Credit Card Fraud.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3efe3a23-acde-4cad-9f96-77f826626c79",
   "metadata": {},
   "source": [
    "## Credit Card Fraud Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bfa650-32bc-4459-8839-5f9bbe841742",
   "metadata": {},
   "source": [
    "### Goal:\n",
    "Try to detect whether credit card transaction is fraudalent or not based on certain predictors.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87f9339-85b4-4fec-b18e-bf0032b2aec1",
   "metadata": {},
   "source": [
    "### Attribute Information:\n",
    "\n",
    "distance_from_home - the distance from home where the transaction happened.\n",
    "\n",
    "distance_from_last_transaction - the distance from last transaction happened.\n",
    "\n",
    "ratio_to_median_purchase_price - Ratio of purchased price transaction to median purchase price.\n",
    "\n",
    "repeat_retailer - Is the transaction happened from same retailer.\n",
    "\n",
    "used_chip - Is the transaction through chip (credit card).\n",
    "\n",
    "used_pin_number - Is the transaction happened by using PIN number.\n",
    "\n",
    "online_order - Is the transaction an online order.\n",
    "\n",
    "fraud - Is the transaction fraudulent. (1 represents a fraudulent transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d31c26f0-e3be-4e58-b9cc-f1d8bb9e507a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import essential libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61818b2f-dc0d-49cb-a89c-2bafe74b6e15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.877857</td>\n",
       "      <td>0.311140</td>\n",
       "      <td>1.945940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.829943</td>\n",
       "      <td>0.175592</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.091079</td>\n",
       "      <td>0.805153</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.247564</td>\n",
       "      <td>5.600044</td>\n",
       "      <td>0.362663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.190936</td>\n",
       "      <td>0.566486</td>\n",
       "      <td>2.222767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   distance_from_home  distance_from_last_transaction  \\\n",
       "0           57.877857                        0.311140   \n",
       "1           10.829943                        0.175592   \n",
       "2            5.091079                        0.805153   \n",
       "3            2.247564                        5.600044   \n",
       "4           44.190936                        0.566486   \n",
       "\n",
       "   ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "0                        1.945940              1.0        1.0   \n",
       "1                        1.294219              1.0        0.0   \n",
       "2                        0.427715              1.0        0.0   \n",
       "3                        0.362663              1.0        1.0   \n",
       "4                        2.222767              1.0        1.0   \n",
       "\n",
       "   used_pin_number  online_order  fraud  \n",
       "0              0.0           0.0    0.0  \n",
       "1              0.0           0.0    0.0  \n",
       "2              0.0           1.0    0.0  \n",
       "3              0.0           1.0    0.0  \n",
       "4              0.0           1.0    0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the data\n",
    "card_url = \"https://drive.google.com/file/d/1XdIqSj2n4fZ_B2lLyWNtBisDLDScxSd8/view?usp=drive_link\"\n",
    "card_path = \"https://drive.google.com/uc?id=\" + card_url.split(\"/\")[-2]\n",
    "card = pd.read_csv(card_path)\n",
    "\n",
    "# create a copy of data frame to keep original\n",
    "real_card = card.copy()\n",
    "card.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "466f0fb7-5772-449a-84b8-9a2d00774ef7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data dimensions\n",
    "card.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045e56f8-5cac-4f04-8222-88c01f1769b1",
   "metadata": {},
   "source": [
    "This data frame consists of 1000000 observations and 8 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3261d73c-7759-4ebb-9fd0-3b48ae177119",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['distance_from_home', 'distance_from_last_transaction',\n",
       "       'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',\n",
       "       'used_pin_number', 'online_order', 'fraud'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column names\n",
    "card.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "327745b6-5dc9-440c-957f-af90d5ba3369",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000000 entries, 0 to 999999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                          Non-Null Count    Dtype  \n",
      "---  ------                          --------------    -----  \n",
      " 0   distance_from_home              1000000 non-null  float64\n",
      " 1   distance_from_last_transaction  1000000 non-null  float64\n",
      " 2   ratio_to_median_purchase_price  1000000 non-null  float64\n",
      " 3   repeat_retailer                 1000000 non-null  float64\n",
      " 4   used_chip                       1000000 non-null  float64\n",
      " 5   used_pin_number                 1000000 non-null  float64\n",
      " 6   online_order                    1000000 non-null  float64\n",
      " 7   fraud                           1000000 non-null  float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 61.0 MB\n"
     ]
    }
   ],
   "source": [
    "# column information\n",
    "card.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41f25c09-11f7-41c6-8c11-c1b045f26cc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distance_from_home</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>26.628792</td>\n",
       "      <td>65.390784</td>\n",
       "      <td>0.004874</td>\n",
       "      <td>3.878008</td>\n",
       "      <td>9.967760</td>\n",
       "      <td>25.743985</td>\n",
       "      <td>10632.723672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.036519</td>\n",
       "      <td>25.843093</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.296671</td>\n",
       "      <td>0.998650</td>\n",
       "      <td>3.355748</td>\n",
       "      <td>11851.104565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1.824182</td>\n",
       "      <td>2.799589</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.475673</td>\n",
       "      <td>0.997717</td>\n",
       "      <td>2.096370</td>\n",
       "      <td>267.802942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repeat_retailer</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>0.323157</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_chip</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.350399</td>\n",
       "      <td>0.477095</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_pin_number</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.100608</td>\n",
       "      <td>0.300809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.650552</td>\n",
       "      <td>0.476796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fraud</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.087403</td>\n",
       "      <td>0.282425</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count       mean        std       min  \\\n",
       "distance_from_home              1000000.0  26.628792  65.390784  0.004874   \n",
       "distance_from_last_transaction  1000000.0   5.036519  25.843093  0.000118   \n",
       "ratio_to_median_purchase_price  1000000.0   1.824182   2.799589  0.004399   \n",
       "repeat_retailer                 1000000.0   0.881536   0.323157  0.000000   \n",
       "used_chip                       1000000.0   0.350399   0.477095  0.000000   \n",
       "used_pin_number                 1000000.0   0.100608   0.300809  0.000000   \n",
       "online_order                    1000000.0   0.650552   0.476796  0.000000   \n",
       "fraud                           1000000.0   0.087403   0.282425  0.000000   \n",
       "\n",
       "                                     25%       50%        75%           max  \n",
       "distance_from_home              3.878008  9.967760  25.743985  10632.723672  \n",
       "distance_from_last_transaction  0.296671  0.998650   3.355748  11851.104565  \n",
       "ratio_to_median_purchase_price  0.475673  0.997717   2.096370    267.802942  \n",
       "repeat_retailer                 1.000000  1.000000   1.000000      1.000000  \n",
       "used_chip                       0.000000  0.000000   1.000000      1.000000  \n",
       "used_pin_number                 0.000000  0.000000   0.000000      1.000000  \n",
       "online_order                    0.000000  1.000000   1.000000      1.000000  \n",
       "fraud                           0.000000  0.000000   0.000000      1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74ffa15f-bdca-46de-8d55-e49f194adbbd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance_from_home                0\n",
       "distance_from_last_transaction    0\n",
       "ratio_to_median_purchase_price    0\n",
       "repeat_retailer                   0\n",
       "used_chip                         0\n",
       "used_pin_number                   0\n",
       "online_order                      0\n",
       "fraud                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "card.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab47ec9-dbdf-4d04-9dbe-50cac8e1b6ae",
   "metadata": {},
   "source": [
    "We have no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076ed4b1-6da0-424d-bb2b-89c6ca53597d",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e582887e-dc7d-42fd-9106-b7d47f2b703f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for correlation between variables\n",
    "cor = card.corr()\n",
    "sns.heatmap(cor, annot = True, fmt = '.2f', cmap = 'crest', linewidth = 0.7)\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057adb58-823b-480d-8dbf-d4430e807332",
   "metadata": {},
   "source": [
    "Looking at the correlation heatap above, we can see we have a strong correlation between our target variable fraud and the ratio to median purchase median price feature. Therefore, we must have ratio to median purchase price as a predictor for our models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f79148b-a668-48e9-8bf7-6d94d5e309f3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of class on our target variable\n",
    "card['fraud'].value_counts()\n",
    "sns.countplot(x = 'fraud', data = card, palette = 'magma')\n",
    "plt.title(\"Fraud Classes Count\")\n",
    "plt.xticks(ticks = [0,1], labels = ['Legitimate','Fraud'])\n",
    "plt.xlabel('Fraud or Not')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b079e2-bfdd-4002-bb5d-597a393d6060",
   "metadata": {},
   "source": [
    "We can clearly see the distribution between the number of fraudlent purchases and non-fraudlent purchase where there is a significant amount of non-fraudulent purchases in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32ee7144-805f-4334-8175-57ca1d4b37b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "online_order     0.0     1.0\n",
      "fraud                       \n",
      "0.0           344756  567841\n",
      "1.0             4692   82711\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if for fraud occurrences in online purchases\n",
    "sns.countplot(x = 'online_order',hue = 'fraud', data = card, palette = 'magma')\n",
    "print(pd.crosstab(index = card['fraud'], columns = card['online_order']))\n",
    "plt.title(\"fraudulent Transaction Dependent on Online Purchases\")\n",
    "plt.xticks(ticks = [0,1], labels = ['No','Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0efa5ec-2a15-434c-989d-1782a265a4e8",
   "metadata": {},
   "source": [
    "As we can see there was definitely more fraud purchases done online compared to purchases not done online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1205804e-25b6-4306-988c-ce49d09ffcea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "used_pin_number     0.0     1.0\n",
      "fraud                          \n",
      "0.0              812262  100335\n",
      "1.0               87130     273\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if for fraud occurrences if the card pin was used\n",
    "sns.countplot(x = 'used_pin_number',hue = 'fraud', data = card, palette = 'magma')\n",
    "print(pd.crosstab(index = card['fraud'], columns = card['used_pin_number']))\n",
    "plt.title(\"fraudulent Transaction Dependent on Pin Number Usage\")\n",
    "plt.xticks(ticks = [0,1], labels = ['No','Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bfeb707-577e-446e-8676-3ea5ecdf6816",
   "metadata": {},
   "source": [
    "Now comparing fraudulent purchase where the card pin was used we can see a majority of fraudulent purchases occured without using the card pin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3fa603b6-22ed-40da-aed4-8c31fb37780f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "used_chip     0.0     1.0\n",
      "fraud                    \n",
      "0.0        584608  327989\n",
      "1.0         64993   22410\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if for fraud occurrences if the card chip was used\n",
    "sns.countplot(x = 'used_chip',hue = 'fraud', data = card, palette = 'magma')\n",
    "plt.title(\"fraudulent Transaction Dependent on Used Chip\")\n",
    "print(pd.crosstab(index = card['fraud'], columns = card['used_chip']))\n",
    "plt.xticks(ticks = [0,1], labels = ['No','Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d3f9bc-73ec-4b16-b41c-5e6bc40ff561",
   "metadata": {},
   "source": [
    "There was more fraudulent purchases where the chip on the card was not used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab3ece0f-9a9c-48dd-a8dc-48dabbf175ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "repeat_retailer     0.0     1.0\n",
      "fraud                          \n",
      "0.0              107986  804611\n",
      "1.0               10478   76925\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if for fraud occurrences if it was in a repeated retailer\n",
    "sns.countplot(x = 'repeat_retailer',hue = 'fraud', data = card, palette = 'magma')\n",
    "print(pd.crosstab(index = card['fraud'], columns = card['repeat_retailer']))\n",
    "plt.title(\"fraudulent Transaction Dependent on Repeated Retailer\")\n",
    "plt.xticks(ticks = [0,1], labels = ['No','Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "880e1a14-1158-480b-886b-1b3ddd55bbb9",
   "metadata": {},
   "source": [
    "This one was interesting to me since more fraudulent purchases occured where the card was used to purchase an item previously. The probability of a fraudulent purchase in a repeated retailer was greater than a purchase on a unknown location. I don't see this as a coincidence or perhaps the retailer is a common place where fraudster go and buy items. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0d6a2667-1be2-46b1-860d-78232b226a00",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'fraud', y = 'distance_from_last_transaction', \n",
    "                data = card, palette = 'Set2')\n",
    "plt.title(\"fraudulent Transactions by Distance of Last Transaction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf4bd79e-c303-4e94-93d8-5eedec806fc2",
   "metadata": {},
   "source": [
    "This one was also interesting to me, where fraudulent purchases occured relatively close to the lastest transaction on the same card. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a08d86a3-2986-4231-90f4-062198150341",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XL0ezZs2Mzpy01L1796RDKea0adMGvXr1wrRp0+Dj44Nu3brhxx9/NHvIx5z09HSMGTMGfn5+cHV1Rfny5aXfRUt+xy5cuAAnJyfUrVs3z8c8d+4cgCch5NnX5bZt20xek/n17Gvp8uXLcHJyMvl5aDQaeHl5Sb97Bs8+R8OHjcDAQLPtcv821K9fH+Hh4SY3wyT/p+sFTF8LSqUSVatWNam3YsWKJh9U1Gr1C+u15O9jccc5TZRvT+/dMMjNzcW//vUvpKenY8KECahduzbKlCmD69evY/DgwUZvQgqFQppH8Ow2LGXpNg11vfXWW3nOlXr67DYAeZ6ZZO7xrenZvUwAMGPGDHz00UcYMmQIPvnkE5QtWxZOTk4YO3as2Td+ObXPnj0bgwcPxvr167Ft2zaMHj0aMTEx2L9/PypWrIgLFy6gXbt2qF27NubMmYPAwEAolUr89ttvmDt3rkV7bArLi56vq6sr9uzZg507d2LTpk3YsmULVq5ciVdffRXbtm2z2lloAwcOxJgxY3Dt2jVkZ2dj//79RpO3LfX48WP8/fffzw2mhvV/9u/fjw0bNmDr1q0YMmQIZs+ejf37979wvt0bb7yBffv2Yfz48WjYsCHc3d2h1+vRoUMHi3/HXsSw3Z9++gkajcakv1Spgr11mXstAfL3gOb1HG31t+FFLK3Xkr+PxR1DE1nF8ePH8ffffyMuLg4DBw6U2hMSEkzGent7m91d/eyno8qVK2P79u24d++e0R/2s2fPWrzNZ5UvXx4eHh7Izc1FeHj4c8fmhyWHnyzx3//+F6+88gp++OEHo/aMjAz4+PhYvN369eujfv36+PDDD7Fv3z60aNECixcvxqeffooNGzYgOzsb8fHxRp+4nz1kUq1aNQDAiRMnnrtHRe73ynBGlLmf/5kzZ+Dj42PRqeNOTk5o164d2rVrhzlz5mDGjBmYPHkydu7c+cLfCcMekaf9/fffcHNzk85IA4A+ffogOjoaP//8s7T21JtvvpnvWp/13//+Fw8fPkRERMQLxzZr1gzNmjXDZ599hhUrVqB///745ZdfMHTo0Dx/Bnfu3MH27dsxbdo0TJkyRWo397zlqlatGvR6PU6dOoWGDRvmOQYAfH19rfq6zEvlypWh1+tx7tw51KlTR2pPTU1FRkaG3S3s+/RroWrVqlL7o0ePcOnSJat9zwrr76Mj4+E5sgrDJ5anP1EJIfDVV1+ZjK1WrRrOnDljtJrx0aNHTc5Y6tixI3JycoxOZc/NzcX8+fMt3qa5unv16oU1a9bgxIkTJv2WrrhcpkwZiw5J5pezs7PJp9jVq1fj+vXrFm1Pp9MhJyfHqK1+/fpwcnKSDueY+1lnZmbixx9/NLpf+/bt4eHhgZiYGGRlZRn1PX1fud8rf39/NGzYEHFxcUanVZ84cQLbtm1Dx44d5T3Jp6Snp5u0Gd7I5Ry+SkxMNJrTcfXqVaxfvx7t27c3+hTv4+OD1157DcuWLcPy5cvRoUOHAoVa4Mnv99ixY+Ht7W10xuKz7ty5Y/I78uxzNJwB++zp6uZ+1gAKtIJ79+7d4eTkhOnTp5vsqTI8TkREBDw9PTFjxgyz87CstRK6geF359nnNWfOHABAp06drPp4BRUeHg6lUomvv/7a6Gfzww8/IDMz02r1FtbfR0fGPU1kFbVr10a1atXw/vvv4/r16/D09MSaNWvMHssfMmQI5syZg4iICERFRSEtLQ2LFy9GvXr1pMmiANClSxe0aNECEydOxD///IO6devi119/NfsGK3eb5sycORM7d+5E06ZN8fbbb6Nu3bpIT0/H4cOH8fvvv5t9Y32R0NBQrFy5EtHR0WjSpAnc3d3RpUuXfG/nRTp37ozp06cjMjISzZs3x/Hjx7F8+XKjT5/5sWPHDowcORK9e/dGzZo1kZOTg59++kn64wk8CUNKpRJdunTBu+++i3v37uH777+Hr68vbty4IW3L09MTc+fOxdChQ9GkSRNpXZyjR4/iwYMHiIuLA5C/79UXX3yB1157DWFhYYiKipKWHFCr1RZdT3D69OnYs2cPOnXqhMqVKyMtLQ0LFy5ExYoVpfV7nic4OBgRERFGSw4AkFbXftrAgQOlBSo/+eSTfNX5xx9/ICsrS5rsv3fvXsTHx0OtVmPt2rVmD2EZxMXFYeHChejRoweqVauGu3fv4vvvv4enp6cUFlxdXVG3bl2sXLkSNWvWRNmyZREcHIzg4GC0bt0as2bNwuPHj1GhQgVs27YNly5dylf9T6tevTomT56MTz75BK1atULPnj2hUqlw6NAhBAQEICYmBp6enli0aBEGDBiAl156CX369EH58uVx5coVbNq0CS1atLDK4U2DBg0aYNCgQfjuu++QkZGBNm3a4ODBg4iLi0P37t3xyiuvWO2xrKF8+fKYNGkSpk2bhg4dOqBr1644e/YsFi5ciCZNmljlBAODwvj76NCK8lQ9chzPW3Lg6VNfn3bq1CkRHh4u3N3dhY+Pj3j77bfF0aNHTU5hFUKIZcuWiapVqwqlUikaNmwotm7danZ5gNu3b4sBAwYIT09PoVarxYABA6TT4i3dJsycWp2amipGjBghAgMDRenSpYVGoxHt2rUT33333Qu/J+ZOjb93757o16+f8PLyEgBkLz/wvCUHzJ12npWVJd577z3h7+8vXF1dRYsWLURiYqLJEgxya7948aIYMmSIqFatmnBxcRFly5YVr7zyivj999+N7hcfHy9CQkKEi4uLqFKlivj8889FbGys0anuT49t3ry5cHV1FZ6enuLll18WP//88wu/V+a+r0II8fvvv4sWLVpI2+vSpYs4deqU0RjDqeXPLiXw7On427dvF926dRMBAQFCqVSKgIAA0bdvX/H333+bfK+fBUCMGDFCLFu2TNSoUUOoVCrRqFGjPE8vz87OFt7e3kKtVpsswZCXZ09FL126tChfvrxo3bq1+Oyzz0RaWprJfZ59jocPHxZ9+/YVlSpVEiqVSvj6+orOnTsbLZUghBD79u0ToaGhQqlUGv0OXrt2TfTo0UN4eXkJtVotevfuLVJSUkx+T+V+zw1iY2NFo0aNhEqlEt7e3qJNmzYiISHB5PlHREQItVotXFxcRLVq1cTgwYNNan/Wi5YcMLfExOPHj8W0adNEUFCQKF26tAgMDBSTJk0SWVlZRuMqV64sOnXqZHJ/w++DnDqe9aKlJfL6u/vNN9+I2rVri9KlSws/Pz8xfPhwcefOHaMxbdq0EfXq1TO5b36eh5y/jyWFQggbz1AjIioBcnJyEBAQgC5dupjMQSMix8A5TURERWDdunW4efOm0YkSRORYuKeJiKgQHThwAMeOHcMnn3wCHx+fErcYIFFxwj1NRESFyHBdN19fX/zf//2frcshogLgniYiIiIiGbiniYiIiEgGhiYiIiIiGbi4pZXo9XqkpKTAw8OjyC6hQURERAUjhMDdu3cREBBgcl3VZzE0WUlKSorJFaOJiIjIMVy9ehUVK1Z87hiGJivx8PAA8OSb7unpaeNqiIiISA6dTofAwEDpffx5GJqsxHBIztPTk6GJiIjIwciZWsOJ4EREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0ERERPSUpKQmjR49GUlKSrUshO8PQRERE9D/Z2dmIjY3FrVu3EBsbi+zsbFuXRHaEoYmIiOh/1q9fj4yMDABARkYG4uPjbVsQ2RWGJiIiIgBarRYbNmyAEAIAIIRAfHw8tFqtjSsje8HQREREJZ4QAkuXLpUC04vaqWRiaCIiohIvJSUFx44dg16vN2rX6/U4duwYUlJSbFQZ2ROGJiIiKvECAgIQEhICJyfjt0UnJyeEhIQgICDARpWRPWFoIiKiEk+hUGDw4MFQKBQm7ZGRkSbtVDIxNBEREQHQaDTo0qWLFJAUCgW6du0KPz8/G1dG9oKhiYiI6H+6desGLy8vAIC3tze6du1q24LIrjA0ERER/Y9KpcKQIUPg4+ODyMhIqFQqW5dEdqSUrQsgIiKyJ6GhoQgNDbV1GWSHuKeJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGm4amPXv2oEuXLggICIBCocC6deuM+oUQmDJlCvz9/eHq6orw8HCcO3fOaEx6ejr69+8PT09PeHl5ISoqCvfu3TMac+zYMbRq1QouLi4IDAzErFmzTGpZvXo1ateuDRcXF9SvXx+//fab1Z8vEREROS6bhqb79++jQYMGWLBggdn+WbNm4euvv8bixYtx4MABlClTBhEREcjKypLG9O/fHydPnkRCQgI2btyIPXv24J133pH6dTod2rdvj8qVKyMpKQlffPEFpk6diu+++04as2/fPvTt2xdRUVE4cuQIunfvju7du+PEiROF9+SJiIjIsQg7AUCsXbtW+lqv1wuNRiO++OILqS0jI0OoVCrx888/CyGEOHXqlAAgDh06JI3ZvHmzUCgU4vr160IIIRYuXCi8vb1Fdna2NGbChAmiVq1a0tdvvPGG6NSpk1E9TZs2Fe+++67s+jMzMwUAkZmZKfs+REREZFv5ef+22zlNly5dglarRXh4uNSmVqvRtGlTJCYmAgASExPh5eWFxo0bS2PCw8Ph5OSEAwcOSGNat24NpVIpjYmIiMDZs2dx584daczTj2MYY3gcc7Kzs6HT6YxuREREVHzZbWjSarUAAD8/P6N2Pz8/qU+r1cLX19eov1SpUihbtqzRGHPbePox8hpj6DcnJiYGarVaugUGBub3KRIREZEDsdvQZO8mTZqEzMxM6Xb16lVbl0RERESFyG5Dk0ajAQCkpqYataempkp9Go0GaWlpRv05OTlIT083GmNuG08/Rl5jDP3mqFQqeHp6Gt2IiIio+LLb0BQUFASNRoPt27dLbTqdDgcOHEBYWBgAICwsDBkZGUhKSpLG7NixA3q9Hk2bNpXG7NmzB48fP5bGJCQkoFatWvD29pbGPP04hjGGxyEiIiKyaWi6d+8ekpOTkZycDODJ5O/k5GRcuXIFCoUCY8eOxaeffor4+HgcP34cAwcOREBAALp37w4AqFOnDjp06IC3334bBw8exN69ezFy5Ej06dMHAQEBAIB+/fpBqVQiKioKJ0+exMqVK/HVV18hOjpaqmPMmDHYsmULZs+ejTNnzmDq1Kn466+/MHLkyKL+lhAREZG9KoKz+fK0c+dOAcDkNmjQICHEk2UHPvroI+Hn5ydUKpVo166dOHv2rNE2bt++Lfr27Svc3d2Fp6eniIyMFHfv3jUac/ToUdGyZUuhUqlEhQoVxMyZM01qWbVqlahZs6ZQKpWiXr16YtOmTfl6LlxygIiIyPHk5/1bIYQQNsxsxYZOp4NarUZmZibnNxERETmI/Lx/2+2cJiIiIiJ7wtBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EFkhKSsLo0aORlJRk61KIiKiIMDQR5VN2djZiY2Nx69YtxMbGIjs729YlERFREWBoIsqn9evXIyMjAwCQkZGB+Ph42xZERERFgqGJKB+0Wi02bNgAIQQAQAiB+Ph4aLVaG1dGRESFjaGJSCYhBJYuXSoFphe1ExFR8cLQRCRTSkoKjh07Br1eb9Su1+tx7NgxpKSk2KgyIiIqCgxNRDIFBAQgJCQETk7GLxsnJyeEhIQgICDARpUREVFRYGgikkmhUGDw4MFQKBQm7ZGRkSbtRERUvDA0EeWDRqNBly5dpICkUCjQtWtX+Pn52bgyIiIqbAxNRPnUrVs3eHl5AQC8vb3RtWtX2xZERERFgqGJKJ9UKhWGDBkCHx8fREZGQqVS2bokIiIqAqVsXQCRIwoNDUVoaKityyAioiLEPU1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMth1aMrNzcVHH32EoKAguLq6olq1avjkk08ghJDGCCEwZcoU+Pv7w9XVFeHh4Th37pzRdtLT09G/f394enrCy8sLUVFRuHfvntGYY8eOoVWrVnBxcUFgYCBmzZpVJM+RiIiIHINdh6bPP/8cixYtwjfffIPTp0/j888/x6xZszB//nxpzKxZs/D1119j8eLFOHDgAMqUKYOIiAhkZWVJY/r374+TJ08iISEBGzduxJ49e/DOO+9I/TqdDu3bt0flypWRlJSEL774AlOnTsV3331XpM+XiIiI7JdCPL3bxs507twZfn5++OGHH6S2Xr16wdXVFcuWLYMQAgEBAXjvvffw/vvvAwAyMzPh5+eHpUuXok+fPjh9+jTq1q2LQ4cOoXHjxgCALVu2oGPHjrh27RoCAgKwaNEiTJ48GVqtFkqlEgAwceJErFu3DmfOnJFVq06ng1qtRmZmJjw9Pa38nSAiIqLCkJ/3b7ve09S8eXNs374df//9NwDg6NGj+PPPP/Haa68BAC5dugStVovw8HDpPmq1Gk2bNkViYiIAIDExEV5eXlJgAoDw8HA4OTnhwIED0pjWrVtLgQkAIiIicPbsWdy5c8dsbdnZ2dDpdEY3IiIiKr5K2bqA55k4cSJ0Oh1q164NZ2dn5Obm4rPPPkP//v0BAFqtFgDg5+dndD8/Pz+pT6vVwtfX16i/VKlSKFu2rNGYoKAgk20Y+ry9vU1qi4mJwbRp06zwLImIiMgR2PWeplWrVmH58uVYsWIFDh8+jLi4OHz55ZeIi4uzdWmYNGkSMjMzpdvVq1dtXRIREREVIrve0zR+/HhMnDgRffr0AQDUr18fly9fRkxMDAYNGgSNRgMASE1Nhb+/v3S/1NRUNGzYEACg0WiQlpZmtN2cnBykp6dL99doNEhNTTUaY/jaMOZZKpUKKpWq4E+SiIiIHIJd72l68OABnJyMS3R2doZerwcABAUFQaPRYPv27VK/TqfDgQMHEBYWBgAICwtDRkYGkpKSpDE7duyAXq9H06ZNpTF79uzB48ePpTEJCQmoVauW2UNzREREVPLYdWjq0qULPvvsM2zatAn//PMP1q5dizlz5qBHjx4AAIVCgbFjx+LTTz9FfHw8jh8/joEDByIgIADdu3cHANSpUwcdOnTA22+/jYMHD2Lv3r0YOXIk+vTpg4CAAABAv379oFQqERUVhZMnT2LlypX46quvEB0dbaunTkRERPZG2DGdTifGjBkjKlWqJFxcXETVqlXF5MmTRXZ2tjRGr9eLjz76SPj5+QmVSiXatWsnzp49a7Sd27dvi759+wp3d3fh6ekpIiMjxd27d43GHD16VLRs2VKoVCpRoUIFMXPmzHzVmpmZKQCIzMxMy58wERERFan8vH/b9TpNjoTrNBERETmeYrNOExEREZG9YGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZLA5NP/30E1q0aIGAgABcvnwZADBv3jysX7/easURERER2QuLQtOiRYsQHR2Njh07IiMjA7m5uQAALy8vzJs3z5r1EREREdkFi0LT/Pnz8f3332Py5MlwdnaW2hs3bozjx49brTgiIiIie2FRaLp06RIaNWpk0q5SqXD//v0CF0VERERkbywKTUFBQUhOTjZp37JlC+rUqVPQmoiIiIjsTilL7hQdHY0RI0YgKysLQggcPHgQP//8M2JiYrBkyRJr10hERERkcxaFpqFDh8LV1RUffvghHjx4gH79+iEgIABfffUV+vTpY+0aiYiIiGxOIYQQBdnAgwcPcO/ePfj6+lqrJoek0+mgVquRmZkJT09PW5dDREREMuTn/duiPU1Pc3Nzg5ubW0E3Q0RERGTXLApNt2/fxpQpU7Bz506kpaVBr9cb9aenp1ulOCIiIiJ7YVFoGjBgAM6fP4+oqCj4+flBoVBYuy4iIiIiu2JRaPrjjz/w559/okGDBtauh4iIiMguWbROU+3atfHw4UNr10JERERktywKTQsXLsTkyZOxe/du3L59GzqdzuhGREREVNxYdHjOy8sLOp0Or776qlG7EAIKhUK6gC8RERFRcWFRaOrfvz9Kly6NFStWcCI4ERERlQgWhaYTJ07gyJEjqFWrlrXrISIiIrJLFs1paty4Ma5evWrtWoiIiIjslkV7mkaNGoUxY8Zg/PjxqF+/PkqXLm3UHxISYpXiiIiIiOyFRdeec3Iy3UGlUChK9ERwXnuOiIjI8RT6tecuXbpkUWFEREREjsqi0FS5cmVr10FERERk1ywKTQBw4cIFzJs3D6dPnwYA1K1bF2PGjEG1atWsVhwRERGRvbDo7LmtW7eibt26OHjwIEJCQhASEoIDBw6gXr16SEhIsHaNRERERDZn0UTwRo0aISIiAjNnzjRqnzhxIrZt24bDhw9brUBHwYngREREjic/798W7Wk6ffo0oqKiTNqHDBmCU6dOWbJJIiIiIrtmUWgqX748kpOTTdqTk5Ph6+tb0JqIiIiI7I5FE8HffvttvPPOO7h48SKaN28OANi7dy8+//xzREdHW7VAIiIiIntg0ZwmIQTmzZuH2bNnIyUlBQAQEBCA8ePHY/To0SXyAr6c00REROR48vP+bVFoetrdu3cBAB4eHgXZjMNjaCIiInI8hb4i+NNKelgiIiKiksGiieCpqakYMGAAAgICUKpUKTg7OxvdiIiIiIobi/Y0DR48GFeuXMFHH30Ef3//EjmHiYiIiEoWi0LTn3/+iT/++AMNGza0cjlERERE9smiw3OBgYEo4PxxIiIiIodiUWiaN28eJk6ciH/++cfK5RARERHZJ9mH57y9vY3mLt2/fx/VqlWDm5sbSpcubTQ2PT3dehUSERER2QHZoWnevHmFWAYRERGRfZMdmgYNGpTvjc+cORPDhg2Dl5dXvu9LREREZE8smtMk14wZM3iojoiIiIqFQg1NPMOOiIiIiotCDU1ERERExQVDExEREZEMDE1EREREMth9aLp+/TreeustlCtXDq6urqhfvz7++usvqV8IgSlTpsDf3x+urq4IDw/HuXPnjLaRnp6O/v37w9PTE15eXoiKisK9e/eMxhw7dgytWrWCi4sLAgMDMWvWrCJ5fkREROQYCjU0tWrVCq6urhbf/86dO2jRogVKly6NzZs349SpU5g9eza8vb2lMbNmzcLXX3+NxYsX48CBAyhTpgwiIiKQlZUljenfvz9OnjyJhIQEbNy4EXv27ME777wj9et0OrRv3x6VK1dGUlISvvjiC0ydOhXfffedxbUTERFR8aIQBTjFLS0tDWlpadDr9UbtISEhBS4MACZOnIi9e/fijz/+MNsvhEBAQADee+89vP/++wCAzMxM+Pn5YenSpejTpw9Onz6NunXr4tChQ2jcuDEAYMuWLejYsSOuXbuGgIAALFq0CJMnT4ZWq4VSqZQee926dThz5oysWnU6HdRqNTIzM+Hp6WmFZ09ERESFLT/v3xbtaUpKSkJwcDD8/f0REhKChg0bolGjRtK/1hIfH4/GjRujd+/e8PX1RaNGjfD9999L/ZcuXYJWq0V4eLjUplar0bRpUyQmJgIAEhMT4eXlJQUmAAgPD4eTkxMOHDggjWndurUUmAAgIiICZ8+exZ07d8zWlp2dDZ1OZ3QjIiKi4sui0DRkyBDUrFkT+/btw8WLF3Hp0iWjf63l4sWLWLRoEWrUqIGtW7di+PDhGD16NOLi4gAAWq0WAODn52d0Pz8/P6lPq9XC19fXqL9UqVIoW7as0Rhz23j6MZ4VExMDtVot3QIDAwv4bImIiMieyb6MytMuXryINWvWoHr16taux4her0fjxo0xY8YMAECjRo1w4sQJLF682KLLuljTpEmTEB0dLX2t0+kYnIiIiIoxi/Y0tWvXDkePHrV2LSb8/f1Rt25do7Y6dergypUrAACNRgMASE1NNRqTmpoq9Wk0GqSlpRn15+TkID093WiMuW08/RjPUqlU8PT0NLpRyZGUlITRo0cjKSnJ1qUQEVERsWhP05IlSzBo0CCcOHECwcHBKF26tFF/165drVJcixYtcPbsWaO2v//+G5UrVwYABAUFQaPRYPv27WjYsCGAJ3t8Dhw4gOHDhwMAwsLCkJGRgaSkJISGhgIAduzYAb1ej6ZNm0pjJk+ejMePH0vPJSEhAbVq1TI6U48IeDKfLTY2Fnfu3EFsbCyCg4OhUqlsXRYRERU2YYH4+HihVquFQqEwuTk5OVmySbMOHjwoSpUqJT777DNx7tw5sXz5cuHm5iaWLVsmjZk5c6bw8vIS69evF8eOHRPdunUTQUFB4uHDh9KYDh06iEaNGokDBw6IP//8U9SoUUP07dtX6s/IyBB+fn5iwIAB4sSJE+KXX34Rbm5u4ttvv5Vda2ZmpgAgMjMzrfPkyW6tXLlS9OvXT/Tt21f069dPrFq1ytYlERGRhfLz/m3R4blRo0bhrbfewo0bN6DX641uubm5Vgt0TZo0wdq1a/Hzzz8jODgYn3zyCebNm4f+/ftLYz744AOMGjUK77zzDpo0aYJ79+5hy5YtcHFxkcYsX74ctWvXRrt27dCxY0e0bNnSaA0mtVqNbdu24dKlSwgNDcV7772HKVOmGK3lRAQ8OTFgw4YN0sWohRCIj4/P84QBIiIqPixap8nDwwPJycmoVq1aYdTkkLhOU/EnhMDnn3+OEydOGK1N5uTkhODgYEyYMAEKhcKGFRIRUX4V+jpNPXv2xM6dOy0qjshRpaSk4NixYyaLuer1ehw7dgwpKSk2qoyIiIqCRRPBa9asiUmTJuHPP/9E/fr1TSaCjx492irFEdmTgIAAhISE5LmnKSAgwIbVERFRYbPo8FxQUFDeG1QorLrApaPg4bmSQavVYvz48UZz95ydnfHll1+aLJBKRET2Lz/v3xbtabp06ZJFhRE5Oo1Ggy5dumD9+vUQQkChUKBr164MTEREJYBFc5qeJoSABTuriBxWt27d4OXlBQDw9va22rpkRERk3ywOTf/3f/+H+vXrw9XVFa6urggJCcFPP/1kzdqI7JJKpcKQIUPg4+ODyMhILmxJRFRCWHR4bs6cOfjoo48wcuRItGjRAgDw559/YtiwYbh16xbGjRtn1SKJ7E1oaKi0wjwREZUMFk8EnzZtGgYOHGjUHhcXh6lTp5bIOU+cCE5EROR4Cn2dphs3bqB58+Ym7c2bN8eNGzcs2SQRERGRXbMoNFWvXh2rVq0yaV+5ciVq1KhR4KKIiIiI7I1Fc5qmTZuGN998E3v27JHmNO3duxfbt283G6aIiIiIHJ1Fe5p69eqFgwcPwsfHB+vWrcO6devg4+ODgwcPokePHtaukYiIiMjm8r2n6fHjx3j33Xfx0UcfYdmyZYVRExEREZHdyfeeptKlS2PNmjWFUQsRERGR3bLo8Fz37t2xbt06K5dCREREZL8smgheo0YNTJ8+HXv37kVoaCjKlClj1D969GirFEdERERkLyxe3DLPDSoUuHjxYoGKckRc3JKIiMjx5Of9W/aeJp1OJ22sJK74TURERCWb7DlN3t7eSEtLAwC8+uqryMjIKKyaiIiIiOyO7NDk7u6O27dvAwB27dqFx48fF1pRRERERPZG9uG58PBwvPLKK6hTpw4AoEePHlAqlWbH7tixwzrVEREREdkJ2aFp2bJliIuLw4ULF7B7927Uq1cPbm5uhVkbERERkd2w6Oy5V155BWvXroWXl1chlOSYePYcERGR48nP+7dFi1vu3LlTVmDy9PQskcsPEBERUfFjUWiSy4KdWERERER2qVBDE1FxlZSUhNGjRyMpKcnWpRARURFhaCLKp+zsbMTGxuLWrVuIjY1Fdna2rUsiIqIiwNBElE/r16+XFnfNyMhAfHy8bQsiIqIiUaihSaFQFObmiYqcVqvFhg0bpPl6QgjEx8dDq9XauDIiIipsnAhOJJMQAkuXLjX5vc6rnYiIipcChaZHjx7h7NmzyMnJMdu/efNmVKhQoSAPQWQ3UlJScOzYMej1eqN2vV6PY8eOISUlxUaVERFRUbAoND148ABRUVFwc3NDvXr1cOXKFQDAqFGjMHPmTGlcy5YtoVKprFMpkY0FBAQgJCQETk7GLxsnJyeEhIQgICDARpUREVFRsCg0TZo0CUePHsWuXbvg4uIitYeHh2PlypVWK47InigUCgwePNhkrp5CoUBkZCTn8BERFXMWhaZ169bhm2++QcuWLY3eKOrVq4cLFy5YrTgie6PRaNClSxejtq5du8LPz89GFRERUVGxKDTdvHkTvr6+Ju3379/np20q9jp06CD9nisUCkRERNi4IiIiKgoWhabGjRtj06ZN0teGN5AlS5YgLCzMOpUR2aktW7YYLTmwdetWG1dERERFoZQld5oxYwZee+01nDp1Cjk5Ofjqq69w6tQp7Nu3D7t377Z2jUR2w7BO09Pi4+PRqlUraDQaG1VFRERFwaI9TS1btkRycjJycnJQv359bNu2Db6+vkhMTERoaKi1aySyC1yniYioZLNoTxMAVKtWDd9//701ayGya4Z1mp719DpNXJeMiKj4smhP02+//WZ2HsfWrVuxefPmAhdFZI8M6zSZW3KA6zQRFR9JSUkYPXo0kpKSbF0K2RmLQtPEiRORm5tr0i6EwMSJEwtcFJE9MqzTZA7XaSIqHrKzsxEbG4tbt24hNjYW2dnZti6J7IhFoencuXOoW7euSXvt2rVx/vz5AhdF5Gg4n4moeFi/fj0yMjIAABkZGYiPj7dtQWRXLApNarUaFy9eNGk/f/48ypQpU+CiiOyRYcK3ucNznAhO5PgMZ8c+vaRIfHw8tFqtjSsje2FRaOrWrRvGjh1rtPr3+fPn8d5776Fr165WK47InvCCvUTFF8+OJTksCk2zZs1CmTJlULt2bQQFBSEoKAh16tRBuXLl8OWXX1q7RiK7wAv2EhVf/FBEcli05IBarca+ffuQkJCAo0ePwtXVFSEhIWjdurW16yOyG4aJ4OPHjzdp50RwIsdm+FB04sQJo+Dk5OSE4OBgfigiAIBCcJ+jVeh0OqjVamRmZsLT09PW5VAhWrVqFdavXw8hBBQKBbp3747evXvbuiwiKiCtVovx48cbnR3u7OyML7/8khflLsby8/5t8eKW27dvx/bt25GWlmayOzM2NtbSzRLZvW7dumH37t24c+cOvL29OY+PqJjQaDTo0qWL0Yeirl27MjCRxKI5TdOmTUP79u2xfft23Lp1C3fu3DG6ERVnKpUKQ4YMgY+PDyIjI6FSqWxdEhFZSbdu3eDl5QUA/FBEJiw6POfv749Zs2ZhwIABhVGTQ+LhOSKi4iEpKQlxcXEYNGgQr6daAhT64blHjx6hefPmFhVHRERkz0JDQxmWyCyLDs8NHToUK1assHYtRERERHbLotCUlZWFOXPmoE2bNhg1ahSio6ONboVl5syZUCgUGDt2rFEtI0aMQLly5eDu7o5evXohNTXV6H5XrlxBp06d4ObmBl9fX4wfPx45OTlGY3bt2oWXXnoJKpUK1atXx9KlSwvteZDj4wU9iYovvr4pLxYdnjt27BgaNmwIADhx4oRRX2GtVXPo0CF8++23CAkJMWofN24cNm3ahNWrV0OtVmPkyJHo2bMn9u7dCwDIzc1Fp06doNFosG/fPty4cQMDBw5E6dKlMWPGDADApUuX0KlTJwwbNgzLly/H9u3bMXToUPj7+yMiIqJQng85LsMFPe/cuYPY2FgEBwdzMjhRMcHXNz2PQ6zTdO/ePbz00ktYuHAhPv30UzRs2BDz5s1DZmYmypcvjxUrVuD1118HAJw5cwZ16tRBYmIimjVrhs2bN6Nz585ISUmRThtdvHgxJkyYgJs3b0KpVGLChAnYtGmTUQDs06cPMjIysGXLFlk1ciJ4ycF1moiKL76+S578vH9bdHiuqI0YMQKdOnVCeHi4UXtSUhIeP35s1F67dm1UqlQJiYmJAIDExETUr1/faJ2NiIgI6HQ6nDx5Uhrz7LYjIiKkbRAZ8IKeRMUXX9/0IhYvbvnXX39h1apVuHLlCh49emTU9+uvvxa4MINffvkFhw8fxqFDh0z6tFotlEqltKaGgZ+fn/RLrtVqTRYmM3z9ojE6nQ4PHz6Eq6uryWNnZ2cjOztb+lqn0+X/yZFDedEFPSdMmMBLqRA5KL6+SQ6L9jT98ssvaN68OU6fPo21a9fi8ePHOHnyJHbs2AG1Wm214q5evYoxY8Zg+fLlcHFxsdp2rSEmJgZqtVq6BQYG2rokKmS8oCdR8cXXN8lhUWiaMWMG5s6diw0bNkCpVOKrr77CmTNn8MYbb6BSpUpWKy4pKQlpaWl46aWXUKpUKZQqVQq7d+/G119/jVKlSsHPzw+PHj1CRkaG0f1SU1Oh0WgAPFkW/9mz6Qxfv2iMp6en2b1MADBp0iRkZmZKt6tXr1rjKZMdM1zQ08nJ+GXj5OSEkJAQXtCTyIHx9U1yWBSaLly4gE6dOgEAlEol7t+/D4VCgXHjxuG7776zWnHt2rXD8ePHkZycLN0aN26M/v37S/8vXbo0tm/fLt3n7NmzuHLlCsLCwgAAYWFhOH78ONLS0qQxCQkJ8PT0RN26daUxT2/DMMawDXNUKhU8PT2NblS8KRQKDB482GQXvUKhQGRkJHfdEzkwvr5JDotCk7e3N+7evQsAqFChgnTWWUZGBh48eGC14jw8PBAcHGx0K1OmDMqVK4fg4GCo1WpERUUhOjoaO3fuRFJSEiIjIxEWFoZmzZoBANq3b4+6detiwIABOHr0KLZu3YoPP/wQI0aMkE4jHTZsGC5evIgPPvgAZ86cwcKFC7Fq1SqMGzfOas+FigfDBT2fxgt6EhUPhte3ISDxgr30LItCU+vWrZGQkAAA6N27N8aMGYO3334bffv2Rbt27axa4IvMnTsXnTt3Rq9evdC6dWtoNBqjiejOzs7YuHEjnJ2dERYWhrfeegsDBw7E9OnTpTFBQUHYtGkTEhIS0KBBA8yePRtLlizhGk1kVocOHYz+qPL3hKj46NatG9zc3AAAZcqU4QV7yYhF6zSlp6cjKysLAQEB0Ov1mDVrFvbt24caNWrgww8/hLe3d2HUate4TlPJsWrVKqxbt076ukePHlzHhaiYyM7OxqhRo3Dv3j24u7tj/vz5XNyymCv0C/aWLVtW+r+TkxMmTpxoyWaIHI5hHZenxcfHo1WrVtKJBUTkuNavX4/79+8DAO7fv4/4+Hh+KCKJRYfnnJ2djSZWG9y+fRvOzs4FLorIHr1oHRcHWFyfiJ6Di1vSi1gUmvJ6c8jOzoZSqSxQQUT2iuu4EBVf/FBEcuTr8NzXX38N4Mnk1yVLlsDd3V3qy83NxZ49e1C7dm3rVkhkJwICAlCrVi2cPXvWpK927dpcx4XIgRk+FD3r6Q9FFSpUsEFlZE/yFZrmzp0L4EnyXrx4sdGhOKVSiSpVqmDx4sXWrZDIAfBTKJFjMyxueeLECaO9yU5OTggODuaHIgKQz9B06dIlAMArr7yCX3/9tUSeJUclV0pKitm9TMCTRVX5SZTIcRkWt3z//fdN+ri4JRlYNKdp586dRoEpNzcXycnJuHPnjtUKI7I3hk+i5lYM5mUWiByfRqNB9erVjdpq1KjBxS1JYlFoGjt2LH744QcATwJT69at8dJLLyEwMBC7du2yZn1EdsPwSdTctan4SZTI8Wm1Wly4cMGo7fz58zx7jiQWhabVq1ejQYMGAIANGzbgn3/+wZkzZzBu3DhMnjzZqgUS2RNeZoGoeOLZcySHRaHp9u3b0kJ+v/32G3r37o2aNWtiyJAhOH78uFULJLI33bp1g5eXF4An12HkZRaIHB+XFCE5LApNfn5+OHXqFHJzc7Flyxb861//AgA8ePCAi1tSsadSqTBkyBD4+PggMjKSl1ggKgY4Z5HksOgyKpGRkXjjjTfg7+8PhUKB8PBwAMCBAwe4ThOVCKGhoQgNDbV1GURkJQqFAp06dTJZq0kIgc6dO3POIgGwcE/T1KlTsWTJErzzzjvYu3ev9Enb2dmZ16GjEiEpKQmjR49GUlKSrUshIisQQuDXX38127dmzRrOaSIAFu5pAoDXX3/dpG3QoEEFKobIEWRnZyM2NhZ37txBbGwsgoODeYiOyMFdv379ueuwXb9+HRUrViziqsjeyA5NX3/9Nd555x24uLhIl1PJy+jRowtcGJG9Wr9+PTIyMgAAGRkZvAo6EVEJITs0zZ07F/3794eLi4t0ORVzFAoFQxMVW3ldBb1Vq1bSGaVE5HgCAgLg4uKCrKwskz5XV1dOBCcA+QhNhkuoPPt/opLiReu4TJgwgZNFiRxUSkqK2cAEAA8fPkRKSgoPz5H80BQdHS1rnEKhwOzZsy0uiMhe8SroREQlm+zQdOTIEaOvDx8+jJycHNSqVQsA8Pfff8PZ2ZmnYVOxxaugExVfFSpUQFBQkNkjKUFBQfxARADyEZp27twp/X/OnDnw8PBAXFycdOHeO3fuIDIyEq1atbJ+lUR2wHDtufHjx5u089pzRI5PqVTmq51KHovWaZo9ezZiYmKkwAQ8uZzEp59+ykNzVKxpNBq89tprRm0dO3bkteeIHFxKSspzlxzgZVQIsDA06XQ63Lx506T95s2buHv3boGLIrJn5i6zQESOzXD43cnJ+G3RycmJl1EhiUWhqUePHoiMjMSvv/6Ka9eu4dq1a1izZg2ioqLQs2dPa9dIZDe0Wi02bdpk1LZp0yZotVobVURE1mA4/G7u7FgeficDi0LT4sWL8dprr6Ffv36oXLkyKleujH79+qFDhw5YuHChtWsksguGpQWevQp6bm6u2aUIiKh44GubDCwKTW5ubli4cCFu376NI0eO4MiRI0hPT8fChQtRpkwZa9dIZBfyWnIAgLTkABE5phetw8bgREABrj0HAGXKlEFISIi1aiGyay+a7M3J4ESOS86HIi47QBbtaSIqiZ5edsOSfiKyXwEBAdK6g8+qVasWJ4ITAIYmItleffVVODs7m+1zdnbGq6++WsQVERFRUWJoIpLJ2dkZQ4cONdv37rvv5hmoiMj+cZ0mkoOhiSgf8tp9X7169SKuhIisies0kRwMTUQyGc6iMfdHlWfXEDk2rtNEcjA0EclkOLvm2XWa9Ho9lxwgKibMhSZ+ICIDhiYimbj7nqj4EkLg22+/Ndv37bffMjgRAIYmItkMu+/NtXP3PZFju379+nMngl+/fr2IKyJ7xNBElA8ajQZeXl5GbV5eXlzYkoioBGBoIsqH48ePIz093ajt9u3bOH78uI0qIiJrqFChAoKCgsz2Va1alauBEwCGJiLZ9Ho95s+fb7Zv/vz5JhPEicixKJVKs+2lS5cu4krIXjE0EcmUnJyMe/fume27d+8ekpOTi7YgIrIaLm5JcjA0EcnUoEEDkzPnDJycnNCgQYMiroiIrMXf3x9ubm5m+9zc3ODv71/EFZE9YmgikunGjRt5HoLT6/W4ceNGEVdERNaSkpKCBw8emO178OAB9zQRAIYmItletE4L13EhIireGJqIZHrROkxcp4nIcfn7+z/38DsPzxHA0EQkW0BAwHPnPHBFcCLHdfTo0ecefj969GgRV0T2iKGJSKYbN248d84D5zQROa6GDRs+90NRw4YNi7YgsksMTUQy+fv7w93d3Wyfu7s7d98TOTCFQgFPT0+zfZ6enjz8TgAYmohku3HjxnPXaeKeJiLHde3aNWi1WrN9Wq0W165dK+KKyB4xNBHJpNFonjtRVKPRFHFFRGQtqampBeqnkoGhiUim5OTk504U5YrgRETFG0MTkUzlypUrUD8R2a9GjRrlOW9JoVCgUaNGRVwR2SOGJiKZ8rouldx+IrJfN27cyHOBWiEE5ywSAIYmItlq165doH4isl95HXqX208lA0MTkUxcEZyo+Lp582aB+qlksPvQFBMTgyZNmsDDwwO+vr7o3r27yWGQrKwsjBgxAuXKlYO7uzt69eplcqbDlStX0KlTJ7i5ucHX1xfjx49HTk6O0Zhdu3bhpZdegkqlQvXq1bF06dLCfnrkQF60e56774kcV8OGDZ97diwXtyTAAULT7t27MWLECOzfvx8JCQl4/Pgx2rdvj/v370tjxo0bhw0bNmD16tXYvXs3UlJS0LNnT6k/NzcXnTp1wqNHj7Bv3z7ExcVh6dKlmDJlijTm0qVL6NSpE1555RUkJydj7NixGDp0KLZu3Vqkz5fs14suo8DLLBA5Lq1W+9yzY/Naw4lKFoVwsEuz37x5E76+vti9ezdat26NzMxMlC9fHitWrMDrr78OADhz5gzq1KmDxMRENGvWDJs3b0bnzp2RkpICPz8/AMDixYsxYcIE3Lx5E0qlEhMmTMCmTZtw4sQJ6bH69OmDjIwMbNmy5YV16XQ6qNVqZGZm5rmqLDm2gwcPYt68eXn2jx07Fi+//HLRFUREVqPX6zF06FBkZWWZ9Lm6uuL777/Pc08UObb8vH873G9AZmYmAKBs2bIAgKSkJDx+/Bjh4eHSmNq1a6NSpUpITEwEACQmJqJ+/fpSYAKAiIgI6HQ6nDx5Uhrz9DYMYwzbIGrcuPFzT0lu3LhxEVdERNZy/fp1s4EJAB4+fIjr168XcUVkjxwqNOn1eowdOxYtWrRAcHAwgCe7VJVKJby8vIzG+vn5SbtTtVqtUWAy9Bv6njdGp9Ph4cOHJrVkZ2dDp9MZ3ah40+v1zz0lmWfXEDmutLS0AvVTyeBQoWnEiBE4ceIEfvnlF1uXgpiYGKjVaukWGBho65KokK1Zs6ZA/URkvxo0aFCgfioZHCY0jRw5Ehs3bsTOnTtRsWJFqV2j0eDRo0fIyMgwGp+amipdC0yj0ZicTWf4+kVjPD094erqalLPpEmTkJmZKd2uXr1a4OdI9q1KlSoF6ici+/WiyyDxMkkEOEBoEkJg5MiRWLt2LXbs2IGgoCCj/tDQUJQuXRrbt2+X2s6ePYsrV64gLCwMABAWFobjx48b7V5NSEiAp6cn6tatK415ehuGMYZtPEulUsHT09PoRkRERMWX3YemESNGYNmyZVixYgU8PDyg1Wqh1WqleUZqtRpRUVGIjo7Gzp07kZSUhMjISISFhaFZs2YAgPbt26Nu3boYMGAAjh49iq1bt+LDDz/EiBEjoFKpAADDhg3DxYsX8cEHH+DMmTNYuHAhVq1ahXHjxtnsuZN9uXTpUoH6ich+vWgdJq7TRIADhKZFixYhMzMTbdu2hb+/v3RbuXKlNGbu3Lno3LkzevXqhdatW0Oj0eDXX3+V+p2dnbFx40Y4OzsjLCwMb731FgYOHIjp06dLY4KCgrBp0yYkJCSgQYMGmD17NpYsWYKIiIgifb5kvwwh3NJ+IrJfPDxHcpSydQEvImcZKRcXFyxYsAALFizIc0zlypXx22+/PXc7bdu2xZEjR/JdI5UMvIwKUfHFa8+RHHa/p4nIXhjW9LK0n4js14sWruTClgQwNBHJdvny5QL1E5H98vX1LVA/lQwMTUQypaenF6ifiOwXD8+RHAxNRDK1bt26QP1EZL9edMksXlKLAIYmItlSUlIK1E9E9utFa+1xLT4CGJqIZOvatWuB+onIfr3ogry8YC8BDE1EssXFxRWon4js14ABAwrUTyUDQxORTM9emzC//URkvzZs2FCgfioZGJqIZMrKyipQPxHZr9DQ0AL1U8nA0EQkk+E6hZb2E5H92r9/f4H6qWRgaCKS6dGjRwXqJyL7dfHixQL1U8nA0EQkU926dQvUT0T2Kycnp0D9VDIwNBHJVLVq1QL1E5H9evDgQYH6qWRgaCKSae/evQXqJyL7FRYWVqB+KhkYmohkysjIKFA/Edmv48ePF6ifSgaGJiKZHj58WKB+IrJfd+/eLVA/lQwMTUQyOTs7F6ifiOxXpUqVCtRPJQNDExERlXinT58uUD+VDAxNRDJxRXCi4svd3b1A/VQyMDQRyXT79u0C9ROR/dLpdAXqp5KBoYlIprJlyxaon4jsV25uboH6qWRgaCKSiYfniIovhUJRoH4qGRiaiGTiisFExdfjx48L1E8lA0MTERGVeK6urgXqp5KBoYmIiEo87kkmORiaiIioxONEcJKDoYmIiEo8vV5foH4qGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhlK2boAIiIqHoQQyM7OtnUZhSYrK8vWJVhEpVJBoVDYuoxigaGJiIisIjs7G0OGDLF1GYXGUZ9bbGwsXFxcbF1GscDDc0REREQycE8TERFZhUqlQmxsrK3LsIicvUiO+txUKpWtSyg2GJqIiMgqFAqFwx4GWrFiBfr16/fcfiKGJiIiGyvuE6gdRWxsrNk9TrGxsQ47Cby4sJfJ7AxNz1iwYAG++OILaLVaNGjQAPPnz8fLL79s67KKjeL+5uCof1jt5Q9SSVXcJ1A7Ov5sbM9eJrMzND1l5cqViI6OxuLFi9G0aVPMmzcPEREROHv2LHx9fW1aW3EJG9nZ2Rg+fLityyg0jvrHddGiRQ4/78GRg58QwtYlENk1e3mNKIS9VGIHmjZtiiZNmuCbb74BAOj1egQGBmLUqFGYOHHic++r0+mgVquRmZkJT09Pq9f28OFDREVFWX27RMXFDz/8AFdXV1uXYZHMzMxi/WGCqKAWLVoEtVpdKNvOz/s3lxz4n0ePHiEpKQnh4eFSm5OTE8LDw5GYmGgyPjs7GzqdzuhW2PURUd74GiGiwsbQ9D+3bt1Cbm4u/Pz8jNr9/Pyg1WpNxsfExECtVku3wMDAoiqViIoZpVJp6xKI7Jq9vEY4p8lCkyZNQnR0tPS1Tqcr1ODk4eGBRYsWFdr2i4oQwuH3CIwdO9akbd68eUVehzUplUqHnQ9k4OHhYesSLObi4uKwawA9rTi8vouj4vD6tpc5lwxN/+Pj4wNnZ2ekpqYataempkKj0ZiMV6lURfpDdHJyKrTjuVRwtj5RgBybI69v9CxHnVdGJAcPz/2PUqlEaGgotm/fLrXp9Xps374dYWFhNqyM7M2zi9xx0TsiopKBe5qeEh0djUGDBqFx48Z4+eWXMW/ePNy/fx+RkZG2Lo3sDIMSEVHJw9D0lDfffBM3b97ElClToNVq0bBhQ2zZssVkcjgRERGVPFynyUoKe50mIiIisj6u00RERERkZQxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQy8jIqVGBZW1+l0Nq6EiIiI5DK8b8u5QApDk5XcvXsXABAYGGjjSoiIiCi/7t69C7Va/dwxvPaclej1eqSkpMDDwwMKhcLW5VAh0+l0CAwMxNWrV3mtQaJihq/vkkUIgbt37yIgIABOTs+ftcQ9TVbi5OSEihUr2roMKmKenp78o0pUTPH1XXK8aA+TASeCExEREcnA0EREREQkA0MTkQVUKhU+/vhjqFQqW5dCRFbG1zflhRPBiYiIiGTgniYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJqI8LFiwAFWqVIGLiwuaNm2KgwcPPnf86tWrUbt2bbi4uKB+/fr47bffiqhSIsqPPXv2oEuXLggICIBCocC6deteeJ9du3bhpZdegkqlQvXq1bF06dJCr5PsD0MTkRkrV65EdHQ0Pv74Yxw+fBgNGjRAREQE0tLSzI7ft28f+vbti6ioKBw5cgTdu3dH9+7dceLEiSKunIhe5P79+2jQoAEWLFgga/ylS5fQqVMnvPLKK0hOTsbYsWMxdOhQbN26tZArJXvDJQeIzGjatCmaNGmCb775BsCTawsGBgZi1KhRmDhxosn4N998E/fv38fGjRultmbNmqFhw4ZYvHhxkdVNRPmjUCiwdu1adO/ePc8xEyZMwKZNm4w+BPXp0wcZGRnYsmVLEVRJ9oJ7moie8ejRIyQlJSE8PFxqc3JyQnh4OBITE83eJzEx0Wg8AEREROQ5nogcB1/fZMDQRPSMW7duITc3F35+fkbtfn5+0Gq1Zu+j1WrzNZ6IHEder2+dToeHDx/aqCqyBYYmIiIiIhkYmoie4ePjA2dnZ6Smphq1p6amQqPRmL2PRqPJ13gichx5vb49PT3h6upqo6rIFhiaiJ6hVCoRGhqK7du3S216vR7bt29HWFiY2fuEhYUZjQeAhISEPMcTkePg65sMGJqIzIiOjsb333+PuLg4nD59GsOHD8f9+/cRGRkJABg4cCAmTZokjR8zZgy2bNmC2bNn48yZM5g6dSr++usvjBw50lZPgYjycO/ePSQnJyM5ORnAkyUFkpOTceXKFQDApEmTMHDgQGn8sGHDcPHiRXzwwQc4c+YMFi5ciFWrVmHcuHG2KJ9sSRCRWfPnzxeVKlUSSqVSvPzyy2L//v1SX5s2bcSgQYOMxq9atUrUrFlTKJVKUa9ePbFp06YirpiI5Ni5c6cAYHIzvKYHDRok2rRpY3Kfhg0bCqVSKapWrSp+/PHHIq+bbI/rNBERERHJwMNzRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERV7Qgi88847KFu2LBQKhbQSdFEZPHgwunfvXqSPSUTWV8rWBRARFbYtW7Zg6dKl2LVrF6pWrQofHx9bl0REDoihiYiKvQsXLsDf3x/Nmzc32//o0SMolcoiroqIHA0PzxFRsTZ48GCMGjUKV65cgUKhQJUqVdC2bVuMHDkSY8eOhY+PDyIiIgAAc+bMQf369VGmTBkEBgbi3//+N+7duydta+rUqWjYsKHR9ufNm4cqVapIX+fm5iI6OhpeXl4oV64cPvjgA/BqVUTFA0MTERVrX331FaZPn46KFSvixo0bOHToEAAgLi4OSqUSe/fuxeLFiwEATk5O+Prrr3Hy5EnExcVhx44d+OCDD/L1eLNnz8bSpUsRGxuLP//8E+np6Vi7dq3VnxcRFT0eniOiYk2tVsPDwwPOzs7QaDRSe40aNTBr1iyjsWPHjpX+X6VKFXz66acYNmwYFi5cKPvx5s2bh0mTJqFnz54AgMWLF2Pr1q0FexJEZBcYmoioRAoNDTVp+/333xETE4MzZ85Ap9MhJycHWVlZePDgAdzc3F64zczMTNy4cQNNmzaV2kqVKoXGjRvzEB1RMcDDc0RUIpUpU8bo63/++QedO3dGSEgI1qxZg6SkJCxYsADAk4niwJPDd8+Gn8ePHxdNwURkcwxNREQAkpKSoNfrMXv2bDRr1gw1a9ZESkqK0Zjy5ctDq9UaBaen13xSq9Xw9/fHgQMHpLacnBwkJSUVev1EVPgYmoiIAFSvXh2PHz/G/PnzcfHiRfz000/SBHGDtm3b4ubNm5g1axYuXLiABQsWYPPmzUZjxowZg5kzZ2LdunU4c+YM/v3vfyMjI6MInwkRFRaGJiIiAA0aNMCcOXPw+eefIzg4GMuXL0dMTIzRmDp16mDhwoVYsGABGjRogIMHD+L99983GvPee+9hwIABGDRoEMLCwuDh4YEePXoU5VMhokKiEJydSERERPRC3NNEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJ8P8AAnfgpbjlCaIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'fraud', y = 'distance_from_home', \n",
    "                data = card, palette = 'Set2')\n",
    "plt.title(\"fraudulent Transactions by Distance from Home\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a5c1223-bd0c-4da1-970a-564ba749db8c",
   "metadata": {},
   "source": [
    "## Preprocessing for Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c689c5-e59b-4cbf-8ceb-0e9a3769f51f",
   "metadata": {},
   "source": [
    "We have to be careful the way we split the data since we observed there were a huge difference between legitimate purchases and fraudulent purchases, there were way more legitimate purchases in our dataset. We want to split the data so that when we train the model so that it has a good amount of samples to learn normal purchases but also enough samples to test our model to check whether a observation was fraudulent. To solve this issue we will use a specific strategy called stratified sampling where instead of pulling a certain subset of the data we will pull a shuffled sampling from the data. Note: This does not solve the problem of class imbalance in our dataset, however, it provides a better method of splitting the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca47bf85-60c9-4547-81d9-1c10a0a7d269",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# metrics we will use to find the best model\n",
    "from sklearn.metrics import f1_score,accuracy_score,recall_score,precision_score,confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf1f1cf6-d200-4a4c-901c-7cf2ef8c7f6b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# convert to integer data type since they are categorical variables\n",
    "card[['repeat_retailer','used_chip','used_pin_number','online_order','fraud']] = card[['repeat_retailer','used_chip','used_pin_number','online_order','fraud']].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fab124c-a9d6-4a6e-8ebc-e952d8f81be3",
   "metadata": {},
   "source": [
    "### Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dbd12dc4-83b1-4a6a-a29f-baec8cabff22",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# split the data\n",
    "\n",
    "# our predictor variables\n",
    "X = card.drop(['fraud'], axis = 1)\n",
    "\n",
    "# our target variable\n",
    "y = card['fraud']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f70b86a5-36b7-42fb-98e1-52ab42ebd434",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "807042d1-6a6c-4c8c-9176-bb8a009677bc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# splitting our data into training and testing sets\n",
    "# we startify our target variable \n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.30, random_state = 42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4224ad0d-4136-420f-87b2-b5ea64253a14",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((700000, 7), (700000,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training sets shape\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc30dbf3-4d51-4eb8-90bf-818b2ff67eb9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300000, 7), (300000,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing sets shape\n",
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af436557-9dd2-4575-b4cc-6a664139ec69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>661471</th>\n",
       "      <td>4.805367</td>\n",
       "      <td>1.379477</td>\n",
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       "      <td>27.052054</td>\n",
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       "      <td>7.360691</td>\n",
       "      <td>0.164692</td>\n",
       "      <td>0.983346</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>854266</th>\n",
       "      <td>535.761269</td>\n",
       "      <td>15.150715</td>\n",
       "      <td>2.220614</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>3.089058</td>\n",
       "      <td>0.065205</td>\n",
       "      <td>1.039158</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>806884</th>\n",
       "      <td>6.176133</td>\n",
       "      <td>9.156109</td>\n",
       "      <td>0.414412</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>62473</th>\n",
       "      <td>18.062687</td>\n",
       "      <td>1.129769</td>\n",
       "      <td>1.039432</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661343</th>\n",
       "      <td>17.333841</td>\n",
       "      <td>0.074861</td>\n",
       "      <td>6.977444</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467315</th>\n",
       "      <td>4.529391</td>\n",
       "      <td>45.922502</td>\n",
       "      <td>1.831249</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>911151</th>\n",
       "      <td>19.110160</td>\n",
       "      <td>0.439835</td>\n",
       "      <td>0.166790</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>700000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "661471            4.805367                        1.379477   \n",
       "75275            27.052054                        1.766070   \n",
       "386916            7.360691                        0.164692   \n",
       "854266          535.761269                       15.150715   \n",
       "2376              3.089058                        0.065205   \n",
       "...                    ...                             ...   \n",
       "806884            6.176133                        9.156109   \n",
       "62473            18.062687                        1.129769   \n",
       "661343           17.333841                        0.074861   \n",
       "467315            4.529391                       45.922502   \n",
       "911151           19.110160                        0.439835   \n",
       "\n",
       "        ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "661471                        1.236960                1          0   \n",
       "75275                         0.415689                1          0   \n",
       "386916                        0.983346                1          0   \n",
       "854266                        2.220614                1          1   \n",
       "2376                          1.039158                1          0   \n",
       "...                                ...              ...        ...   \n",
       "806884                        0.414412                1          0   \n",
       "62473                         1.039432                1          1   \n",
       "661343                        6.977444                1          1   \n",
       "467315                        1.831249                1          1   \n",
       "911151                        0.166790                1          0   \n",
       "\n",
       "        used_pin_number  online_order  \n",
       "661471                0             0  \n",
       "75275                 0             0  \n",
       "386916                0             1  \n",
       "854266                1             1  \n",
       "2376                  1             1  \n",
       "...                 ...           ...  \n",
       "806884                0             0  \n",
       "62473                 0             0  \n",
       "661343                0             1  \n",
       "467315                0             1  \n",
       "911151                1             0  \n",
       "\n",
       "[700000 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85e10ad8-5e66-42b0-81a2-6fbf92f5c253",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fraud\n",
       "0    638818\n",
       "1     61182\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "46f3ad9a-fe9a-48c2-8c74-b2ec5ef90185",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fraud\n",
       "0    273779\n",
       "1     26221\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93608af5",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85eb20c-aacb-40a0-915f-8191890c5045",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aaaf325d-de3c-409d-98d0-4a696b369ba7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import our random forest classifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f3aa4c4-f68d-4740-b1b5-35573a61f752",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier scores:\n",
      "Mean accuracy score of 5-fold CV on Training set: 0.9999857142857141 (std: 7.824607964335488e-06)\n",
      "Mean accuracy score of 5-fold CV on Testing set: 0.9999733333333334 (std: 2.260776661041507e-05)\n"
     ]
    }
   ],
   "source": [
    "# instantiate our model\n",
    "rfc = RandomForestClassifier(random_state = 0)\n",
    "# train our model\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "# stratified shuffle \n",
    "skf = StratifiedKFold(shuffle = True, n_splits = 5, random_state = 5)\n",
    "\n",
    "# cross Validation scores\n",
    "tr_sc = cross_val_score(rfc, X_train, y_train, cv = skf)\n",
    "te_sc = cross_val_score(rfc, X_test, y_test, cv = skf)\n",
    "\n",
    "print('Random Forest Classifier scores:')\n",
    "print(f'Mean accuracy score of 5-fold CV on Training set: {tr_sc.mean()} (std: {tr_sc.std()})')\n",
    "print(f'Mean accuracy score of 5-fold CV on Testing set: {te_sc.mean()} (std: {te_sc.std()})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24b23d73",
   "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>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ratio_to_median_purchase_price</td>\n",
       "      <td>0.510585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>online_order</td>\n",
       "      <td>0.175434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>distance_from_home</td>\n",
       "      <td>0.137584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>used_pin_number</td>\n",
       "      <td>0.064779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>used_chip</td>\n",
       "      <td>0.059978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>distance_from_last_transaction</td>\n",
       "      <td>0.044310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>repeat_retailer</td>\n",
       "      <td>0.007331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          columns  importance\n",
       "2  ratio_to_median_purchase_price    0.510585\n",
       "6                    online_order    0.175434\n",
       "0              distance_from_home    0.137584\n",
       "5                 used_pin_number    0.064779\n",
       "4                       used_chip    0.059978\n",
       "1  distance_from_last_transaction    0.044310\n",
       "3                 repeat_retailer    0.007331"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a feature importance data frame\n",
    "dict1 = {'columns': rfc.feature_names_in_, 'importance': rfc.feature_importances_}\n",
    "forest_importances = pd.DataFrame(dict1).sort_values('importance', ascending = False)\n",
    "forest_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc41a39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a horizontal bar chart on feature importance \n",
    "sns.barplot(data = forest_importances, y = 'columns', x = 'importance')\n",
    "plt.title(\"Random Forest Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a15486f2-8d9d-4da1-a9fd-5fdff334282d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39722ea4-8ab1-44da-bd19-c1b284090ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import our xgboost classifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1fe7c12f-e8fd-4827-8a1a-e653a4ff05c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Classifier scores:\n",
      "Mean accuracy score of 5-fold CV on Training set: 0.9999757142857142 (std: 1.069044967647206e-05)\n",
      "Mean accuracy score of 5-fold CV on Testing set: 0.9999566666666666 (std: 3.091206165164894e-05)\n"
     ]
    }
   ],
   "source": [
    "# instantiate our model\n",
    "boost = XGBClassifier(random_state = 5)\n",
    "# train our boosting model\n",
    "boost.fit(X_train,y_train)\n",
    "\n",
    "# our stratified shuffle\n",
    "skf = StratifiedKFold(shuffle = True, n_splits = 5, random_state = 5)\n",
    "# 5-fold cross validation\n",
    "tr_sc = cross_val_score(boost, X_train, y_train, cv = skf)\n",
    "te_sc = cross_val_score(boost, X_test, y_test, cv = skf)\n",
    "\n",
    "# accuracy mean score\n",
    "print('XGBoost Classifier scores:')\n",
    "print(f'Mean accuracy score of 5-fold CV on Training set: {tr_sc.mean()} (std: {tr_sc.std()})')\n",
    "print(f'Mean accuracy score of 5-fold CV on Testing set: {te_sc.mean()} (std: {te_sc.std()})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7e68f60",
   "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>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ratio_to_median_purchase_price</td>\n",
       "      <td>0.536974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>online_order</td>\n",
       "      <td>0.260885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>used_pin_number</td>\n",
       "      <td>0.069966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>used_chip</td>\n",
       "      <td>0.055687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>distance_from_home</td>\n",
       "      <td>0.054601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>distance_from_last_transaction</td>\n",
       "      <td>0.021888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>repeat_retailer</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          columns  importance\n",
       "2  ratio_to_median_purchase_price    0.536974\n",
       "6                    online_order    0.260885\n",
       "5                 used_pin_number    0.069966\n",
       "4                       used_chip    0.055687\n",
       "0              distance_from_home    0.054601\n",
       "1  distance_from_last_transaction    0.021888\n",
       "3                 repeat_retailer    0.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a feature importance data frame\n",
    "dict1 = {'columns': boost.feature_names_in_, 'importance': boost.feature_importances_}\n",
    "boost_importances = pd.DataFrame(dict1).sort_values('importance', ascending = False)\n",
    "boost_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c00bd75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a horizontal bar chart on feature importance \n",
    "sns.barplot(data = boost_importances, y = 'columns', x = 'importance')\n",
    "plt.title(\"XGBClassifier Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d777a10f-cbf9-4104-aff7-a35488aa0fd9",
   "metadata": {},
   "source": [
    "## Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9e2bc239-9c24-4050-80dd-3e345a990628",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import our logistic regression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c6884c81-799d-4d7e-bc58-8f060a03d508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression with L1 penalty, scores:\n",
      "Mean accuracy score of 5-fold CV on Training set: 0.9584185714285715 (std: 0.00024212494666165768)\n",
      "Mean accuracy score of 5-fold CV on Testing set: 0.9595733333333334 (std: 0.00043736585244952714)\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression with Lasso penalty (L1)\n",
    "log_l1 = LogisticRegression(solver = 'liblinear',penalty = 'l1')\n",
    "log_l1.fit(X_train,y_train)\n",
    "\n",
    "# stratified shuffle \n",
    "skf = StratifiedKFold(shuffle = True, n_splits = 5, random_state = 5)\n",
    "tr_sc = cross_val_score(log_l1, X_train, y_train, cv = skf)\n",
    "te_sc = cross_val_score(log_l1, X_test, y_test, cv = skf)\n",
    "\n",
    "# accuracy mean scores\n",
    "print('Logistic Regression with L1 penalty, scores:')\n",
    "print(f'Mean accuracy score of 5-fold CV on Training set: {tr_sc.mean()} (std: {tr_sc.std()})')\n",
    "print(f'Mean accuracy score of 5-fold CV on Testing set: {te_sc.mean()} (std: {te_sc.std()})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "99c1763d-aac2-4dc1-ab9a-ea37c6c0dcd6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression with L2 penalty, scores:\n",
      "Mean accuracy score of 5-fold CV on Training set: 0.9582785714285714 (std: 0.00020503857277725471)\n",
      "Mean accuracy score of 5-fold CV on Testing set: 0.95922 (std: 0.00044825587930704194)\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression With Ridge penalty (L2)\n",
    "log_l2 = LogisticRegression(solver = 'liblinear',penalty = 'l2')\n",
    "log_l2.fit(X_train,y_train)\n",
    "\n",
    "skf = StratifiedKFold(shuffle = True, n_splits = 5, random_state = 5)\n",
    "tr_sc = cross_val_score(log_l2, X_train, y_train, cv = skf)\n",
    "te_sc = cross_val_score(log_l2, X_test, y_test, cv = skf)\n",
    "\n",
    "\n",
    "print('Logistic Regression with L2 penalty, scores:')\n",
    "print(f'Mean accuracy score of 5-fold CV on Training set: {tr_sc.mean()} (std: {tr_sc.std()})')\n",
    "print(f'Mean accuracy score of 5-fold CV on Testing set: {te_sc.mean()} (std: {te_sc.std()})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2747815d-6dfb-498a-8037-a3519994bffb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic(L1) model on testing set is: 95.91866666666667% \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# L1 penalty performed slightly better will make a prediction with that model\n",
    "y_pred = log_l1.predict(X_test)\n",
    "\n",
    "# Confusion Matrix\n",
    "log_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(log_matrix, annot=True, fmt=\"d\")\n",
    "plt.title('Logistic Regression L1 penalty Confusion Matrix')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "\n",
    "print(f\"The accuracy of Logistic(L1) model on testing set is: {accuracy_score(y_test, y_pred)*100}% \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9279a4ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98    273779\n",
      "           1       0.90      0.60      0.72     26221\n",
      "\n",
      "    accuracy                           0.96    300000\n",
      "   macro avg       0.93      0.80      0.85    300000\n",
      "weighted avg       0.96      0.96      0.96    300000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# logistic regression with lasso penalty (L1) Classification Report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd804f2a-5f17-4554-af87-30aadfecab3b",
   "metadata": {},
   "source": [
    "Creating a confusion matrix on the testing set and using sklearn [confusion matrix documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) the upper left corner represents the true negatives. In our scenario the true negatives represents all non-fraudulent purchase predicted accurately by the Logistic Regression model. The lower right corner are the true positives or the fraudulent purchases predicted accurately by our model. However in the top right corner we have the false positives, which represents all the observations categorized as fraudulent purchases but actually weren't fraudulent purchases. The number of false positives in our model was about 1841 purchases that were considered fraudulent transactions but actually were not. Now this represents a problem since we are denying a good amount of purchases, since they were considered fraudulent but weren't, this mistake might cost a company a huge amount of money. So we want to reduce the amount of false positives and categorized them as verified purchases. Note we might classify actual fraudulent purchases as non-fraudulent purchases but this scenario would cost the company less expense rather than considering worst case scenario as a fraudulent purchase. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eaee5dea-7e36-4568-b837-b2cb766a7c39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.37994249e-01, 6.20057509e-02],\n",
       "       [9.99988736e-01, 1.12638207e-05],\n",
       "       [9.99740316e-01, 2.59683762e-04],\n",
       "       ...,\n",
       "       [9.99964233e-01, 3.57665568e-05],\n",
       "       [9.88930950e-01, 1.10690505e-02],\n",
       "       [1.95772219e-01, 8.04227781e-01]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_l1.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "51432ade-f2e4-4544-a32c-180f1f2f6eab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic(L1) model on testing set is: 94.71133333333334% \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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cHNLS0mBtbS0mQPlbRkaGOPkx/4/428+DhYVFgeGsfE5OTkqP839Zf/rppwXO9eeff4rnksvl+P7777Fv3z7Y2NjA09MToaGhSExMFOtq2bIlfH19MWvWLFhaWqJr165YvXp1gXkEbyrN6yBf9erVlR7nX2tRyVZ5qFmzptJjZ2dn6OjoiPOP4uLiIAgCatasWeB5v3btmvi857O3ty+QNFeqVKnANa9ZswYffvihOP/DysoKe/bsQVpaWpmvZcCAAUhISMCxY8cAAH/99ReSkpLQv3//Yo8zNTUFADx//lyl89y7d6/I10H+/jdp4nXwzTffIDU1FbVq1YKrqysmTJiAixcvFnvMvXv3oKOjU+B9aWtrC3Nz8xKvAyj8Z1uSvn37isPBO3bsQN++fYuMTUhIgL+/PywsLGBiYgIrKyu0bNkSAEr1Wnn790lxLCwssHjxYly8eBFmZmZYvHixysfSf8d7PadGE+rUqYMbN25g9+7d2L9/P7Zu3Yply5Zh+vTpmDVrlsbbY2lpCS8vLwCAt7c3XFxc0KlTJyxatEj8ZKdQKGBtbY0NGzYUWse/WZHz5ie0/HMBr+fV2NraFoh/c1XHmDFj0LlzZ+zYsQMHDhzAtGnTEBISgoMHD+Kjjz6CTCbDli1bcPLkSfzxxx84cOAABg0ahPnz5+PkyZMwMTEpc7vfVNT8DOGNSYjvmrcTEoVCAZlMhn379hV6PW8/V6pc8/r16+Hv7w8fHx9MmDAB1tbW0NXVRUhISIGJ6KXh7e0NGxsbrF+/Hp6enli/fj1sbW3F13FRXFxcAACXLl2Cj49Pmc9fFCleB3l5eUqPPT09cfv2bezcuRN//vknVq5ciQULFiAsLKzIe8PkU/WGfOq6ji5dukAul8PPzw9ZWVno2bNnoXF5eXn47LPP8PTpUwQHB8PFxQXGxsZ48OAB/P39C8yJKc7bv09KcuDAAQCvE8/79+9LuiqNysd7m9RYWVnByMgIN27cKLDv+vXr0NHRQbVq1QAADg4OSqsa8hVWVpj8Gfa9evVCdnY2unfvjjlz5mDy5MkwMDAo1d1AnZ2dcfnyZZXjS9KxY0e0bNkSc+fOxZdffgljY2M4Ozvjr7/+wieffFLsL438ruVbt24pfWJ68uSJyp/ynJ2dAbyezFfSH6n8+HHjxmHcuHGIi4tDw4YNMX/+fKxfv16MadasGZo1a4Y5c+Zg48aN6NevH3777bdC/wiU5nXwXxIXF6f0M7l16xYUCoU46drZ2RmCIMDJyQm1atVSyzm3bNmCGjVqYNu2bUqv6fwVQMUp7j2gq6uLvn37Ijw8HN9//z127NhRYBizMM2bN0elSpWwadMmTJkypcR4BweHIl8H+fvVpVKlSgVuNJidnY1Hjx4ViLWwsMDAgQMxcOBAZGRkwNPTEzNnziwyqXFwcIBCoUBcXJzYywS8ngidmpqq1ut4k6GhIXx8fLB+/XrxRoeFuXTpEm7evIk1a9ZgwIABYvmbK7ryqfNOyfv378fKlSsxceJEbNiwAX5+fjh16lS5Locn9Xtvh590dXXRtm1b7Ny5U2lJcFJSEjZu3IjmzZuL3dfe3t6Ijo5GbGysGPf06dMiezLe9OTJE6XH+vr6qFu3LgRBEOd95M8LUOVuqr6+vrhw4UKBFQxA2T8hBgcH48mTJ/jll18AvF45k5eXh2+//bZAbG5urtjONm3aQE9PD8uXL1eKWbJkicrn9vb2hqmpKebOnVvoPJj8pdIvXrwosIrC2dkZFStWFIeXnj17VuA5yJ8HVdQQVGleB/8l+Uth8/30008AIK5U6t69O3R1dTFr1qwCz5kgCAVet6rITxrerO/UqVOIjo4u8diS3gP9+/fHs2fP8OWXXyIjI0Npfk9RjIyMEBwcjGvXriE4OLjQ98f69esRExMDAOjQoQNiYmKU2puZmYkVK1bA0dERdevWLfGcqnJ2dsbRo0eVylasWFGgp+btn4OJiQk++OCDYodUO3ToAOD16qs3/fjjjwBef5CRyvjx4zFjxgxMmzatyJjCXieCIGDRokUFYkvzu7E4qamp4gqyuXPnYuXKlTh37hzmzp37r+qld4/Wp6i//vor9u/fX6B89OjRmD17NiIjI9G8eXN89dVX0NPTw88//4ysrCyle3BMnDgR69evx2effYbAwEBxSXf16tXx9OnTYj9NtG3bFra2tvjkk09gY2ODa9euYcmSJejYsaM4gdHNzQ3A65uB9e7dGxUqVEDnzp0LnQQ5YcIEbNmyBZ9//jkGDRoENzc3PH36FLt27UJYWFiR9/QoTvv27VG/fn38+OOPCAgIQMuWLfHll18iJCQEsbGxaNu2LSpUqIC4uDhERERg0aJF6NGjB2xsbDB69GjMnz8fXbp0Qbt27XDhwgXs27cPlpaWKn3KMjU1xfLly9G/f380atQIvXv3hpWVFRISErBnzx588sknWLJkCW7evIk2bdqgZ8+eqFu3LvT09LB9+3YkJSWhd+/eAF7P51i2bBm6desGZ2dnPH/+HL/88gtMTU3FX/SFUfV1oC4//vgjjIyMlMp0dHQwZcoUAK+H4u7du4cXL14AAI4ePYrZs2cDeP3HXZVP2vHx8eLPJDo6GuvXr0ffvn3F14ezszNmz56NyZMn4+7du/Dx8UHFihURHx+P7du3Y9iwYRg/fnyprqtTp07Ytm0bunXrho4dOyI+Ph5hYWGoW7cuMjIyij22pPfARx99hPr16yMiIgJ16tRBo0aNVGrThAkTcOXKFcyfPx+HDh1Cjx49YGtri8TEROzYsQMxMTE4ceIEAGDSpEnYtGkT2rdvj1GjRsHCwgJr1qxBfHw8tm7dCh0d9X0GHDJkCIYPHw5fX1989tlnuHDhAg4cOFCgd6Nu3bpo1aoV3NzcYGFhgTNnzmDLli0YOXJkkXU3aNAAfn5+WLFiBVJTU9GyZUvExMRgzZo18PHxQevWrdV2HYWdu6TfQS4uLnB2dsb48ePx4MEDmJqaYuvWrYX27ua/LkaNGgVvb2/o6uqK7/fSGD16NJ48eYK//voLurq6aNeuHYYMGYLZs2eja9euZfq9Se8oja+30pD85bdFbf/8848gCIJw7tw5wdvbWzAxMRGMjIyE1q1bCydOnChQ3/nz54UWLVoIcrlcsLe3F0JCQoTFixcLAITExEQx7u0l3T///LPg6ekpVK5cWZDL5YKzs7MwYcIEIS0tTan+b7/9Vqhataqgo6OjtLT17SWegvB6OfnIkSOFqlWrCvr6+oK9vb3g5+cnpKSkFPucODg4FLk8NDw8vMAyyBUrVghubm6CoaGhULFiRcHV1VWYOHGi8PDhQzEmNzdXmDZtmmBraysYGhoKn376qXDt2jWhcuXKwvDhwwv8PIpabn3o0CHB29tbMDMzEwwMDARnZ2fB399fOHPmjCAIgpCSkiIEBAQILi4ugrGxsWBmZia4u7sLv//+u1jHuXPnhD59+gjVq1cX5HK5YG1tLXTq1EmsIx8KWU6ryuugqGvIX+J76NChQq8tX/7S1sI2XV1dMS5/CXVhm6rnuHr1qtCjRw+hYsWKQqVKlYSRI0cWeiv9rVu3Cs2bNxeMjY0FY2NjwcXFRQgICBBu3Lih1J7Cbkvg5+cnODg4iI8VCoUwd+5cwcHBQZDL5cJHH30k7N69u0CcIBT+MyjqPZAvNDRUACDMnTu32OegMFu2bBHatm0rWFhYCHp6ekKVKlWEXr16CYcPH1aKu337ttCjRw/B3NxcMDAwEJo2bSrs3r1bKSb/5x0REaFUXthS4qKWdOfl5QnBwcGCpaWlYGRkJHh7ewu3bt0q8H6fPXu20LRpU8Hc3FwwNDQUXFxchDlz5ih9dcrbS7oF4fVXJ8yaNUtwcnISKlSoIFSrVk2YPHmy8OrVK6W4on4nvP17rCh463YQhSnsObh69arg5eUlmJiYCJaWlsLQoUOFCxcuFHj+cnNzhcDAQMHKykqQyWTideY/14Xd4uHtn8POnTsFAML8+fOV4tLT0wUHBwehQYMGavsqGip/MkF4h2c3vuPGjBmDn3/+GRkZGSrd3Ot9kZqaikqVKmH27Nn4+uuvy7s575WZM2di1qxZSE5OLvWNGN91ixYtwtixY3H37t1CV+wQEb23c2pK683bdwOvx7rXrVuH5s2bv9cJzdvPC/C/sfy3vy6CqKwEQcCqVavQsmVLJjREVCStn1OjLh4eHmjVqhXq1KmDpKQkrFq1Cunp6cVOiHsfbN68GeHh4ejQoQNMTExw/PhxbNq0CW3btsUnn3xS3s2j/7jMzEzs2rULhw4dwqVLl7Bz587ybhIRvcOY1KioQ4cO2LJlC1asWAGZTIZGjRph1apV8PT0LO+mlasPP/wQenp6CA0NRXp6ujh5OH9iK9G/kZycjL59+8Lc3BxTpkxBly5dyrtJRPQO45waIiIi0gqcU0NERERagUkNERERaQUmNURERKQVtHKicE7KnfJuAtE7ydCuRXk3geidk5v9QPJzqOvvUgXLGmqpR1uxp4aIiIi0glb21BAREb1TFHklx9C/xqSGiIhIaoKivFvwXmBSQ0REJDUFkxpN4JwaIiIi0grsqSEiIpKYwOEnjWBSQ0REJDUOP2kEh5+IiIhIK7CnhoiISGocftIIJjVERERS431qNILDT0RERKQV2FNDREQkNQ4/aQSTGiIiIqlx9ZNGcPiJiIiItAJ7aoiIiCTGm+9pBpMaIiIiqXH4SSOY1BAREUmNPTUawTk1REREpBXYU0NERCQ13nxPI5jUEBERSY3DTxrB4SciIiLSCuypISIikhpXP2kEkxoiIiKpcfhJIzj8RERERFqBPTVERERS4/CTRjCpISIikpggcEm3JnD4iYiISAuFhISgSZMmqFixIqytreHj44MbN24oxbRq1QoymUxpGz58uFJMQkICOnbsCCMjI1hbW2PChAnIzc1Vijl8+DAaNWoEuVyODz74AOHh4QXas3TpUjg6OsLAwADu7u6IiYlR2v/q1SsEBASgcuXKMDExga+vL5KSkkp1zUxqiIiIpCYo1LOVwpEjRxAQEICTJ08iMjISOTk5aNu2LTIzM5Xihg4dikePHolbaGiouC8vLw8dO3ZEdnY2Tpw4gTVr1iA8PBzTp08XY+Lj49GxY0e0bt0asbGxGDNmDIYMGYIDBw6IMZs3b0ZQUBBmzJiBc+fOoUGDBvD29sbjx4/FmLFjx+KPP/5AREQEjhw5gocPH6J79+6lumaZIAhCqY74D8hJuVPeTSB6JxnatSjvJhC9c3KzH0h+jlfndqmlHoNGXcp8bHJyMqytrXHkyBF4enoCeN1T07BhQyxcuLDQY/bt24dOnTrh4cOHsLGxAQCEhYUhODgYycnJ0NfXR3BwMPbs2YPLly+Lx/Xu3RupqanYv38/AMDd3R1NmjTBkiVLAAAKhQLVqlVDYGAgJk2ahLS0NFhZWWHjxo3o0aMHAOD69euoU6cOoqOj0axZM5WukT01REREUlNTT01WVhbS09OVtqysLJWakJaWBgCwsLBQKt+wYQMsLS1Rv359TJ48GS9evBD3RUdHw9XVVUxoAMDb2xvp6em4cuWKGOPl5aVUp7e3N6KjowEA2dnZOHv2rFKMjo4OvLy8xJizZ88iJydHKcbFxQXVq1cXY1TBpIaIiOg/IiQkBGZmZkpbSEhIiccpFAqMGTMGn3zyCerXry+W9+3bF+vXr8ehQ4cwefJkrFu3Dl988YW4PzExUSmhASA+TkxMLDYmPT0dL1++REpKCvLy8gqNebMOfX19mJubFxmjCq5+IiIikpqavtBy8uTJCAoKUiqTy+UlHhcQEIDLly/j+PHjSuXDhg0T/+3q6ooqVaqgTZs2uH37NpydndXSZk1iUkNERCQ1Nd1RWC6Xq5TEvGnkyJHYvXs3jh49Cnt7+2Jj3d3dAQC3bt2Cs7MzbG1tC6xSyl+RZGtrK/7/7VVKSUlJMDU1haGhIXR1daGrq1tozJt1ZGdnIzU1Vam35s0YVXD4iYiISAsJgoCRI0di+/btOHjwIJycnEo8JjY2FgBQpUoVAICHhwcuXbqktEopMjISpqamqFu3rhgTFRWlVE9kZCQ8PDwAAPr6+nBzc1OKUSgUiIqKEmPc3NxQoUIFpZgbN24gISFBjFEFe2qIiIikVg53FA4ICMDGjRuxc+dOVKxYUZybYmZmBkNDQ9y+fRsbN25Ehw4dULlyZVy8eBFjx46Fp6cnPvzwQwBA27ZtUbduXfTv3x+hoaFITEzE1KlTERAQIPYYDR8+HEuWLMHEiRMxaNAgHDx4EL///jv27NkjtiUoKAh+fn5o3LgxmjZtioULFyIzMxMDBw4U2zR48GAEBQXBwsICpqamCAwMhIeHh8ornwAu6SZ6r3BJN1FBGlnSHb1JLfUYePRROVYmkxVavnr1avj7++Off/7BF198gcuXLyMzMxPVqlVDt27dMHXqVJiamorx9+7dw4gRI3D48GEYGxvDz88P3333HfT0/tcvcvjwYYwdOxZXr16Fvb09pk2bBn9/f6XzLlmyBPPmzUNiYiIaNmyIxYsXi8NdwOub740bNw6bNm1CVlYWvL29sWzZslINPzGpIXqPMKkhKkhbk5r3EYefiIiIpMYvtNQIJjVERERSY1KjEVz9RERERFqBPTVEREQSEwT13HyPisekhoiISGocftIIJjVERERSU9Mdhal4nFNDREREWoE9NURERFLj8JNGMKkhIiKSGoefNILDT0RERKQV2FNDREQkNQ4/aQSTGiIiIqlx+EkjOPxEREREWoE9NURERFLj8JNGMKkhIiKSGpMajeDwExEREWkF9tQQERFJjROFNYJJDRERkdQ4/KQRTGqIiIikxp4ajeCcGiIiItIK7KkhIiKSGoefNIJJDRERkdQ4/KQRHH4iIiIircCeGiIiIqlx+EkjmNQQERFJjUmNRnD4iYiIiLQCe2qIiIikJgjl3YL3ApMaIiIiqXH4SSM4/ERERERagT01REREUmNPjUYwqSEiIpIab76nEUxqiIiIpMaeGo3gnBoiIiLSCuypISIikhqXdGsEkxoiIiKpcfhJIzj8RERERFqBPTVERERSY0+NRjCpISIikhqXdGsEh5+IiIhIK7CnhoiISGKCgqufNIFJDRERkdQ4p0YjOPxEREREWoE9NURERFLjRGGNYFJDREQkNc6p0QgmNURERFLjnBqN4JwaIiIi0grsqSEiIpIae2o0gkkNERGR1Pgt3RrB4SciIiLSCuypeY/9snYz/jryN+Lv3YeBXB8NXeti7IhBcHKwBwA8eJQE7x7+hR47/9sp8P60Ba7H3cGq9b/j3MUrSE1Nh10VG/T06YD+PX3E2JhzFzEoMLhAHYd3bYBlZQsAwJnYS1i9cQuuXr+F5CdPsShkGtp4fqwU/+LFSyxYvhoHj51AatpzVLWzQb8eXdGrW0f1PCFEpdCiuTvGjRuBRh+5ws7OFt17DMKuXQcAAHp6evj2m4lo1+5T1HByQFpaOqIOHseUr+fi0aMkpXo6tG+DqV+PgatrHbx6lYWjx07Ct8dgcf+CH7/Bxx83Qf16tXHt+i00btJWo9dJasLhJ41gUvMeOxN7CX26d0b9OrWQm5eHRT+HY9jYr7Fzw88wMjSArbUlDu/aoHRMxM59WL1xK1o0awwAuHojDhaVzPHd9AmwtbZC7OVrmPX9Yujq6KBvjy5Kx+7e9AtMjI3ExxaVzMV/v3z5CrU/qIFuHdtizJTZhbY39KcVOHX2AkKmT0TVKjY4EXMWs+cvhbVlZbRu0UxNzwqRaoyNjXDx4lWsDv8NWyNWKe0zMjLERw1dMWfuIly8eBWVzM2w4MdZ2L5tNZp5dBDjunXrgJ+Xh2LqtO9x6PDf0NPTRb16LgXOFR7+G5o2bQRX1zqSXxdJhEu6NYJJzXvs5x+Vk4c5XwfBs1MfXL0Rh8YNXaGrqyv2pOSLOnoC3m1awMjIEADQvZO30v5qVavgwuVr+OvIiQJJjUUlc5hWNCm0LS08mqCFR5Ni2xt76Rq6tvdC00YfAgA+79oBETv34dK1G0xqSOP2HziE/QcOFbovPf052nXoo1Q2avRUnIzei2rV7PDPPw+hq6uLBfO/QfCk2Vgd/psYd+1anNJxY4OmAwCsrCozqSEqQbnOqUlJSUFoaCi6desGDw8PeHh4oFu3bpg3bx6Sk5PLs2nvpYzMFwAAM9OKhe6/cj0O1+PuFEhk3vY8IxNmpgWTlx7+AWjVpS+GjJ6CcxevlLp9DV3r4NDxk0hKToEgCIg5ewF3Ex7g46aNSl0XkaaZmZlCoVAgNTUdANDoI1fY21eBQqHA6ZgD+OfeOezetQ716tUu55aSJASFejYqVrklNadPn0atWrWwePFimJmZwdPTE56enjAzM8PixYvh4uKCM2fOlFfz3jsKhQLfLfoZH31YFzVrOBYas233AdRwrIaPXOsWWc/5S1dxIOooenRpL5ZZVbbA9AmBWDBnKhbMmQpbG0sMGhmMqzdulaqNU8aOgLNjdbTx6Y+PWnbGl+Om4utxX6FxQ9dS1UOkaXK5HHPnTsFvm3fg+fMMAIBTjeoAgOnTxmFuyCJ09fHDs9Q0REVuQaU3hmZJSygE9WxUrHIbfgoMDMTnn3+OsLAwyGQypX2CIGD48OEIDAxEdHR0sfVkZWUhKytLqUwnKwtyuVztbdZms+cvxa07d7F2+Q+F7n+VlYW9kYfxpX+fQvcDQNyduxg1aRZGDOqHT9zdxHInB3tx8jEAfORaF/cfPMLazdvx3fQJKrdxw5ZduHjlOpZ8PwNVbG1wNvYS5sxfBmvLyvBo8pHK9RBpkp6eHn7b9Pr3XMDIyWK5js7rz5Qh3y3G9u17AQCDhwThXvwZ9PDthF9Wri+X9hL9l5VbT82FCxcwduzYAgkNAMhkMowdOxaxsbEl1hMSEgIzMzOl7ftFYRK0WHvNmb8MR07E4NefvoettVWhMX8eOo6Xr7LQpV2bQvffjr+HwaMmo0eX9sUmPvnq16mNf+4/VLmNr7KysOjnNZgwahhaNW+G2h84oW+PLmjXxhPhm7aqXA+RJuUnNNWr26Nd+z5iLw0AJD56DAC4du2mWJadnY34+HuoXr2qxttK0hIUCrVsVLxyS2psbW0RExNT5P6YmBjY2NiUWM/kyZORlpamtAWPHq7OpmotQRAwZ/4yRB09gV8Xfwd7O9siY7ftPoDWzd2VVizlu3XnHgYGTkLX9l4Y/aW/Sue+HnenwCTk4uTm5iI3Nxc6byXBuro6UPCNTu+g/ITmgw+c4N2uF54+faa0/+y5i3j16hVq1XJWOsbBoRru3buv6eaS1Dj8pBHlltSMHz8ew4YNw+jRo7Fr1y6cOnUKp06dwq5duzB69GgMHz4cEydOLLEeuVwOU1NTpY1DT6qZPX8pdv95EN/PnAhjI0OkPHmKlCdP8eqt4byE+w9xNvYyfDu3K1BH3J27GBQYjI+bNoJf725iHU+fpYox6zZvx8Fj0Ui4/xBxd+7iu4VhiDl3AX18O4kxL168xPWbt3H95m0AwIOHSbh+8zYeJb7+NGtibIzGH7li/tJViDl3EfcfJmLHnkjs2heFNi2V72dDpAnGxkZo0KAeGjSoBwBwcqyOBg3qoVo1O+jp6eH3zSvg1qgBBvgFQldXFzY2VrCxsUKFChUAAM+fZ+DnFesxY/p4fObliVq1nLF0SQgAYMvW3eJ5nJ0d0aBBPdjYWMPQ0EA8Z3499B9RDhOFQ0JC0KRJE1SsWBHW1tbw8fHBjRs3lGJevXqFgIAAVK5cGSYmJvD19UVSkvK9lBISEtCxY0cYGRnB2toaEyZMQG5urlLM4cOH0ahRI8jlcnzwwQcIDw8v0J6lS5fC0dERBgYGcHd3L9CxoUpbSiIThPK7d/PmzZuxYMECnD17Fnl5eQAAXV1duLm5ISgoCD179ixTvTkpd9TZTK1V/5P2hZbPnhIEn46fiY8XhoVj958H8eeWcHEeQL6lq9Zj+a8b3q4CdrbW+HPrGgDArxsiELFzHx4nP4GBgRy1nJ0wYmBfNHVrIMYXdYO+ru29MGfqOABAypOnWBgWjhMx55CW/hx2ttbo0bU9BvTqVugwJhVkaNeivJugNVp6eiDqry0Fytes/R3ffDsft+NOFXpcG68eOHL09VxBPT09zJ09Gf36+cLQ0AAxMecRNH4Grl7935BUVGQEWhaSuDvXdGePjprkZj+Q/ByZs79QSz3GU1Wfa9WuXTv07t0bTZo0QW5uLqZMmYLLly/j6tWrMDY2BgCMGDECe/bsQXh4OMzMzDBy5Ejo6Ojg77//BgDk5eWhYcOGsLW1xbx58/Do0SMMGDAAQ4cOxdy5cwEA8fHxqF+/PoYPH44hQ4YgKioKY8aMwZ49e+Dt/Xq17ObNmzFgwACEhYXB3d0dCxcuREREBG7cuAFra2uV2qKKck1q8uXk5CAlJQUAYGlp+a8/gTCpISockxqigjSS1HzTTy31GE8v+CFSVcnJybC2tsaRI0fg6emJtLQ0WFlZYePGjejRowcA4Pr166hTpw6io6PRrFkz7Nu3D506dcLDhw/FKSFhYWEIDg5GcnIy9PX1ERwcjD179uDy5cviuXr37o3U1FTs378fAODu7o4mTZpgyZIlAF6vuK1WrRoCAwMxadIkldqiinfiu58qVKiAKlWqoEqVKuxSJSIi7aNQqGXLyspCenq60vb2CuCipKWlAQAsLF7PZzx79ixycnLg5eUlxri4uKB69eriyuPo6Gi4uroqzXH19vZGeno6rly5Isa8WUd+TH4d2dnZOHv2rFKMjo4OvLy8xBhV2qKKdyKpISIiopIVtuI3JCSkxOMUCgXGjBmDTz75BPXr1wcAJCYmQl9fH+bm5kqxNjY2SExMFGPeXrST/7ikmPT0dLx8+RIpKSnIy8srNObNOkpqiyr4NQlERERSU9PKpclfT0ZQUJBSmSqLYwICAnD58mUcP35cLe14VzGpISIikpqavuJALpeXeoXvyJEjsXv3bhw9ehT29v+7EaqtrS2ys7ORmpqq1EOSlJQEW1tbMebtVUr5K5LejHl7lVJSUhJMTU1haGgIXV1d6OrqFhrzZh0ltUUVHH4iIiLSQoIgYOTIkdi+fTsOHjwIJycnpf1ubm6oUKECoqKixLIbN24gISEBHh4eAAAPDw9cunQJjx8/FmMiIyNhamqKunXrijFv1pEfk1+Hvr4+3NzclGIUCgWioqLEGFXaogr21BAREUmtHG6cFxAQgI0bN2Lnzp2oWLGiODfFzMwMhoaGMDMzw+DBgxEUFAQLCwuYmpoiMDAQHh4e4mqjtm3bom7duujfvz9CQ0ORmJiIqVOnIiAgQOwxGj58OJYsWYKJEydi0KBBOHjwIH7//Xfs2bNHbEtQUBD8/PzQuHFjNG3aFAsXLkRmZiYGDhwotqmktqiCSQ0REZHEyuMrDpYvXw4AaNWqlVL56tWr4e/vDwBYsGABdHR04Ovri6ysLHh7e2PZsmVirK6uLnbv3o0RI0bAw8MDxsbG8PPzwzfffCPGODk5Yc+ePRg7diwWLVoEe3t7rFy5UrxHDQD06tULycnJmD59OhITE9GwYUPs379fafJwSW1RxTtxnxp1431qiArH+9QQFaSJ+9RkTPZVSz0mIfyuu+Kwp4aIiEhq/N4mjWBSQ0REJDUmNRrBpIaIiEhqalrSTcXjkm4iIiLSCuypISIikhqHnzSCSQ0REZHEBCY1GsHhJyIiItIK7KkhIiKSGntqNIJJDRERkdTK4Y7C7yMOPxEREZFWYE8NERGR1Dj8pBFMaoiIiKTGpEYjOPxEREREWoE9NURERBITBPbUaAKTGiIiIqlx+EkjmNQQERFJjUmNRnBODREREWkF9tQQERFJjN/9pBlMaoiIiKTGpEYjOPxEREREWoE9NURERFLjVz9pBJMaIiIiiXFOjWZw+ImIiIi0AntqiIiIpMaeGo1gUkNERCQ1zqnRCA4/ERERkVZgTw0REZHEOFFYM5jUEBERSY3DTxrBpIaIiEhi7KnRDM6pISIiIq3AnhoiIiKpcfhJI5jUEBERSUxgUqMRHH4iIiIircCeGiIiIqmxp0YjmNQQERFJjMNPmsHhJyIiItIK7KkhIiKSGntqNIJJDRERkcQ4/KQZTGqIiIgkxqRGMzinhoiIiLQCe2qIiIgkxp4azWBSQ0REJDVBVt4teC9w+ImIiIi0AntqiIiIJMbhJ81gUkNERCQxQcHhJ03g8BMRERFpBfbUEBERSYzDT5rBpIaIiEhiAlc/aQSHn4iIiEgrsKeGiIhIYhx+0gyVk5r09HSVKzU1NS1TY4iIiLQRVz9phspJjbm5OWQy1X4oeXl5ZW4QERGRthGE8m7B+0HlpObQoUPiv+/evYtJkybB398fHh4eAIDo6GisWbMGISEh6m8lERERUQlkglD6/LFNmzYYMmQI+vTpo1S+ceNGrFixAocPH1ZX+8okJ+VOuZ6f6F1laNeivJtA9M7JzX4g+TnuNfJSSz0O5/5SSz3aqkyrn6Kjo9G4ceMC5Y0bN0ZMTMy/bhQREZE2ERQytWxUvDIlNdWqVcMvv/xSoHzlypWoVq3av24UERERUWmVaUn3ggUL4Ovri3379sHd3R0AEBMTg7i4OGzdulWtDSQiIvqv40RhzShTT02HDh1w8+ZNdO7cGU+fPsXTp0/RuXNn3Lx5Ex06dFB3G4mIiP7Tymv46ejRo+jcuTPs7Owgk8mwY8cOpf3+/v6QyWRKW7t27ZRinj59in79+sHU1BTm5uYYPHgwMjIylGIuXryIFi1awMDAANWqVUNoaGiBtkRERMDFxQUGBgZwdXXF3r17lZ8jQcD06dNRpUoVGBoawsvLC3FxcaW63jLffK9atWqYO3duWQ8nIiIiiWVmZqJBgwYYNGgQunfvXmhMu3btsHr1avGxXC5X2t+vXz88evQIkZGRyMnJwcCBAzFs2DBs3LgRwOv72LVt2xZeXl4ICwvDpUuXMGjQIJibm2PYsGEAgBMnTqBPnz4ICQlBp06dsHHjRvj4+ODcuXOoX78+ACA0NBSLFy/GmjVr4OTkhGnTpsHb2xtXr16FgYGBStdbptVPAHDs2DH8/PPPuHPnDiIiIlC1alWsW7cOTk5OaN68eVmqVBuufiIqHFc/ERWkidVPt+t7q6Ue58sHynysTCbD9u3b4ePjI5b5+/sjNTW1QA9OvmvXrqFu3bo4ffq0uEBo//796NChA+7fvw87OzssX74cX3/9NRITE6Gvrw8AmDRpEnbs2IHr168DAHr16oXMzEzs3r1brLtZs2Zo2LAhwsLCIAgC7OzsMG7cOIwfPx4AkJaWBhsbG4SHh6N3794qXWOZhp+2bt0Kb29vGBoa4ty5c8jKyhIbwN4bIiIiZYJCPVtWVhbS09OVtvy/wWV1+PBhWFtbo3bt2hgxYgSePHki7ouOjoa5ubnSimcvLy/o6Ojg1KlTYoynp6eY0ACAt7c3bty4gWfPnokxXl7Ky9q9vb0RHR0NAIiPj0diYqJSjJmZGdzd3cUYVZQpqZk9ezbCwsLwyy+/oEKFCmL5J598gnPnzpWlSiIiIipBSEgIzMzMlLZ/c9Pbdu3aYe3atYiKisL333+PI0eOoH379uI3AyQmJsLa2lrpGD09PVhYWCAxMVGMsbGxUYrJf1xSzJv73zyusBhVlGlOzY0bN+Dp6Vmg3MzMDKmpqWWpkoiISGspBPXcY2by5MkICgpSKnt7DkxpvDms4+rqig8//BDOzs44fPgw2rRpU+Z6y0uZempsbW1x69atAuXHjx9HjRo1/nWjiIiItIkgyNSyyeVymJqaKm3/Jql5W40aNWBpaSn+jbe1tcXjx4+VYnJzc/H06VPY2tqKMUlJSUox+Y9Linlz/5vHFRajijIlNUOHDsXo0aNx6tQpyGQyPHz4EBs2bMD48eMxYsSIslRJRESktf4rdxS+f/8+njx5gipVqgAAPDw8kJqairNnz4oxBw8ehEKhEO9T5+HhgaNHjyInJ0eMiYyMRO3atVGpUiUxJioqSulckZGR4vdHOjk5wdbWVikmPT0dp06dEmNUUabhp0mTJkGhUKBNmzZ48eIFPD09IZfLMX78eAQGBpalSiIiIlKzjIwMpZGV+Ph4xMbGwsLCAhYWFpg1axZ8fX1ha2uL27dvY+LEifjggw/g7f16tVadOnXQrl07DB06FGFhYcjJycHIkSPRu3dv2NnZAQD69u2LWbNmYfDgwQgODsbly5exaNEiLFiwQDzv6NGj0bJlS8yfPx8dO3bEb7/9hjNnzmDFihUAXq/MGjNmDGbPno2aNWuKS7rt7OyUVmuVpMxLugEgOzsbt27dQkZGBurWrQsTE5OyVqVWXNJNVDgu6SYqSBNLuq/VVM+NaevE7S056A2HDx9G69atC5T7+flh+fLl8PHxwfnz55Gamgo7Ozu0bdsW3377rdKE3adPn2LkyJH4448/oKOjA19fXyxevFjpb/7FixcREBCA06dPw9LSEoGBgQgODlY6Z0REBKZOnYq7d++iZs2aCA0NVbphryAImDFjBlasWIHU1FQ0b94cy5YtQ61atVS+3jIlNYMGDcKiRYtQsWJFpfLMzEwEBgbi119/LW2VasWkhqhwTGqICtJEUnPVuaNa6ql7e49a6tFWZZpTs2bNGrx8+bJA+cuXL7F27dp/3SgiIiKi0irVnJr09HQIggBBEPD8+XOl2xbn5eVh7969BdazExERve/UtaSbileqpMbc3Fz8wqvCxrhkMhlmzZqltsYRERFpA4FJjUaUKqk5dOgQBEHAp59+iq1bt8LCwkLcp6+vDwcHB3E2NBEREZEmlSqpadmyJYDXS8KqV68OmYyZJxERUUnKvs6YSqNME4UPHjyILVu2FCiPiIjAmjVr/nWjiIiItIlCkKllo+KVKakJCQmBpaVlgXJra2t+SzcRERGVizLdUTghIQFOTk4Fyh0cHJCQkPCvG0VERKRNOFFYM8rUU2NtbY2LFy8WKL9w4QIqV678rxtFRESkTQRBPRsVr0w9NX369MGoUaNQsWJFeHp6AgCOHDmC0aNHK32NOREREfE+NZpSpqTm22+/xd27d9GmTRvo6b2uQqFQYMCAAZxTQ0REROXiX32h5c2bN3HhwgUYGhrC1dUVDg4O6mxbmVmaqv7lV0TvkxxFXnk3geidk5ZxW/JznK7aTS31NHmwXS31aKsy9dTkq1WrVqm+PZOIiOh9xOEnzVA5qQkKCsK3334LY2NjBAUFFRv7448//uuGEREREZWGyknN+fPnkZOTI/67KLzLMBERkTIuXNIMlZOaQ4cOFfpvIiIiKh6HnzSjTPepISIiInrXqNxT0717d5Ur3bZtW5kaQ0REpI14R2HNUDmpMTMzE/8tCAK2b98OMzMzNG7cGABw9uxZpKamlir5ISIieh8oyrsB7wmVk5rVq1eL/w4ODkbPnj0RFhYGXV1dAEBeXh6++uormJqaqr+VRERERCUo0833rKyscPz4cdSuXVup/MaNG/j444/x5MkTtTWwLHjzPaLC8eZ7RAVp4uZ7R20/V0s9nokRaqlHW5VponBubi6uX79eoPz69etQKNjJRkRE9CaFoJ6NilemOwoPHDgQgwcPxu3bt9G0aVMAwKlTp/Ddd99h4MCBam0gERHRf50CnCisCWVKan744QfY2tpi/vz5ePToEQCgSpUqmDBhAsaNG6fWBhIRERGp4l99oSUApKenA8A7NUGYc2qICsc5NUQFaWJOTZRNL7XU0yZps1rq0VZlvvlebm4u/vrrL2zatEn8aoSHDx8iIyNDbY0jIiLSBgo1bVS8Mg0/3bt3D+3atUNCQgKysrLw2WefoWLFivj++++RlZWFsLAwdbeTiIiIqFhl6qkZPXo0GjdujGfPnsHQ0FAs79atG6KiotTWOCIiIm0gQKaWjYpXpp6aY8eO4cSJE9DX11cqd3R0xIMHD9TSMCIiIm3BoSPNKFNPjUKhQF5ewQmH9+/fR8WKFf91o4iIiIhKq0xJTdu2bbFw4ULxsUwmQ0ZGBmbMmIEOHTqoq21ERERagROFNaPM96lp164d6tati1evXqFv376Ii4uDpaUlNm3apO42EhER/adxPoxmlCmpqVatGi5cuIDNmzfjwoULyMjIwODBg9GvXz+licNEREREmlLqpCYnJwcuLi7YvXs3+vXrh379+knRLiIiIq2hYEeNRpQ6qalQoQJevXolRVuIiIi0Er/7STPKNFE4ICAA33//PXJzc9XdHiIiIq0jqGmj4pVpTs3p06cRFRWFP//8E66urjA2Nlbav23bNrU0joiIiEhVZUpqzM3N4evrq+62EBERaSUux9aMUiU1CoUC8+bNw82bN5GdnY1PP/0UM2fO5IonIiKiYihknFOjCaWaUzNnzhxMmTIFJiYmqFq1KhYvXoyAgACp2kZERESkslIlNWvXrsWyZctw4MAB7NixA3/88Qc2bNgAhYIda0REREXhRGHNKFVSk5CQoPQ1CF5eXpDJZHj48KHaG0ZERKQt+DUJmlGqpCY3NxcGBgZKZRUqVEBOTo5aG0VERERUWqWaKCwIAvz9/SGXy8WyV69eYfjw4UrLurmkm4iI6H94R2HNKFVS4+fnV6Dsiy++UFtjiIiItBHvKKwZpUpqVq9eLVU7iIiIiP6VMt18j4iIiFTHlUuawaSGiIhIYpxToxlMaoiIiCTG5diaUaZv6SYiIiJ617CnhoiISGKcU6MZTGqIiIgkxjk1msHhJyIiItIK7KkhIiKSGCcKawaTGiIiIokxqdEMDj8RERGRVmBPDRERkcQEThTWCPbUEBERSUyhpq20jh49is6dO8POzg4ymQw7duxQ2i8IAqZPn44qVarA0NAQXl5eiIuLU4p5+vQp+vXrB1NTU5ibm2Pw4MHIyMhQirl48SJatGgBAwMDVKtWDaGhoQXaEhERARcXFxgYGMDV1RV79+4tdVtKwqSGiIhIS2VmZqJBgwZYunRpoftDQ0OxePFihIWF4dSpUzA2Noa3tzdevXolxvTr1w9XrlxBZGQkdu/ejaNHj2LYsGHi/vT0dLRt2xYODg44e/Ys5s2bh5kzZ2LFihVizIkTJ9CnTx8MHjwY58+fh4+PD3x8fHD58uVStaUkMkEQtO6eQJamtcq7CUTvpBxFXnk3geidk5ZxW/JzLKn2hVrqGfnP+jIfK5PJsH37dvj4+AB43TNiZ2eHcePGYfz48QCAtLQ02NjYIDw8HL1798a1a9dQt25dnD59Go0bNwYA7N+/Hx06dMD9+/dhZ2eH5cuX4+uvv0ZiYiL09fUBAJMmTcKOHTtw/fp1AECvXr2QmZmJ3bt3i+1p1qwZGjZsiLCwMJXaogr21BAREUlMUNOWlZWF9PR0pS0rK6tMbYqPj0diYiK8vLzEMjMzM7i7uyM6OhoAEB0dDXNzczGhAQAvLy/o6Ojg1KlTYoynp6eY0ACAt7c3bty4gWfPnokxb54nPyb/PKq0RRVMaoiIiCSmkKlnCwkJgZmZmdIWEhJSpjYlJiYCAGxsbJTKbWxsxH2JiYmwtrZW2q+npwcLCwulmMLqePMcRcW8ub+ktqiCq5+IiIj+IyZPnoygoCClMrlcXk6tefewp4aIiEhi6lr9JJfLYWpqqrSVNamxtbUFACQlJSmVJyUliftsbW3x+PFjpf25ubl4+vSpUkxhdbx5jqJi3txfUltUwaSGiIhIYuW1pLs4Tk5OsLW1RVRUlFiWnp6OU6dOwcPDAwDg4eGB1NRUnD17Vow5ePAgFAoF3N3dxZijR48iJydHjImMjETt2rVRqVIlMebN8+TH5J9HlbaogkkNERGRlsrIyEBsbCxiY2MBvJ6QGxsbi4SEBMhkMowZMwazZ8/Grl27cOnSJQwYMAB2dnbiCqk6deqgXbt2GDp0KGJiYvD3339j5MiR6N27N+zs7AAAffv2hb6+PgYPHowrV65g8+bNWLRokdIw2ejRo7F//37Mnz8f169fx8yZM3HmzBmMHDkSAFRqiyo4p4aIiEhi5XXvlDNnzqB169bi4/xEw8/PD+Hh4Zg4cSIyMzMxbNgwpKamonnz5ti/fz8MDAzEYzZs2ICRI0eiTZs20NHRga+vLxYvXizuNzMzw59//omAgAC4ubnB0tIS06dPV7qXzccff4yNGzdi6tSpmDJlCmrWrIkdO3agfv36YowqbSkJ71ND9B7hfWqICtLEfWpCHdRzn5qJ98p+n5r3AYefiIiISCtw+ImIiEhi6p7kS4VjUkNERCQxrZvn8Y7i8BMRERFpBfbUEBERSUzBvhqNYFJDREQkMc6p0QwmNURERBJjP41mcE4NERERaQX21BAREUmMw0+awaSGiIhIYgpZebfg/cDhJyIiItIK7KkhIiKSGJd0awaTGiIiIokxpdEMDj8RERGRVmBPDRERkcS4+kkzmNQQERFJjHNqNIPDT0RERKQV2FNDREQkMfbTaAaTGiIiIolxTo1mMKkhIiKSGOfUaAbn1BAREZFWYE8NERGRxNhPoxlMaoiIiCTGOTWaweEnIiIi0grsqSEiIpKYwAEojWBSQ0REJDEOP2kGh5+IiIhIK7CnhoiISGK8T41mMKkhIiKSGFMazeDwExEREWkFJjWkxOPjxtiwOQyXbxxDSvpNtO/oVWTsDwtmISX9Jr78yq/Avs+8W+HAwQj8k3QRt+6dxtqNy5T2V7Wvgk0RK5CQeAHXbkdj5rcToaurK+7/pHlTpKTfLLBZW1uq72KJVPTxJ03w2+8rcD3uBNIybqNjp8+U9nfu0hbbd4Yj/t4ZpGXchqtrnULradL0I/yxZz0eJl3CPw9jsffAJhgYyMX94yd8hT//isCjx5dx7/75ItvTt58v/j65B0kpV3ErPgY//DhTLddJ0lFAUMtGxePwEykxMjbC5cvXsWHdVqzduLTIuA6dPoNbk4Z49DCpwL5OXdpiwU+zMWfWjzh29CR0dXVRp24tcb+Ojg42RazA46QUdPisN2xsrbD051Dk5ORizjc/KtXl3qgtnqdniI+Tk5+o4SqJSsfI6PX7Yv26LdiwaXmh+6Ojz2D7tr34aWlIoXU0afoRtm5fjQXzl2PC+FnIzc2Fq2sdKBT/+0NVQb8Cdmzfi5iYc+g/oGeh9QSMHISRowZj2tff4eyZCzAyMkR1B3v1XChJhqufNINJDSmJijyKqMijxcbYVrHBd/Om4fNug7ApYoXSPl1dXcz9fipmTg3FhnVbxPKbN26L/27dpjlqu3wA3y7+SE5+gsuXruG72QsxfdYEhIb8hJycHDE2OfkJ0tOeq+nqiMrmr8gj+CvySJH7N/+2AwBQvXrVImNCvvsaP4etwYIffxbLbsXFK8fMWQTgdU9MYczNTTF1ehB69xyGI4dPiOVXrtwo8RqofPE+NZrB4ScqFZlMhuUrQrFk8UrcuH6rwP4PG9aDXVVbKBQKHDy2A1duHsdvW1fCpU5NMaZJ04a4euWmUq/LwajjMDWrCJc6HyjVd/j4Tly5eRxbdqxGU/dG0l0YkYQsrSqjSdOPkJz8BH/+FYG4O6ewZ/9GNPNwK1U9rT9tDh0dHVSpYoOYswdw9cZxhK9djKpVq0jUcqL/lnc6qfnnn38waNCgYmOysrKQnp6utAkCO/qkMmrsMOTm5WHF8rWF7nd0rAYAmDg5ED/OW4a+Pb9EWmoadu5dD/NKZgAAa2srJCenKB2X/Pj1Y2sbKwBAUmIyxo2ehoH9A+H/RSAePHiEnXvX4cMGdaW6NCLJ5L8vJk8ehTXhv8HXZyAuxF7Brt3rUMPZsVT16OjIMG7CCEyeOBsDvhiJSpXMseOPNahQoYJErSd1UKhpo+K900nN06dPsWbNmmJjQkJCYGZmprS9zH6moRa+Xxo0rIdhIwYgcPikImN0dGQAgAU/hGH3rj9xIfYKAkdMgiAI6OrTTuVz3boVjzWrN+NC7BWcjjmP0QFTcPrUeQwPGPivr4NI03R0Xv+qXf3rJmxYvxUXL17FlElzEBcXj/79e5SqHn19fQSP/wZRUcdw5nQsBg0cA2dnR3h6NpOq+aQGgpr+o+KV65yaXbt2Fbv/zp07JdYxefJkBAUFKZU5VeUwhRSafdwYVlaVEXv1sFimp6eHb+ZMwpcj/NDI9VMkJSYDgNLQVHZ2Du7d/QdV7e0AAI8fJ+Mjtw+V6rb6/1VNj5OSizz/ubMX4V7K7nqid0FS4mMAwPW3hmxv3rgN+2p2KteTWEg9T1Ke4smTZ6Wqh0hblWtS4+PjA5lMBkEoOvuUyWTF1iGXyyGXy5XKZLJ3ugPqP+v333biyKETSmUR23/F77/txKb1WwEAsbGX8epVFj6o6YRTJ88CeJ34VKteFff/eQgAOB0Ti7HjR8DS0gIpKU8BAK1af4L0tOeFztPJV9+1jpg0Ef2X3Lt3Hw8fJqJmrRpK5R984IjIP4uegPy2k///nqpZqwYePkwEAFSqZIbKlSvhn4QH6mswqR2HjjSjXJOaKlWqYNmyZejatWuh+2NjY+Hmxk/mmmRsbASnGg7iYwdHe9R3rYNnz1Lx4P4jPHuaqhSfk5ODx4+TcevW61UcGc8zEf7rJgRPGYUHDx7hfsJDjBw9BACwc8c+AMChqOO4cf0Wlv0yD7OmzYO1jSUmTxuDVb9sQHb265VPX37lh4S793H9ehzkcjm+8PscLVo2Qw+f4udYEUnB2NgINd58XzjYw/X/3xf37z9CpUpmsLe3g20VGwAQk5ekpGQ8/v/5YosX/oLJX4/B5UvXcOniNfTp1x01azljwBcjxXrt7augUiVz2FerAl1dHfF+N3fu3ENm5gvcvnUXu/+IxHeh0zA68Gs8f56BGTPH4+bN2zh69KSmng4qA0UxH95Jfco1qXFzc8PZs2eLTGpK6sUh9Wv4UX3s3LtefDw7ZAoAYNOGbQgcUfRcmjfNnBqKvNw8LFsxD4YGBjh75gK6dRqAtNR0AIBCoUDfnl9i3oJZ2PfXZrx48RKbN27Hd/+/nBUA9CtUwKy5k1Clig1evnyJq5dvwLeLP44fO6XGqyVSzUeNXLFn30bxccj3UwEAG9ZvxVfDJ6J9By8s/zlU3L96zeLXcXMX4bu5r/+9fFk4DAzkmPvdVFSqZIbLl67Dp8sAxMcniMdNmToW/b7433Lu49G7AQAd2/cVX/vDh41HyHdfI2LLSigUCvz9dwx8fQYhNzdXoqsn+u+QCeWYNRw7dgyZmZlo167wCaSZmZk4c+YMWrZsWap6LU1rlRxE9B7KUeSVdxOI3jlpGbdLDvqXvnDorpZ61t/bppZ6tFW59tS0aNGi2P3GxsalTmiIiIjeNfyKA83gjFoiIiLSCvyaBCIiIonxHjOawaSGiIhIYlzSrRlMaoiIiCTGOTWawTk1REREpBXYU0NERCQxzqnRDCY1REREEuOcGs3g8BMRERFpBfbUEBERSYxf+aMZTGqIiIgkxtVPmsHhJyIiItIK7KkhIiKSGCcKawaTGiIiIolxSbdmcPiJiIiItAJ7aoiIiCTGicKawZ4aIiIiiQmCoJatNGbOnAmZTKa0ubi4iPtfvXqFgIAAVK5cGSYmJvD19UVSUpJSHQkJCejYsSOMjIxgbW2NCRMmIDc3Vynm8OHDaNSoEeRyOT744AOEh4cXaMvSpUvh6OgIAwMDuLu7IyYmplTXoiomNURERBJTqGkrrXr16uHRo0fidvz4cXHf2LFj8ccffyAiIgJHjhzBw4cP0b17d3F/Xl4eOnbsiOzsbJw4cQJr1qxBeHg4pk+fLsbEx8ejY8eOaN26NWJjYzFmzBgMGTIEBw4cEGM2b96MoKAgzJgxA+fOnUODBg3g7e2Nx48fl+GKiicTtPCOQJamtcq7CUTvpBxFXnk3geidk5ZxW/JzeFdrr5Z6DvyzT+XYmTNnYseOHYiNjS2wLy0tDVZWVti4cSN69OgBALh+/Trq1KmD6OhoNGvWDPv27UOnTp3w8OFD2NjYAADCwsIQHByM5ORk6OvrIzg4GHv27MHly5fFunv37o3U1FTs378fAODu7o4mTZpgyZIlAACFQoFq1aohMDAQkyZNKutTUSj21BAREUlMUNN/WVlZSE9PV9qysrKKPG9cXBzs7OxQo0YN9OvXDwkJCQCAs2fPIicnB15eXmKsi4sLqlevjujoaABAdHQ0XF1dxYQGALy9vZGeno4rV66IMW/WkR+TX0d2djbOnj2rFKOjowMvLy8xRp2Y1BAREUlMAUEtW0hICMzMzJS2kJCQQs/p7u6O8PBw7N+/H8uXL0d8fDxatGiB58+fIzExEfr6+jA3N1c6xsbGBomJiQCAxMREpYQmf3/+vuJi0tPT8fLlS6SkpCAvL6/QmPw61Imrn4iIiP4jJk+ejKCgIKUyuVxeaGz79v8b8vrwww/h7u4OBwcH/P777zA0NJS0neWFPTVEREQSU9fqJ7lcDlNTU6WtqKTmbebm5qhVqxZu3boFW1tbZGdnIzU1VSkmKSkJtra2AABbW9sCq6HyH5cUY2pqCkNDQ1haWkJXV7fQmPw61IlJDRERkcTUNfz0b2RkZOD27duoUqUK3NzcUKFCBURFRYn7b9y4gYSEBHh4eAAAPDw8cOnSJaVVSpGRkTA1NUXdunXFmDfryI/Jr0NfXx9ubm5KMQqFAlFRUWKMOjGpISIi0kLjx4/HkSNHcPfuXZw4cQLdunWDrq4u+vTpAzMzMwwePBhBQUE4dOgQzp49i4EDB8LDwwPNmjUDALRt2xZ169ZF//79ceHCBRw4cABTp05FQECA2Ds0fPhw3LlzBxMnTsT169exbNky/P777xg7dqzYjqCgIPzyyy9Ys2YNrl27hhEjRiAzMxMDBw5U+zVzTg0REZHEyuO7n+7fv48+ffrgyZMnsLKyQvPmzXHy5ElYWVkBABYsWAAdHR34+voiKysL3t7eWLZsmXi8rq4udu/ejREjRsDDwwPGxsbw8/PDN998I8Y4OTlhz549GDt2LBYtWgR7e3usXLkS3t7eYkyvXr2QnJyM6dOnIzExEQ0bNsT+/fsLTB5WB96nhug9wvvUEBWkifvUeFZto5Z6jj6IKjnoPcbhJyIiItIKHH4iIiKSmNYNibyjmNQQERFJjN/SrRlMaoiIiCTGpEYzOKeGiIiItAJ7aoiIiCSmhQuN30lMaoiIiCTG4SfN4PATERERaQX21BAREUmsPO4o/D5iUkNERCQxzqnRDA4/ERERkVZgTw0REZHEOFFYM5jUEBERSYzDT5rB4SciIiLSCuypISIikhiHnzSDSQ0REZHEuKRbM5jUEBERSUzBOTUawTk1REREpBXYU0NERCQxDj9pBpMaIiIiiXH4STM4/ERERERagT01REREEuPwk2YwqSEiIpIYh580g8NPREREpBXYU0NERCQxDj9pBpMaIiIiiXH4STM4/ERERERagT01REREEuPwk2YwqSEiIpKYICjKuwnvBSY1REREElOwp0YjOKeGiIiItAJ7aoiIiCQmcPWTRjCpISIikhiHnzSDw09ERESkFdhTQ0REJDEOP2kGkxoiIiKJ8Y7CmsHhJyIiItIK7KkhIiKSGO8orBlMaoiIiCTGOTWaweEnIiIi0grsqSEiIpIY71OjGUxqiIiIJMbhJ81gUkNERCQxLunWDM6pISIiIq3AnhoiIiKJcfhJM5jUEBERSYwThTWDw09ERESkFdhTQ0REJDEOP2kGkxoiIiKJcfWTZnD4iYiIiLQCe2qIiIgkxi+01AwmNURERBLj8JNmcPiJiIiItAJ7aoiIiCTG1U+awaSGiIhIYpxToxlMaoiIiCTGnhrN4JwaIiIi0grsqSEiIpIYe2o0g0kNERGRxJjSaAaHn4iIiEgryAT2iZFEsrKyEBISgsmTJ0Mul5d3c4jeGXxvEEmDSQ1JJj09HWZmZkhLS4OpqWl5N4foncH3BpE0OPxEREREWoFJDREREWkFJjVERESkFZjUkGTkcjlmzJjBiZBEb+F7g0ganChMREREWoE9NURERKQVmNQQERGRVmBSQ0RERFqBSQ0RERFpBSY1JJmlS5fC0dERBgYGcHd3R0xMTHk3iahcHT16FJ07d4adnR1kMhl27NhR3k0i0ipMakgSmzdvRlBQEGbMmIFz586hQYMG8Pb2xuPHj8u7aUTlJjMzEw0aNMDSpUvLuylEWolLukkS7u7uaNKkCZYsWQIAUCgUqFatGgIDAzFp0qRybh1R+ZPJZNi+fTt8fHzKuylEWoM9NaR22dnZOHv2LLy8vMQyHR0deHl5ITo6uhxbRkRE2oxJDaldSkoK8vLyYGNjo1RuY2ODxMTEcmoVERFpOyY1REREpBWY1JDaWVpaQldXF0lJSUrlSUlJsLW1LadWERGRtmNSQ2qnr68PNzc3REVFiWUKhQJRUVHw8PAox5YREZE20yvvBpB2CgoKgp+fHxo3boymTZti4cKFyMzMxMCBA8u7aUTlJiMjA7du3RIfx8fHIzY2FhYWFqhevXo5toxIO3BJN0lmyZIlmDdvHhITE9GwYUMsXrwY7u7u5d0sonJz+PBhtG7dukC5n58fwsPDNd8gIi3DpIaIiIi0AufUEBERkVZgUkNERERagUkNERERaQUmNURERKQVmNQQERGRVmBSQ0RERFqBSQ0RERFpBSY1RFRqMpkMO3bsKO9mEBEpYVJD9I6Ljo6Grq4uOnbsWKrjHB0dsXDhQmkaRUT0DmJSQ/SOW7VqFQIDA3H06FE8fPiwvJtDRPTOYlJD9A7LyMjA5s2bMWLECHTs2LHA9wP98ccfaNKkCQwMDGBpaYlu3boBAFq1aoV79+5h7NixkMlkkMlkAICZM2eiYcOGSnUsXLgQjo6O4uPTp0/js88+g6WlJczMzNCyZUucO3dOysskIlILJjVE77Dff/8dLi4uqF27Nr744gv8+uuvyP+6tj179qBbt27o0KEDzp8/j6ioKDRt2hQAsG3bNtjb2+Obb77Bo0eP8OjRI5XP+fz5c/j5+eH48eM4efIkatasiQ4dOuD58+eSXCMRkbrolXcDiKhoq1atwhdffAEAaNeuHdLS0nDkyBG0atUKc+bMQe/evTFr1iwxvkGDBgAACwsL6OrqomLFirC1tS3VOT/99FOlxytWrIC5uTmOHDmCTp06/csrIiKSDntqiN5RN27cQExMDPr06QMA0NPTQ69evbBq1SoAQGxsLNq0aaP28yYlJWHo0KGoWbMmzMzMYGpqioyMDCQkJKj9XERE6sSeGqJ31KpVq5Cbmws7OzuxTBAEyOVyLFmyBIaGhqWuU0dHRxy+ypeTk6P02M/PD0+ePMGiRYvg4OAAuVwODw8PZGdnl+1CiIg0hD01RO+g3NxcrF27FvPnz0dsbKy4XbhwAXZ2dti0aRM+/PBDREVFFVmHvr4+8vLylMqsrKyQmJiolNjExsYqxfz9998YNWoUOnTogHr16kEulyMlJUWt10dEJAX21BC9g3bv3o1nz55h8ODBMDMzU9rn6+uLVatWYd68eWjTpg2cnZ3Ru3dv5ObmYu/evQgODgbw+j41R48eRe/evSGXy2FpaYlWrVohOTkZoaGh6NGjB/bv3499+/bB1NRUrL9mzZpYt24dGjdujPT0dEyYMKFMvUJERJrGnhqid9CqVavg5eVVIKEBXic1Z86cgYWFBSIiIrBr1y40bNgQn376KWJiYsS4b775Bnfv3oWzszOsrKwAAHXq1MGyZcuwdOlSNGjQADExMRg/fnyBcz979gyNGjVC//79MWrUKFhbW0t7wUREaiAT3h5gJyIiIvoPYk8NERERaQUmNURERKQVmNQQERGRVmBSQ0RERFqBSQ0RERFpBSY1REREpBWY1BAREZFWYFJDREREWoFJDREREWkFJjVERESkFZjUEBERkVZgUkNERERa4f8AA37lYqSmqRYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# will change the probability threshold for predicting a fraudulent transaction\n",
    "THRESHOLD = .70\n",
    "y_pred = np.where(log_l1.predict_proba(X_test)[:,1] > THRESHOLD, 1, 0)\n",
    "\n",
    "# Confusion Matrix\n",
    "log_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(log_matrix, annot=True, fmt=\"d\")\n",
    "plt.title('Logistic Regression L1 penalty Confusion Matrix')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "\n",
    "print(f\"The accuracy of Logistic(L1) model on testing set is: {accuracy_score(y_test, y_pred)*100}% \\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9d2220-2f88-454e-a0e3-de54cff52890",
   "metadata": {},
   "source": [
    "Observe how increasing the threshold for a fraudulent purchase probability to be greater than 75%, we were able to reduce the number of false positives by 700. However, this came at a cost of reducing the number of true positive, the number of predicting a fraudulent purchase. We also increased the number of false negatives, the fraudulent purchases classified as non-fraudulent purchases. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149590cc",
   "metadata": {},
   "source": [
    "## Finding best Threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df38f1b",
   "metadata": {},
   "source": [
    "We have to consider we want to find the best model that has the highest prediction for fraudulent transactions while having a relatively low error on misclassified fraudulent transactions. In other words we want our true positives to be high and false positives to be low in our confusion matrix. The reason we want this is that it can cost the company a good amount of money to by misidentify a fraudulent transaction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "807e9649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to find the best threshold for a given model\n",
    "def find_best_threshold(model, num_steps):\n",
    "    highest_f1 = 0\n",
    "    best_threshold = 0\n",
    "    best_acc = 0\n",
    "    best_rec = 0\n",
    "    best_pre = 0\n",
    "    # Iterate over a range of thresholds\n",
    "    for threshold in np.linspace(0, 1, num_steps):\n",
    "        # Predict the target variable using the given threshold\n",
    "        y_predict = (model.predict_proba(X_test)[:, 1] >= threshold)\n",
    "        # Calculate various evaluation metrics\n",
    "        f1 = f1_score(y_test, y_predict)\n",
    "        acc = accuracy_score(y_test, y_predict)\n",
    "        rec = recall_score(y_test, y_predict)\n",
    "        pre = precision_score(y_test, y_predict)\n",
    "        # Update the best threshold and metrics if F1 score improves\n",
    "        if f1 > highest_f1:\n",
    "            best_threshold, highest_f1, best_acc, best_rec, best_pre = \\\n",
    "                threshold, f1, acc, rec, pre\n",
    "    # Return the best threshold and evaluation metrics\n",
    "    return best_threshold, highest_f1, best_acc, best_rec, best_pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b8add8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first we define our models and objects into a list\n",
    "model_names = [\"Logistic Regression\", \"Random Forest\", \"XGBClassifier\"]\n",
    "models = [log_l1, rfc, boost]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8742ff65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\luis_\\Documents\\Github - Main\\Credit-Card-Fraud-Detection-\\cardenv\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Best Threshold</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.794844</td>\n",
       "      <td>0.965407</td>\n",
       "      <td>0.766714</td>\n",
       "      <td>0.825118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.999981</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.999962</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.999886</td>\n",
       "      <td>0.999980</td>\n",
       "      <td>0.999886</td>\n",
       "      <td>0.999886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Best Threshold  F1 Score  Accuracy    Recall  \\\n",
       "0  Logistic Regression        0.315789  0.794844  0.965407  0.766714   \n",
       "1        Random Forest        0.315789  0.999981  0.999997  0.999962   \n",
       "2        XGBClassifier        0.105263  0.999886  0.999980  0.999886   \n",
       "\n",
       "   Precision  \n",
       "0   0.825118  \n",
       "1   1.000000  \n",
       "2   0.999886  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty list to store the results\n",
    "chart = list()\n",
    "\n",
    "# Iterate over the models and find the best threshold for each one\n",
    "for item, name in zip(models, model_names):\n",
    "    best_thresh, high_f1, high_acc, high_rec, high_pre = find_best_threshold(item, 20)\n",
    "    # Append the results to the chart list\n",
    "    chart.append([name, best_thresh, high_f1, high_acc, high_rec, high_pre])\n",
    "\n",
    "# Create a pandas dataframe from the chart list and display it\n",
    "chart = pd.DataFrame(chart, columns=['Model', 'Best Threshold', 'F1 Score', 'Accuracy', 'Recall', 'Precision'])\n",
    "#chart.to_csv('model_evaluation_scores.csv')\n",
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0769f7ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_confusion_matrix_val(model, threshold=0.5):\n",
    "    \"\"\"\n",
    "    Create a confusion matrix plot for the given model and threshold. \n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    model : sklearn classifier\n",
    "        The classification model to evaluate.\n",
    "    threshold : float, default=0.5\n",
    "        Probability threshold for binary classification.\n",
    "        \n",
    "    Returns:\n",
    "    --------\n",
    "    None\n",
    "    \n",
    "    \"\"\"\n",
    "    # Predict class 1 if probability of being in class 1 is greater than threshold\n",
    "    # (model.predict(X_test) does this automatically with a threshold of 0.5)\n",
    "    y_predict = (model.predict_proba(X_test)[:, 1] >= threshold)\n",
    "    \n",
    "    # calculate the confusion matrix\n",
    "    fraud_confusion = confusion_matrix(y_test, y_predict)\n",
    "    \n",
    "    # plot the confusion matrix as heatmap\n",
    "    plt.figure(dpi=100)\n",
    "    sns.set(font_scale=1)\n",
    "    sns.heatmap(fraud_confusion, cmap=plt.cm.Blues, annot=True, square=True, fmt='d',\n",
    "            xticklabels=['Not Fraud', 'Fraud'],\n",
    "            yticklabels=['Not Fraud', 'Fraud'])\n",
    "    \n",
    "    # calculate TP, FP, FN, and TN values from the confusion matrix\n",
    "    TP = fraud_confusion[0][0]\n",
    "    FP = fraud_confusion[0][1]\n",
    "    FN = fraud_confusion[1][0]\n",
    "    TN = fraud_confusion[1][1]\n",
    "    \n",
    "    # rotate y-axis ticks\n",
    "    plt.yticks(rotation = 0)\n",
    "    \n",
    "    # set plot title, x and y labels\n",
    "    plt.title('Predicted vs. Actual', fontsize = 20, pad = 10)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0bc4e378",
   "metadata": {},
   "outputs": [
    {
     "data": {
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S6dOnM3v2bG7fvs2qVavMFzo8qy1bthAREQHAyJEjadGixWP3jYmJwcfHhxs3bhAUFET37t3N90BLuG1BUldAPmzZsmXcunXLfNPTp0lY//SkcHvjxo0kt8+cOZPbt2+TJk0a89WhjzKZTApV8srSVYEiyZAlSxZzINm8efNj143ExcXRuHFjfHx8zJfBw78LuU+fPv3YcHXnzh2LBe/JUa5cOQBz1SUpJ0+eZObMmYwYMYITJ048tc+KFSuav960adNj9zt48CBly5alQYMGrFu3DgAXFxfz8U86duvWrU8dx5N4enqa1ydt3rzZ/PyVKlVKVG26dOkSfn5+VKtWLck747u4uNC3b1/zNKwRH16csGjdycnpsVeZJkibNq15Tdi1a9csPg8y4YKMW7du8ccffyR5fMJ918aNG2d+HYAnrslKuLLy5s2bj13ndujQoSduL168+GOnTQ8ePGiueBl5+wqR/wIFK5FkatmyJRAfjubOnZvkPgsXLuT06dOEh4dbVIbq1atnniYbPnx4kh9fM3HixKdOnz0qoRJy69Ytpk6dmuQ+kydPBuLfTB++/UHCfY4eHUuZMmXMa5Rmz56d6O7pED9NNGrUKKKjo7l06ZLFG2zCmA4dOkRQUFCiY2/cuMG0adOSe4qPlRBGNm3aZL6v0qOL1iF+zc+JEye4du0aixYtSjIUh4aGmq9+fNKi++QIDw8337upXr16yVo39/BFAkuXLjV/XbduXTJnzgzAuHHjkvy9WbRokfmWDAk3hIV/P1kgqWMSLqKIiYlh8+bNidoPHDjA9u3bkxxrQr+XLl1Kcrrw5s2bDBs2zPz9kz6qScQWKViJJNOHH35IiRIlABg7diwDBw7kr7/+4saNG5w4cYIvv/zS/PEdBQoUwM/Pz3xsxowZ6devHxC/SLlt27bs37+fGzduEBwcTL9+/Vi8eHGSH7PzJGXLluWtt94CYNasWQwaNIjjx48TERHBn3/+Sffu3c03x+zWrZvFm3zCG/a2bdsICwuzuN/Q4MGDcXBw4NatW7z33nssXryYixcvcu3aNbZv387HH39svnrxk08+sbijd7Nmzcy3BggMDGTChAmcP3+e69ev8+uvv/L+++/zzz//PPdVbgk33Dxy5AhXr17FyckpyfuLpU2bljZt2pjPtVOnTuzbt4+rV69y6dIl1q9fT/v27c33A0t4PRP069ePhg0b0rBhw2SN64cffjBXgR633utRRYsWpWzZskD87S5Onz4NxFe8En5v9u3bx0cffcSuXbuIiIjg1KlTTJw4kTFjxgBQp04d84eMw78/3xMnTnD06FEiIiLMIadOnTrmYD1o0CBWr17NlStXuHDhArNnz6Z9+/ZkyJAhybEm3D0+IiKCTp06cejQIa5fv865c+dYsmQJzZs3Jzg42Ly/Ne6tJfIy0RorkWRKmzYts2bNonPnzhw+fJiVK1eap3weVqBAAWbPnm2ebknwzjvvcO3aNSZOnMi+ffto1aqVRXvJkiUpXLgwP/74Y4rGNXToUCIjI/nll1/4/vvvLa5GS+Dn50e7du0stlWuXJk///yTY8eOUbNmTRwdHTl06BCOjo6UL1+eyZMn07dvX27cuMEXX3zBF198kajfli1b0r17d4ttdnZ2TJ06lU8//ZRDhw4xc+ZMZs6cabFP3759mTx58jMvYId/bwKaUFl5UnXI39+fY8eOsWnTJrZs2cKWLVsS7ePi4sKUKVMS3XLjn3/+4ezZs8keV1IfYZMc77zzjjmsLl26lEGDBgHxFcCrV68yceJEDhw4kORnJFaoUIFx48ZZbKtcuTKzZ8/m7t275jvLL1y4kMqVK5MnTx569OjB+PHjuXnzpsW0dcLYv/zyS9q3b5/ouTp27MiWLVs4ffo0O3fuZOfOnYn2KVeuHBkyZGDr1q2cP38+2a+BiC1QxUokBdzd3Vm6dCmjR4/Gx8eHrFmz4uDggKurK+XKlWPAgAH8+OOPj51O8vf3Z/ny5bzxxhu89tprpE2blvz589OpUyeWLFnyTJfVOzk5MXXqVL7++mvq1KljHpObmxt16tRh3rx5BAYGJjqua9eu+Pn54e7ujqOjI1myZOHy5cvm9rp167Jx40b8/f0pXrw4rq6uODo6kj17dho0aMC8efMYPnx4klW2zJkzs3DhQoYPH065cuXIlCmTef3V119//cx3Xn/Uw1N/T6oOOTo68vXXXzN+/Hhq1qxJtmzZzD+3okWL0r59e9avX0/16tWfazwHDhwwT502btw4RRXIRo0amcP4jz/+aDHN5u/vz6pVq3j77bfJnTu3+WNrypcvz9ChQ1m0aFGiUOnj48PgwYMpUKCA+ef78J33O3bsyLx586hTpw5ubm7m38V27dqxevVqihQpkuQ4M2XKxPLly/n000/x8PAgbdq0ODo6moPk6NGjWbx4sfmzEM+dO5fkPbxEbJWdSXdvExERETGEKlYiIiIiBlGwEhERETGIgpWIiIiIQRSsRERERAyiYCUiIiJiEAUrEREREYMoWImIiIgYRMFKRERExCAKViIiIiIGUbASERERMYiClYiIiIhBFKxEREREDKJgJSIiImIQhxc9AHk+6ct1fdFDEEk1EfumvughiKQqJyu8Cxv1PhF1SP8/JoeClYiIiC2z0+SUNenVFhERETGIKlYiIiK2zM7uRY/glaJgJSIiYss0FWhVClYiIiK2TBUrq1KMFRERETGIKlYiIiK2TFOBVqVgJSIiYss0FWhVirEiIiIiBlHFSkRExJZpKtCqFKxERERsmaYCrUoxVkRERMQgqliJiIjYMk0FWpWClYiIiC3TVKBVKcaKiIiIGEQVKxEREVumqUCrUrASERGxZZoKtCoFKxEREVumipVV6dUWERERMYgqViIiIrZMFSurUrASERGxZfZaY2VNirEiIiKSqm7cuMHgwYOpWbMm5cuX54MPPmD//v3m9rZt2+Lp6Wnx8PPzM7dHR0czdOhQqlatSrly5ejTpw/Xr1+3eI5du3bx9ttvU7ZsWRo2bMjPP/9s0W5EH8mhYCUiImLL7OyNeTyH3r17c+jQISZMmMDKlSspXrw4n3zyCWfOnAHgxIkT/O9//2P79u3mx5QpU8zHJ7RNmTKFBQsWcObMGbp3725uP336NP7+/vj4+BAUFETLli3p168fu3btMrSP5NBUoIiIiC17wbdbOH/+PDt27GDJkiVUqFABgM8//5xt27axZs0aWrduzbVr1yhbtizu7u6Jjg8LC+OHH35gxowZVKxYEYAJEybQsGFDDh06RLly5ViwYAGenp706tULAA8PD44dO8acOXOoWrWqIX0klypWIiIikmrc3NyYNWsWpUuXNm+zs7PDzs6OW7duceLECezs7ChYsGCSxx84cACAKlWqmLcVLFiQHDlysG/fPgD279+fKPxUqVKFAwcOYDKZDOkjuVSxEhERsWUGXRVYt27dJ7Zv2rQpye0ZM2akVq1aFts2bNjA+fPnGThwICEhIWTIkIFhw4axY8cOnJ2dadiwIZ07dyZt2rSEhYXh5uZGunTpLPrInj07ly9fBuDy5cvkzJkzUXtUVBQRERGG9JElS5Ynnn8CVaxERERsmZ2dMQ+DHDx4kICAAOrXr0/t2rUJCQkhOjqaMmXKMGfOHDp16sSKFSsIDAwEICoqirRp0ybqJ126dERHRwNw7969RPskfB8TE2NIH8mlipWIiIg81eMqUinx66+/0rdvX8qXL8+4ceMAGDZsGP379ydTpkwAFC1aFEdHR3r16kW/fv1wcnJKMthER0eTPn16ID4gPbpPwvfp06c3pI/kUsVKRETElr0EVwUCLF68mG7dulGnTh1mzJhhnpZzcHAwh6oERYoUAf6dnrtx40ai0BMeHk6OHDkAyJUrF+Hh4YnanZ2dyZAhgyF9JJeClYiIiC17CaYClyxZwhdffEGrVq2YMGGCxZSbn58fAQEBFvsfOXIER0dHChQoQIUKFYiLizMvQAc4e/YsYWFheHt7A1CxYkX27t1r0cfu3bspX7489vb2hvSRXApWIiIituwFV6zOnj3LyJEjef311/H39+fq1atcuXKFK1eucPv2bRo0aMCPP/7Id999R2hoKGvXrmXMmDF88sknuLq6kiNHDho3bkxgYCB79uzh8OHD9O7dm0qVKuHl5QXEh7PDhw8zbtw4Tp8+zbx581i/fj3t27cHMKSPZL/cppRcQygvnfTlur7oIYikmoh9U1/0EERSlZMVVjqnbzjBkH6i1vd+puNmzJjBxIkTk2xr3rw5o0aN4ttvv+Xbb78lNDQUd3d33n33XTp27GiuFN29e5eRI0eyYcMGAGrWrElgYCBubm7mvrZu3crYsWM5d+4cefLkoVu3bjRq1MjcbkQfyaFg9R+nYCW2TMFKbJ1VgtUbSYealIpa18uQfmydrgoUERGxZQbdx0qSR6+2iIiIiEFUsRIREbFlL/izAl81ClYiIiK2TFOBVqVXW0RERMQgqliJiIjYMlWsrErBSkRExJZpjZVVKcaKiIiIGEQVKxEREVumqUCrUrASERGxZZoKtCoFKxEREVumipVV6dUWERERMYgqViIiIrZMU4FWpWAlIiJiw+wUrKxKU4EiIiIiBlHFSkRExIapYmVdClYiIiK2TLnKqjQVKCIiImIQVaxERERsmKYCrUvBSkRExIYpWFmXpgJFREREDKKKlYiIiA1Txcq6FKxERERsmIKVdSlYiYiI2DLlKqvSGisRERERg6hiJSIiYsM0FWhdClYiIiI2TMHKujQVKCIiImIQVaxERERsmCpW1qVgJSIiYsMUrKxLU4EiIiIiBlHFSkRExJapYGVVClYiIiI2TFOB1qWpQBERERGDqGIlIiJiw1Sxsi4FKxERERumYGVdClYiIiK2TLnKqrTGSkRERMQgqliJiIjYME0FWpeClYiIiA1TsLIuTQWKiIiIGEQVKxERERumipV1KViJiIjYMAUr69JUoIiIiIhBVLESERGxZSpYWZWClYiIiA3TVKB1aSpQRERExCCqWImIiNgwVaysS8FKRETEhilYWZeClYiIiC1TrrIqrbESERERMcgLD1a+vr74+vpy586dRG0DBgzAz88v2X2ZTCZWrVrFtWvXnvh8np6eST4WL178TOfwPPz8/BgwYIDVn9fW2NnZ0f6dGuxdFsCVHeM5tuZ/jOnzNhlcnMz7vOaeiW9GfMTFLaMJ2zaWn2d0paxnHot+PAvm4Puv/AnfPo5Lv41mydhP8MjnbrFPobzZiDo0NdFj/4qBSY4td/bM/LN1DD4ViiRqq17Og1/n9iRs21hC1g5j3GctcHVOZ8ArIq+6Xj268sbrvhbb9uzeRbuPWlOjqje+NavTu0c3Qi9csNgnJiaGyV9NoEHd2lQqX4aWzd9kw/q1j32e37ZspmxJz1Q5BzGGnZ2dIQ9JnpdiKvDSpUuMGTOGYcOGPVc/+/btY8CAAWzatOmJ+7Vr14527dol2u7q6vpczy8vTp+P6zGkcxMmLtzElr0nKJIvO4M7N6FE4ddo0mkqrs7p+GVuT6Lvx9JtxHfci44loENDfpreFe93R3L56i3yv5aVzd/05ubtKHqNXkH4tVu0bV6N3+b3oXqrMVz45zqAOYw17DiZqHsx5jHcfejrBHlyZGb1tC5kzuCcqK14oZz8NL0rO/84Tev+83gte2ZG9HiLArmz8U7Pman0Ssmr4Kc1P7L511947bXc5m2HDh6gU8dPqF2nLl+OHkdU1F1mzZjGR60/YOWPa3BzywLAwP592bVzBz169SFf/gL8tPpH+vftjauLK9V9alo8z769ewjo18eq5yYpp1BkXS9FsMqbNy/Lli2jYcOGVKtW7Zn7MZlMydrP2dkZd3f3p+8o/wl2dnb0/vh15qzcweApqwHYsucE129Gsmh0O8qXyEf96iXIktmFcm8P5/LVWwAcPHaenUv6U7NiEZavP0C3VrVxdkpL9VZjOHcpvur5665gti7sw9CuTWk7aAEAZYrm4eLlCH7fF/LEMbVqUokvezV/7B+19xt5YzKZeLfXLCKj4kOZQxp7pgZ+QL5cblz4J8Kw10heHeHhYYweOYIcOXNabP9m7mwKeXgwbuIk7O3jJyu8ypWnQd3arP5hFR+1/YSDB/bzy8YNfD1jFjV8agFQuUpVQi+cZ/v2reZgFRl5h3lzZvPN3Nm4umYA7lr1HEVeZi98KhDgzTffpGrVqgwaNCjJKcEEN27cYOjQodSqVYsyZcrw/vvvs2fPHgD27NlDmzZtAKhbty5BQUHPPB5fX19Gjx5No0aNqFy5Mnv37uXmzZsEBgbi4+NDyZIlqVq1KoGBgURFRZmf39PTk4sXL5r7eXRbTEwMI0eOpGrVqlSoUIGxY8cSFxf3zOOUeBldnPju570sX7ffYvuJc2EAFMqTjeb1vFj16x/mUAUQdu02Hg0CWb7+AACeBXNy/Mxlc6iC+LC+7cApGvqUNG8r45mHP0/8+3NOSukirzFl0Pss+Xkvn3y+IMl9nNI5cj/2AXfv3Tdvu34zEoAsmVySc+oiiQwdHEjV6tWpXLmqxfbSZcrSyu8jc6gCyJ49B66uGQgNjZ8O/GXjevLmzWcOVRD/j4QF3y6lf0Cgeduqld+z8vvlBAQO5oNWrVP5jOR5aSrQul6KYGVnZ8eIESO4efMmo0ePTnKfBw8e0K5dO/bv38/YsWMJCgqiaNGifPLJJxw+fJhy5coxZcoUAFasWEGjRo2ea0yLFy8mMDCQOXPm4OXlxYABAzh27BhTp05lw4YNBAQE8MMPP7Bs2bJk9zl8+HDWrl3LqFGjWLp0KZcvX2b//v1PP1Ce6OadKPqM+Z5df56x2N60dhkAjp+5TPGCuTh5LozBnRtzZuMIbu2dxPpZ3Sle6N9/1V+7EUnObBlxcLD836JQnmxkzuCMW8b46byynrnJ4OLElvm9idg9kbO/jOSL7m9aHBd6OYJSbw6l//gg7kbdJykLftgFwJg+b5MlkwvFC+VkYMdGHAm5xOGQS8//wsgrJ+j7FRw7dpSAQZ8nauvg34nmb79jsW3/vr3cunUTD4/49X8ngoMpXKQIa39aw1tN36B8mRK81fQNNm/61eK4WrV9WbdxMy3ffT/1TkYMo2BlXS/FVCBA7ty56d+/P4MHD6ZBgwbUqFHDon379u0cPXqUNWvWULRoUQCGDh3KkSNHmDt3LpMmTSJTpkwAZMmSBScnp0TPkWDmzJnMmzfPYlvTpk0t1njVqlXLYlqyevXqeHt74+kZv0gzT548LF68mJCQx08HPezOnTsEBQUxZMgQatWK/9fgyJEj2b17d7KOl5TxLpWfvm1f56ffjxB+7RaOjmno1tqXsxev0nnYEtKldeDzTo3ZOKcnld77kn+u3GThj7t5742KzP2iDUOmruHWnXt82Nib16uVAMAlfVrs7e3IncONNGnSMGjSD1z45zp1KnnS5+N65MnhZp4ujLh1l4hbT54eOXb6HwZN+pGvBrxL11Z1ADj/9zXqtZtIXFzyprVFEvz99yXGjfmSYcO/NK+XepKIiOsMHfI57tmz8+Zbb8Vvu36dC+fPcezYUbp170U2d3eWLV1C7x5d+Xr6LPNUYN58+VLzVET+016aYAXw3nvvsWHDBgIDA/npp58s2kJCQsiQIYM5VEF8Cq9YsSLbt29P0fO8//77ia42fHThev78+S2+//DDD9m8eTOrVq3i3LlznDp1iosXL1KoUKFkPefZs2e5f/8+pUuXNm9Lly4dJUqUSNHY5emqli3Eysmfcu7va/gPWUz6dI7mtje7fG1ez3Tw2AWO/DiYT9+ryZCpa9i8J5i2A+cz5rMWvNuwIgCbdgczbv5GBndqwt1797l7L4bGn07l1IVw82L27QdOER0Ty9CuTRk1Zz0nzoYla5x9277OF92bMWPp7/yw+U+yZXZhQIc3WDuzO/XaTST8+m2DXxmxVSaTiSGBA6lRsxb16jd46v5XroTTqeMnXL0Szsy583Fxif/7d//+fa5cucLSFUEULxE//V2pchVavt2MmTOmJVq8Lv8RL0Gx6caNG0yYMIHffvuNO3fu4OnpSZ8+fahYMf5v7a5duxg7diynT58mV65cdOvWjcaNG5uPj46OZtSoUaxfv5579+7h6+vLoEGDyJLl339EWKOP5HgppgIfNnz4cG7fvs2XX35psf1xC9NNJhMODinLh5kyZSJ//vwWj6xZs1rs83DFKy4uDn9/f4YPH46DgwONGjVi5syZlC9f/onP8+DBA/PXCWXUR88jpWOXJ3unfnl+ntGV0MvXaeQ/hes3I7l99x4AW/efNIcqiJ+uCz4bRtli/95yYem6/RSoN5BSzYbiUX8QTTpNxSFNGh48iOPG7bvci77P5j3B5lCVYP22v4D4he3JkSaNPQM6NOS7n/fSa/QKft8XwspfDtHIfzI5s2Wk10f1nvelkFfI0iXfcjLkBP36DyQ2NpbY2Fjz35rY2FiLtZwnQ07g98F7hF0OY9rMOZQpU9bc5uzigru7uzlUAaRJk4YqVaoSfPyY9U5IDPUyTAX27t2bQ4cOMWHCBFauXEnx4sX55JNPOHPmDKdPn8bf3x8fHx+CgoJo2bIl/fr1Y9euXebj//e//7F9+3amTJnCggULOHPmDN27dze3W6uP5Hjp3tVfe+01BgwYQGBgIHnz5iVXrlwAeHp6cvv2bUJCQsxVK5PJxIEDByhcuDCQepeUHj9+nK1bt7J8+XLKlo3/I3T//n0uXLhA3rx5AXB0jK+KPLz4/ty5c+avCxYsSLp06Th48CDFixcH4v/gBQcHU7ly5VQZ96ump19dRvRsxtb9J3mvz2xu3YkPVLfu3CP8+m3SpU386+7okIZ7/7943LNgDiqUzM+Sn/Zy+sIV8z5exfJy5OQl4uJMeORzp7Z3Ub7fcJCbd6LM+6R3SgvAlYjkVZnc3VxxSZ8u0bqwKxF3CDkfTnGPnI85UiSxX3/ZQEREBHVr10jUVqFsST7t3JVOXbqxd89uenXvgqtrBr5Z9C2FC1veWy1//vyEXf4Hk8lk8fc0NjaWdOkev7xCXm4ven3U+fPn2bFjB0uWLKFChQoAfP7552zbto01a9Zw7do1PD096dWrFwAeHh4cO3aMOXPmULVqVcLCwvjhhx+YMWOGucI1YcIEGjZsyKFDhyhXrhwLFixI9T6S66WrWAG0bNmSGjVqEBoaat5Wo0YNihcvTp8+fdi7dy+nT59m2LBhhISE8NFHHwHxt1EACA4OJjIy0rDxZMuWDQcHB9atW0doaChHjhyhZ8+eXLlyhZiY+ApI0aJFcXZ2ZtasWVy4cIFt27bxzTffmPtwcXGhdevWTJ48mY0bN3L69GmGDBlCWFjypo3kyT5pUZ0vezdn5S+HeLPLNHOoSrBh+1HqVPIka+Z/r7Yrkj87RfNnZ8eh0wCU8MjF3C/aUCR/dvM+xQrl5PWqxVmz5TAAObNlZGrgB7z9ejmL/t+pX56bt6M4dCyU5Ai/fptrNyKpXq6wxfasmV0oks+dsxcff5NbkUd9PmQoS5Z9b/GoWasO7u7uLFn2PS1avsvx48fo1uVTcuTMxaLvliUKVQA1fGpx48YNdu3cYd52PyaGHTu2Uf7/3xBFUsrNzY1Zs2ZZLIVJqILdunWL/fv3JwouVapU4cCBA+YCSsK2BAULFiRHjhzs27cPwCp9JNdLV7FKMHz4cJo2bWr+Pk2aNMybN4/Ro0fTtWtXYmJiKFWqFPPnz8fLywuIDze1atWiZ8+e9O7dO8mbgD6LHDlyMGrUKKZMmcK3336Lu7s7tWvX5uOPP2bz5s1A/BqtsWPHMm7cOBo1akSxYsXo378/Xbp0MffTp08f0qVLx7Bhw4iMjOSNN97A19f3cU8ryZQjawbG9GnBuUtXmbH0d8oVz2vRfubiVUbOWkfTOmVYM60rX85aR1rHNPyva1MuhkXwzaqdAKzffpTTF64wf+THDJv+Exld0jOy51ucvXSVKd9uAWDnoTNs3hPMqN7NSe/kyPEzl3mjRkk6f1CL/uODLKpYTxIXZ2L4jJ+ZOOBdbkXeI+iXg2TL7ErfdvV5EGdi0qIn3+RW5GEFCiZe65k5c2YcHdNSslT8m1m3zp8Sez+Wzl26cfmff7j8zz/mfd3cspA3Xz4aNWnKd0sWM7B/X7r37EOOHDn4dvFCwi5fZtyESVY7HzHWi76gL2PGjOaLthJs2LCB8+fPM3DgQFatWkXOR+67lj17dqKiooiIiCAsLAw3NzfSpUuXaJ/Lly8DcPny5VTv4+G1WE/ywoNVQjB5VK5cuRLdiiBLliyPvR0DQNq0aZk1a9YzPd/T9mnatKlF0EsQEBBg/rpevXrUq2e5NubEiRPmr9OkSUOPHj3o0aPHU8cgydegRkmc06elQO5sbPqmd6L2DoMXsXjNHup8PIHh3Zsxd3gbHsTFsXl3MP3GBXHnbjQAUffu82bXrxnb9x3mj/yYe9GxbNxxjCFTV3M7Mr4CZjKZeL/PHAb5v0G3VnXImS0TZy5epcvw75i/KmXz8DOWbeXG7Sh6+PnS5s3KXLsRyY5Dp3mv92zO/62KlRjnYmioeY1Un17dE7W/2aw5X4wchaOjI7PmfMPkSROYMnkidyMjKVa8BLPmzrdYdyX/LUZNBdatW/eJ7U/71JMEBw8eJCAggPr161O7dm3u3btH2rRpLfZJ+D4mJoaoqKhE7RB/AVh0dPzfb2v0kVwvPFiJPK+FP+5m4Y9Pv21F8JnLT/2omDOhV2nRY8YT97kdeY8BE1YxYMKqZI1v24GTpC/XNcm2pWv3sXTtvmT1I5ISX4wcZf46T968/Hn0xBP2/lfGTJkIHDyUwMFDk7V/py7d6NSl2zONUV49v/76K3379qV8+fKMGzcOiA83jwaXhO/Tp0+Pk5NTksEmOjqa9OnTW62P5FKwEhERsWFGTQUmtyL1OIsXL2bEiBE0bNiQ0aNHm6tBuXLlIjw83GLf8PBwnJ2dyZAhAzlz5uTGjRvExMRYVJTCw8PJkSOH1fpIrpdy8bqIiIgY42W43cKSJUv44osvaNWqFRMmTLAINxUrVmTv3r0W++/evZvy5ctjb29PhQoViIuLMy9Ah/h7Q4aFheHt7W21PpJLwUpERERSzdmzZxk5ciSvv/46/v7+XL16lStXrnDlyhVu376Nn58fhw8fZty4cZw+fZp58+axfv162rdvD8RfQNa4cWMCAwPZs2cPhw8fpnfv3lSqVMl88Zo1+kguO1NKriGUl87j1u6I2IKIfVNf9BBEUpWTFRbkFBuwwZB+gkc9/a7+SZkxYwYTJ05Msq158+aMGjWKrVu3MnbsWM6dO0eePHno1q2bxWf+3r17l5EjR7JhQ/y51KxZk8DAQNzc3Mz7WKOP5FCw+o9TsBJbpmAlts4awarEwI2G9HNsZH1D+rF1mgoUERERMYiuChQREbFhL/oGoa8aBSsREREb9qI/K/BVo2AlIiJiw5SrrEtrrEREREQMooqViIiIDdNUoHUpWImIiNgwBSvr0lSgiIiIiEFUsRIREbFhKlhZl4KViIiIDdNUoHVpKlBERETEIKpYiYiI2DAVrKxLwUpERMSGaSrQujQVKCIiImIQVaxERERsmApW1qVgJSIiYsM0FWhdClYiIiI2TLnKurTGSkRERMQgqliJiIjYME0FWpeClYiIiA1TrrIuTQWKiIiIGEQVKxERERumqUDrUrASERGxYcpV1qWpQBERERGDqGIlIiJiwzQVaF0KViIiIjZMucq6NBUoIiIiYhBVrERERGyYpgKtS8FKRETEhilYWZeClYiIiA1TrrIurbESERERMYgqViIiIjZMU4HWpWAlIiJiw5SrrEtTgSIiIiIGUcVKRETEhmkq0LoUrERERGyYcpV1aSpQRERExCCqWImIiNgwe5WsrErBSkRExIYpV1mXpgJFREREDKKKlYiIiA3TVYHWpWAlIiJiw+yVq6xKwUpERMSGqWJlXVpjJSIiImIQVaxERERsmApW1qVgJSIiYsPsULKyJk0FioiIiBhEFSsREREbpqsCrUvBSkRExIbpqkDr0lSgiIiIiEFUsRIREbFhKlhZl4KViIiIDbNXsrIqTQWKiIiIGEQVKxERERumgpV1KViJiIjYMF0VaF0KViIiIjZMucq6tMZKRERExCCqWImIiNgwXRVoXapYiYiI2DA7gx5GmTlzJn5+fhbbAgMD8fT0tHj4+vqa2+Pi4pg8eTI+Pj54eXnRoUMHQkNDLfo4fvw4rVu3xsvLC19fXxYuXGjRbkQfyaFgJSIiIlbx7bff8tVXXyXafuLECT799FO2b99ufnz//ffm9mnTprFkyRK++OILli5dSlxcHO3btycmJgaAiIgI2rZtS758+Vi5ciVdunRh3LhxrFy50tA+kkNTgSIiIjbsZbgqMCwsjCFDhrBnzx4KFChg0WYymTh16hQdO3bE3d090bExMTHMmzePvn37Urt2bQAmTpyIj48PGzdupEmTJixfvhxHR0eGDRuGg4MDHh4enD9/nlmzZtGiRQtD+kiuZAWrYsWKJfsHY2dnx7Fjx5I9ABEREUk99i8+V3H06FEcHR1ZvXo1X3/9NZcuXTK3Xbhwgbt371KoUKEkjw0ODiYyMpKqVauat2XMmJESJUqwb98+mjRpwv79+6lUqRIODv/GmipVqjBz5kyuXr3K33///dx9ZMuWLVnnmqxg1aVLl5ci8YqIiMiLUbdu3Se2b9q06bFtvr6+FmumHhYSEgLAokWL2Lp1K/b29tSsWZNevXqRIUMGLl++DECuXLksjsuePbu57fLlyxQtWjRRO8A///xjSB+GBqtu3bolqzMRERF5ubzshZGQkBDs7e3Jnj07M2bM4MKFC4wZM4aTJ0+yYMECoqKiAEibNq3FcenSpePmzZsA3Lt3L8l2gOjoaEP6SK5nWmMVFhbGgQMHzAu+IH61fVRUFPv372fixInP0q2IiIgYzKhc9aSK1PPo1KkTH374IW5ubgAULVoUd3d33n33XY4cOYKTkxMQv9Yq4WuIDzvp06cHwMnJySKTJLQDODs7G9JHcqU4WK1fv56+ffsSGxtrTsEmk8n89ePmSEVEREQeZW9vbw5VCYoUKQLET88lTN+Fh4eTL18+8z7h4eF4enoCkDNnTsLDwy36SPg+R44cxMbGPncfyT6fZO/5/2bMmEHJkiUJCgri7bffplmzZvz888989tlnpEmThoEDB6a0SxEREUkldnZ2hjxSS79+/fj4448tth05cgSAwoULU6xYMVxdXdmzZ4+5/datWxw7dgxvb28AvL29OXDgAA8ePDDvs3v3bgoWLEjWrFkN6SO5Uhyszp49S4cOHShRogSVK1cmODgYDw8P2rVrR5s2bZgxY0ZKuxQREZFUYm9nzCO1NGjQgF27djF16lQuXLjA77//zsCBA2nSpAkeHh6kTZuW1q1bM27cODZt2kRwcDC9evUiZ86c1K9fH4AWLVpw584dBg0axKlTpwgKCmL+/Pn4+/sDGNJHcqV4KtDe3p5MmTIBkD9/fs6cOUNcXJx5Ff+qVatS2qWIiIikkpd98XrdunX56quvmDVrFrNnzyZDhgw0bdqUnj17mvfp3r07sbGxBAYGcu/ePby9vZk7dy6Ojo4AZM2alTlz5jBixAiaN2+Ou7s7/fr1o3nz5ob2kRx2JpPJlJIDWrRoQf369fH39+f27dt4e3sTFBREiRIl2LhxIwEBARw4cCBFg5Bnl75c1xc9BJFUE7Fv6osegkiqcrLCbbrbLj1iSD/fvF/akH5sXYp/pO+//z5Dhgzh7t279OrViypVqhAQEMA777zD4sWLKVmyZGqMU0RERJ7By12vsj0pDlYtW7YkJiaGixcvAvDFF1/QoUMHRowYQe7cuRk0aJDhgxQREZFnY/+STwXammcqQrZq1cr8dd68eVm3bh0RERFkyZLFsIGJiIiI/NcYMrtrZ2enUCUiIvISUsHKulIcrJLzgczHjx9/5gGJiIiIcV72qwJtTYqDVVIfyBwZGcnBgwe5cOECffv2NWxwIiIiIv8lKQ5WT/pA5n79+vHXX3/RokWL5xqUiIiIGEMFK+tK8Z3Xn6R58+asXbvWyC5FRETkOdjb2RnykOQxNFhduHDB/EGHIiIiIq+aFE8FTp2a+E7IcXFxXL58mbVr11KnTh1DBiYiIiLPT8Um6zIkWAG4urpSr149AgICnntQIiIiYgxdFWhdKQ5WwcHBqTEOeUbX9+qz1MR23bmnpQVi25xcU//DAg1d8yNPleLXOyAggNDQ0CTbzpw5w6effvrcgxIRERH5L0pWVP7777/NX69atYp69eqRJk2aRPtt3bqVnTt3Gjc6EREReS6aCrSuZAWroUOHsnXrViD+B9S1a9ck9zOZTFSvXt240YmIiMhzsVeusqpkBathw4axc+dOTCYTAwcOpFOnTuTLl89iH3t7ezJmzEjlypVTZaAiIiIiL7tkBascOXLQvHlzIL5iVbt2bTJmzGieDrx37x73798nQ4YMqTdSERERSTFVrKwrxYvXmzRpwldffcW7775r3nbw4EGqVq3K6NGjiYuLM3SAIiIi8uzs7OwMeUjypDhYTZkyhdWrV9OkSRPzthIlStC3b1+WL1/OnDlzDB2giIiIyH9Fim+gsWbNGvr378/7779v3pY5c2Y+/vhjHBwcWLhwIR07djR0kCIiIvJsNBVoXSkOVhEREeTNmzfJtkKFCnH58uXnHpSIiIgYQ7N41pXiqcBChQqxYcOGJNs2b95M/vz5n3tQIiIiIv9FKa5YtWnThgEDBnDjxg3q1atH1qxZuX79Olu2bGHdunV8+eWXqTFOEREReQb2KllZVYqD1VtvvUVkZCTTpk1j48aN5u1ubm4MHjyYZs2aGTpAEREReXb6rEDreqZPf2zVqhUffvghZ8+e5caNG2TMmJEMGTKwYsUKfH192bJli9HjFBERkWeggpV1PfPHatvZ2VGoUCG2bdvG3Llz+f3334mNjSVPnjxGjk9ERETkP+OZgtX169f5/vvvWb58OZcuXcLV1ZXmzZvTrFkzKlasaPQYRURE5BlpjZV1pShY7d69m2XLlvHrr7/y4MEDKlSowKVLl/j666+pVKlSao1RREREnpFylXUlK1jNnz+fZcuWcfbsWfLnz0/nzp1p3rw5zs7OVKpUSbe6FxERESGZwWrUqFF4enqycOFCi8rU7du3U21gIiIi8vx053XrStZVmI0bN+b8+fP4+/vTuXNnfvnlF2JjY1N7bCIiIvKc7O3sDHlI8iSrYjV+/Hju3LnDmjVrCAoKolu3bri5uVGvXj196rWIiIjI/7MzmUymlB508uRJVq5cyZo1a7h27Rr58uWjcePGNG7cmMKFC6fGOOUxou6/6BGIpJ7IaFXGxbZlc33mux4l2xe/njKkn8/r6f09OZ4pWCWIjY1ly5YtrFy5ku3bt/PgwQOKFCnC6tWrjRyjPIGCldgyBSuxddYIViM2GROsBtVVsEqO5/qJOjg48Prrr/P6669z9epVVq1axapVq4wam4iIiMh/ynNVrOTFU8VKbJkqVmLrrFGxGrnptCH9DKzrYUg/ti71f6IiIiLywuh2C9alYCUiImLDFKysK1n3sRIRERGRp1PFSkRExIbpXpPWpWAlIiJiwzQVaF2aChQRERExiCpWIiIiNkwzgdalYCUiImLD9AHK1qWpQBERERGDqGIlIiJiw7R43boUrERERGyYZgKtS1OBIiIiIgZRxUpERMSG2aOSlTUpWImIiNgwTQVal4KViIiIDdPidevSGisRERERg6hiJSIiYsN0g1DrUrASERGxYcpV1qWpQBERERGDqGIlIiJiwzQVaF0KViIiIjZMucq6NBUoIiIiYhAFKxERERtmb9DDKDNnzsTPz89i2/Hjx2ndujVeXl74+vqycOFCi/a4uDgmT56Mj48PXl5edOjQgdDQUKv3kRwKViIiIjbMzs7OkIcRvv32W7766iuLbREREbRt25Z8+fKxcuVKunTpwrhx41i5cqV5n2nTprFkyRK++OILli5dSlxcHO3btycmJsaqfSSH1liJiIhIqgoLC2PIkCHs2bOHAgUKWLQtX74cR0dHhg0bhoODAx4eHpw/f55Zs2bRokULYmJimDdvHn379qV27doATJw4ER8fHzZu3EiTJk2s0kdyqWIlIiJiw+wMejyPo0eP4ujoyOrVqylbtqxF2/79+6lUqRIODv/WeqpUqcK5c+e4evUqwcHBREZGUrVqVXN7xowZKVGiBPv27bNaH8mlipWIiIgNM+p2C3Xr1n1i+6ZNmx7b5uvri6+vb5Jtly9fpmjRohbbsmfPDsA///zD5cuXAciVK1eifRLarNFHtmzZHnt+D1PFSkRExIa9DBWrJ7l37x5p06a12JYuXToAoqOjiYqKAkhyn+joaKv1kVyqWImIiMhTPaki9TycnJzMC8gTJAQZZ2dnnJycAIiJiTF/nbBP+vTprdZHcqliJSIiYsPs7Ix5pJacOXMSHh5usS3h+xw5cpin75LaJ0eOHFbrI7kUrERERGzYy3S7haR4e3tz4MABHjx4YN62e/duChYsSNasWSlWrBiurq7s2bPH3H7r1i2OHTuGt7e31fpILgUrEREReWFatGjBnTt3GDRoEKdOnSIoKIj58+fj7+8PxK+Lat26NePGjWPTpk0EBwfTq1cvcubMSf369a3WR3JpjZWIiIgNe9krKFmzZmXOnDmMGDGC5s2b4+7uTr9+/WjevLl5n+7duxMbG0tgYCD37t3D29ubuXPn4ujoaNU+ksPOZDKZDHhd5AWJuv+iRyCSeiKjY1/0EERSVTbX1K9vLP/jb0P6edfrNUP6sXUve5AVERER+c/QVKCIiIgNS817UEliClYiIiI2LDWv6JPENBUoIiIiYhBVrERERGyYKijWpWAlIiJiwzQVaF0KViIiIjZMscq6VCEUERERMYgqViIiIjZMM4HWpWAlIiJiw+w1GWhVmgoUERERMYgqViIiIjZMU4HWpWAlIiJiw+w0FWhVmgoUERERMYgqViIiIjZMU4HWpWAlIiJiw3RVoHVpKlBERETEIKpYiYiI2DBNBVqXgpWIiIgNU7CyLgUrERERG6bbLViX1liJiIiIGEQVKxERERtmr4KVVSlYiYiI2DBNBVqXpgJFREREDKKKlYiIiA3TVYHWpWAlIiJiwzQVaF2aChQRERExyCtXsfLz82Pv3r1JtrVr147+/ftbbSwDBgzg0qVLLFq0yGrPKXD4zz+Y/NV4/vrrCM7pnalWw4feffqRJWtWi/1iY2Np2+ZDqlX3oVOXbon6eVz7Jx/7cWB/0r9jAH/8dcK4k5FXVlxcHD8GrWDViqX8fSkUtyxZqVGrDu39u+Li6grAxdDzTB4/mj8PHSRNmjTUqdeAzt17m9sfNWXCGE4EH2PqrPmJ2pYvWcT3y77lypVwChQoRIfO3alWo+ZjxxfQtwchwcdY+dMvhpyvPDtdFWhdr1ywAnjjjTcYNGhQou3p06d/AaMRazp29C86tGtD5SrVmPDVVK5cCWfKVxPoeb4LC79dat4vOjqawIH9OHL4T6pV90nUz5PaB34+hMg7dyy2hYZe4POB/Wnxzrupc2Lyyvl2wVxmT5/CB35tqVipCqHnzzF7xhTOnD7FV1/P5s6d23T3b0eWbNkIHDqSiOvXmDZ5Av/8fZEJU2cl6u+7RfNZ+u0CylXwTtS2dPF8pk2eQNsOnShWohQ//biSAb27MmXmN5QtVyHR/hvWrmHrll/Jmeu1VDl3SRlNBVrXKxmsnJyccHd3f9HDkBfgqwlj8SxWgq+mTMPePn4m3NXFlTGjRnDpYii58+Tl4IH9jBoxjLCwsCT7eFq7h0dhi+8fPHjA6C+HU9SzGP0CEgd6kZSKi4vj2wXzaPZ2Szp16wWAd+WqZMycmSEBfQk+fpR9u3dx8+ZN5n37PZnd3ABwz5GTvt0/5fAfBynjVR6Avy9dZMrEsezYugVX1wyJniv63j3mz5nJ+60/om2HTgBUqVYD/7atmDd7OpOmzbHY/8qVcCaOHUn2HDlT8yUQeWlpjdUj/Pz8+Pzzz2nZsiUVK1Zk9erVxMTEMHr0aHx9fSlVqhSVKlWiR48eXL9+HYCLFy/i6enJnj17zP08us1kMjFt2jRq1qyJl5cXAQEBREdHv5BzfFXduBHB/n17ee/9D8yhCqDu6/XZsOl3cufJC0CPbp3Imes1vlsRlGQ/T2t/1PcrlnL82FEGDR6Ko2Pa5z8ReeVFRt6hQaOmvN6wscX2/AUKAnDpYih7d++gbLny5lAFUKlKNZxdXNi1Y5t52+Txo7l44TyTZ8yjiGexRM919K/D3L59i5p16pm32dnZUcu3Hof27yX63j2L/Ud9MZhKVapTwbuyIecqz8/OzpiHJM8rWbF6mhUrVjB27Fg8PT1xd3dnzJgxbNmyhVGjRpE7d25OnDhBQEAA06dPT3JKMSmzZs1izpw5DBs2jBIlSrBs2TKCgoKoVKlSKp+NJDgZcoK4uDjcsmQhoH8fft+yGZMJ6tZ7nX4BgWTMmBGAefMXU6So52P7eVr7w+7ejWT61Mk0btqM0qXLGHIeIhkyZKRXv4GJtm/7bTMAhQoV5tzZM9R9vaFFe5o0aXjttdxcOH/WvK1j5+4U9CiM3WPeOc+fPQNAvnz5LbbnyZOPBw8ecOliKIUKFwFg9arvOXH8GIuX/8jUr8Y++wmKoZSJrOuVDFZr1qxhw4YNFtsqVKjAnDnxJe3ixYvTtGlTc1vp0qVp2LAhFStWBCB37txUq1aNkJCQZD2fyWRi0aJFtGnThiZNmgAQEBBgUeGS1JdQYRzy+UBq1KjJxMnTuHD+HJMnTeDixVC+WbgEOzu7p4am5IYqgB+CVnLr1i3ad/B/rrGLPM3RI4dZNH8O1WvWplDhIkTeuY2Lq0ui/ZydXYi8E2n+PiEUPc6dO7fjj3OxXPDu7BLfd2Rk/HrCy//8zZSJYxg4ZLhFlUxePHuVm6zqlQxWvr6+9O3b12Kbk5OT+ev8+S3/ZdasWTN27tzJuHHjOHfuHGfOnOHs2bPmoPU0ERERXLlyhdKlS1ts9/Ly4vTp0894FpJSsffvA1CiREmGDBsBQOUqVcmQISMD+vVm184dVKtew9DnXLb0W2rV9jVP0YikhsN/HKRfzy689lpuBg2J/92Oi4t77P72KbhMLM5kemK7nb09JpOJkUMDqVq9JnXq1k923yK26JVcY+Xi4kL+/PktHjly5DC3PxyyAAYPHkyvXr24f/8+vr6+jB8/nsaNGz/arYUHDx6Yv04osZse+QPl4PBK5toXJuFf2D616lhsr1Yj/qq+E8HHDH2+kBPBnD93jkZNmj59Z5Fn9OvGdfTo3J4cOXMyafo8MmXODICrawbuRt5NtH9kZCQuSSxSfxzX/781w927kRbbEypVrq6urFy+hNMnQ+jRpz+xsbHExsaa/97FxsY+MeRJ6rMz6CHJo3f2p4iIiGDZsmVMnDiRRo0ambefOXMGZ2dnABwdHQG489Al9ufOnTN/7ebmRq5cuThw4AD16v27APSvv/4yHyupL1/+AgDcj4mx2B4bGwtAunROjx7yXLb+/htO6dPjU7O2of2KJFiy8BumTR5PuQrefDluMq4Z/g1M+fIX4GLoBYv9Hzx4wN9/X6SWb71Hu3qsfPn/f0F86AUylvy36n4p9AKOjo68ljsvv236ghs3InizQe1Ex9eqXJZ2HTvziX+XFJ6dGEapyKoUrJ7C1dWVDBkysGnTJkqWLMm9e/dYvHgxR48epWzZsgBkz56d3Llzs2DBAgoUKMCNGzeYNGmSxWLQDh06MHr0aAoVKkTFihX58ccfOXz4MBUqJL4HjKSOQoU8eC13btav+5n3P2xt/vn8vmUTAOUrJG9qN7mOHP6D4sVLJKqAihjhh5XL+XrSOOrWf4PPh41MdMWpd5VqLFk4j4iI67i5ZQFg7+6dRN29S6Uq1ZL9PKXLeJE+fXq2bNpI8f8PViaTid82/4pXeW/Spk3LZwOHJKpofTNrGsHHjzF64lSyuWd/zrMV+e9QsHoKR0dHJk2axKhRo2jatCmZMmWicuXK9O7dm5kzZxIVFUX69OkZM2YMI0eOpFmzZuTPn5+AgAA6duxo7qdVq1bExcUxffp0rl69io+PD++88w5nz559wrOLkezs7OjVpx/9+vSkf99evP3Ou5w5fYqpkydS7/UGFCtewtDnO3kyhKrVjF2zJQJw7eoVJo8fTa7XctPi3Q84EXzcoj13nrw0b/k+K5ctoWfn9rTr0JmbN28wbfJ4qlT3oXTZcsl+Lqf06fnAry3fzJ6Og4MjpcuW4+cfgzhx/BhTZ30DkOQawoyZMuPo6EjxEqWe72TluekGodb1ygWrp318TFLt1atXZ82aNYm2+/v/e6VXxYoVCQqyvK/RsWOWa3b8/Pzw8/NLyXDFYK/Xb8ikKdOZOeNrunfxJ1OmTLzz7vt07d7L8Oe6fu2a+RYOIkbatWMb0dH3+OfvS3Ru3yZR+8Ahw2n8ZnOmzPyGSeNHMTSwP84uLvjWa0CXnp+l+PnaduhEmjRpWB20gu8Wz6dgQQ9GT5xqvsmovNx0UaB12ZkeXVEt/ylR91/0CERST2R07Isegkiqyuaa+vWNvWduGtJPpUKZDOnH1r1yFSsREZFXiQpW1qVgJSIiYsuUrKzqlbyPlYiIiEhqUMVKRETEhumqQOtSsBIREbFhuirQuhSsREREbJhylXVpjZWIiIiIQVSxEhERsWUqWVmVgpWIiIgN0+J169JUoIiIiIhBVLESERGxYboq0LoUrERERGyYcpV1aSpQRERExCCqWImIiNgylaysSsFKRETEhumqQOvSVKCIiIiIQVSxEhERsWG6KtC6VLESERGxYXYGPZ5HWFgYnp6eiR5BQUEAHD9+nNatW+Pl5YWvry8LFy60OD4uLo7Jkyfj4+ODl5cXHTp0IDQ01GIfI/owgoKViIiILXsJklVwcDDp0qVj27ZtbN++3fxo1KgRERERtG3blnz58rFy5Uq6dOnCuHHjWLlypfn4adOmsWTJEr744guWLl1KXFwc7du3JyYmBsCQPoyiqUARERFJVSEhIRQoUIDs2bMnaluwYAGOjo4MGzYMBwcHPDw8OH/+PLNmzaJFixbExMQwb948+vbtS+3atQGYOHEiPj4+bNy4kSZNmrB8+fLn7sMoqliJiIjYMDuD/nseJ06cwMPDI8m2/fv3U6lSJRwc/q31VKlShXPnznH16lWCg4OJjIykatWq5vaMGTNSokQJ9u3bZ1gfRlGwEhERsWF2dsY8nkdISAjXr1+nVatWVKtWjQ8++ICtW7cCcPnyZXLmzGmxf0Jl659//uHy5csA5MqVK9E+CW1G9GEUTQWKiIjIU9WtW/eJ7Zs2bUpye2xsLGfOnKFw4cIMGDAAV1dXfv75Zzp27Mg333zDvXv3SJs2rcUx6dKlAyA6OpqoqCiAJPe5efMmgCF9GEXBSkRExIa96LstODg4sGfPHtKkSYOTkxMApUqV4uTJk8ydOxcnJ6dEC8ijo6MBcHZ2Nh8TExNj/jphn/Tp0wMY0odRFKxERERsmUHJ6nEVqeRwcXFJtK1IkSJs376dnDlzEh4ebtGW8H2OHDmIjY01b8uXL5/FPp6engCG9GEUrbESERGRVHPy5EnKly/Pnj17LLb/9ddfFC5cGG9vbw4cOMCDBw/Mbbt376ZgwYJkzZqVYsWK4erqanH8rVu3OHbsGN7e3gCG9GEUBSsREREb9qKvCvTw8KBQoUIMGzaM/fv3c/r0ab788kv++OMPOnXqRIsWLbhz5w6DBg3i1KlTBAUFMX/+fPz9/YH4dVGtW7dm3LhxbNq0ieDgYHr16kXOnDmpX78+gCF9GMXOZDKZDO1RrCrq/osegUjqiYyOfdFDEElV2VxTf0XOict3DenHM6fzMx979epVxo8fz7Zt27h16xYlSpSgb9++VKxYEYDDhw8zYsQIjh07hru7O+3ataN169bm4x88eMCECRMICgri3r17eHt7M3jwYPLkyWPex4g+jKBg9R+nYCW2TMFKbN2rEqxeJVq8LiIiYsNe9FWBrxoFKxEREVumZGVVClYiIiI27Hk/jkZSRlcFioiIiBhEFSsREREb9ryf8ycpo2AlIiJiw5SrrEtTgSIiIiIGUcVKRETElqlkZVUKViIiIjZMVwVal6YCRURERAyiipWIiIgN01WB1qVgJSIiYsOUq6xLwUpERMSWKVlZldZYiYiIiBhEFSsREREbpqsCrUvBSkRExIZp8bp1aSpQRERExCCqWImIiNgwFaysS8FKRETEhmkq0Lo0FSgiIiJiEFWsREREbJpKVtakYCUiImLDNBVoXZoKFBERETGIKlYiIiI2TAUr61KwEhERsWGaCrQuBSsREREbpo+0sS6tsRIRERExiCpWIiIitkwFK6tSsBIREbFhylXWpalAEREREYOoYiUiImLDdFWgdSlYiYiI2DBdFWhdmgoUERERMYgqViIiIrZMBSurUrASERGxYcpV1qWpQBERERGDqGIlIiJiw3RVoHUpWImIiNgwXRVoXQpWIiIiNkwVK+vSGisRERERgyhYiYiIiBhEU4EiIiI2TFOB1qWKlYiIiIhBVLESERGxYboq0LoUrERERGyYpgKtS1OBIiIiIgZRxUpERMSGqWBlXQpWIiIitkzJyqo0FSgiIiJiEFWsREREbJiuCrQuBSsREREbpqsCrUvBSkRExIYpV1mX1liJiIiIGEQVKxEREVumkpVVKViJiIjYMC1ety5NBYqIiIgYRBUrERERG6arAq3LzmQymV70IERERERsgaYCRURERAyiYCUiIiJiEAUrEREREYMoWImIiIgYRMFKRERExCAKViIiIiIGUbASERERMYiClYiIiIhBFKxEREREDKJgJSIiImIQBSsRERERgyhYiYiIiBhEwUpERETEIA4vegAiyeXr6wvA6tWrcXV1tWgbMGAAly5dYtGiRcnqy2Qy8cMPP1CzZk2yZs362Oe7dOlSkm2ff/45rVu3TsHon5+fnx+5c+dm1KhRVn1eefn5+fmxd+/eJNvatWtH//79rTaWlP6/KGJrFKzkP+XSpUuMGTOGYcOGPVc/+/btY8CAAWzatOmJ+7Vr14527dol2v5osBN50d544w0GDRqUaHv69OlfwGhEXl0KVvKfkjdvXpYtW0bDhg2pVq3aM/djMpmStZ+zszPu7u7P/Dwi1uLk5KTfVZGXgNZYyX/Km2++SdWqVRk0aBB37tx57H43btxg6NCh1KpVizJlyvD++++zZ88eAPbs2UObNm0AqFu3LkFBQc88Hl9fX0aPHk2jRo2oXLkye/fu5ebNmwQGBuLj40PJkiWpWrUqgYGBREVFmZ/f09OTixcvmvt5dFtMTAwjR46katWqVKhQgbFjxxIXF/fM45RXm5+fH59//jktW7akYsWKrF69mpiYGEaPHo2vry+lSpWiUqVK9OjRg+vXrwNw8eJFPD09zf/fJLXNZDIxbdo0atasiZeXFwEBAURHR7+QcxR5WShYyX+KnZ0dI0aM4ObNm4wePTrJfR48eEC7du3Yv38/Y8eOJSgoiKJFi/LJJ59w+PBhypUrx5QpUwBYsWIFjRo1eq4xLV68mMDAQObMmYOXlxcDBgzg2LFjTJ06lQ0bNhAQEMAPP/zAsmXLkt3n8OHDWbt2LaNGjWLp0qVcvnyZ/fv3P9c45dW2YsUK2rRpw5IlS/Dx8WHMmDFs3LiRUaNGsWHDBkaNGsXu3buZPn16svucNWsWc+bMoV+/fgQFBZExY0bWrl2bimch8vLTVKD85+TOnZv+/fszePBgGjRoQI0aNSzat2/fztGjR1mzZg1FixYFYOjQoRw5coS5c+cyadIkMmXKBECWLFlwcnJ67HPNnDmTefPmWWxr2rSpxRqvWrVqWUxLVq9eHW9vbzw9PQHIkycPixcvJiQkJFnnd+fOHYKCghgyZAi1atUCYOTIkezevTtZx8urac2aNWzYsMFiW4UKFZgzZw4AxYsXp2nTpua20qVL07BhQypWrAjE/39VrVq1ZP+emkwmFi1aRJs2bWjSpAkAAQEBFhUukVeRgpX8J7333nts2LCBwMBAfvrpJ4u2kJAQMmTIYA5VEF/pqlixItu3b0/R87z//vv4+flZbHt04Xr+/Pktvv/www/ZvHkzq1at4ty5c5w6dYqLFy9SqFChZD3n2bNnuX//PqVLlzZvS5cuHSVKlEjR2OXV4uvrS9++fS22PfyPhkd/T5s1a8bOnTsZN24c586d48yZM5w9e9YctJ4mIiKCK1euWPyeAnh5eXH69OlnPAuR/z4FK/nPGj58OE2bNuXLL7+02P64hekmkwkHh5T9ymfKlCnRG9KjHn7ziouLw9/fn5MnT9KkSRMaNWpEyZIl+fzzz5/Yx4MHD8xf29nZmcf7sJSOXV4tLi4uT/xdfbQyO3jwYDZs2MBbb72Fr68vXbp0Ye7cuYSFhT22D/2eijyd/g+Q/6zXXnuNAQMGEBgYSN68ecmVKxcAnp6e3L59m5CQEHPVymQyceDAAQoXLgz8+6ZgtOPHj7N161aWL19O2bJlAbh//z4XLlwgb968ADg6OgJYLL4/d+6c+euCBQuSLl06Dh48SPHixQGIjY0lODiYypUrp8q45dUSERHBsmXLmDhxosUawzNnzuDs7Aw8/ffUzc2NXLlyceDAAerVq2fe/tdff5mPFXkVafG6/Ke1bNmSGjVqEBoaat5Wo0YNihcvTp8+fdi7dy+nT59m2LBhhISE8NFHHwGY3zyCg4OJjIw0bDzZsmXDwcGBdevWERoaypEjR+jZsydXrlwhJiYGgKJFi+Ls7MysWbO4cOEC27Zt45tvvjH34eLiQuvWrZk8eTIbN27k9OnTDBky5ImVBJGUcHV1JUOGDGzatInz589z4sQJPv/8c44ePWr+Pc2ePTu5c+dmwYIFnD59mgMHDjBp0iSLf5R06NCBb7/9lhUrVnD27Fm++uorDh8+/KJOS+SloGAl/3nDhw8nQ4YM5u/TpEnDvHnzKFGiBF27dqVFixacPHmS+fPn4+XlBcSHm1q1atGzZ88UXa33NDly5GDUqFFs3ryZRo0a0aNHD3LkyMHHH3/MX3/9BcS/qY0dO5Zjx47RqFEjJk2alOjO2H369OHDDz9k2LBhvPPOO5hMJvOd50Wel6OjI5MmTSIkJISmTZvSvn17oqKi6N27N6dOnSIqKgo7OzvGjBnDnTt3aNasGYMHD6Z3797Y2//7ttGqVSs+++wzpk+fTrNmzTh58iTvvPPOCzwzkRfPzpTcOyWKiIiIyBOpYiUiIiJiEAUrEREREYMoWImIiIgYRMFKRERExCAKViIiIiIGUbASERERMYiClYjYHN1FRkReFAUrEUnEz88PT09Pi0epUqWoXbs2Q4cO5ebNm6nyvEFBQXh6enLx4kUApkyZgqenZ7KPv3z5Mh07duTSpUvPPZaLFy/i6elJUFDQc/clIq8OfVagiCSpRIkSDBkyxPz9/fv3OXr0KBMmTOD48eN89913qfaZiwlatmyJj49PsvffuXMnv//+eyqOSETkyRSsRCRJrq6u5o8ASuDt7U1kZCSTJ0/mzz//TNRutJw5c5IzZ85UfQ4RESNpKlBEUqRUqVIA/P333/j5+dG3b1+6d++Ol5cXbdu2BSA6OpoxY8ZQq1YtSpUqRdOmTVm7dq1FP3FxcUybNo3atWtTtmxZOnfunGiKMampwB9++IHmzZtTtmxZateuzfjx44mJiSEoKIiAgAAA6taty4ABA8zHrFixgsaNG5unM6dMmcKDBw8s+t24cSNvvvkmZcqUoXnz5gQHBxvzgonIK0UVKxFJkbNnzwKQN29eANatW8ebb77J9OnTiYuLw2Qy0aVLFw4ePEj37t3x8PDgl19+oVevXsTExPDWW28BMHbsWBYuXEinTp0oW7Ys69atY/z48U987m+//ZZhw4bRsmVLevfuTWhoKGPGjOHmzZv07NmTTp06MX36dKZOnWoOZDNnzmTixIm0bt2agIAAjh8/zpQpU/jnn38YOXIkAJs3b6Z79+40bdqUzz77jOPHj/PZZ5+l0isoIrZMwUpEkmQymYiNjTV/f/PmTfbu3cv06dMpV66cuXLl6OjI0KFDSZs2LQA7duxg27ZtTJw4kUaNGgHg4+NDVFQU48aNo0mTJty9e5dFixbRtm1bunbtat4nPDycbdu2JTmeuLg4vv76a+rVq8fw4cPN26Oiovj555/JkCED+fLlA6B48eLkyZOH27dvM23aNN577z0CAwMBqFGjBpkzZyYwMJC2bdtSpEgRvv76a8qUKcPYsWPNYwGeGvRERB6lqUARSdK+ffsoWbKk+VGtWjV69+5NqVKlGD9+vHnheqFChcyhCmDXrl3Y2dlRq1YtYmNjzQ9fX1+uXLnCyZMn+eOPP7h//z516tSxeM433njjseM5e/Ys165d4/XXX7fY/sknnxAUFISjo2OiYw4dOsS9e/fw9fVNNBaID4H37t3j6NGjKRqLiMjjqGIlIkkqWbIkQ4cOBcDOzo506dKRK1cuXF1dLfZzcXGx+P7GjRuYTCbKly+fZL/h4eHcunULADc3N4s2d3f3x47nxo0bAGTNmjXZ55BwTMeOHR87lps3b2IymRKNJXv27Ml+HhGRBApWIpIkFxcXSpcuneLjMmTIgLOzMwsXLkyyPX/+/Bw+fBiAa9euUahQIXNbQhBKSsaMGQG4fv26xfaIiAiOHTtGuXLlHnvMuHHjKFCgQKL2bNmykTlzZuzt7bl69apF25PGIiLyOJoKFBFDVapUibt372IymShdurT5ERISwtdff01sbCzlypXDycmJ9evXWxy7ZcuWx/ZbqFAh3NzcEu3z448/0rFjR+7fv4+9veWftLJly+Lo6EhYWJjFWBwcHJgwYQIXL14kXbp0lCtXjo0bN1rcsX3z5s0GvBoi8qpRxUpEDFWrVi28vb3p3LkznTt3xsPDg8OHDzN58mR8fHzIkiULAJ07d+arr74iffr0VKlShd9///2JwSpNmjR069aNYcOGkTVrVnx9fTl79iyTJ0+mVatWZMqUyVyh+uWXX6hZsyYeHh60b9+eSZMmcefOHSpXrkxYWBiTJk3Czs6OYsWKAdC7d28++ugjunbtynvvvcfZs2eZMWNG6r9YImJzFKxExFD29vbMmjWLSZMmMXPmTK5du0aOHDlo27YtXbp0Me/n7++Ps7MzCxYsYMGCBZQrV47+/fvzv//977F9t2rVCmdnZ+bOncuyZcvImTMnHTp0oEOHDgBUrlyZatWqMX78eHbt2sWsWbPo2bMn7u7uLFmyhDlz5pApUyaqVq1K7969yZAhAwAVK1Zk9uzZTJgwga5du5InTx5GjhzJp59+mqqvlYjYHjuTPq1URERExBBaYyUiIiJiEAUrEREREYMoWImIiIgYRMFKRERExCAKViIiIiIGUbASERERMYiClYiIiIhBFKxEREREDKJgJSIiImIQBSsRERERgyhYiYiIiBhEwUpERETEIP8Hjo6xDBHXDs8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic regression confusion matrix with best threshold\n",
    "make_confusion_matrix_val(log_l1, threshold=0.315789)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eda5d44a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGBoost confusion matrix with best threshold\n",
    "make_confusion_matrix_val(boost, threshold=0.105263)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6653b6bb-6a2b-4e8b-8b5f-9c14dc9a1d93",
   "metadata": {},
   "source": [
    "# Credit Card Fraud Predictor System "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a969e35e",
   "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>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.131956</td>\n",
       "      <td>56.372401</td>\n",
       "      <td>6.358667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.803057</td>\n",
       "      <td>67.241081</td>\n",
       "      <td>1.872950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15.694986</td>\n",
       "      <td>175.989182</td>\n",
       "      <td>0.855623</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>26.711462</td>\n",
       "      <td>1.552008</td>\n",
       "      <td>4.603601</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10.664474</td>\n",
       "      <td>1.565769</td>\n",
       "      <td>4.886521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999908</th>\n",
       "      <td>45.296658</td>\n",
       "      <td>0.882736</td>\n",
       "      <td>8.856861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999916</th>\n",
       "      <td>167.139756</td>\n",
       "      <td>0.282253</td>\n",
       "      <td>0.308468</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999919</th>\n",
       "      <td>124.640118</td>\n",
       "      <td>0.004416</td>\n",
       "      <td>0.434885</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999939</th>\n",
       "      <td>51.412900</td>\n",
       "      <td>3.429330</td>\n",
       "      <td>29.914254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999949</th>\n",
       "      <td>15.724799</td>\n",
       "      <td>1.875906</td>\n",
       "      <td>11.009366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87403 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "13                2.131956                       56.372401   \n",
       "24                3.803057                       67.241081   \n",
       "29               15.694986                      175.989182   \n",
       "35               26.711462                        1.552008   \n",
       "36               10.664474                        1.565769   \n",
       "...                    ...                             ...   \n",
       "999908           45.296658                        0.882736   \n",
       "999916          167.139756                        0.282253   \n",
       "999919          124.640118                        0.004416   \n",
       "999939           51.412900                        3.429330   \n",
       "999949           15.724799                        1.875906   \n",
       "\n",
       "        ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "13                            6.358667              1.0        0.0   \n",
       "24                            1.872950              1.0        0.0   \n",
       "29                            0.855623              1.0        0.0   \n",
       "35                            4.603601              1.0        1.0   \n",
       "36                            4.886521              1.0        0.0   \n",
       "...                                ...              ...        ...   \n",
       "999908                        8.856861              1.0        0.0   \n",
       "999916                        0.308468              1.0        0.0   \n",
       "999919                        0.434885              1.0        0.0   \n",
       "999939                       29.914254              1.0        0.0   \n",
       "999949                       11.009366              1.0        1.0   \n",
       "\n",
       "        used_pin_number  online_order  fraud  \n",
       "13                  0.0           1.0    1.0  \n",
       "24                  0.0           1.0    1.0  \n",
       "29                  0.0           1.0    1.0  \n",
       "35                  0.0           1.0    1.0  \n",
       "36                  0.0           1.0    1.0  \n",
       "...                 ...           ...    ...  \n",
       "999908              0.0           1.0    1.0  \n",
       "999916              0.0           1.0    1.0  \n",
       "999919              0.0           1.0    1.0  \n",
       "999939              0.0           1.0    1.0  \n",
       "999949              0.0           1.0    1.0  \n",
       "\n",
       "[87403 rows x 8 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_card.loc[real_card['fraud']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "76e2a879-178f-4845-a99e-e3f46f980f2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we choose a random observation\n",
    "sample = real_card.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8c972de3-fc13-4124-a0d4-37f2cdb5b568",
   "metadata": {
    "tags": []
   },
   "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>246643</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distance_from_home</th>\n",
       "      <td>47.020068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <td>0.138581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <td>1.646250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repeat_retailer</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_chip</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_pin_number</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fraud</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   246643\n",
       "distance_from_home              47.020068\n",
       "distance_from_last_transaction   0.138581\n",
       "ratio_to_median_purchase_price   1.646250\n",
       "repeat_retailer                  1.000000\n",
       "used_chip                        0.000000\n",
       "used_pin_number                  1.000000\n",
       "online_order                     1.000000\n",
       "fraud                            0.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if the example is a fraudulent purchase or not\n",
    "sample.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5087302b-0985-4541-83f8-c1c53ec7108d",
   "metadata": {},
   "source": [
    "Lets see if our credit card fraud detector system is able to predict the status of this transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "45d1eb54-7b8d-497f-bff4-a8f3c0a193ca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we drop the fraud column from our sample\n",
    "example = sample.drop(['fraud'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b625243f-a63c-4b2c-8d90-b6b907a300a5",
   "metadata": {
    "tags": []
   },
   "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>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>246643</th>\n",
       "      <td>47.020068</td>\n",
       "      <td>0.138581</td>\n",
       "      <td>1.64625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "246643           47.020068                        0.138581   \n",
       "\n",
       "        ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "246643                         1.64625              1.0        0.0   \n",
       "\n",
       "        used_pin_number  online_order  \n",
       "246643              1.0           1.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if fraud column from sample was dropped\n",
    "example.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8f113d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(data):\n",
    "  # convert data type\n",
    "  data[['repeat_retailer','used_chip','used_pin_number','online_order']] = data[['repeat_retailer','used_chip','used_pin_number','online_order']].astype('int')\n",
    "\n",
    "  # scale some columns\n",
    "  # data[['distance_from_home','distance_from_last_transaction','ratio_to_median_purchase_price']] = scaler.transform(data[['distance_from_home','distance_from_last_transaction','ratio_to_median_purchase_price']])\n",
    "  \n",
    "  return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "87d90584-c4f2-4cb6-a6af-63611fd48468",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# credit card fraud detection system\n",
    "\n",
    "def fraud_prediction(obs):\n",
    "    obs = preprocess(obs)\n",
    "    result = np.where((boost.predict_proba(obs)[:, 1] >= 0.105263),1,0)\n",
    "    if (result == 1):\n",
    "        return \"This is a fraudulent purchase!\"\n",
    "    else: \n",
    "        return \"This transaction is verified\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "973654c4-06c6-4755-a1db-f6d4116a9609",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This transaction is verified'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the results \n",
    "fraud_prediction(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2034ea",
   "metadata": {},
   "source": [
    "## Deployment of Model using Gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fc71a159",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"
     ]
    }
   ],
   "source": [
    "# import shap library\n",
    "import shap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592f7781",
   "metadata": {},
   "source": [
    "SHAP (SHapley Additive exPlanations) is a method used for explaining the output of machine learning models. It provides a way to understand the contribution of individual features to the prediction made by the model. SHAP values are based on cooperative game theory and provide a fair way to allocate the \"credit\" of the model's prediction to each feature.\n",
    "\n",
    "Here's a brief overview of how SHAP feature importance works:\n",
    "\n",
    "1. Background Dataset: To calculate SHAP values, a background dataset is required. This dataset represents the \"average\" or \"baseline\" state of the features and is used as a reference point to measure the impact of individual features on predictions.\n",
    "\n",
    "2. Model Prediction: For each instance in the dataset you want to explain, the model predicts an output (e.g., a probability score or a regression value).\n",
    "\n",
    "3. SHAP Values Calculation: SHAP values are computed by running the model on all possible combinations of features (subsets) for each instance. The contribution of each feature to the prediction is determined by comparing the model's output when the feature is included or excluded.\n",
    "\n",
    "4. Shapley Values: SHAP values are calculated using the concept of Shapley values from cooperative game theory. These values fairly distribute the model's prediction among the individual features.\n",
    "\n",
    "5. Feature Importance: After calculating SHAP values for all instances, you can aggregate them to get feature importances. This can be done by averaging the absolute SHAP values for each feature across the entire dataset or by using other summary statistics.\n",
    "\n",
    "6. Interpretation: Positive SHAP values indicate that the feature pushes the model's prediction higher, while negative values indicate the opposite. The magnitude of the SHAP value represents the strength of the feature's impact.\n",
    "\n",
    "SHAP values provide a powerful and model-agnostic way to understand the influence of features on the model's predictions, making it easier to interpret and trust the model's decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ba72395b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shap values for an example observation\n",
    "# this cell is to prepare a shap importance plot for our web app\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "\n",
    "explainer = shap.TreeExplainer(boost)\n",
    "shap_values = explainer.shap_values(example)\n",
    "scores_desc = list(zip(shap_values[0], example.columns))\n",
    "scores_desc = sorted(scores_desc)\n",
    "fig_m = plt.figure(tight_layout=True)\n",
    "plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])\n",
    "plt.title(\"Feature Shap Values\")\n",
    "plt.ylabel(\"Shap Value\")\n",
    "plt.xlabel(\"Feature Importance\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8d36abeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(*data):\n",
    "      columns = ['distance_from_home', 'distance_from_last_transaction',\n",
    "            'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',\n",
    "            'used_pin_number', 'online_order']\n",
    "      df = pd.DataFrame([data], columns = columns)\n",
    "      df = preprocess(df)\n",
    "      prob_pred = boost.predict_proba(df)\n",
    "      return {\"Normal\": float(prob_pred[0][0]), \"Fraud\": float(prob_pred[0][1])}\n",
    "\n",
    "def interpret(*data):\n",
    "\n",
    "      plt.style.use(\"fivethirtyeight\")\n",
    "      \n",
    "      columns = ['distance_from_home', 'distance_from_last_transaction',\n",
    "            'ratio_to_median_purchase_price', 'repeat_retailer', 'used_chip',\n",
    "            'used_pin_number', 'online_order']\n",
    "      df = pd.DataFrame([data], columns = columns)\n",
    "      \n",
    "      explainer = shap.TreeExplainer(boost)\n",
    "      shap_values = explainer.shap_values(df)\n",
    "      scores_desc = list(zip(shap_values[0], df.columns))\n",
    "      scores_desc = sorted(scores_desc)\n",
    "      fig_m = plt.figure(tight_layout=True)\n",
    "      plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])\n",
    "      plt.title(\"Feature Shap Values\")\n",
    "      plt.ylabel(\"Shap Value\")\n",
    "      plt.xlabel(\"Feature Importance\")\n",
    "      plt.tight_layout()\n",
    "      return fig_m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d154a0-4ddd-4b82-88bf-9310028fee4d",
   "metadata": {},
   "source": [
    "## Conclusion: \n",
    "The XGBoost Classifier algorithm returned an accuracy score on both the training and testing data of above 99.99%. The accuracy score recieved was the mean of a 5-fold stratified cross validation on both data sets. Therefore, we can conclude that the model was not over fitting or being biased by the data it was trained on. This makes the credit card fraud detection system a pretty accurate detector for fraudulent transactions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d429f492",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deployment module\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3bfd3b11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7870\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7870/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gr.Blocks() as demo:\n",
    "    gr.HTML(\"\"\"\n",
    "    <h1 align=\"center\">Credit Card Fraud Prediction System</h1>\n",
    "    <p>This is a Web App that predicts Whether a Credit Card Transaction is Fraudulent or not. Just input the following parameters and click the predict button. If you want to see the influence that each parameter had on the outcome click the explain button</P>\n",
    "    \"\"\")\n",
    "    with gr.Row():\n",
    "        with gr.Column():\n",
    "            repeated_retailer = gr.Radio([\"No\",\"Yes\"], type = \"index\", label = \"Repeat Retailer\", info =\"Was the transaction at at a repeated store?\")\n",
    "            \n",
    "            online_order = gr.Radio([\"No\",\"Yes\"], type = \"index\", label = \"Online Order\", info =\"Was the transaction an online order?\")\n",
    "            \n",
    "            used_chip = gr.Radio([\"No\",\"Yes\"], type = \"index\", label = \"Used Chip\", info =\"did the purchase use the security chip of the card?\")\n",
    "\n",
    "            used_pin = gr.Radio([\"No\",\"Yes\"], type = \"index\", label = \"Used Pin Number\", info =\"Did the transaction use the pin code of the card?\")\n",
    "            \n",
    "            distance_home = gr.Number(value = 25, label = \"Distance From Home (miles)\", info = \"How far was the transaction from the card owner's house? (in Miles)\")\n",
    "            \n",
    "            distance_last = gr.Number(value = 5, label = \"Distance From Last Transaction (miles)\", info = \"How far away was the it from the last transaction that happened? (in Miles)\")\n",
    "            \n",
    "            gr.HTML(\"\"\"\n",
    "                <h4 align=\"center\">Ratio Median Purchase Price Equation</h4>\n",
    "                    \"\"\")\n",
    "            ratio_median = gr.Number(value = 1.8, label = \"Ratio Median Purchase Price\", info = \"Divide the purchase price by card owners median purchase price?\")\n",
    "        \n",
    "            \n",
    "        with gr.Column():\n",
    "            label = gr.Label()\n",
    "            plot = gr.Plot()\n",
    "            with gr.Row():\n",
    "                predict_btn = gr.Button(value=\"Predict\")\n",
    "                interpret_btn = gr.Button(value=\"Explain\")\n",
    "            predict_btn.click(\n",
    "                predict,\n",
    "                inputs= [\n",
    "                    distance_home,\n",
    "                    distance_last,\n",
    "                    ratio_median,\n",
    "                    repeated_retailer,\n",
    "                    used_chip,\n",
    "                    used_pin,\n",
    "                    online_order   \n",
    "                ],\n",
    "                outputs=[label],\n",
    "            )\n",
    "            interpret_btn.click(\n",
    "                interpret,\n",
    "                inputs=[\n",
    "                    distance_home,\n",
    "                    distance_last,\n",
    "                    ratio_median,\n",
    "                    repeated_retailer,\n",
    "                    used_chip,\n",
    "                    used_pin,\n",
    "                    online_order   \n",
    "                ],\n",
    "                outputs=[plot],\n",
    "            )\n",
    "\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "35b3b5cb",
   "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>246643</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distance_from_home</th>\n",
       "      <td>47.020068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <td>0.138581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <td>1.646250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repeat_retailer</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_chip</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_pin_number</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   246643\n",
       "distance_from_home              47.020068\n",
       "distance_from_last_transaction   0.138581\n",
       "ratio_to_median_purchase_price   1.646250\n",
       "repeat_retailer                  1.000000\n",
       "used_chip                        0.000000\n",
       "used_pin_number                  1.000000\n",
       "online_order                     1.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "001d1c39",
   "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>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distance_from_home</th>\n",
       "      <td>2.131956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <td>56.372401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <td>6.358667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repeat_retailer</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_chip</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_pin_number</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fraud</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       13\n",
       "distance_from_home               2.131956\n",
       "distance_from_last_transaction  56.372401\n",
       "ratio_to_median_purchase_price   6.358667\n",
       "repeat_retailer                  1.000000\n",
       "used_chip                        0.000000\n",
       "used_pin_number                  0.000000\n",
       "online_order                     1.000000\n",
       "fraud                            1.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_card.loc[real_card.index == 13].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "413171e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # save our models\n",
    "# import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e659cd5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_name = \"boost.sav\"\n",
    "# pickle.dump(boost,open(model_name,\"wb\"))"
   ]
  }
 ],
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