notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d367b0fd-7b69-40b3-b617-16aa13c0cb73",
   "metadata": {},
   "source": [
    "# **Machine Learning for Fraud Detection.**\n",
    "\n",
    "##### **Notebook Author**: A.D. Téllez\n",
    "\n",
    "You can find me on GitHub for collaborations.\n",
    "\n",
    "# Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6fd7a084-7481-4835-924d-8a96fee66e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5745c3da-a61d-42a2-84dc-ca3fd716b47a",
   "metadata": {},
   "source": [
    "##### This data sets is under public domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c03ee255-ab94-4657-844b-d951a1181abf",
   "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>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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.829943</td>\n",
       "      <td>0.175592</td>\n",
       "      <td>1.294219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "</div>"
      ],
      "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_df = pd.read_csv('card_transdata.csv')\n",
    "\n",
    "#quick look to our data.\n",
    "card_df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1628ff8-d13d-4173-ab5a-62926fbf02a0",
   "metadata": {},
   "source": [
    "## Preparing the data for the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8bf26c-40ee-403d-acb8-27e467d0a70a",
   "metadata": {},
   "source": [
    "#### 1. Total observations in our database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6b2b8246-61fe-4268-8afe-fe1284118cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000 observations.\n"
     ]
    }
   ],
   "source": [
    "print(card_df.shape[0],\"observations.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ace146-1198-42f0-96e4-f82b09e97614",
   "metadata": {},
   "source": [
    "#### 2. Looking for null values.\n",
    "\n",
    "Sometimes we have null values in our data sets. Since they can compromise the performance of our model, we need to identify and treat them properly. Just deleting is not always the best solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "900bdfdb-ed7e-4fab-93b9-4beb37c34757",
   "metadata": {},
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca29408f-063e-41b8-bd9f-d28a73f840e4",
   "metadata": {},
   "source": [
    "#### 3. Fraud incidences distribution on raw data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "80db07c9-51e4-40b6-b263-12e56285d35c",
   "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>Number of cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fraud</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>912597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>87403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of cases\n",
       "fraud                 \n",
       "0.0             912597\n",
       "1.0              87403"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_df = card_df.value_counts(card_df['fraud']).rename(index = 'Number of cases').to_frame() #counts how many incidences belongs to each cases.  \n",
    "fraud_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b46f1c02-dde3-4b71-9b8a-cfa4c5b67ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: >], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 310x310 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visual raw data distribution \n",
    "colors = ['lightgrey','slategray']\n",
    "fraud_df.plot(kind='pie', subplots = True,autopct='%1.0f%%',colors=colors,labels = {'Legit':0,'Fraud':1},ylabel='',figsize=(3.1,3.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5019d149-1b5e-4613-9731-e589c770df5d",
   "metadata": {
    "tags": []
   },
   "source": [
    "####  Aiming for an equal outcome distribution \n",
    "\n",
    "There's an unfair distribution between normal and fraudulent transactions. This can bias our model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c490b0-f78c-4114-bad6-c8708468e349",
   "metadata": {},
   "source": [
    "##### Separate the two classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d6a6e7b8-73c6-45cf-ab4a-2b8232fae30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    912597.000000\n",
       "mean         22.832976\n",
       "std          52.828655\n",
       "min           0.004874\n",
       "25%           3.828942\n",
       "50%           9.673847\n",
       "75%          24.158057\n",
       "max        8777.136420\n",
       "Name: distance_from_home, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separating the normal transactions\n",
    "legit = card_df[card_df.fraud == 0]\n",
    "\n",
    "#look for some statistical info about normal transactions\n",
    "legit.distance_from_home.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "359e5e55-e1ee-4860-bd94-1598e0b52756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    87403.000000\n",
       "mean        66.261876\n",
       "std        134.391608\n",
       "min          0.025847\n",
       "25%          4.585729\n",
       "50%         15.454219\n",
       "75%        101.110104\n",
       "max      10632.723672\n",
       "Name: distance_from_home, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separating the abnormal transactions\n",
    "fraud = card_df[card_df.fraud == 1]\n",
    "\n",
    "#look for some statistical info about fraudulent transactions\n",
    "fraud.distance_from_home.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81df0866-89cf-4c34-8b9a-d36dff76210b",
   "metadata": {},
   "source": [
    "**After that, we see fraudulent transactions has a correlation with the distance.** \n",
    "\n",
    "We can extend this exploratory analysis for other attributes on the dataframe. We selected just one in order to eliminate distracting values.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162f07c7-aaee-4f66-9f79-7d3a748ee88c",
   "metadata": {},
   "source": [
    "##### Pick an equal distribution of each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4bce943d-2463-4e35-8d64-7fcc8ec1e161",
   "metadata": {},
   "outputs": [],
   "source": [
    "legit_sample = legit.sample(n = 87403) #n is the number of random observations to make a sample set. Also n = number of fraudulent transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00eba46b-e42e-4087-8316-e280c6c11b8f",
   "metadata": {},
   "source": [
    "We join the now 2 balanced datasets:\n",
    "\n",
    "**legit_sample** ---> the reduced dataset of legit transactions\n",
    "\n",
    "**fraud** ---> dataset with fraudulent transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "67c3d2e1-68fd-41e3-82a8-ceeece268bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = pd.concat( [legit_sample, fraud] , axis = 0) # axis = 0 concatenate rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a0ea6093-6e63-4fb1-ac08-5f3604a5f69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving to csv file\n",
    "sample.to_csv('model_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ce027df9-451c-4514-99b9-bc3f7eb3e88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_table = sample.value_counts(sample['fraud'].rename(index = 'Number of cases'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "886ac523-32f8-4fc6-a59f-217b47acb9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of cases\n",
       "0.0    87403\n",
       "1.0    87403\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['lightgrey','slategrey']\n",
    "sample_table.plot(kind='pie',subplots = True, autopct='%1.0f%%',colors=colors,labels = {'Legit':0,'Fraud':1},ylabel='',figsize=(2.5,2.5))\n",
    "sample_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a799497a-eebf-4464-8a43-e4ef12729b85",
   "metadata": {},
   "source": [
    "## FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b204512a-2e0d-4b9c-b50a-41a689bc09de",
   "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",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distance_from_home</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015344</td>\n",
       "      <td>-0.075312</td>\n",
       "      <td>0.153612</td>\n",
       "      <td>-0.102418</td>\n",
       "      <td>-0.035313</td>\n",
       "      <td>0.059393</td>\n",
       "      <td>0.210722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <td>-0.015344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038010</td>\n",
       "      <td>-0.016129</td>\n",
       "      <td>-0.035670</td>\n",
       "      <td>-0.018531</td>\n",
       "      <td>0.021759</td>\n",
       "      <td>0.113495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <td>-0.075312</td>\n",
       "      <td>-0.038010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038285</td>\n",
       "      <td>0.031616</td>\n",
       "      <td>-0.087848</td>\n",
       "      <td>0.135949</td>\n",
       "      <td>0.475037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>repeat_retailer</th>\n",
       "      <td>0.153612</td>\n",
       "      <td>-0.016129</td>\n",
       "      <td>-0.038285</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007685</td>\n",
       "      <td>0.003546</td>\n",
       "      <td>0.073308</td>\n",
       "      <td>-0.000705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_chip</th>\n",
       "      <td>-0.102418</td>\n",
       "      <td>-0.035670</td>\n",
       "      <td>0.031616</td>\n",
       "      <td>-0.007685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018021</td>\n",
       "      <td>-0.027102</td>\n",
       "      <td>-0.113198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_pin_number</th>\n",
       "      <td>-0.035313</td>\n",
       "      <td>-0.018531</td>\n",
       "      <td>-0.087848</td>\n",
       "      <td>0.003546</td>\n",
       "      <td>0.018021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077227</td>\n",
       "      <td>-0.231616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>0.059393</td>\n",
       "      <td>0.021759</td>\n",
       "      <td>0.135949</td>\n",
       "      <td>0.073308</td>\n",
       "      <td>-0.027102</td>\n",
       "      <td>-0.077227</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.394338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fraud</th>\n",
       "      <td>0.210722</td>\n",
       "      <td>0.113495</td>\n",
       "      <td>0.475037</td>\n",
       "      <td>-0.000705</td>\n",
       "      <td>-0.113198</td>\n",
       "      <td>-0.231616</td>\n",
       "      <td>0.394338</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                distance_from_home  \\\n",
       "distance_from_home                        1.000000   \n",
       "distance_from_last_transaction           -0.015344   \n",
       "ratio_to_median_purchase_price           -0.075312   \n",
       "repeat_retailer                           0.153612   \n",
       "used_chip                                -0.102418   \n",
       "used_pin_number                          -0.035313   \n",
       "online_order                              0.059393   \n",
       "fraud                                     0.210722   \n",
       "\n",
       "                                distance_from_last_transaction  \\\n",
       "distance_from_home                                   -0.015344   \n",
       "distance_from_last_transaction                        1.000000   \n",
       "ratio_to_median_purchase_price                       -0.038010   \n",
       "repeat_retailer                                      -0.016129   \n",
       "used_chip                                            -0.035670   \n",
       "used_pin_number                                      -0.018531   \n",
       "online_order                                          0.021759   \n",
       "fraud                                                 0.113495   \n",
       "\n",
       "                                ratio_to_median_purchase_price  \\\n",
       "distance_from_home                                   -0.075312   \n",
       "distance_from_last_transaction                       -0.038010   \n",
       "ratio_to_median_purchase_price                        1.000000   \n",
       "repeat_retailer                                      -0.038285   \n",
       "used_chip                                             0.031616   \n",
       "used_pin_number                                      -0.087848   \n",
       "online_order                                          0.135949   \n",
       "fraud                                                 0.475037   \n",
       "\n",
       "                                repeat_retailer  used_chip  used_pin_number  \\\n",
       "distance_from_home                     0.153612  -0.102418        -0.035313   \n",
       "distance_from_last_transaction        -0.016129  -0.035670        -0.018531   \n",
       "ratio_to_median_purchase_price        -0.038285   0.031616        -0.087848   \n",
       "repeat_retailer                        1.000000  -0.007685         0.003546   \n",
       "used_chip                             -0.007685   1.000000         0.018021   \n",
       "used_pin_number                        0.003546   0.018021         1.000000   \n",
       "online_order                           0.073308  -0.027102        -0.077227   \n",
       "fraud                                 -0.000705  -0.113198        -0.231616   \n",
       "\n",
       "                                online_order     fraud  \n",
       "distance_from_home                  0.059393  0.210722  \n",
       "distance_from_last_transaction      0.021759  0.113495  \n",
       "ratio_to_median_purchase_price      0.135949  0.475037  \n",
       "repeat_retailer                     0.073308 -0.000705  \n",
       "used_chip                          -0.027102 -0.113198  \n",
       "used_pin_number                    -0.077227 -0.231616  \n",
       "online_order                        1.000000  0.394338  \n",
       "fraud                               0.394338  1.000000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.corr() #correlation matrix for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "55d948eb-6a2f-4154-b4ad-087fb4791970",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "803f54c2-8a2f-4a6a-b3e6-4e0a630554ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(sample.corr(),annot=True,cmap=\"rocket\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a51a99-ab4d-4a2b-85de-f39c14942fdd",
   "metadata": {},
   "source": [
    "#### 4. Standardization\n",
    "\n",
    "This process prepares our data in such a way that the mean is zero. This sets our values within the range [-1,1] preventing extreme values from weighting more in our model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cfc393e6-6dd9-49c6-9577-86e1832c8dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# allows us to standardize our data.\n",
    "from sklearn import preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5342b6a-1f00-4fa9-8293-0157b352d1f1",
   "metadata": {},
   "source": [
    "#### Understanding the process\n",
    "\n",
    "When setting our model, we need some input data called independent variables. But we also need to define what would be the expected outcome. The variable we would like to predict, categorize or cluster is called the dependent variable.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f83dd9e1-0398-4b56-aa18-993f2fc8a9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_var = sample.drop(columns='fraud',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6b4980c5-8c7c-4fd7-b0bb-9a621caa9a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0290005 , -0.16859783, -0.71990974, ..., -0.66822951,\n",
       "         4.07957287, -1.90548363],\n",
       "       [ 0.82083363, -0.18341737, -0.72788744, ...,  1.49649182,\n",
       "        -0.2451237 ,  0.52480115],\n",
       "       [-0.16511895, -0.22387451, -0.63319252, ...,  1.49649182,\n",
       "         4.07957287, -1.90548363],\n",
       "       ...,\n",
       "       [ 0.77276453, -0.23022876, -0.68193008, ..., -0.66822951,\n",
       "        -0.2451237 ,  0.52480115],\n",
       "       [ 0.06776155, -0.13759069,  5.43976469, ..., -0.66822951,\n",
       "        -0.2451237 ,  0.52480115],\n",
       "       [-0.27582951, -0.17960815,  1.51396982, ...,  1.49649182,\n",
       "        -0.2451237 ,  0.52480115]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform = preprocessing.StandardScaler()  # performs the standardization.\n",
    "\n",
    "X = transform.fit_transform(ind_var) #Independent variables should go on the x-axis.\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1bbbdc62-a261-4c1a-ba73-574c4622a51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = sample['fraud'].to_numpy()\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395706d3-4b8a-454c-befd-4f3b5941996f",
   "metadata": {},
   "source": [
    "#### 5. Splitting into train data and test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0f9e6931-b3e5-4079-ba9a-b4a2882ddf3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allows us to split our data into training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1436a008-c657-4b95-8fda-dbee431fc346",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split (X, Y, test_size = 0.2, stratify = Y, random_state = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86df90a2-4092-406e-a619-8a51f2f5dab5",
   "metadata": {},
   "source": [
    "## Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c55dec45-b11c-414f-97bd-00d6ead6b15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b82ffed-56c1-4299-a88c-35415afa4141",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "69dc0dcc-0160-4f86-98a7-8fd5af51290e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "470b45e7-fa4d-4e40-bbdb-ab4201a7745e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 100,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': None,\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b891f08e-48c1-4092-83ce-e4d916376e19",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3503733-c028-48a2-bef1-36501a06a07e",
   "metadata": {},
   "source": [
    "We use this method in order to predict the value for Y_train which is known, using X_train as input for our model. Then we compare it to see how accurately it performed. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c341c04-d5ca-44e9-9374-6302eb3592ad",
   "metadata": {},
   "source": [
    "##### Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "86a73ce9-d925-475d-9b84-ab7160ad1560",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_prediction = model.predict(X_train)\n",
    "training_accuracy = accuracy_score(Y_train_prediction, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "47615d82-6af8-4633-9bed-6cd842d367d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.942 \n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy: %.3f \" % training_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcca1b1-df30-49a4-a190-5234fba9abde",
   "metadata": {},
   "source": [
    "##### Test Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "30656401-e963-499e-bb8f-c7d5450e18bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test_prediction = model.predict(X_test)\n",
    "testing_accuracy = accuracy_score(Y_test_prediction, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ecd4e876-5dd7-4971-bf4b-a233efa7f5fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.940 \n"
     ]
    }
   ],
   "source": [
    "print(\"Test Accuracy: %.3f \" % testing_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d652015a-a400-46ba-969f-60b7d855d251",
   "metadata": {},
   "source": [
    "Our **accuracy is similar** for both train and test data.\n",
    "\n",
    "- Train accuracy >> Test accuracy implies an Overfitted model.\n",
    "- Train accuracy << Test accuracy implies an Underfitted model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9555985a-a564-4ebb-b33a-c4f983a3b1e6",
   "metadata": {},
   "source": [
    "##### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "6d9981de-5a0a-46ad-a59f-b810d3847205",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b5006017-bbde-4634-b857-5f37f518feb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(Y_test,Y_test_prediction, labels = model.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7c05ee10-4390-49d6-890a-46da5a3f722d",
   "metadata": {},
   "outputs": [],
   "source": [
    "displayCM = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = ['legit','fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "011e93ca-da7e-4f88-b225-b8fab90d84cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e51bc95ed0>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayCM.plot(cmap='Blues') #plot the confusion matrix changing the color scale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7f3079-821e-427e-b034-89ed1e1d9a8c",
   "metadata": {},
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c674c8-429f-44e4-876c-e113fc8fe16c",
   "metadata": {},
   "source": [
    "# Model Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e1ed0319-183b-40e1-92c1-c1e2b89fa678",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}