td>\n",
       "      <td>0.023712</td>\n",
       "      <td>0.419117</td>\n",
       "      <td>14278.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568625</th>\n",
       "      <td>568625</td>\n",
       "      <td>-0.833437</td>\n",
       "      <td>0.061886</td>\n",
       "      <td>-0.899794</td>\n",
       "      <td>0.904227</td>\n",
       "      <td>-1.002401</td>\n",
       "      <td>0.481454</td>\n",
       "      <td>-0.370393</td>\n",
       "      <td>0.189694</td>\n",
       "      <td>-0.938153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167503</td>\n",
       "      <td>0.419731</td>\n",
       "      <td>1.288249</td>\n",
       "      <td>-0.900861</td>\n",
       "      <td>0.560661</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>3.308968</td>\n",
       "      <td>0.081564</td>\n",
       "      <td>4394.16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568626</th>\n",
       "      <td>568626</td>\n",
       "      <td>-0.670459</td>\n",
       "      <td>-0.202896</td>\n",
       "      <td>-0.068129</td>\n",
       "      <td>-0.267328</td>\n",
       "      <td>-0.133660</td>\n",
       "      <td>0.237148</td>\n",
       "      <td>-0.016935</td>\n",
       "      <td>-0.147733</td>\n",
       "      <td>0.483894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.388161</td>\n",
       "      <td>-0.154257</td>\n",
       "      <td>-0.846452</td>\n",
       "      <td>-0.153443</td>\n",
       "      <td>1.961398</td>\n",
       "      <td>-1.528642</td>\n",
       "      <td>1.704306</td>\n",
       "      <td>4653.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568627</th>\n",
       "      <td>568627</td>\n",
       "      <td>-0.311997</td>\n",
       "      <td>-0.004095</td>\n",
       "      <td>0.137526</td>\n",
       "      <td>-0.035893</td>\n",
       "      <td>-0.042291</td>\n",
       "      <td>0.121098</td>\n",
       "      <td>-0.070958</td>\n",
       "      <td>-0.019997</td>\n",
       "      <td>-0.122048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140788</td>\n",
       "      <td>0.536523</td>\n",
       "      <td>-0.211100</td>\n",
       "      <td>-0.448909</td>\n",
       "      <td>0.540073</td>\n",
       "      <td>-0.755836</td>\n",
       "      <td>-0.487540</td>\n",
       "      <td>-0.268741</td>\n",
       "      <td>23572.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568628</th>\n",
       "      <td>568628</td>\n",
       "      <td>0.636871</td>\n",
       "      <td>-0.516970</td>\n",
       "      <td>-0.300889</td>\n",
       "      <td>-0.144480</td>\n",
       "      <td>0.131042</td>\n",
       "      <td>-0.294148</td>\n",
       "      <td>0.580568</td>\n",
       "      <td>-0.207723</td>\n",
       "      <td>0.893527</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.060381</td>\n",
       "      <td>-0.195609</td>\n",
       "      <td>-0.175488</td>\n",
       "      <td>-0.554643</td>\n",
       "      <td>-0.099669</td>\n",
       "      <td>-1.434931</td>\n",
       "      <td>-0.159269</td>\n",
       "      <td>-0.076251</td>\n",
       "      <td>10160.83</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568629</th>\n",
       "      <td>568629</td>\n",
       "      <td>-0.795144</td>\n",
       "      <td>0.433236</td>\n",
       "      <td>-0.649140</td>\n",
       "      <td>0.374732</td>\n",
       "      <td>-0.244976</td>\n",
       "      <td>-0.603493</td>\n",
       "      <td>-0.347613</td>\n",
       "      <td>-0.340814</td>\n",
       "      <td>0.253971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534853</td>\n",
       "      <td>-0.291514</td>\n",
       "      <td>0.157303</td>\n",
       "      <td>0.931030</td>\n",
       "      <td>-0.349423</td>\n",
       "      <td>-1.090974</td>\n",
       "      <td>-1.575113</td>\n",
       "      <td>0.722936</td>\n",
       "      <td>21493.92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568630 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id        V1        V2        V3        V4        V5        V6  \\\n",
       "0            0 -0.260648 -0.469648  2.496266 -0.083724  0.129681  0.732898   \n",
       "1            1  0.985100 -0.356045  0.558056 -0.429654  0.277140  0.428605   \n",
       "2            2 -0.260272 -0.949385  1.728538 -0.457986  0.074062  1.419481   \n",
       "3            3 -0.152152 -0.508959  1.746840 -1.090178  0.249486  1.143312   \n",
       "4            4 -0.206820 -0.165280  1.527053 -0.448293  0.106125  0.530549   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "568625  568625 -0.833437  0.061886 -0.899794  0.904227 -1.002401  0.481454   \n",
       "568626  568626 -0.670459 -0.202896 -0.068129 -0.267328 -0.133660  0.237148   \n",
       "568627  568627 -0.311997 -0.004095  0.137526 -0.035893 -0.042291  0.121098   \n",
       "568628  568628  0.636871 -0.516970 -0.300889 -0.144480  0.131042 -0.294148   \n",
       "568629  568629 -0.795144  0.433236 -0.649140  0.374732 -0.244976 -0.603493   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "0       0.519014 -0.130006  0.727159  ... -0.110552  0.217606 -0.134794   \n",
       "1       0.406466 -0.133118  0.347452  ... -0.194936 -0.605761  0.079469   \n",
       "2       0.743511 -0.095576 -0.261297  ... -0.005020  0.702906  0.945045   \n",
       "3       0.518269 -0.065130 -0.205698  ... -0.146927 -0.038212 -0.214048   \n",
       "4       0.658849 -0.212660  1.049921  ... -0.106984  0.729727 -0.161666   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "568625 -0.370393  0.189694 -0.938153  ...  0.167503  0.419731  1.288249   \n",
       "568626 -0.016935 -0.147733  0.483894  ...  0.031874  0.388161 -0.154257   \n",
       "568627 -0.070958 -0.019997 -0.122048  ...  0.140788  0.536523 -0.211100   \n",
       "568628  0.580568 -0.207723  0.893527  ... -0.060381 -0.195609 -0.175488   \n",
       "568629 -0.347613 -0.340814  0.253971  ...  0.534853 -0.291514  0.157303   \n",
       "\n",
       "             V24       V25       V26       V27       V28    Amount  Class  \n",
       "0       0.165959  0.126280 -0.434824 -0.081230 -0.151045  17982.10      0  \n",
       "1      -0.577395  0.190090  0.296503 -0.248052 -0.064512   6531.37      0  \n",
       "2      -1.154666 -0.605564 -0.312895 -0.300258 -0.244718   2513.54      0  \n",
       "3      -1.893131  1.003963 -0.515950 -0.165316  0.048424   5384.44      0  \n",
       "4       0.312561 -0.414116  1.071126  0.023712  0.419117  14278.97      0  \n",
       "...          ...       ...       ...       ...       ...       ...    ...  \n",
       "568625 -0.900861  0.560661 -0.006018  3.308968  0.081564   4394.16      1  \n",
       "568626 -0.846452 -0.153443  1.961398 -1.528642  1.704306   4653.40      1  \n",
       "568627 -0.448909  0.540073 -0.755836 -0.487540 -0.268741  23572.85      1  \n",
       "568628 -0.554643 -0.099669 -1.434931 -0.159269 -0.076251  10160.83      1  \n",
       "568629  0.931030 -0.349423 -1.090974 -1.575113  0.722936  21493.92      1  \n",
       "\n",
       "[568630 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CreditCard = pd.read_csv(\"creditcard_2023.csv\")\n",
    "CreditCard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "\n",
    "### Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No invalid data found.\n"
     ]
    }
   ],
   "source": [
    "if CreditCard.isna().sum().sum() > 1:\n",
    "    CreditCard.dropna(inplace=True)  # drop rows containing invalid values\n",
    "    print(\"Removed invalid data.\")\n",
    "else:\n",
    "    print(\"No invalid data found.\")\n",
    "\n",
    "CreditCard.drop(columns=['id'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data set, we can make the following observations:\n",
    "\n",
    "* The first column just contains an ascending id, so we removed this redundant column.\n",
    "* No invalid entries are found, so no cleaning of rows was needed.\n",
    "* Attributes V1 to V28 are anonymized to protect the privacy of customers in the data set.\n",
    "\n",
    "We can also have a look at the scatter matrix of the first attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(CreditCard[['V1', 'V2', 'V3']]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraudulent Transactions: 284315\n",
      "Normal Transactions: 284315\n"
     ]
    }
   ],
   "source": [
    "print(\"Fraudulent Transactions:\", sum(CreditCard['Class'] == 1))\n",
    "print(\"Normal Transactions:\", sum(CreditCard['Class'] == 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the fraction of fraudulent transactions is exactly $0.5$, this is explained by the data set just being designed this way. This balanced distribution allows us to measure the model accuracy using standard methods. Credit card data sets which are not balanced often have a fraction of fraudulent activity $\\leq 0.1 \\%$. In those cases special accuracy definitions like \"Area Under the Precision-Recall Curve\" should be used, since the standard methods are not adequate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collinearity Detection\n",
    "\n",
    "Collinear parameters are defined by a very high correlation ($> 0.8$), meaning an effect can't be attributed to a single parameter. This leads to numeric instability in the regression calculation.\n",
    "\n",
    "For detecting if a pair of parameters are collinear, we can just look if the correlation matrix contains entries $> 0.8$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "      <th>V1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.561184</td>\n",
       "      <td>0.484499</td>\n",
       "      <td>-0.498963</td>\n",
       "      <td>0.517462</td>\n",
       "      <td>0.354728</td>\n",
       "      <td>0.573381</td>\n",
       "      <td>-0.226757</td>\n",
       "      <td>0.548973</td>\n",
       "      <td>0.599108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034669</td>\n",
       "      <td>-0.073729</td>\n",
       "      <td>-0.068917</td>\n",
       "      <td>-0.014651</td>\n",
       "      <td>-0.008508</td>\n",
       "      <td>0.009281</td>\n",
       "      <td>-0.122772</td>\n",
       "      <td>0.070111</td>\n",
       "      <td>-0.001280</td>\n",
       "      <td>-0.505761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>-0.561184</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.627810</td>\n",
       "      <td>0.579638</td>\n",
       "      <td>-0.631669</td>\n",
       "      <td>-0.341040</td>\n",
       "      <td>-0.694022</td>\n",
       "      <td>0.191321</td>\n",
       "      <td>-0.585095</td>\n",
       "      <td>-0.621798</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013570</td>\n",
       "      <td>0.035346</td>\n",
       "      <td>0.151906</td>\n",
       "      <td>-0.027515</td>\n",
       "      <td>0.132443</td>\n",
       "      <td>0.012219</td>\n",
       "      <td>0.053835</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>0.491878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>0.484499</td>\n",
       "      <td>-0.627810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.687726</td>\n",
       "      <td>0.510351</td>\n",
       "      <td>0.508974</td>\n",
       "      <td>0.634336</td>\n",
       "      <td>-0.263018</td>\n",
       "      <td>0.648615</td>\n",
       "      <td>0.707676</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021710</td>\n",
       "      <td>-0.041970</td>\n",
       "      <td>-0.058884</td>\n",
       "      <td>0.076460</td>\n",
       "      <td>-0.076332</td>\n",
       "      <td>-0.052056</td>\n",
       "      <td>-0.190582</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>-0.002001</td>\n",
       "      <td>-0.682095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>-0.498963</td>\n",
       "      <td>0.579638</td>\n",
       "      <td>-0.687726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.429243</td>\n",
       "      <td>-0.474403</td>\n",
       "      <td>-0.588648</td>\n",
       "      <td>0.199013</td>\n",
       "      <td>-0.676648</td>\n",
       "      <td>-0.712839</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013093</td>\n",
       "      <td>0.091197</td>\n",
       "      <td>0.043266</td>\n",
       "      <td>-0.102508</td>\n",
       "      <td>0.029402</td>\n",
       "      <td>0.136679</td>\n",
       "      <td>0.188036</td>\n",
       "      <td>-0.011316</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>0.735981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>0.517462</td>\n",
       "      <td>-0.631669</td>\n",
       "      <td>0.510351</td>\n",
       "      <td>-0.429243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245187</td>\n",
       "      <td>0.586828</td>\n",
       "      <td>-0.314975</td>\n",
       "      <td>0.479614</td>\n",
       "      <td>0.563874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034147</td>\n",
       "      <td>-0.119152</td>\n",
       "      <td>-0.113919</td>\n",
       "      <td>-0.083243</td>\n",
       "      <td>-0.047845</td>\n",
       "      <td>0.047771</td>\n",
       "      <td>-0.043759</td>\n",
       "      <td>0.108422</td>\n",
       "      <td>-0.000016</td>\n",
       "      <td>-0.338639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>0.354728</td>\n",
       "      <td>-0.341040</td>\n",
       "      <td>0.508974</td>\n",
       "      <td>-0.474403</td>\n",
       "      <td>0.245187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.418703</td>\n",
       "      <td>-0.604491</td>\n",
       "      <td>0.432241</td>\n",
       "      <td>0.471000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040153</td>\n",
       "      <td>0.036896</td>\n",
       "      <td>0.308598</td>\n",
       "      <td>-0.005237</td>\n",
       "      <td>-0.195340</td>\n",
       "      <td>-0.067605</td>\n",
       "      <td>-0.260783</td>\n",
       "      <td>-0.065641</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>-0.435088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0.573381</td>\n",
       "      <td>-0.694022</td>\n",
       "      <td>0.634336</td>\n",
       "      <td>-0.588648</td>\n",
       "      <td>0.586828</td>\n",
       "      <td>0.418703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.180986</td>\n",
       "      <td>0.601789</td>\n",
       "      <td>0.678004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019627</td>\n",
       "      <td>-0.104043</td>\n",
       "      <td>-0.111177</td>\n",
       "      <td>-0.004152</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>-0.006488</td>\n",
       "      <td>-0.036557</td>\n",
       "      <td>0.040732</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>-0.491234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8</th>\n",
       "      <td>-0.226757</td>\n",
       "      <td>0.191321</td>\n",
       "      <td>-0.263018</td>\n",
       "      <td>0.199013</td>\n",
       "      <td>-0.314975</td>\n",
       "      <td>-0.604491</td>\n",
       "      <td>-0.180986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.208557</td>\n",
       "      <td>-0.199995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056416</td>\n",
       "      <td>-0.098752</td>\n",
       "      <td>-0.463649</td>\n",
       "      <td>0.083272</td>\n",
       "      <td>0.322639</td>\n",
       "      <td>0.040448</td>\n",
       "      <td>0.298398</td>\n",
       "      <td>0.046017</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>0.144294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V9</th>\n",
       "      <td>0.548973</td>\n",
       "      <td>-0.585095</td>\n",
       "      <td>0.648615</td>\n",
       "      <td>-0.676648</td>\n",
       "      <td>0.479614</td>\n",
       "      <td>0.432241</td>\n",
       "      <td>0.601789</td>\n",
       "      <td>-0.208557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.748487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131001</td>\n",
       "      <td>-0.204723</td>\n",
       "      <td>-0.042371</td>\n",
       "      <td>0.044006</td>\n",
       "      <td>-0.034885</td>\n",
       "      <td>-0.131000</td>\n",
       "      <td>-0.111842</td>\n",
       "      <td>0.069959</td>\n",
       "      <td>-0.001589</td>\n",
       "      <td>-0.585522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V10</th>\n",
       "      <td>0.599108</td>\n",
       "      <td>-0.621798</td>\n",
       "      <td>0.707676</td>\n",
       "      <td>-0.712839</td>\n",
       "      <td>0.563874</td>\n",
       "      <td>0.471000</td>\n",
       "      <td>0.678004</td>\n",
       "      <td>-0.199995</td>\n",
       "      <td>0.748487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037426</td>\n",
       "      <td>-0.150957</td>\n",
       "      <td>-0.056285</td>\n",
       "      <td>0.045935</td>\n",
       "      <td>-0.014045</td>\n",
       "      <td>-0.053684</td>\n",
       "      <td>-0.134907</td>\n",
       "      <td>0.035646</td>\n",
       "      <td>-0.001259</td>\n",
       "      <td>-0.673665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V11</th>\n",
       "      <td>-0.525797</td>\n",
       "      <td>0.558863</td>\n",
       "      <td>-0.688436</td>\n",
       "      <td>0.708642</td>\n",
       "      <td>-0.440100</td>\n",
       "      <td>-0.497611</td>\n",
       "      <td>-0.587660</td>\n",
       "      <td>0.223052</td>\n",
       "      <td>-0.633556</td>\n",
       "      <td>-0.713066</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111608</td>\n",
       "      <td>0.022153</td>\n",
       "      <td>0.013596</td>\n",
       "      <td>-0.104340</td>\n",
       "      <td>0.051535</td>\n",
       "      <td>0.133635</td>\n",
       "      <td>0.290912</td>\n",
       "      <td>0.059732</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.724278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12</th>\n",
       "      <td>0.580715</td>\n",
       "      <td>-0.574935</td>\n",
       "      <td>0.705497</td>\n",
       "      <td>-0.722597</td>\n",
       "      <td>0.473002</td>\n",
       "      <td>0.498993</td>\n",
       "      <td>0.603318</td>\n",
       "      <td>-0.211999</td>\n",
       "      <td>0.667266</td>\n",
       "      <td>0.736783</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080394</td>\n",
       "      <td>-0.072096</td>\n",
       "      <td>-0.019261</td>\n",
       "      <td>0.080407</td>\n",
       "      <td>-0.010350</td>\n",
       "      <td>-0.114272</td>\n",
       "      <td>-0.216563</td>\n",
       "      <td>-0.053136</td>\n",
       "      <td>-0.001245</td>\n",
       "      <td>-0.768579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V13</th>\n",
       "      <td>-0.020567</td>\n",
       "      <td>0.012801</td>\n",
       "      <td>-0.019272</td>\n",
       "      <td>0.011519</td>\n",
       "      <td>-0.115317</td>\n",
       "      <td>-0.117637</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>0.273958</td>\n",
       "      <td>-0.006167</td>\n",
       "      <td>-0.019246</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025529</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>0.060097</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>0.043750</td>\n",
       "      <td>0.058483</td>\n",
       "      <td>-0.101488</td>\n",
       "      <td>-0.002718</td>\n",
       "      <td>-0.071105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V14</th>\n",
       "      <td>0.494427</td>\n",
       "      <td>-0.523294</td>\n",
       "      <td>0.673179</td>\n",
       "      <td>-0.714847</td>\n",
       "      <td>0.387454</td>\n",
       "      <td>0.510123</td>\n",
       "      <td>0.535612</td>\n",
       "      <td>-0.216410</td>\n",
       "      <td>0.633212</td>\n",
       "      <td>0.698939</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.189902</td>\n",
       "      <td>0.052023</td>\n",
       "      <td>-0.007601</td>\n",
       "      <td>0.138718</td>\n",
       "      <td>-0.087040</td>\n",
       "      <td>-0.142472</td>\n",
       "      <td>-0.299951</td>\n",
       "      <td>-0.127969</td>\n",
       "      <td>-0.001363</td>\n",
       "      <td>-0.805669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V15</th>\n",
       "      <td>0.046002</td>\n",
       "      <td>-0.161325</td>\n",
       "      <td>0.098516</td>\n",
       "      <td>-0.098627</td>\n",
       "      <td>0.058686</td>\n",
       "      <td>-0.023851</td>\n",
       "      <td>0.135939</td>\n",
       "      <td>0.101690</td>\n",
       "      <td>0.114613</td>\n",
       "      <td>0.111051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171719</td>\n",
       "      <td>-0.099347</td>\n",
       "      <td>-0.074832</td>\n",
       "      <td>0.023003</td>\n",
       "      <td>-0.027579</td>\n",
       "      <td>0.047833</td>\n",
       "      <td>0.116106</td>\n",
       "      <td>0.100293</td>\n",
       "      <td>0.001190</td>\n",
       "      <td>-0.037948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V16</th>\n",
       "      <td>0.621884</td>\n",
       "      <td>-0.534392</td>\n",
       "      <td>0.614504</td>\n",
       "      <td>-0.593948</td>\n",
       "      <td>0.596898</td>\n",
       "      <td>0.415834</td>\n",
       "      <td>0.667244</td>\n",
       "      <td>-0.230638</td>\n",
       "      <td>0.573957</td>\n",
       "      <td>0.686602</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.117591</td>\n",
       "      <td>-0.101847</td>\n",
       "      <td>-0.057100</td>\n",
       "      <td>-0.023511</td>\n",
       "      <td>0.062484</td>\n",
       "      <td>-0.056184</td>\n",
       "      <td>-0.191742</td>\n",
       "      <td>-0.022328</td>\n",
       "      <td>-0.000479</td>\n",
       "      <td>-0.573511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V17</th>\n",
       "      <td>0.605799</td>\n",
       "      <td>-0.495836</td>\n",
       "      <td>0.578223</td>\n",
       "      <td>-0.532786</td>\n",
       "      <td>0.669625</td>\n",
       "      <td>0.378152</td>\n",
       "      <td>0.655755</td>\n",
       "      <td>-0.277246</td>\n",
       "      <td>0.581604</td>\n",
       "      <td>0.649149</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.079348</td>\n",
       "      <td>-0.144637</td>\n",
       "      <td>-0.044635</td>\n",
       "      <td>-0.072198</td>\n",
       "      <td>0.075609</td>\n",
       "      <td>-0.045189</td>\n",
       "      <td>-0.184550</td>\n",
       "      <td>0.019570</td>\n",
       "      <td>-0.000358</td>\n",
       "      <td>-0.476377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V18</th>\n",
       "      <td>0.577296</td>\n",
       "      <td>-0.482162</td>\n",
       "      <td>0.525509</td>\n",
       "      <td>-0.482267</td>\n",
       "      <td>0.645095</td>\n",
       "      <td>0.328019</td>\n",
       "      <td>0.625680</td>\n",
       "      <td>-0.249986</td>\n",
       "      <td>0.522720</td>\n",
       "      <td>0.596702</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.060862</td>\n",
       "      <td>-0.135994</td>\n",
       "      <td>-0.046262</td>\n",
       "      <td>-0.099745</td>\n",
       "      <td>0.070467</td>\n",
       "      <td>-0.021039</td>\n",
       "      <td>-0.141790</td>\n",
       "      <td>0.052547</td>\n",
       "      <td>-0.001516</td>\n",
       "      <td>-0.410091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V19</th>\n",
       "      <td>-0.377803</td>\n",
       "      <td>0.208821</td>\n",
       "      <td>-0.314396</td>\n",
       "      <td>0.269842</td>\n",
       "      <td>-0.438118</td>\n",
       "      <td>-0.235623</td>\n",
       "      <td>-0.372270</td>\n",
       "      <td>0.253272</td>\n",
       "      <td>-0.294432</td>\n",
       "      <td>-0.375080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136080</td>\n",
       "      <td>0.110066</td>\n",
       "      <td>-0.001529</td>\n",
       "      <td>0.110751</td>\n",
       "      <td>-0.174328</td>\n",
       "      <td>0.041421</td>\n",
       "      <td>0.123266</td>\n",
       "      <td>-0.024368</td>\n",
       "      <td>-0.000400</td>\n",
       "      <td>0.244081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V20</th>\n",
       "      <td>-0.219164</td>\n",
       "      <td>0.263707</td>\n",
       "      <td>-0.253805</td>\n",
       "      <td>0.257236</td>\n",
       "      <td>-0.246694</td>\n",
       "      <td>-0.188360</td>\n",
       "      <td>-0.299436</td>\n",
       "      <td>0.131354</td>\n",
       "      <td>-0.328975</td>\n",
       "      <td>-0.287051</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.529918</td>\n",
       "      <td>0.429362</td>\n",
       "      <td>0.017204</td>\n",
       "      <td>-0.020316</td>\n",
       "      <td>0.030478</td>\n",
       "      <td>0.007677</td>\n",
       "      <td>-0.055183</td>\n",
       "      <td>-0.035727</td>\n",
       "      <td>-0.001405</td>\n",
       "      <td>0.179851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V21</th>\n",
       "      <td>-0.034669</td>\n",
       "      <td>-0.013570</td>\n",
       "      <td>-0.021710</td>\n",
       "      <td>-0.013093</td>\n",
       "      <td>0.034147</td>\n",
       "      <td>-0.040153</td>\n",
       "      <td>0.019627</td>\n",
       "      <td>0.056416</td>\n",
       "      <td>0.131001</td>\n",
       "      <td>0.037426</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.734653</td>\n",
       "      <td>0.096587</td>\n",
       "      <td>-0.059190</td>\n",
       "      <td>0.146164</td>\n",
       "      <td>0.070050</td>\n",
       "      <td>0.373256</td>\n",
       "      <td>0.326677</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.109640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V22</th>\n",
       "      <td>-0.073729</td>\n",
       "      <td>0.035346</td>\n",
       "      <td>-0.041970</td>\n",
       "      <td>0.091197</td>\n",
       "      <td>-0.119152</td>\n",
       "      <td>0.036896</td>\n",
       "      <td>-0.104043</td>\n",
       "      <td>-0.098752</td>\n",
       "      <td>-0.204723</td>\n",
       "      <td>-0.150957</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.734653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000636</td>\n",
       "      <td>0.079790</td>\n",
       "      <td>-0.258956</td>\n",
       "      <td>-0.015127</td>\n",
       "      <td>-0.340640</td>\n",
       "      <td>-0.282893</td>\n",
       "      <td>-0.000942</td>\n",
       "      <td>0.014098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V23</th>\n",
       "      <td>-0.068917</td>\n",
       "      <td>0.151906</td>\n",
       "      <td>-0.058884</td>\n",
       "      <td>0.043266</td>\n",
       "      <td>-0.113919</td>\n",
       "      <td>0.308598</td>\n",
       "      <td>-0.111177</td>\n",
       "      <td>-0.463649</td>\n",
       "      <td>-0.042371</td>\n",
       "      <td>-0.056285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096587</td>\n",
       "      <td>-0.000636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051181</td>\n",
       "      <td>-0.040882</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>-0.151698</td>\n",
       "      <td>0.028059</td>\n",
       "      <td>-0.001981</td>\n",
       "      <td>0.010255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V24</th>\n",
       "      <td>-0.014651</td>\n",
       "      <td>-0.027515</td>\n",
       "      <td>0.076460</td>\n",
       "      <td>-0.102508</td>\n",
       "      <td>-0.083243</td>\n",
       "      <td>-0.005237</td>\n",
       "      <td>-0.004152</td>\n",
       "      <td>0.083272</td>\n",
       "      <td>0.044006</td>\n",
       "      <td>0.045935</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.059190</td>\n",
       "      <td>0.079790</td>\n",
       "      <td>-0.051181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079604</td>\n",
       "      <td>-0.113362</td>\n",
       "      <td>-0.194899</td>\n",
       "      <td>-0.045189</td>\n",
       "      <td>-0.000846</td>\n",
       "      <td>-0.130107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V25</th>\n",
       "      <td>-0.008508</td>\n",
       "      <td>0.132443</td>\n",
       "      <td>-0.076332</td>\n",
       "      <td>0.029402</td>\n",
       "      <td>-0.047845</td>\n",
       "      <td>-0.195340</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.322639</td>\n",
       "      <td>-0.034885</td>\n",
       "      <td>-0.014045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146164</td>\n",
       "      <td>-0.258956</td>\n",
       "      <td>-0.040882</td>\n",
       "      <td>-0.079604</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057546</td>\n",
       "      <td>0.215653</td>\n",
       "      <td>0.176058</td>\n",
       "      <td>-0.000720</td>\n",
       "      <td>0.061847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V26</th>\n",
       "      <td>0.009281</td>\n",
       "      <td>0.012219</td>\n",
       "      <td>-0.052056</td>\n",
       "      <td>0.136679</td>\n",
       "      <td>0.047771</td>\n",
       "      <td>-0.067605</td>\n",
       "      <td>-0.006488</td>\n",
       "      <td>0.040448</td>\n",
       "      <td>-0.131000</td>\n",
       "      <td>-0.053684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070050</td>\n",
       "      <td>-0.015127</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>-0.113362</td>\n",
       "      <td>0.057546</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.193977</td>\n",
       "      <td>0.036830</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>0.071052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V27</th>\n",
       "      <td>-0.122772</td>\n",
       "      <td>0.053835</td>\n",
       "      <td>-0.190582</td>\n",
       "      <td>0.188036</td>\n",
       "      <td>-0.043759</td>\n",
       "      <td>-0.260783</td>\n",
       "      <td>-0.036557</td>\n",
       "      <td>0.298398</td>\n",
       "      <td>-0.111842</td>\n",
       "      <td>-0.134907</td>\n",
       "      <td>...</td>\n",
       "      <td>0.373256</td>\n",
       "      <td>-0.340640</td>\n",
       "      <td>-0.151698</td>\n",
       "      <td>-0.194899</td>\n",
       "      <td>0.215653</td>\n",
       "      <td>0.193977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183233</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.214002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V28</th>\n",
       "      <td>0.070111</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>-0.011316</td>\n",
       "      <td>0.108422</td>\n",
       "      <td>-0.065641</td>\n",
       "      <td>0.040732</td>\n",
       "      <td>0.046017</td>\n",
       "      <td>0.069959</td>\n",
       "      <td>0.035646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.326677</td>\n",
       "      <td>-0.282893</td>\n",
       "      <td>0.028059</td>\n",
       "      <td>-0.045189</td>\n",
       "      <td>0.176058</td>\n",
       "      <td>0.036830</td>\n",
       "      <td>0.183233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001503</td>\n",
       "      <td>0.102024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>-0.001280</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>-0.002001</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>-0.000016</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.001589</td>\n",
       "      <td>-0.001259</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>-0.000942</td>\n",
       "      <td>-0.001981</td>\n",
       "      <td>-0.000846</td>\n",
       "      <td>-0.000720</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>-0.001503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>-0.505761</td>\n",
       "      <td>0.491878</td>\n",
       "      <td>-0.682095</td>\n",
       "      <td>0.735981</td>\n",
       "      <td>-0.338639</td>\n",
       "      <td>-0.435088</td>\n",
       "      <td>-0.491234</td>\n",
       "      <td>0.144294</td>\n",
       "      <td>-0.585522</td>\n",
       "      <td>-0.673665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109640</td>\n",
       "      <td>0.014098</td>\n",
       "      <td>0.010255</td>\n",
       "      <td>-0.130107</td>\n",
       "      <td>0.061847</td>\n",
       "      <td>0.071052</td>\n",
       "      <td>0.214002</td>\n",
       "      <td>0.102024</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              V1        V2        V3        V4        V5        V6        V7  \\\n",
       "V1      1.000000 -0.561184  0.484499 -0.498963  0.517462  0.354728  0.573381   \n",
       "V2     -0.561184  1.000000 -0.627810  0.579638 -0.631669 -0.341040 -0.694022   \n",
       "V3      0.484499 -0.627810  1.000000 -0.687726  0.510351  0.508974  0.634336   \n",
       "V4     -0.498963  0.579638 -0.687726  1.000000 -0.429243 -0.474403 -0.588648   \n",
       "V5      0.517462 -0.631669  0.510351 -0.429243  1.000000  0.245187  0.586828   \n",
       "V6      0.354728 -0.341040  0.508974 -0.474403  0.245187  1.000000  0.418703   \n",
       "V7      0.573381 -0.694022  0.634336 -0.588648  0.586828  0.418703  1.000000   \n",
       "V8     -0.226757  0.191321 -0.263018  0.199013 -0.314975 -0.604491 -0.180986   \n",
       "V9      0.548973 -0.585095  0.648615 -0.676648  0.479614  0.432241  0.601789   \n",
       "V10     0.599108 -0.621798  0.707676 -0.712839  0.563874  0.471000  0.678004   \n",
       "V11    -0.525797  0.558863 -0.688436  0.708642 -0.440100 -0.497611 -0.587660   \n",
       "V12     0.580715 -0.574935  0.705497 -0.722597  0.473002  0.498993  0.603318   \n",
       "V13    -0.020567  0.012801 -0.019272  0.011519 -0.115317 -0.117637 -0.030000   \n",
       "V14     0.494427 -0.523294  0.673179 -0.714847  0.387454  0.510123  0.535612   \n",
       "V15     0.046002 -0.161325  0.098516 -0.098627  0.058686 -0.023851  0.135939   \n",
       "V16     0.621884 -0.534392  0.614504 -0.593948  0.596898  0.415834  0.667244   \n",
       "V17     0.605799 -0.495836  0.578223 -0.532786  0.669625  0.378152  0.655755   \n",
       "V18     0.577296 -0.482162  0.525509 -0.482267  0.645095  0.328019  0.625680   \n",
       "V19    -0.377803  0.208821 -0.314396  0.269842 -0.438118 -0.235623 -0.372270   \n",
       "V20    -0.219164  0.263707 -0.253805  0.257236 -0.246694 -0.188360 -0.299436   \n",
       "V21    -0.034669 -0.013570 -0.021710 -0.013093  0.034147 -0.040153  0.019627   \n",
       "V22    -0.073729  0.035346 -0.041970  0.091197 -0.119152  0.036896 -0.104043   \n",
       "V23    -0.068917  0.151906 -0.058884  0.043266 -0.113919  0.308598 -0.111177   \n",
       "V24    -0.014651 -0.027515  0.076460 -0.102508 -0.083243 -0.005237 -0.004152   \n",
       "V25    -0.008508  0.132443 -0.076332  0.029402 -0.047845 -0.195340  0.000802   \n",
       "V26     0.009281  0.012219 -0.052056  0.136679  0.047771 -0.067605 -0.006488   \n",
       "V27    -0.122772  0.053835 -0.190582  0.188036 -0.043759 -0.260783 -0.036557   \n",
       "V28     0.070111  0.021071  0.005346 -0.011316  0.108422 -0.065641  0.040732   \n",
       "Amount -0.001280 -0.000076 -0.002001  0.001859 -0.000016  0.000734  0.001326   \n",
       "Class  -0.505761  0.491878 -0.682095  0.735981 -0.338639 -0.435088 -0.491234   \n",
       "\n",
       "              V8        V9       V10  ...       V21       V22       V23  \\\n",
       "V1     -0.226757  0.548973  0.599108  ... -0.034669 -0.073729 -0.068917   \n",
       "V2      0.191321 -0.585095 -0.621798  ... -0.013570  0.035346  0.151906   \n",
       "V3     -0.263018  0.648615  0.707676  ... -0.021710 -0.041970 -0.058884   \n",
       "V4      0.199013 -0.676648 -0.712839  ... -0.013093  0.091197  0.043266   \n",
       "V5     -0.314975  0.479614  0.563874  ...  0.034147 -0.119152 -0.113919   \n",
       "V6     -0.604491  0.432241  0.471000  ... -0.040153  0.036896  0.308598   \n",
       "V7     -0.180986  0.601789  0.678004  ...  0.019627 -0.104043 -0.111177   \n",
       "V8      1.000000 -0.208557 -0.199995  ...  0.056416 -0.098752 -0.463649   \n",
       "V9     -0.208557  1.000000  0.748487  ...  0.131001 -0.204723 -0.042371   \n",
       "V10    -0.199995  0.748487  1.000000  ...  0.037426 -0.150957 -0.056285   \n",
       "V11     0.223052 -0.633556 -0.713066  ...  0.111608  0.022153  0.013596   \n",
       "V12    -0.211999  0.667266  0.736783  ... -0.080394 -0.072096 -0.019261   \n",
       "V13     0.273958 -0.006167 -0.019246  ...  0.025529  0.002039 -0.123520   \n",
       "V14    -0.216410  0.633212  0.698939  ... -0.189902  0.052023 -0.007601   \n",
       "V15     0.101690  0.114613  0.111051  ...  0.171719 -0.099347 -0.074832   \n",
       "V16    -0.230638  0.573957  0.686602  ... -0.117591 -0.101847 -0.057100   \n",
       "V17    -0.277246  0.581604  0.649149  ... -0.079348 -0.144637 -0.044635   \n",
       "V18    -0.249986  0.522720  0.596702  ... -0.060862 -0.135994 -0.046262   \n",
       "V19     0.253272 -0.294432 -0.375080  ...  0.136080  0.110066 -0.001529   \n",
       "V20     0.131354 -0.328975 -0.287051  ... -0.529918  0.429362  0.017204   \n",
       "V21     0.056416  0.131001  0.037426  ...  1.000000 -0.734653  0.096587   \n",
       "V22    -0.098752 -0.204723 -0.150957  ... -0.734653  1.000000 -0.000636   \n",
       "V23    -0.463649 -0.042371 -0.056285  ...  0.096587 -0.000636  1.000000   \n",
       "V24     0.083272  0.044006  0.045935  ... -0.059190  0.079790 -0.051181   \n",
       "V25     0.322639 -0.034885 -0.014045  ...  0.146164 -0.258956 -0.040882   \n",
       "V26     0.040448 -0.131000 -0.053684  ...  0.070050 -0.015127  0.001057   \n",
       "V27     0.298398 -0.111842 -0.134907  ...  0.373256 -0.340640 -0.151698   \n",
       "V28     0.046017  0.069959  0.035646  ...  0.326677 -0.282893  0.028059   \n",
       "Amount -0.000208 -0.001589 -0.001259  ...  0.001029 -0.000942 -0.001981   \n",
       "Class   0.144294 -0.585522 -0.673665  ...  0.109640  0.014098  0.010255   \n",
       "\n",
       "             V24       V25       V26       V27       V28    Amount     Class  \n",
       "V1     -0.014651 -0.008508  0.009281 -0.122772  0.070111 -0.001280 -0.505761  \n",
       "V2     -0.027515  0.132443  0.012219  0.053835  0.021071 -0.000076  0.491878  \n",
       "V3      0.076460 -0.076332 -0.052056 -0.190582  0.005346 -0.002001 -0.682095  \n",
       "V4     -0.102508  0.029402  0.136679  0.188036 -0.011316  0.001859  0.735981  \n",
       "V5     -0.083243 -0.047845  0.047771 -0.043759  0.108422 -0.000016 -0.338639  \n",
       "V6     -0.005237 -0.195340 -0.067605 -0.260783 -0.065641  0.000734 -0.435088  \n",
       "V7     -0.004152  0.000802 -0.006488 -0.036557  0.040732  0.001326 -0.491234  \n",
       "V8      0.083272  0.322639  0.040448  0.298398  0.046017 -0.000208  0.144294  \n",
       "V9      0.044006 -0.034885 -0.131000 -0.111842  0.069959 -0.001589 -0.585522  \n",
       "V10     0.045935 -0.014045 -0.053684 -0.134907  0.035646 -0.001259 -0.673665  \n",
       "V11    -0.104340  0.051535  0.133635  0.290912  0.059732  0.000292  0.724278  \n",
       "V12     0.080407 -0.010350 -0.114272 -0.216563 -0.053136 -0.001245 -0.768579  \n",
       "V13     0.060097  0.003580  0.043750  0.058483 -0.101488 -0.002718 -0.071105  \n",
       "V14     0.138718 -0.087040 -0.142472 -0.299951 -0.127969 -0.001363 -0.805669  \n",
       "V15     0.023003 -0.027579  0.047833  0.116106  0.100293  0.001190 -0.037948  \n",
       "V16    -0.023511  0.062484 -0.056184 -0.191742 -0.022328 -0.000479 -0.573511  \n",
       "V17    -0.072198  0.075609 -0.045189 -0.184550  0.019570 -0.000358 -0.476377  \n",
       "V18    -0.099745  0.070467 -0.021039 -0.141790  0.052547 -0.001516 -0.410091  \n",
       "V19     0.110751 -0.174328  0.041421  0.123266 -0.024368 -0.000400  0.244081  \n",
       "V20    -0.020316  0.030478  0.007677 -0.055183 -0.035727 -0.001405  0.179851  \n",
       "V21    -0.059190  0.146164  0.070050  0.373256  0.326677  0.001029  0.109640  \n",
       "V22     0.079790 -0.258956 -0.015127 -0.340640 -0.282893 -0.000942  0.014098  \n",
       "V23    -0.051181 -0.040882  0.001057 -0.151698  0.028059 -0.001981  0.010255  \n",
       "V24     1.000000 -0.079604 -0.113362 -0.194899 -0.045189 -0.000846 -0.130107  \n",
       "V25    -0.079604  1.000000  0.057546  0.215653  0.176058 -0.000720  0.061847  \n",
       "V26    -0.113362  0.057546  1.000000  0.193977  0.036830 -0.000120  0.071052  \n",
       "V27    -0.194899  0.215653  0.193977  1.000000  0.183233  0.001235  0.214002  \n",
       "V28    -0.045189  0.176058  0.036830  0.183233  1.000000 -0.001503  0.102024  \n",
       "Amount -0.000846 -0.000720 -0.000120  0.001235 -0.001503  1.000000  0.002261  \n",
       "Class  -0.130107  0.061847  0.071052  0.214002  0.102024  0.002261  1.000000  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CreditCard.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But often collinearity exists between three or more variables, so better practice is to check if the variance inflation factor (VIF) is bigger than a threshold, often choosen between $5$ and $10$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const     4.028678\n",
       "V1        2.105776\n",
       "V2        3.034875\n",
       "V3        3.022703\n",
       "V4        3.067169\n",
       "V5        2.894080\n",
       "V6        2.506364\n",
       "V7        3.055107\n",
       "V8        2.722728\n",
       "V9        2.970366\n",
       "V10       3.953038\n",
       "V11       3.376454\n",
       "V12       4.039609\n",
       "V13       1.153581\n",
       "V14       3.912997\n",
       "V15       1.165098\n",
       "V16       4.891915\n",
       "V17       6.536299\n",
       "V18       4.035031\n",
       "V19       2.014014\n",
       "V20       1.790715\n",
       "V21       3.575843\n",
       "V22       2.733672\n",
       "V23       1.579823\n",
       "V24       1.133900\n",
       "V25       1.382206\n",
       "V26       1.112986\n",
       "V27       1.560585\n",
       "V28       1.275454\n",
       "Amount    1.000059\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.tools.tools.add_constant(CreditCard[CreditCard.columns.drop(['Class'])])  # regression matrix\n",
    "\n",
    "vif = pd.Series([sm.stats.outliers_influence.variance_inflation_factor(X.values, i) \n",
    "               for i in range(X.shape[1])], \n",
    "              index=X.columns)\n",
    "vif\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['V17'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "highVifColumns = vif[vif > 5].index\n",
    "print(highVifColumns)\n",
    "\n",
    "CreditCard.drop(columns=highVifColumns, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that V17 is the only collinear parameter, which we can then drop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A priori Model\n",
    "### Full-dimensional Regression\n",
    "\n",
    "We now construct at a simple logistic regression model to get a first idea of the classification problem. For this a priori model we use the whole data set as trainings data, since for now we don't need an accurate accuracy measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Class</td>      <th>  No. Observations:  </th>   <td>568630</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>   <td>568601</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>   <td>    28</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>Logit</td>      <th>  Scale:             </th>  <td>  1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th>  <td> -56082.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 23 Jun 2024</td> <th>  Deviance:          </th> <td>1.1216e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:19:05</td>     <th>  Pearson chi2:      </th>  <td>1.26e+12</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>12</td>        <th>  Pseudo R-squ. (CS):</th>   <td>0.6955</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    5.2052</td> <td>    0.053</td> <td>   98.649</td> <td> 0.000</td> <td>    5.102</td> <td>    5.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V1</th>     <td>   -0.5100</td> <td>    0.017</td> <td>  -30.415</td> <td> 0.000</td> <td>   -0.543</td> <td>   -0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V2</th>     <td>    0.0070</td> <td>    0.014</td> <td>    0.512</td> <td> 0.609</td> <td>   -0.020</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V3</th>     <td>   -0.8266</td> <td>    0.017</td> <td>  -48.277</td> <td> 0.000</td> <td>   -0.860</td> <td>   -0.793</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V4</th>     <td>    3.0180</td> <td>    0.021</td> <td>  141.105</td> <td> 0.000</td> <td>    2.976</td> <td>    3.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V5</th>     <td>   -0.0669</td> <td>    0.014</td> <td>   -4.927</td> <td> 0.000</td> <td>   -0.093</td> <td>   -0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V6</th>     <td>   -0.3795</td> <td>    0.016</td> <td>  -23.697</td> <td> 0.000</td> <td>   -0.411</td> <td>   -0.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V7</th>     <td>   -0.6526</td> <td>    0.022</td> <td>  -29.619</td> <td> 0.000</td> <td>   -0.696</td> <td>   -0.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V8</th>     <td>   -2.4866</td> <td>    0.040</td> <td>  -62.073</td> <td> 0.000</td> <td>   -2.565</td> <td>   -2.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V9</th>     <td>   -0.5454</td> <td>    0.019</td> <td>  -28.018</td> <td> 0.000</td> <td>   -0.584</td> <td>   -0.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V10</th>    <td>   -1.2119</td> <td>    0.026</td> <td>  -47.511</td> <td> 0.000</td> <td>   -1.262</td> <td>   -1.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V11</th>    <td>    1.3344</td> <td>    0.015</td> <td>   89.661</td> <td> 0.000</td> <td>    1.305</td> <td>    1.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V12</th>    <td>   -2.1024</td> <td>    0.020</td> <td> -104.371</td> <td> 0.000</td> <td>   -2.142</td> <td>   -2.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V13</th>    <td>   -0.0409</td> <td>    0.008</td> <td>   -4.923</td> <td> 0.000</td> <td>   -0.057</td> <td>   -0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V14</th>    <td>   -2.4085</td> <td>    0.019</td> <td> -123.990</td> <td> 0.000</td> <td>   -2.447</td> <td>   -2.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V15</th>    <td>   -0.2079</td> <td>    0.008</td> <td>  -25.568</td> <td> 0.000</td> <td>   -0.224</td> <td>   -0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V16</th>    <td>   -0.6260</td> <td>    0.021</td> <td>  -29.968</td> <td> 0.000</td> <td>   -0.667</td> <td>   -0.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V18</th>    <td>   -0.9524</td> <td>    0.019</td> <td>  -48.962</td> <td> 0.000</td> <td>   -0.990</td> <td>   -0.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V19</th>    <td>   -0.0963</td> <td>    0.011</td> <td>   -8.520</td> <td> 0.000</td> <td>   -0.118</td> <td>   -0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V20</th>    <td>    0.0529</td> <td>    0.011</td> <td>    4.919</td> <td> 0.000</td> <td>    0.032</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V21</th>    <td>    0.3225</td> <td>    0.027</td> <td>   12.157</td> <td> 0.000</td> <td>    0.271</td> <td>    0.374</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V22</th>    <td>    0.5175</td> <td>    0.014</td> <td>   36.102</td> <td> 0.000</td> <td>    0.489</td> <td>    0.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V23</th>    <td>   -0.2539</td> <td>    0.010</td> <td>  -26.122</td> <td> 0.000</td> <td>   -0.273</td> <td>   -0.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V24</th>    <td>   -0.1293</td> <td>    0.009</td> <td>  -14.419</td> <td> 0.000</td> <td>   -0.147</td> <td>   -0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V25</th>    <td>    0.1269</td> <td>    0.010</td> <td>   12.110</td> <td> 0.000</td> <td>    0.106</td> <td>    0.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V26</th>    <td>   -0.1370</td> <td>    0.009</td> <td>  -14.569</td> <td> 0.000</td> <td>   -0.155</td> <td>   -0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V27</th>    <td>    0.0649</td> <td>    0.020</td> <td>    3.283</td> <td> 0.001</td> <td>    0.026</td> <td>    0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>V28</th>    <td>    0.0852</td> <td>    0.009</td> <td>    8.986</td> <td> 0.000</td> <td>    0.067</td> <td>    0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Amount</th> <td>-2.628e-07</td> <td> 1.14e-06</td> <td>   -0.230</td> <td> 0.818</td> <td>-2.51e-06</td> <td> 1.98e-06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}   &      Class       & \\textbf{  No. Observations:  } &   568630    \\\\\n",
       "\\textbf{Model:}           &       GLM        & \\textbf{  Df Residuals:      } &   568601    \\\\\n",
       "\\textbf{Model Family:}    &     Binomial     & \\textbf{  Df Model:          } &       28    \\\\\n",
       "\\textbf{Link Function:}   &      Logit       & \\textbf{  Scale:             } &    1.0000   \\\\\n",
       "\\textbf{Method:}          &       IRLS       & \\textbf{  Log-Likelihood:    } &   -56082.   \\\\\n",
       "\\textbf{Date:}            & Sun, 23 Jun 2024 & \\textbf{  Deviance:          } & 1.1216e+05  \\\\\n",
       "\\textbf{Time:}            &     20:19:05     & \\textbf{  Pearson chi2:      } &  1.26e+12   \\\\\n",
       "\\textbf{No. Iterations:}  &        12        & \\textbf{  Pseudo R-squ. (CS):} &   0.6955    \\\\\n",
       "\\textbf{Covariance Type:} &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &       5.2052  &        0.053     &    98.649  &         0.000        &        5.102    &        5.309     \\\\\n",
       "\\textbf{V1}     &      -0.5100  &        0.017     &   -30.415  &         0.000        &       -0.543    &       -0.477     \\\\\n",
       "\\textbf{V2}     &       0.0070  &        0.014     &     0.512  &         0.609        &       -0.020    &        0.034     \\\\\n",
       "\\textbf{V3}     &      -0.8266  &        0.017     &   -48.277  &         0.000        &       -0.860    &       -0.793     \\\\\n",
       "\\textbf{V4}     &       3.0180  &        0.021     &   141.105  &         0.000        &        2.976    &        3.060     \\\\\n",
       "\\textbf{V5}     &      -0.0669  &        0.014     &    -4.927  &         0.000        &       -0.093    &       -0.040     \\\\\n",
       "\\textbf{V6}     &      -0.3795  &        0.016     &   -23.697  &         0.000        &       -0.411    &       -0.348     \\\\\n",
       "\\textbf{V7}     &      -0.6526  &        0.022     &   -29.619  &         0.000        &       -0.696    &       -0.609     \\\\\n",
       "\\textbf{V8}     &      -2.4866  &        0.040     &   -62.073  &         0.000        &       -2.565    &       -2.408     \\\\\n",
       "\\textbf{V9}     &      -0.5454  &        0.019     &   -28.018  &         0.000        &       -0.584    &       -0.507     \\\\\n",
       "\\textbf{V10}    &      -1.2119  &        0.026     &   -47.511  &         0.000        &       -1.262    &       -1.162     \\\\\n",
       "\\textbf{V11}    &       1.3344  &        0.015     &    89.661  &         0.000        &        1.305    &        1.364     \\\\\n",
       "\\textbf{V12}    &      -2.1024  &        0.020     &  -104.371  &         0.000        &       -2.142    &       -2.063     \\\\\n",
       "\\textbf{V13}    &      -0.0409  &        0.008     &    -4.923  &         0.000        &       -0.057    &       -0.025     \\\\\n",
       "\\textbf{V14}    &      -2.4085  &        0.019     &  -123.990  &         0.000        &       -2.447    &       -2.370     \\\\\n",
       "\\textbf{V15}    &      -0.2079  &        0.008     &   -25.568  &         0.000        &       -0.224    &       -0.192     \\\\\n",
       "\\textbf{V16}    &      -0.6260  &        0.021     &   -29.968  &         0.000        &       -0.667    &       -0.585     \\\\\n",
       "\\textbf{V18}    &      -0.9524  &        0.019     &   -48.962  &         0.000        &       -0.990    &       -0.914     \\\\\n",
       "\\textbf{V19}    &      -0.0963  &        0.011     &    -8.520  &         0.000        &       -0.118    &       -0.074     \\\\\n",
       "\\textbf{V20}    &       0.0529  &        0.011     &     4.919  &         0.000        &        0.032    &        0.074     \\\\\n",
       "\\textbf{V21}    &       0.3225  &        0.027     &    12.157  &         0.000        &        0.271    &        0.374     \\\\\n",
       "\\textbf{V22}    &       0.5175  &        0.014     &    36.102  &         0.000        &        0.489    &        0.546     \\\\\n",
       "\\textbf{V23}    &      -0.2539  &        0.010     &   -26.122  &         0.000        &       -0.273    &       -0.235     \\\\\n",
       "\\textbf{V24}    &      -0.1293  &        0.009     &   -14.419  &         0.000        &       -0.147    &       -0.112     \\\\\n",
       "\\textbf{V25}    &       0.1269  &        0.010     &    12.110  &         0.000        &        0.106    &        0.147     \\\\\n",
       "\\textbf{V26}    &      -0.1370  &        0.009     &   -14.569  &         0.000        &       -0.155    &       -0.119     \\\\\n",
       "\\textbf{V27}    &       0.0649  &        0.020     &     3.283  &         0.001        &        0.026    &        0.104     \\\\\n",
       "\\textbf{V28}    &       0.0852  &        0.009     &     8.986  &         0.000        &        0.067    &        0.104     \\\\\n",
       "\\textbf{Amount} &   -2.628e-07  &     1.14e-06     &    -0.230  &         0.818        &    -2.51e-06    &     1.98e-06     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Generalized Linear Model Regression Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                  Class   No. Observations:               568630\n",
       "Model:                            GLM   Df Residuals:                   568601\n",
       "Model Family:                Binomial   Df Model:                           28\n",
       "Link Function:                  Logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -56082.\n",
       "Date:                Sun, 23 Jun 2024   Deviance:                   1.1216e+05\n",
       "Time:                        20:19:05   Pearson chi2:                 1.26e+12\n",
       "No. Iterations:                    12   Pseudo R-squ. (CS):             0.6955\n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          5.2052      0.053     98.649      0.000       5.102       5.309\n",
       "V1            -0.5100      0.017    -30.415      0.000      -0.543      -0.477\n",
       "V2             0.0070      0.014      0.512      0.609      -0.020       0.034\n",
       "V3            -0.8266      0.017    -48.277      0.000      -0.860      -0.793\n",
       "V4             3.0180      0.021    141.105      0.000       2.976       3.060\n",
       "V5            -0.0669      0.014     -4.927      0.000      -0.093      -0.040\n",
       "V6            -0.3795      0.016    -23.697      0.000      -0.411      -0.348\n",
       "V7            -0.6526      0.022    -29.619      0.000      -0.696      -0.609\n",
       "V8            -2.4866      0.040    -62.073      0.000      -2.565      -2.408\n",
       "V9            -0.5454      0.019    -28.018      0.000      -0.584      -0.507\n",
       "V10           -1.2119      0.026    -47.511      0.000      -1.262      -1.162\n",
       "V11            1.3344      0.015     89.661      0.000       1.305       1.364\n",
       "V12           -2.1024      0.020   -104.371      0.000      -2.142      -2.063\n",
       "V13           -0.0409      0.008     -4.923      0.000      -0.057      -0.025\n",
       "V14           -2.4085      0.019   -123.990      0.000      -2.447      -2.370\n",
       "V15           -0.2079      0.008    -25.568      0.000      -0.224      -0.192\n",
       "V16           -0.6260      0.021    -29.968      0.000      -0.667      -0.585\n",
       "V18           -0.9524      0.019    -48.962      0.000      -0.990      -0.914\n",
       "V19           -0.0963      0.011     -8.520      0.000      -0.118      -0.074\n",
       "V20            0.0529      0.011      4.919      0.000       0.032       0.074\n",
       "V21            0.3225      0.027     12.157      0.000       0.271       0.374\n",
       "V22            0.5175      0.014     36.102      0.000       0.489       0.546\n",
       "V23           -0.2539      0.010    -26.122      0.000      -0.273      -0.235\n",
       "V24           -0.1293      0.009    -14.419      0.000      -0.147      -0.112\n",
       "V25            0.1269      0.010     12.110      0.000       0.106       0.147\n",
       "V26           -0.1370      0.009    -14.569      0.000      -0.155      -0.119\n",
       "V27            0.0649      0.020      3.283      0.001       0.026       0.104\n",
       "V28            0.0852      0.009      8.986      0.000       0.067       0.104\n",
       "Amount     -2.628e-07   1.14e-06     -0.230      0.818   -2.51e-06    1.98e-06\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.tools.tools.add_constant(CreditCard[CreditCard.columns.drop(['Class'])])  # regression matrix\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "model = sm.api.GLM(y, X, family=sm.api.families.Binomial())\n",
    "res = model.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9618451365562845\n"
     ]
    }
   ],
   "source": [
    "predictions = (res.predict(X) >= 0.5).astype(int)  # convert to binary decision with threshold 0.5\n",
    "\n",
    "trueLabels = CreditCard['Class']\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(trueLabels, predictions)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()\n",
    "\n",
    "print(sl.metrics.accuracy_score(trueLabels, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that our a priori model already yields an accuracy of $96.2 \\%$. But we also observe that our balanced threshold of $0.5$ results in more fraudulent transactions being undetected than normal transactions wrongly classified as fraudulent. We will return to this problem of threshold fine-tuning in the section Bias-Specification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensionality Reduction\n",
    "\n",
    "Our a priori model has 28 predictors (V1 to V28 minus V17 plus Amount). We will try to detect the most significant parameters using the Forward Selection algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features: ['V4', 'V8', 'V12', 'V14', 'V18']\n"
     ]
    }
   ],
   "source": [
    "X = CreditCard[CreditCard.columns.drop(['Class'])] # regression matrix\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "sfs = SFS(sl.linear_model.LinearRegression(),\n",
    "          k_features=5,  # Alternatively use 'best' for auto-select\n",
    "          forward=True,\n",
    "          floating=False,\n",
    "          scoring='neg_mean_squared_error',\n",
    "          cv=5)\n",
    "\n",
    "\n",
    "sfs = sfs.fit(X, y)\n",
    "selected_features = list(sfs.k_feature_names_)\n",
    "print(f\"Selected features: {selected_features}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9527495911225226\n"
     ]
    }
   ],
   "source": [
    "X = sm.tools.tools.add_constant(CreditCard[selected_features])  # regression matrix\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "model = sm.api.GLM(y, X, family=sm.api.families.Binomial())\n",
    "res = model.fit()\n",
    "\n",
    "print(sl.metrics.accuracy_score(CreditCard['Class'], (res.predict(X) >= 0.5).astype(int)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So by reducing our 28 predictors to just the 5 best selected by Forward Selection, we only reduce our accuracy by less than $1 \\%$. This prevents overfitting later on and allows us to experiment with computationally more expensive models like polynomial regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-linear Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9575048801505373\n"
     ]
    }
   ],
   "source": [
    "poly = sl.preprocessing.PolynomialFeatures(degree=2, include_bias=False)\n",
    "\n",
    "X = sm.tools.tools.add_constant(poly.fit_transform(CreditCard[selected_features]))  # polynomial regression matrix\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "model = sm.api.GLM(y, X, family=sm.api.families.Binomial())\n",
    "res = model.fit()\n",
    "\n",
    "print(sl.metrics.accuracy_score(CreditCard['Class'], (res.predict(X) >= 0.5).astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9539841373124879\n"
     ]
    }
   ],
   "source": [
    "X = sm.tools.tools.add_constant(np.concatenate([np.sin(CreditCard[selected_features]), np.cos(CreditCard[selected_features])], axis=1))  # sine/cosine regression matrix\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "model = sm.api.GLM(y, X, family=sm.api.families.Binomial())\n",
    "res = model.fit()\n",
    "\n",
    "print(sl.metrics.accuracy_score(CreditCard['Class'], (res.predict(X) >= 0.5).astype(int)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that neither polynomial no trigonometric regression leads to a significant improvement of accuracy for our data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "\n",
    "### Model Comparison\n",
    "\n",
    "For our a priori model we used the complete data set for training, so we have no way of detecting overfitting or have an accurate measure of model performance on new data. So for our proper models we now divide the data set in training and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = CreditCard[selected_features]\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "X_train, X_test, y_train, y_test = sl.model_selection.train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9523415929514799\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = sm.api.GLM(y_train, sm.tools.tools.add_constant(X_train), family=sm.api.families.Binomial())\n",
    "res = model.fit()\n",
    "prediction = (res.predict(sm.tools.tools.add_constant(X_test)) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our a priori model using the 5 parameters selected by Forward Search we measured an accuracy of $95.3 \\%$. With the split data set the more accurate estimate is $95.2 \\%$, which is almost identical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9473735117739127\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lda = sl.discriminant_analysis.LinearDiscriminantAnalysis(store_covariance=True)\n",
    "lda.fit(X_train, y_train)\n",
    "prediction = (lda.predict(X_test) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA gives us an almost identical accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quadratic Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9381671737333591\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qda = sl.discriminant_analysis.QuadraticDiscriminantAnalysis(store_covariance=True)\n",
    "qda.fit(X_train, y_train)\n",
    "prediction = (qda.predict(X_test) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extending LDA to QDA also does not seem to help, it even lowers the accuracy which indicates some overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9532560716107135\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boost = GBR(n_estimators=50,\n",
    "                   learning_rate=0.01,\n",
    "                   max_depth=4,\n",
    "                   random_state=0)\n",
    "boost.fit(X_train, y_train)\n",
    "\n",
    "prediction = (boost.predict(X_test) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of Decision Trees seem to be similar to other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9531329687142782\n"
     ]
    },
    {
     "data": {
      "image/png": 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pVwqwf1cnVFe2nNg4uTQgPPYa9m51+91eBV6e0RM9g2qw+3wu9uT9gEejirFwZg9Uld8+QaL20zTHRupmztr97i0tLZGQkIB169bhypUrzY5nZ2dj2rRpmDFjBk6ePInFixdj0aJFSE1N1YtbsWIFgoKCkJ2djUWLFgEAqqursXbtWqSnpyMjIwPffPMNHnvsMezduxd79+7F1q1b8c477+Djjz8W29FoNFiyZAm+//577N69G3l5eYiMjDTonurq6lBeXq630e0N+VMF/m9iGboH1GLQQ5VYsvUCAGDfR40ThkdOLcXQMeXw7VOLYePKsXT7z/jlghJH/9tYxm+qqDz81xsIm3ETvYJr8MzrV9GlZx2+THfTi3nihWv4v4ll8OtXg7jVl6FQAP/b00nsS3jsNQQOrkbPoBo8/kwxnnyxEB9t8Lw3XwiiFlhaCXhlwyUoLID1L3dpMcbeUYsl/8rD5R9tb5mIL2Be4hWUXrdC3KO9EDPRD4e+VOGNtDy4etbfmxsgusdkMXn40UcfxYABA/Daa69hy5YtesdSUlIwevRoMVnp3bs3Tp8+jRUrVuglHH/6058QHx8vfs7KykJ9fT02bNiAnj0bx6off/xxbN26FdeuXYOjoyMCAwMxatQo7N+/H9OnTwcAPPXUU2IbPXr0wNq1a3H//fejsrISjo6tW0WQmJiI119//a6+FtQ4qdK3Ty1+yVO2eNzNqwGeXerxywWl+BkAuvWu1Yvz6VWLol8a5xC4/hrT1e+3GBulAHW3OjGmJQGDqlFdYYmSYiu4eHDeDd1bllYCFm66CLWPBgum9WyxWmPnoMWyDy6gttoCr8/2hbbht2GIAQ9W4v4x5Xg8IEg8d/1Jewx66AzGTLuJHetZjZQbHYzwrigznzzc7hWbJsnJyUhLS8Pp06f19p85cwbDhw/X2zd8+HCcP39ebwhp8ODBzdq0t7cXkxoA8PLygq+vr16C4uXlpTfUdOLECUydOhXdunWDk5MTRo4cCQC4fPm3FTl/5OWXX0ZZWZm45efnt/pcAjR1CuT/pLztX5TlNy1RfNUarl6Nx718NHBTa3DlZ/1E6JcLSnh2aYzx61cNa6VOL6ahHriWbwOvLrf/y/WnXDvY2Org4MyVUXRvNSU193XX4B/Te6KipPnfofaOWiT8+wLqNQq8Ftkd9XX6P9KVdo2lylvniekEBSzM+3efbAm/roqSsglmntjIomIDAA899BDCwsLwyiuv6FViBEGAQqH/P6ml1VIODg7N9llb6/8lrlAoWtyn+/VffVVVFcaNG4dx48Zh27Zt8PDwwOXLlxEWFmbQhGSlUgmlsuVqAzX3zuveGDauDJ731aP0uhU+WOOF6gpLjJ12EzVVFti6Uo0HJ5bC1asB1/Jt8H5iZ6hcGzB8QhkAQKEAHn+2GFtXqtEjsAY9+tbgq49ckf+zLf65+SIAwMFJh4kRN7B1lRoe3vXw7KLBx78OMf3fpFIAwOFMZ9wstkJgSDVsbHX4/ltHpCZ3xsMzb8BGyRV6ZFy29lp4d//t54raR4MefWtQUWqJG4XWWLT5InoF1+DVJ7vDwlKAi0djAl5RaomGegvYOTQmNUo7HZbP84W9oxb2jo0JeNkNK+h0CpzJdkBlmSVefDMf21d7oa7WAhNm3oDaRyMO5ZK88O3e0skmsQGApKQkDBgwAL179xb3BQYGIisrSy/u4MGD6N27NywtjTv57ezZs7h+/TqSkpLg4+MDADh+/LhRr0HNXS+wRmK0L8pvWkLl1oA+g6qxZs+P8OpSj7oaBS6etcVXH3dHVbklXD0b0H94JV7ZeBH2jr/9GfpYVDHqaxXY+Np9qCi1RI/AWiT++2d4+/72iyNq0S+wtBSwPKYrNLUW8B9YjeSPfoZTp8ZfBpbWAvakuuOdxUrodEDnbho8+WIBpkRev+dfEzJ9vfvXYMXO357d9MzrVwEAmR+6YNsqNULDGufmbfjqR73zXvxzT/xwyBF+/WoQEFINAEg9dFYv5sn7A3Dtig3Kb1phYXgPRP6jAMk7foaltYBL52yx+G++uHDari1vj6jdyCqxCQ4OxsyZM7Fu3TpxX1xcHIYMGYIlS5Zg+vTpOHToENavX4+3337b6Nfv2rUrbGxssG7dOjzzzDPIzc3FkiVLjH4d0vfKxku3Paa0E5Dw7wutamf6vCK959jcysoamPPaVcx57WqLx4eMqsCQURWtuhaRVD8cckSYd//bHr/Tsdac3+T8D/ZYGN7zD+NIHvjkYelkd/dLlizRG2oaNGgQduzYgfT0dAQFBeHVV1/FG2+8YfBKpdbw8PBAamoqPvroIwQGBiIpKQkrV640+nWIiIha0jQUJXUzZwqBj/dtc+Xl5VCpVCj5sQecnWSXSxIZRUvv2SIyBQ1CPb7BJygrK4Ozc9vMTWr6PTE18ylYO9j88Ql3UF+lwSfj3mvT/sqZrIaiiIiIzBnfFSUdExsiIiKZ4Koo6TguQkRERCaDiQ0REZFMtPfk4cTERCgUCsTGxor7BEHA4sWL4e3tDTs7O4wcORKnTp3SO6+urg7z5s2Du7s7HBwcMGXKlGavSSopKUFERARUKhVUKhUiIiJQWlqqF3P58mVMnjwZDg4OcHd3R0xMjMEvtmZiQ0REJBPtmdgcO3YM77zzDvr166e3f/ny5UhJScH69etx7NgxqNVqjB07FhUVvz0eIzY2Frt27UJ6ejqysrJQWVmJSZMm6b0hIDw8HDk5OcjIyEBGRgZycnIQEREhHtdqtZg4cSKqqqqQlZWF9PR07Ny5E3FxcQbdBxMbIiIiE3Try5jr6upuG1tZWYmZM2di8+bNcHFxEfcLgoA1a9Zg4cKFeOyxxxAUFIS0tDRUV1fjgw8+AACUlZVhy5YtWLVqFcaMGYOBAwdi27ZtOHnyJL766isAja9HysjIwLvvvovQ0FCEhoZi8+bN2LNnD86dOwcAyMzMxOnTp7Ft2zYMHDgQY8aMwapVq7B582aDXibNxIaIiEgmjFmx8fHxEYd9VCoVEhMTb3vd5557DhMnTsSYMWP09ufl5aGwsBDjxo0T9ymVSowYMQIHDx4EAGRnZ6O+vl4vxtvbG0FBQWLMoUOHoFKpMHToUDFm2LBhUKlUejFBQUHw9vYWY8LCwlBXV4fs7OxWfw25KoqIiEgmBEhfrt30cLr8/Hy959jc7h2G6enp+O6773Ds2LFmxwoLCwE0vjD697y8vHDp0iUxxsbGRq/S0xTTdH5hYSE8PT2bte/p6akXc+t1XFxcYGNjI8a0BhMbIiIimTDmcm9nZ+c/fEBffn4+XnjhBWRmZsLW1va2cS29jPrWfbe6Naal+LuJ+SMciiIiIjJT2dnZKCoqQkhICKysrGBlZYUDBw5g7dq1sLKyEisot1ZMioqKxGNqtRoajQYlJSV3jLl27Vqz6xcXF+vF3HqdkpIS1NfXN6vk3AkTGyIiIpm416uiRo8ejZMnTyInJ0fcBg8ejJkzZyInJwc9evSAWq3Gvn37xHM0Gg0OHDiABx54AAAQEhICa2trvZiCggLk5uaKMaGhoSgrK8PRo0fFmCNHjqCsrEwvJjc3FwUFBWJMZmYmlEolQkJCWn1PHIoiIiKSiXv95GEnJycEBQXp7XNwcICbm5u4PzY2FgkJCfDz84Ofnx8SEhJgb2+P8PBwAIBKpcLs2bMRFxcHNzc3uLq6Ij4+HsHBweJk5ICAAIwfPx5RUVHYtGkTAGDOnDmYNGkS/P39AQDjxo1DYGAgIiIisGLFCty8eRPx8fGIiooy6J1XTGyIiIjothYsWICamhpER0ejpKQEQ4cORWZmJpycnMSY1atXw8rKCtOmTUNNTQ1Gjx6N1NRUWFpaijHbt29HTEyMuHpqypQpWL9+vXjc0tISn3/+OaKjozF8+HDY2dkhPDwcK1euNKi/fLv3PcC3e5M54Nu9yVTdy7d7P/jpc7ByaHn1Ums1VNUha8pbfLs3ERERtS9BUECQOBQl9fyOjuUDIiIiMhms2BAREcmEDgrJD+iTen5Hx8SGiIhIJu71qihTxKEoIiIiMhms2BAREckEJw9Lx8SGiIhIJjgUJR0TGyIiIplgxUY6zrEhIiIik8GKDRERkUwIRhiKMveKDRMbIiIimRAASH3Rkbm/J4lDUURERGQyWLEhIiKSCR0UUPDJw5IwsSEiIpIJroqSjkNRREREZDJYsSEiIpIJnaCAgg/ok4SJDRERkUwIghFWRZn5sigORREREZHJYMWGiIhIJjh5WDomNkRERDLBxEY6JjZEREQywcnD0nGODREREZkMVmyIiIhkgquipGNiQ0REJBONiY3UOTZG6kwHxaEoIiIiMhms2BAREckEV0VJx8SGiIhIJoRfN6ltmDMORREREZHJYMWGiIhIJjgUJR0TGyIiIrngWJRkTGyIiIjkwggVG5h5xYZzbIiIiMhksGJDREQkE3zysHRMbIiIiGSCk4el41AUERERmQxWbIiIiORCUEif/GvmFRsmNkRERDLBOTbScSiKiIiITAYrNkRERHLBB/RJxsSGiIhIJrgqSrpWJTZr165tdYMxMTF33RkiIiIiKVqV2KxevbpVjSkUCiY2REREUpj5UJJUrUps8vLy2rofREREZo9DUdLd9aoojUaDc+fOoaGhwZj9ISIiMl+CkTYzZnBiU11djdmzZ8Pe3h59+/bF5cuXATTOrUlKSjJ6B4mIiIhay+DE5uWXX8b333+Pb775Bra2tuL+MWPG4MMPPzRq54iIiMyLwkib+TJ4uffu3bvx4YcfYtiwYVAofvviBQYG4ueffzZq54iIiMwKn2MjmcEVm+LiYnh6ejbbX1VVpZfoEBEREd1rBic2Q4YMweeffy5+bkpmNm/ejNDQUOP1jIiIyNxw8rBkBg9FJSYmYvz48Th9+jQaGhrw5ptv4tSpUzh06BAOHDjQFn0kIiIyD3y7t2QGV2weeOABfPvtt6iurkbPnj2RmZkJLy8vHDp0CCEhIW3RRyIiIqJWuat3RQUHByMtLc3YfSEiIjJrgtC4SW3DnN1VYqPVarFr1y6cOXMGCoUCAQEBmDp1Kqys+E5NIiKiu8ZVUZIZnInk5uZi6tSpKCwshL+/PwDgxx9/hIeHBz799FMEBwcbvZNERERErWHwHJunn34affv2xZUrV/Ddd9/hu+++Q35+Pvr164c5c+a0RR+JiIjMQ9PkYambGTO4YvP999/j+PHjcHFxEfe5uLhg2bJlGDJkiFE7R0REZE4UQuMmtQ1zZnDFxt/fH9euXWu2v6ioCL169TJKp4iIiMwSn2MjWasSm/LycnFLSEhATEwMPv74Y1y5cgVXrlzBxx9/jNjYWCQnJ7d1f4mIiMhINmzYgH79+sHZ2RnOzs4IDQ3FF198IR4XBAGLFy+Gt7c37OzsMHLkSJw6dUqvjbq6OsybNw/u7u5wcHDAlClTcOXKFb2YkpISREREQKVSQaVSISIiAqWlpXoxly9fxuTJk+Hg4AB3d3fExMRAo9EYfE+tGorq1KmT3usSBEHAtGnTxH3Cr2vLJk+eDK1Wa3AniIiICPf8AX1dunRBUlKSOOKSlpaGqVOn4sSJE+jbty+WL1+OlJQUpKamonfv3li6dCnGjh2Lc+fOwcnJCQAQGxuLzz77DOnp6XBzc0NcXBwmTZqE7OxsWFpaAgDCw8Nx5coVZGRkAADmzJmDiIgIfPbZZwAaV1tPnDgRHh4eyMrKwo0bNzBr1iwIgoB169YZdPsKQfjjFe+GPFF4xIgRBnXAHJSXl0OlUqHkxx5wdjJ49I+oQwjzHtDeXSBqEw1CPb7BJygrK4Ozs3ObXKPp94RPyhJY2NlKaktXU4v8+YuQn5+v11+lUgmlUvmH57u6umLFihV46qmn4O3tjdjYWLz00ksAGqszXl5eSE5Oxty5c1FWVgYPDw9s3boV06dPBwBcvXoVPj4+2Lt3L8LCwnDmzBkEBgbi8OHDGDp0KADg8OHDCA0NxdmzZ+Hv748vvvgCkyZNQn5+Pry9vQEA6enpiIyMRFFRkUFf91ZVbJisEBERdSw+Pj56n1977TUsXrz4tvFarRYfffQRqqqqEBoairy8PBQWFmLcuHFijFKpxIgRI3Dw4EHMnTsX2dnZqK+v14vx9vZGUFAQDh48iLCwMBw6dAgqlUpMagBg2LBhUKlUOHjwIPz9/XHo0CEEBQWJSQ0AhIWFoa6uDtnZ2Rg1alSr7/uun6hXXV2Ny5cvNxv/6tev3902SUREZN6M+IC+lio2LTl58iRCQ0NRW1sLR0dH7Nq1C4GBgTh48CAAwMvLSy/ey8sLly5dAgAUFhbCxsZGb6V0U0xhYaEY4+np2ey6np6eejG3XsfFxQU2NjZiTGsZnNgUFxfjb3/7m97kot/jHBsiIqK7ZMTEpmlC8B/x9/dHTk4OSktLsXPnTsyaNUtvCsrv59gCjfNqb93XrAu3xLQUfzcxrWHwhI/Y2FiUlJTg8OHDsLOzQ0ZGBtLS0uDn54dPP/3U0OaIiIioHdnY2KBXr14YPHgwEhMT0b9/f7z55ptQq9UA0KxiUlRUJFZX1Go1NBoNSkpK7hjT0mNiiouL9WJuvU5JSQnq6+ubVXL+iMGJzddff43Vq1djyJAhsLCwQLdu3fDXv/4Vy5cvR2JioqHNERERURMZPHlYEATU1dWhe/fuUKvV2Ldvn3hMo9HgwIEDeOCBBwAAISEhsLa21ospKChAbm6uGBMaGoqysjIcPXpUjDly5AjKysr0YnJzc1FQUCDGZGZmQqlUIiQkxKD+GzwUVVVVJY6Vubq6ori4GL1790ZwcDC+++47Q5sjIiKiX93rJw+/8sormDBhAnx8fFBRUYH09HR88803yMjIgEKhQGxsLBISEuDn5wc/Pz8kJCTA3t4e4eHhAACVSoXZs2cjLi4Obm5ucHV1RXx8PIKDgzFmzBgAQEBAAMaPH4+oqChs2rQJQONy70mTJonvnBw3bhwCAwMRERGBFStW4ObNm4iPj0dUVJTBK9EMTmz8/f1x7tw5+Pr6YsCAAdi0aRN8fX2xceNGdO7c2dDmiIiIqJ1cu3YNERERKCgogEqlQr9+/ZCRkYGxY8cCABYsWICamhpER0ejpKQEQ4cORWZmpvgMGwBYvXo1rKysMG3aNNTU1GD06NFITU0Vn2EDANu3b0dMTIy4emrKlClYv369eNzS0hKff/45oqOjMXz4cNjZ2SE8PBwrV640+J5a9Ryb39u+fTvq6+sRGRmJEydOICwsDDdu3ICNjQ1SU1PFdez0Gz7HhswBn2NDpupePsema/JSozzH5vJL/2zT/sqZwRWbmTNniv89cOBAXLx4EWfPnkXXrl3h7u5u1M4RERERGeKun2PTxN7eHoMGDTJGX4iIiMyaAkaYY2OUnnRcrUps5s+f3+oGU1JS7rozRERERFK0KrE5ceJEqxoz9CE65ubxYQ/BysKmvbtB1CY2XuJzrMg0VVToMLDvPbrYPX4JpilqVWKzf//+tu4HERERGfHJw+aKS3SIiIjIZEiePExERERGwoqNZExsiIiIZOJeP3nYFHEoioiIiEwGKzZERERywaEoye6qYrN161YMHz4c3t7euHTpEgBgzZo1+OSTT4zaOSIiIrMiGGkzYwYnNhs2bMD8+fPx8MMPo7S0FFqtFgDQqVMnrFmzxtj9IyIiImo1gxObdevWYfPmzVi4cKHemzsHDx6MkydPGrVzRERE5qRp8rDUzZwZPMcmLy8PAwcObLZfqVSiqqrKKJ0iIiIyS3zysGQGV2y6d++OnJycZvu/+OILBAYGGqNPRERE5olzbCQzuGLz4osv4rnnnkNtbS0EQcDRo0fx73//G4mJiXj33Xfboo9ERERErWJwYvO3v/0NDQ0NWLBgAaqrqxEeHo777rsPb775JmbMmNEWfSQiIjILfECfdHf1HJuoqChERUXh+vXr0Ol08PT0NHa/iIiIzA+fYyOZpAf0ubu7G6sfRERERJIZnNh0794dCsXtZ1xfuHBBUoeIiIjMljGWa7NiY5jY2Fi9z/X19Thx4gQyMjLw4osvGqtfRERE5odDUZIZnNi88MILLe5/6623cPz4cckdIiIiIrpbRnu794QJE7Bz505jNUdERGR++BwbyYz2du+PP/4Yrq6uxmqOiIjI7HC5t3QGJzYDBw7UmzwsCAIKCwtRXFyMt99+26idIyIiIjKEwYnNI488ovfZwsICHh4eGDlyJPr06WOsfhEREREZzKDEpqGhAb6+vggLC4NarW6rPhEREZknroqSzKDJw1ZWVnj22WdRV1fXVv0hIiIyW01zbKRu5szgVVFDhw7FiRMn2qIvRERERJIYPMcmOjoacXFxuHLlCkJCQuDg4KB3vF+/fkbrHBERkdkx84qLVK1ObJ566imsWbMG06dPBwDExMSIxxQKBQRBgEKhgFarNX4viYiIzAHn2EjW6sQmLS0NSUlJyMvLa8v+EBEREd21Vic2gtCYAnbr1q3NOkNERGTO+IA+6QyaY3Ont3oTERGRRByKksygxKZ3795/mNzcvHlTUoeIiIiI7pZBic3rr78OlUrVVn0hIiIyaxyKks6gxGbGjBnw9PRsq74QERGZNw5FSdbqB/Rxfg0RERHJncGrooiIiKiNsGIjWasTG51O15b9ICIiMnucYyOdwa9UICIiojbCio1kBr8Ek4iIiEiuWLEhIiKSC1ZsJGNiQ0REJBOcYyMdh6KIiIjIZLBiQ0REJBccipKMiQ0REZFMcChKOg5FERERkclgxYaIiEguOBQlGRMbIiIiuWBiIxmHooiIiMhksGJDREQkE4pfN6ltmDMmNkRERHLBoSjJmNgQERHJBJd7S8c5NkRERGQyWLEhIiKSCw5FScbEhoiISE7MPDGRikNRREREZioxMRFDhgyBk5MTPD098cgjj+DcuXN6MYIgYPHixfD29oadnR1GjhyJU6dO6cXU1dVh3rx5cHd3h4ODA6ZMmYIrV67oxZSUlCAiIgIqlQoqlQoREREoLS3Vi7l8+TImT54MBwcHuLu7IyYmBhqNxqB7YmJDREQkE02Th6VurXXgwAE899xzOHz4MPbt24eGhgaMGzcOVVVVYszy5cuRkpKC9evX49ixY1Cr1Rg7diwqKirEmNjYWOzatQvp6enIyspCZWUlJk2aBK1WK8aEh4cjJycHGRkZyMjIQE5ODiIiIsTjWq0WEydORFVVFbKyspCeno6dO3ciLi7OwK+hILDo1cbKy8uhUqkw2jUSVhY27d0dojbx1neftncXiNpERYUOA/sWoaysDM7Ozm1yjabfE0FRCbC0sZXUllZTi9zNryA/P1+vv0qlEkql8o7nFhcXw9PTEwcOHMBDDz0EQRDg7e2N2NhYvPTSSwAaqzNeXl5ITk7G3LlzUVZWBg8PD2zduhXTp08HAFy9ehU+Pj7Yu3cvwsLCcObMGQQGBuLw4cMYOnQoAODw4cMIDQ3F2bNn4e/vjy+++AKTJk1Cfn4+vL29AQDp6emIjIxEUVFRq7/2rNgQERGZIB8fH3HYR6VSITEx8Q/PKSsrAwC4uroCAPLy8lBYWIhx48aJMUqlEiNGjMDBgwcBANnZ2aivr9eL8fb2RlBQkBhz6NAhqFQqMakBgGHDhkGlUunFBAUFiUkNAISFhaGurg7Z2dmtvm9OHiYiIpIJYz7HpqWKzZ0IgoD58+fjwQcfRFBQEACgsLAQAODl5aUX6+XlhUuXLokxNjY2cHFxaRbTdH5hYSE8PT2bXdPT01Mv5tbruLi4wMbGRoxpDSY2REREcmHE5d7Ozs4GDZ09//zz+OGHH5CVldXsmEKh/6IGQRCa7WvWjVtiWoq/m5g/wqEoIiIiMzdv3jx8+umn2L9/P7p06SLuV6vVANCsYlJUVCRWV9RqNTQaDUpKSu4Yc+3atWbXLS4u1ou59TolJSWor69vVsm5EyY2REREMnGvV0UJgoDnn38e//nPf/D111+je/fuese7d+8OtVqNffv2ifs0Gg0OHDiABx54AAAQEhICa2trvZiCggLk5uaKMaGhoSgrK8PRo0fFmCNHjqCsrEwvJjc3FwUFBWJMZmYmlEolQkJCWn1PHIoiIiKSi3v85OHnnnsOH3zwAT755BM4OTmJFROVSgU7OzsoFArExsYiISEBfn5+8PPzQ0JCAuzt7REeHi7Gzp49G3FxcXBzc4Orqyvi4+MRHByMMWPGAAACAgIwfvx4REVFYdOmTQCAOXPmYNKkSfD39wcAjBs3DoGBgYiIiMCKFStw8+ZNxMfHIyoqyqAhNSY2REREcnGPE5sNGzYAAEaOHKm3//3330dkZCQAYMGCBaipqUF0dDRKSkowdOhQZGZmwsnJSYxfvXo1rKysMG3aNNTU1GD06NFITU2FpaWlGLN9+3bExMSIq6emTJmC9evXi8ctLS3x+eefIzo6GsOHD4ednR3Cw8OxcuVKg26fz7G5B/gcGzIHfI4Nmap7+RybfpHGeY7ND6mvtGl/5YwVGyIiIpkw5nJvc8XEhoiISC74dm/JuCqKiIiITAYrNkRERDKhEAQoJE59lXp+R8fEhoiISC44FCUZh6KIiIjIZLBiQ0REJBNcFSUdExsiIiK54FCUZByKIiIiIpPBig0REZFMcChKOiY2REREcsGhKMmY2BAREckEKzbScY4NERERmQxWbIiIiOSCQ1GSMbEhIiKSEXMfSpKKQ1FERERkMlixISIikgtBaNyktmHGmNgQERHJBFdFScehKCIiIjIZrNgQERHJBVdFScbEhoiISCYUusZNahvmjENRREREZDJYsSHZmzb7EiJjL2D31i54Z7kfAOCB0cWY8Jdf0CuwEiqXejz/+GBcOOekd56VtQ5Px/+EEROKoFRqkXPEBW8t640b12wBAJ7eNXhi7iX0v78ELu4a3Cy2wdd71PjwnW5oaGDOT8bz2equ+HxNV719zh4aLD9+FNp6BT5Z2Q25+11w/bIt7Jwa0OfBMjz6j4vo5KUBAFzPV+KfDw5pse2ot88gZOINAMDbswOQf9oBFTdsYO/cgIAHS/Hoy7+1AwAfLu6Bn4854+qP9lD3qsY/v8hpm5umu8OhKMmY2BgoMjISpaWl2L17d3t3xSz49S3H+Mev4sI5B739tnZanM5RISvTEy+8fq7Fc+e+dB5DR95A8oJAlJdaIyr+JyxefxIvTB8MnU4Bn+7VsLAQsO4NfxTk26FbryrELD4LWzsttqzqdS9uj8yId+8qvLA9V/xsYdn420dTY4HLuQ54OCYfXQKqUF1mhY/e6I63ZwfglT3fAwBcveuQfOyIXntZ/1Yjc2MX9B1ZIu7rHVqG8c9dgcpTg9JCG+xc1h3vPNMHC3b98NuJAvDAtGvIy3HCL2ft2/CO6W5wVZR0HSaxiYyMRFpaWrP958+fR69e/CVkimztGrAg6TTWvu6PGXMu6R37eo8aQGPVpSX2jg0Y91gBVr0cgJzDrgCAFS8HIm3fQQwYdhPfHXRD9reNW5PCK3b4T2pXPDz9FyY2ZHQWVgJUnvXN9ts5axG7/ZTevumvX0DSlAG4+YsSrvfVwcISzc7NyXBDyKRi2Dr8NqFizNNXxf9261KHsOgr2BgVAG29ApbWgtg2AFTctGZiI0d8jo1kHarePn78eBQUFOht3bt314vRaDS3OZs6muiF53H0f25iYmIIv8AKWFsL+O7Qb+feLFbi0k8OCBhQftvzHJwaUFlmfVf9JbqTojw7vDRkCBYOH4x3n/dH8WXlbWNrKiyhUAiwc25o8filkw7IP+2I4dOv3baNqlIrHN3tgR4h5WJSQ2QOOlRio1QqoVar9bbRo0fj+eefx/z58+Hu7o6xY8cCAFJSUhAcHAwHBwf4+PggOjoalZWVYluLFy/GgAED9Npfs2YNfH19xc9arRbz589Hp06d4ObmhgULFkBoRSZcV1eH8vJyvY0M89D4a+gVWIHUNT3u6nwXdw3qNQpUlusnKaU3bODi3nLyq+5Sg8lPXMHeHd53dU2i2+k+oAKRKT8iZusp/DX5PMqKbbDisf6oLGleNK+vVWBXki+GTC2GnZO2xfa+TVdD3asaPQdXNDv2n0RfxPQJRVz/Ybj5ixLPvnvG6PdDbadpKErqZs46VGJzO2lpabCyssK3336LTZs2AQAsLCywdu1a5ObmIi0tDV9//TUWLFhgULurVq3Ce++9hy1btiArKws3b97Erl27/vC8xMREqFQqcfPx8bmr+zJX7l61mPuP81jxj0DUayyN2rZCgRYn1rl61GHJxu+RlemJL//DxIaMK2hUCQY9fAP39alGwINleP79xqGnwx976sVp6xV4d14fCDoFnlj6c4ttaWotcOxTj9tWa8bNvYKFe08gZlsuLCwFpP69t7mPTHQsgpE2M9Zh5tgAwJ49e+Do6Ch+njBhAgCgV69eWL58uV5sbGys+N/du3fHkiVL8Oyzz+Ltt99u9fXWrFmDl19+GX/+858BABs3bsSXX375h+e9/PLLmD9/vvi5vLycyY0B/PpWwMWtHms/PC7us7QSEBRSislP/IKpISOg0ynu2EbJdRtY2whwdK7Xq9qoXDU4naPSi3X1qEPSlhM4+70z1r7ub9ybIWqB0l4Hb/8qFF20E/dp6xV457k+uJ5vi7//++RtqzXf7XWDpsYCw/7ccmLj6NoAR9cGePWoRede1Xh52P3I+84JPUKaV3eITFGHSmxGjRqFDRs2iJ8dHBzwxBNPYPDgwc1i9+/fj4SEBJw+fRrl5eVoaGhAbW0tqqqq4ODg0Cz+VmVlZSgoKEBoaKi4z8rKCoMHD/7D4SilUgml8vbj53RnOYdd8Oyj+ktb/77kLK7k2eOj97r+YVIDAOdPO6G+XoGBoSX435eNfxW7uNehW68qvJfSU4xz82xMas6fdsLqRQEQhD9um0iq+joFCn+yh9/9jcPUTUlNcZ4t/p5+Eo4uLc+tAYBvP1Sj35ibcHK7fUyTph9V9RqTKM6bBa6Kkq5DJTYODg4troC6NVG5dOkSHn74YTzzzDNYsmQJXF1dkZWVhdmzZ6O+vnFlgYWFRbMEpekYta+aaitc+slRb19tjSXKS63F/Y7O9fDsXAtXz8b5Ml18qwE0VmpKbihRXWmFzP90xtPxP6G81AoVZdZ4Ou4nXDzvKE5GdvWoQ9J7J1BcoMSWVb2gcvlt7k3JDSamZDwfL/VFvzE34epdh4ob1ti7ritqKy0x7M9F0DYAm57tg/xcRzz33mnotAqUFTVWGR06NcDK5refU0UXbfHTEWc8n3qq2TXychxxMccJvYaUw17VgOuXbfFZSld4dKtBj0Hlem3UVVmivNga9bUWyD/V+POzs1+13rWonXBVlGQdKrFprePHj6OhoQGrVq2ChUXjXyo7duzQi/Hw8EBhYSEEQYBC0fhXek5OjnhcpVKhc+fOOHz4MB566CEAQENDA7KzszFo0KB7cyN0W8NGXcf8pWfFz/9YeRoAsP1tX2zf0LhS7p3lvaDVKvDyylOwUerw/REXpDwfIFZ8Bj1wE/d1q8F93Wqw9b8H9dp/OHjUPboTMgelhUpsmeePyhJrOLrWo8fACizY9T3cutTher4SP+xrfOzA0gkD9c77e/pJ+IeWiZ8P7vBCJ7UGAQ+VNruGja0OORlu2LO6K+pqLKHy0KDvyBLMXn8O1srfftFtfckP5w//Nhy77OHGay7NOgZ3nzpj3jZRuzDJxKZnz55oaGjAunXrMHnyZHz77bfYuHGjXszIkSNRXFyM5cuX4/HHH0dGRga++OILODs7izEvvPACkpKS4Ofnh4CAAKSkpKC0tPQe3w0BwD+e0v+B/9UnnfHVJ53veE69xhIbE3tjY2LvFo+3pg0iY3h6fcsPkQQAd586bLyU1ap2HllwCY8suNTisfv6VOPv6bktHvu9uA9Ptupa1D44FCWdSQ68DhgwACkpKUhOTkZQUBC2b9+OxMREvZiAgAC8/fbbeOutt9C/f38cPXoU8fHxejFxcXF48sknERkZidDQUDg5OeHRRx+9l7dCRETmhKuiJFMIrXkwC0lSXl4OlUqF0a6RsLKwae/uELWJt777tL27QNQmKip0GNi3CGVlZXpVfWNq+j0ROv4NWFnbSmqrob4WhzJebdP+yplJDkURERF1RByKko6JDRERkVzohMZNahtmjIkNERGRXBhjjox55zWmOXmYiIiIzBMrNkRERDKhgBHm2BilJx0XExsiIiK54JOHJeNQFBEREZkMVmyIiIhkgsu9pWNiQ0REJBdcFSUZh6KIiIjIZLBiQ0REJBMKQYBC4uRfqed3dExsiIiI5EL36ya1DTPGoSgiIiIyGazYEBERyQSHoqRjYkNERCQXXBUlGRMbIiIiueCThyXjHBsiIiIyGazYEBERyQSfPCwdExsiIiK54FCUZByKIiIiIpPBig0REZFMKHSNm9Q2zBkTGyIiIrngUJRkHIoiIiIyY//v//0/TJ48Gd7e3lAoFNi9e7fecUEQsHjxYnh7e8POzg4jR47EqVOn9GLq6uowb948uLu7w8HBAVOmTMGVK1f0YkpKShAREQGVSgWVSoWIiAiUlpbqxVy+fBmTJ0+Gg4MD3N3dERMTA41GY9D9MLEhIiKSC8FImwGqqqrQv39/rF+/vsXjy5cvR0pKCtavX49jx45BrVZj7NixqKioEGNiY2Oxa9cupKenIysrC5WVlZg0aRK0Wq0YEx4ejpycHGRkZCAjIwM5OTmIiIgQj2u1WkycOBFVVVXIyspCeno6du7cibi4OIPuh0NRREREMmHMVyqUl5fr7VcqlVAqlc3iJ0yYgAkTJrTYliAIWLNmDRYuXIjHHnsMAJCWlgYvLy988MEHmDt3LsrKyrBlyxZs3boVY8aMAQBs27YNPj4++OqrrxAWFoYzZ84gIyMDhw8fxtChQwEAmzdvRmhoKM6dOwd/f39kZmbi9OnTyM/Ph7e3NwBg1apViIyMxLJly+Ds7Nyq+2fFhoiIyAT5+PiIwz4qlQqJiYkGt5GXl4fCwkKMGzdO3KdUKjFixAgcPHgQAJCdnY36+nq9GG9vbwQFBYkxhw4dgkqlEpMaABg2bBhUKpVeTFBQkJjUAEBYWBjq6uqQnZ3d6j6zYkNERCQXRpw8nJ+fr1flaKla80cKCwsBAF5eXnr7vby8cOnSJTHGxsYGLi4uzWKazi8sLISnp2ez9j09PfVibr2Oi4sLbGxsxJjWYGJDREQkFwIAqcu1f82LnJ2dWz1880cUCoX+JQSh2b5m3bglpqX4u4n5IxyKIiIikommOTZSN2NRq9UA0KxiUlRUJFZX1Go1NBoNSkpK7hhz7dq1Zu0XFxfrxdx6nZKSEtTX1zer5NwJExsiIiJqUffu3aFWq7Fv3z5xn0ajwYEDB/DAAw8AAEJCQmBtba0XU1BQgNzcXDEmNDQUZWVlOHr0qBhz5MgRlJWV6cXk5uaioKBAjMnMzIRSqURISEir+8yhKCIiIrkQYIQ5NoaFV1ZW4qeffhI/5+XlIScnB66urujatStiY2ORkJAAPz8/+Pn5ISEhAfb29ggPDwcAqFQqzJ49G3FxcXBzc4Orqyvi4+MRHBwsrpIKCAjA+PHjERUVhU2bNgEA5syZg0mTJsHf3x8AMG7cOAQGBiIiIgIrVqzAzZs3ER8fj6ioKIOG1JjYEBERyUU7PHn4+PHjGDVqlPh5/vz5AIBZs2YhNTUVCxYsQE1NDaKjo1FSUoKhQ4ciMzMTTk5O4jmrV6+GlZUVpk2bhpqaGowePRqpqamwtLQUY7Zv346YmBhx9dSUKVP0np1jaWmJzz//HNHR0Rg+fDjs7OwQHh6OlStXGnQ/CkEw82cv3wPl5eVQqVQY7RoJKwub9u4OUZt467tP27sLRG2iokKHgX2LUFZWZrTJuLdq+j3xp/4vwcrS8NVLv9egrcPX3ye3aX/ljBUbIiIiudABaP0CoNu3YcaY2BAREcmEMZ88bK64KoqIiIhMBis2REREctEOk4dNDRMbIiIiuWBiIxmHooiIiMhksGJDREQkF6zYSMbEhoiISC643FsyJjZEREQyweXe0nGODREREZkMVmyIiIjkgnNsJGNiQ0REJBc6AVBITEx05p3YcCiKiIiITAYrNkRERHLBoSjJmNgQERHJhhESG5h3YsOhKCIiIjIZrNgQERHJBYeiJGNiQ0REJBc6AZKHkrgqioiIiMg0sGJDREQkF4KucZPahhljYkNERCQXnGMjGRMbIiIiueAcG8k4x4aIiIhMBis2REREcsGhKMmY2BAREcmFACMkNkbpSYfFoSgiIiIyGazYEBERyQWHoiRjYkNERCQXOh0Aic+h0Zn3c2w4FEVEREQmgxUbIiIiueBQlGRMbIiIiOSCiY1kHIoiIiIik8GKDRERkVzwlQqSMbEhIiKSCUHQQZD4dm6p53d0TGyIiIjkQhCkV1w4x4aIiIjINLBiQ0REJBeCEebYmHnFhokNERGRXOh0gELiHBkzn2PDoSgiIiIyGazYEBERyQWHoiRjYkNERCQTgk4HQeJQlLkv9+ZQFBEREZkMVmyIiIjkgkNRkjGxISIikgudACiY2EjBoSgiIiIyGazYEBERyYUgAJD6HBvzrtgwsSEiIpIJQSdAkDgUJTCxISIiIlkQdJBeseFybyIiIiKTwIoNERGRTHAoSjomNkRERHLBoSjJmNjcA03Zc4Ogkfz9SiRXFRX85ibTVFnZ+L19LyohDaiX/Hy+BtQbpzMdFBObe6CiogIAcKDkg3buCVHbGdi3vXtA1LYqKiqgUqnapG0bGxuo1WpkFe41SntqtRo2NjZGaaujUQjmPhh3D+h0Oly9ehVOTk5QKBTt3R2TV15eDh8fH+Tn58PZ2bm9u0NkdPwev7cEQUBFRQW8vb1hYdF2a25qa2uh0WiM0paNjQ1sbW2N0lZHw4rNPWBhYYEuXbq0dzfMjrOzM3/ok0nj9/i901aVmt+ztbU122TEmLjcm4iIiEwGExsiIiIyGUxsyOQolUq89tprUCqV7d0VojbB73Gi2+PkYSIiIjIZrNgQERGRyWBiQ0RERCaDiQ0RERGZDCY2RK30zTffQKFQoLS0tL27QiRZZGQkHnnkkfbuBpHRMbGhdhEZGQmFQoGkpCS9/bt37+bTmcmkNX3v37r99NNP7d01IpPAxIbaja2tLZKTk1FSUmK0No31OHKitjR+/HgUFBTobd27d9eL4fcy0d1hYkPtZsyYMVCr1UhMTLxtzM6dO9G3b18olUr4+vpi1apVesd9fX2xdOlSREZGQqVSISoqCqmpqejUqRP27NkDf39/2Nvb4/HHH0dVVRXS0tLg6+sLFxcXzJs3D1qtVmxr27ZtGDx4MJycnKBWqxEeHo6ioqI2u38yX0qlEmq1Wm8bPXo0nn/+ecyfPx/u7u4YO3YsACAlJQXBwcFwcHCAj48PoqOjUVlZKba1ePFiDBgwQK/9NWvWwNfXV/ys1Woxf/58dOrUCW5ubliwYME9eVM1UXtgYkPtxtLSEgkJCVi3bh2uXLnS7Hh2djamTZuGGTNm4OTJk1i8eDEWLVqE1NRUvbgVK1YgKCgI2dnZWLRoEQCguroaa9euRXp6OjIyMvDNN9/gsccew969e7F3715s3boV77zzDj7++GOxHY1GgyVLluD777/H7t27kZeXh8jIyLb8EhDpSUtLg5WVFb799lts2rQJQOO75tauXYvc3FykpaXh66+/xoIFCwxqd9WqVXjvvfewZcsWZGVl4ebNm9i1a1db3AJR+xOI2sGsWbOEqVOnCoIgCMOGDROeeuopQRAEYdeuXULTt2V4eLgwduxYvfNefPFFITAwUPzcrVs34ZFHHtGLef/99wUAwk8//STumzt3rmBvby9UVFSI+8LCwoS5c+feto9Hjx4VAIjn7N+/XwAglJSUGH7DRL+aNWuWYGlpKTg4OIjb448/LowYMUIYMGDAH56/Y8cOwc3NTfz82muvCf3799eLWb16tdCtWzfxc+fOnYWkpCTxc319vdClSxfx3yCRKWHFhtpdcnIy0tLScPr0ab39Z86cwfDhw/X2DR8+HOfPn9cbQho8eHCzNu3t7dGzZ0/xs5eXF3x9feHo6Ki37/dDTSdOnMDUqVPRrVs3ODk5YeTIkQCAy5cvS7o/oluNGjUKOTk54rZ27VoALX8v79+/H2PHjsV9990HJycnPPnkk7hx4waqqqpada2ysjIUFBQgNDRU3GdlZdXitYhMARMbancPPfQQwsLC8Morr+jtFwSh2QopoYV5AQ4ODs32WVtb631WKBQt7tPpdACAqqoqjBs3Do6Ojti2bRuOHTsmluo5iZOMzcHBAb169RK3zp07i/t/79KlS3j44YcRFBSEnTt3Ijs7G2+99RYAoL6+HkDjUNWt/y6ajhGZI6v27gARACQlJWHAgAHo3bu3uC8wMBBZWVl6cQcPHkTv3r1haWlp1OufPXsW169fR1JSEnx8fAAAx48fN+o1iAx1/PhxNDQ0YNWqVbCwaPw7dMeOHXoxHh4eKCws1PtDICcnRzyuUqnQuXNnHD58GA899BAAoKGhAdnZ2Rg0aNC9uRGie4gVG5KF4OBgzJw5E+vWrRP3xcXF4b///S+WLFmCH3/8EWlpaVi/fj3i4+ONfv2uXbvCxsYG69atw4ULF/Dpp59iyZIlRr8OkSF69uyJhoYG8fty69at2Lhxo17MyJEjUVxcjOXLl+Pnn3/GW2+9hS+++EIv5oUXXkBSUhJ27dqFs2fPIjo6mg+aJJPFxIZkY8mSJXol9UGDBmHHjh1IT09HUFAQXn31VbzxxhttslLJw8MDqamp+OijjxAYGIikpCSsXLnS6NchMsSAAQOQkpKC5ORkBAUFYfv27c0ejxAQEIC3334bb731Fvr374+jR482S/7j4uLw5JNPIjIyEqGhoXBycsKjjz56L2+F6J5RCC1NWiAiIiLqgFixISIiIpPBxIaIiIhMBhMbIiIiMhlMbIiIiMhkMLEhIiIik8HEhoiIiEwGExsiIiIyGUxsiIiIyGQwsSEyE4sXL8aAAQPEz5GRkXjkkUfueT8uXrwIhUKh9z6jW/n6+mLNmjWtbjM1NRWdOnWS3DeFQoHdu3dLboeI2g8TG6J2FBkZCYVCIb59vEePHoiPj0dVVVWbX/vNN99Eampqq2Jbk4wQEckB3+5N1M7Gjx+P999/H/X19fjf//6Hp59+GlVVVdiwYUOz2Pr6elhbWxvluiqVyijtEBHJCSs2RO1MqVRCrVbDx8cH4eHhmDlzpjgc0jR89N5776FHjx5QKpUQBAFlZWWYM2cOPD094ezsjD/96U/4/vvv9dpNSkqCl5cXnJycMHv2bNTW1uodv3UoSqfTITk5Gb169YJSqUTXrl2xbNkyAED37t0BAAMHDoRCocDIkSPF895//30EBATA1tYWffr0wdtvv613naNHj2LgwIGwtbXF4MGDceLECYO/RikpKQgODoaDgwN8fHwQHR2NysrKZnG7d+9G7969YWtri7FjxyI/P1/v+GeffYaQkBDY2tqiR48eeP3119HQ0GBwf4hIvpjYEMmMnZ0d6uvrxc8//fQTduzYgZ07d4pDQRMnTkRhYSH27t2L7OxsDBo0CKNHj8bNmzcBADt27MBrr72GZcuW4fjx4+jcuXOzhONWL7/8MpKTk7Fo0SKcPn0aH3zwAby8vAA0JicA8NVXX6GgoAD/+c9/AACbN2/GwoULsWzZMpw5cwYJCQlYtGgR0tLSAABVVVWYNGkS/P39kZ2djcWLFzd783RrWFhYYO3atcjNzUVaWhq+/vprLFiwQC+muroay5YtQ1paGr799luUl5djxowZ4vEvv/wSf/3rXxETE4PTp09j06ZNSE1NFZM3IjIRAhG1m1mzZglTp04VPx85ckRwc3MTpk2bJgiCILz22muCtbW1UFRUJMb897//FZydnYXa2lq9tnr27Cls2rRJEARBCA0NFZ555hm940OHDhX69+/f4rXLy8sFpVIpbN68ucV+5uXlCQCEEydO6O338fERPvjgA719S5YsEUJDQwVBEIRNmzYJrq6uQlVVlXh8w4YNLbb1e926dRNWr1592+M7duwQ3NzcxM/vv/++AEA4fPiwuO/MmTMCAOHIkSOCIAjC//3f/wkJCQl67WzdulXo3Lmz+BmAsGvXrttel4jkj3NsiNrZnj174OjoiIaGBtTX12Pq1KlYt26deLxbt27w8PAQP2dnZ6OyshJubm567dTU1ODnn38GAJw5cwbPPPOM3vHQ0FDs37+/xT6cOXMGdXV1GD16dKv7XVxcjPz8fMyePRtRUVHi/oaGBnH+zpkzZ9C/f3/Y29vr9cNQ+/fvR0JCAk6fPo3y8nI0NDSgtrYWVVVVcHBwAABYWVlh8ODB4jl9+vRBp06dcObMGdx///3Izs7GsWPH9Co0Wq0WtbW1qK6u1usjEXVcTGyI2tmoUaOwYcMGWFtbw9vbu9nk4KZf3E10Oh06d+6Mb775pllbd7vk2c7OzuBzdDodgMbhqKFDh+ods7S0BAAIgnBX/fm9S5cu4eGHH8YzzzyDJUuWwNXVFVlZWZg9e7bekB3QuFz7Vk37dDodXn/9dTz22GPNYmxtbSX3k4jkgYkNUTtzcHBAr169Wh0/aNAgFBYWwsrKCr6+vi3GBAQE4PDhw3jyySfFfYcPH75tm35+frCzs8N///tfPP30082O29jYAGiscDTx8vLCfffdhwsXLmDmzJktthsYGIitW7eipqZGTJ7u1I+WHD9+HA0NDVi1ahUsLBqnBe7YsaNZXENDA44fP477778fAHDu3DmUlpaiT58+ABq/bufOnTPoa01EHQ8TG6IOZsyYMQgNDcUjjzyC5ORk+Pv74+rVq9i7dy8eeeQRDB48GC+88AJmzZqFwYMH48EHH8T27dtx6tQp9OjRo8U2bW1t8dJLL2HBggWwsbHB8OHDUVxcjFOnTmH27Nnw9PSEnZ0dMjIy0KVLF9ja2kKlUmHx4sWIiYmBs7MzJkyYgLq6Ohw/fhwlJSWYP38+wsPDsXDhQsyePRv//Oc/cfHiRaxcudKg++3ZsycaGhqwbt06TJ48Gd9++y02btzYLM7a2hrz5s3D2rVrYW1tjeeffx7Dhg0TE51XX30VkyZNgo+PD/7yl7/AwsICP/zwA06ePImlS5ca/j+CiGSJq6KIOhiFQoG9e/fioYcewlNPPYXevXtjxowZuHjxoriKafr06Xj11Vfx0ksvISQkBJcuXcKzzz57x3YXLVqEuLg4vPrqqwgICMD06dNRVFQEoHH+ytq1a7Fp0yZ4e3tj6tSpAICnn34a7777LlJTUxEcHIwRI0YgNTVVXB7u6OiIzz77DKdPn8bAgQOxcOFCJCcnG3S/AwYMQEpKCpKTkxEUFITt27cjMTGxWZy9vT1eeuklhIeHIzQ0FHZ2dkhPTxePh4WFYc+ePdi3bx+GDBmCYcOGISUlBd26dTOoP0QkbwrBGIPgRERERDLAig0RERGZDCY2REREZDKY2BAREZHJYGJDREREJoOJDREREZkMJjZERERkMpjYEBERkclgYkNEREQmg4kNERERmQwmNkRERGQymNgQERGRyfj/baT7pp6CIlkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = int(0.1 * len(X_train))  # too expensive for whole data set\n",
    "\n",
    "svm = sklearn.svm.SVC(C=10, kernel='linear')\n",
    "svm.fit(X_train[:n_samples], y_train[:n_samples])\n",
    "\n",
    "prediction = (svm.predict(X_test) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training on the whole data set is too expensive, so for now we train it only on $10 \\%$ of it. But even with a higher fraction of trainings data the accuracy does not increase significantly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### k-Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9948032991576244\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = sl.neighbors.KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, y_train)\n",
    "prediction = (knn.predict(X_test) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearest neighbour with $k = 3$ leads to a phenomenal accuracy rate of $99.5 \\%$, we will later verify with Cross-Validation if that was just a lucky result or is really independent of training and test data splitting. But first we try different values for $k$:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for k=1: 0.9967553593725269\n",
      "Accuracy for k=2: 0.9967729455005891\n",
      "Accuracy for k=3: 0.9948032991576244\n",
      "Accuracy for k=4: 0.994952781246153\n",
      "Accuracy for k=5: 0.9932029615039657\n",
      "Accuracy for k=6: 0.9934227881047429\n",
      "Accuracy for k=7: 0.991998311731706\n"
     ]
    }
   ],
   "source": [
    "for k in range(1, 8):\n",
    "    knn = sl.neighbors.KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    predictions = knn.predict(X_test)\n",
    "    \n",
    "    accuracy = sl.metrics.accuracy_score(y_test, predictions)\n",
    "    print(f'Accuracy for k={k}: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe the very high accuracy rate even for $k = 1$, and it even tends to increase for higher $k$. This would imply that the classification of the most similar transaction is a very accurate classifier. We will look at the confusion matrix of $k = 1$ and then validate this with Cross-Validation and Bootstrap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9967553593725269\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = sl.neighbors.KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "prediction = (knn.predict(X_test) >= 0.5).astype(int)\n",
    "\n",
    "print(sl.metrics.accuracy_score(y_test, prediction))\n",
    "\n",
    "cm = sl.metrics.confusion_matrix(y_test, prediction)\n",
    "disp = sl.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Fraud'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation\n",
    "#### Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9972917362784236\n",
      "0.9971510472539261\n",
      "0.9969927721013664\n",
      "0.9974675975590455\n",
      "0.9969927721013664\n",
      "0.9971686333819882\n",
      "0.9970103582294286\n",
      "0.9973093224064857\n",
      "0.9974148391748588\n",
      "0.9969400137171799\n"
     ]
    }
   ],
   "source": [
    "kf = sl.model_selection.KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "\n",
    "X = CreditCard[selected_features]\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "for idxTrain, idxTest in kf.split(X):\n",
    "    X_train, X_test = X.iloc[idxTrain], X.iloc[idxTest]\n",
    "    y_train, y_test = y.iloc[idxTrain], y.iloc[idxTest]\n",
    "\n",
    "    knn = sl.neighbors.KNeighborsClassifier(n_neighbors=1)\n",
    "    knn.fit(X_train, y_train)\n",
    "    predictions = knn.predict(X_test)\n",
    "\n",
    "    print(sl.metrics.accuracy_score(y_test, predictions))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the phenomenal accuracy of $> 99.5 \\%$ is actually validated with Cross-Validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9983908692823101\n",
      "0.9980567328491287\n",
      "0.998285352513937\n",
      "0.9980303536570353\n",
      "0.9978544923764134\n",
      "0.9982501802578126\n",
      "0.9982501802578126\n",
      "0.9981534565534707\n",
      "0.9981358704254084\n",
      "0.9981710426815328\n"
     ]
    }
   ],
   "source": [
    "n_bootstrap_samples = 10\n",
    "\n",
    "X = CreditCard[selected_features]  # selected features\n",
    "#X = CreditCard[CreditCard.columns.drop(['Class'])]  # all features\n",
    "y = CreditCard['Class'] == 1\n",
    "\n",
    "for i in range(n_bootstrap_samples):\n",
    "    X_resampled, y_resampled = sl.utils.resample(X, y, random_state=i)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = sl.model_selection.train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)\n",
    "    \n",
    "    knn = sl.neighbors.KNeighborsClassifier(n_neighbors=1)\n",
    "    knn.fit(X_train, y_train)\n",
    "    predictions = knn.predict(X_test)\n",
    "\n",
    "    print(sl.metrics.accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since both validation methods support our accuracy measurements, we can now be confident in having found a highly accurate model. Looking at the confusion matrix for $k = 1$, we only have 8 fraudulent transaction classified as normal. We also see how the dimensionality reduction paid of drastically, using the full data set results in an $8$ fold increase of the runtime and a drop in the accuracy to $95.8 \\%$.\n",
    "\n",
    "We discussed earlier that in the case of credit card fraud the classification should not be unbiased, since not detecting a fraudulent transaction is worse than wrongly rejecting a normal transaction. This invites us to experiment with the threshold for the binary decision, which for more illustrative results we will try with the worse performing Logistic Regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = sm.api.GLM(y_train, sm.tools.tools.add_constant(X_train), family=sm.api.families.Binomial())\n",
    "res = model.fit()\n",
    "\n",
    "thresholds = np.arange(0.1, 1.0, 0.05)\n",
    "accuracies = []\n",
    "undetected_fraud = []\n",
    "mislabeled_innocents = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "    predictions = (res.predict(sm.tools.tools.add_constant(X_test)) >= threshold).astype(int)\n",
    "    \n",
    "    accuracy = sl.metrics.accuracy_score(y_test, predictions)\n",
    "    accuracies.append(accuracy)\n",
    "    \n",
    "    cm = sl.metrics.confusion_matrix(y_test, predictions)\n",
    "    tn, fp, fn, tp = cm.ravel()\n",
    "    \n",
    "    undetected_fraud.append(fn)\n",
    "    mislabeled_innocents.append(fp)\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "ax1.plot(thresholds, undetected_fraud, marker='x', color='r', label='Undetected Fraud')\n",
    "ax1.plot(thresholds, mislabeled_innocents, marker='s', color='b', label='Mislabeled Innocents')\n",
    "ax1.set_xlabel('Threshold')\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.tick_params(axis='y')\n",
    "ax1.legend(loc='upper left')\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "ax2.plot(thresholds, accuracies, marker='o', color='g', label='Accuracy')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.tick_params(axis='y')\n",
    "ax2.legend(loc='upper right')\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.title('Model Performance for Different Thresholds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graphical overview over false positives, false negatives and the accuracy allows us to choose the right threshold for our application."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Mia Müßig"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}