10. Credit Card Transactions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "863ab025",
   "metadata": {},
   "source": [
    "# Goal of Aanalysis:\n",
    "\n",
    "### 1. create a list to identify those users who never went above the monthly credit card limit (calendar month). The goal of this is to automatically increase their limit\n",
    "\n",
    "### 2. oimplement an algorithm that as soon as a user goes above her monthly limit, it triggers an alert so that the user can be notiﬁed about that\n",
    "\n",
    "### 3.  implement an unsupervised algorithm that returns all transactions that seem unusual and are worth being investigated further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6617aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1b74288",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit_card</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>credit_card_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1280981422329509</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>PA</td>\n",
       "      <td>18612</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9737219864179988</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4749889059323202</td>\n",
       "      <td>Auburn</td>\n",
       "      <td>MA</td>\n",
       "      <td>1501</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9591503562024072</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>WV</td>\n",
       "      <td>26412</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2095640259001271</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10001</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        credit_card      city state  zipcode  credit_card_limit\n",
       "0  1280981422329509    Dallas    PA    18612               6000\n",
       "1  9737219864179988   Houston    PA    15342              16000\n",
       "2  4749889059323202    Auburn    MA     1501              14000\n",
       "3  9591503562024072   Orlando    WV    26412              18000\n",
       "4  2095640259001271  New York    NY    10001              20000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info = pd.read_csv('cc_info.csv')\n",
    "info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a9ab3ad",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit_card</th>\n",
       "      <th>date</th>\n",
       "      <th>transaction_dollar_amount</th>\n",
       "      <th>Long</th>\n",
       "      <th>Lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-09-11 00:32:40</td>\n",
       "      <td>43.78</td>\n",
       "      <td>-80.174132</td>\n",
       "      <td>40.267370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-24 22:23:08</td>\n",
       "      <td>103.15</td>\n",
       "      <td>-80.194240</td>\n",
       "      <td>40.180114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-26 18:19:36</td>\n",
       "      <td>48.55</td>\n",
       "      <td>-80.211033</td>\n",
       "      <td>40.313004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-22 19:41:10</td>\n",
       "      <td>136.18</td>\n",
       "      <td>-80.174138</td>\n",
       "      <td>40.290895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-26 20:08:22</td>\n",
       "      <td>71.82</td>\n",
       "      <td>-80.238720</td>\n",
       "      <td>40.166719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "        credit_card                date  transaction_dollar_amount       Long  \\\n",
       "0  1003715054175576 2015-09-11 00:32:40                      43.78 -80.174132   \n",
       "1  1003715054175576 2015-10-24 22:23:08                     103.15 -80.194240   \n",
       "2  1003715054175576 2015-10-26 18:19:36                      48.55 -80.211033   \n",
       "3  1003715054175576 2015-10-22 19:41:10                     136.18 -80.174138   \n",
       "4  1003715054175576 2015-10-26 20:08:22                      71.82 -80.238720   \n",
       "\n",
       "         Lat  \n",
       "0  40.267370  \n",
       "1  40.180114  \n",
       "2  40.313004  \n",
       "3  40.290895  \n",
       "4  40.166719  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction = pd.read_csv('transactions.csv', parse_dates = ['date'])\n",
    "transaction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c912618d",
   "metadata": {},
   "source": [
    "I will first merge two dataset into one and do my analysis based on this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9fa49e2",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>credit_card</th>\n",
       "      <th>date</th>\n",
       "      <th>transaction_dollar_amount</th>\n",
       "      <th>Long</th>\n",
       "      <th>Lat</th>\n",
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       "      <td>43.78</td>\n",
       "      <td>-80.174132</td>\n",
       "      <td>40.267370</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-24 22:23:08</td>\n",
       "      <td>103.15</td>\n",
       "      <td>-80.194240</td>\n",
       "      <td>40.180114</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
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       "      <td>20000</td>\n",
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       "      <th>2</th>\n",
       "      <td>1003715054175576</td>\n",
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       "      <td>48.55</td>\n",
       "      <td>-80.211033</td>\n",
       "      <td>40.313004</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-22 19:41:10</td>\n",
       "      <td>136.18</td>\n",
       "      <td>-80.174138</td>\n",
       "      <td>40.290895</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>2015-10-26 20:08:22</td>\n",
       "      <td>71.82</td>\n",
       "      <td>-80.238720</td>\n",
       "      <td>40.166719</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        credit_card                date  transaction_dollar_amount       Long  \\\n",
       "0  1003715054175576 2015-09-11 00:32:40                      43.78 -80.174132   \n",
       "1  1003715054175576 2015-10-24 22:23:08                     103.15 -80.194240   \n",
       "2  1003715054175576 2015-10-26 18:19:36                      48.55 -80.211033   \n",
       "3  1003715054175576 2015-10-22 19:41:10                     136.18 -80.174138   \n",
       "4  1003715054175576 2015-10-26 20:08:22                      71.82 -80.238720   \n",
       "\n",
       "         Lat     city state  zipcode  credit_card_limit  \n",
       "0  40.267370  Houston    PA    15342              20000  \n",
       "1  40.180114  Houston    PA    15342              20000  \n",
       "2  40.313004  Houston    PA    15342              20000  \n",
       "3  40.290895  Houston    PA    15342              20000  \n",
       "4  40.166719  Houston    PA    15342              20000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(left=transaction, right=info, on='credit_card', how='left')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36b03a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((984, 5), (294588, 5), (294588, 9))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "info.shape, transaction.shape, data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a6654ae",
   "metadata": {},
   "outputs": [
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       "      <th>credit_card</th>\n",
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       "      <th>Long</th>\n",
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       "      <td>43.78</td>\n",
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       "      <td>103.15</td>\n",
       "      <td>-80.194240</td>\n",
       "      <td>40.180114</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
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       "      <td>5</td>\n",
       "      <td>22</td>\n",
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       "      <th>2</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>48.55</td>\n",
       "      <td>-80.211033</td>\n",
       "      <td>40.313004</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>18</td>\n",
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       "      <th>3</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>136.18</td>\n",
       "      <td>-80.174138</td>\n",
       "      <td>40.290895</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
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       "      <th>4</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>71.82</td>\n",
       "      <td>-80.238720</td>\n",
       "      <td>40.166719</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
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      "text/plain": [
       "        credit_card  transaction_dollar_amount       Long        Lat     city  \\\n",
       "0  1003715054175576                      43.78 -80.174132  40.267370  Houston   \n",
       "1  1003715054175576                     103.15 -80.194240  40.180114  Houston   \n",
       "2  1003715054175576                      48.55 -80.211033  40.313004  Houston   \n",
       "3  1003715054175576                     136.18 -80.174138  40.290895  Houston   \n",
       "4  1003715054175576                      71.82 -80.238720  40.166719  Houston   \n",
       "\n",
       "  state  zipcode  credit_card_limit  month  day  weekday  hour  \n",
       "0    PA    15342              20000      9   11        4     0  \n",
       "1    PA    15342              20000     10   24        5    22  \n",
       "2    PA    15342              20000     10   26        0    18  \n",
       "3    PA    15342              20000     10   22        3    19  \n",
       "4    PA    15342              20000     10   26        0    20  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract month, weekday, and hour information\n",
    "data['month'] = data['date'].apply(lambda x: x.month)\n",
    "data['day'] = data['date'].apply(lambda x: x.day)\n",
    "data['weekday'] = data['date'].apply(lambda x: x.dayofweek)\n",
    "data['hour'] = data['date'].apply(lambda x: x.hour)\n",
    "\n",
    "# drop useless features\n",
    "data = data.drop(labels='date', axis=1)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34caa808",
   "metadata": {},
   "source": [
    "### Part 1: identify users who never went above the credit limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53a1624b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def monthly_transaction(df):\n",
    "    \"\"\" function to calculate montly total transactions \"\"\"\n",
    "    total_trans = df['transaction_dollar_amount'].sum()\n",
    "    credit_limit = df['credit_card_limit'].values[0]\n",
    "    \n",
    "    return pd.Series([total_trans, credit_limit], index=['total_transaction', 'credit_limit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22a93bf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid users:\t 862\n"
     ]
    },
    {
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       "      <th>3</th>\n",
       "      <td>1073931538936472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1077622576192810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        credit_card\n",
       "0  1003715054175576\n",
       "1  1013870087888817\n",
       "2  1023820165155391\n",
       "3  1073931538936472\n",
       "4  1077622576192810"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find users who never exceed credit limit\n",
    "grouped = data.groupby(['credit_card', 'month']).apply(monthly_transaction)\n",
    "grouped = grouped.reset_index()\n",
    "\n",
    "grouped['exceed'] = (grouped['total_transaction'] > grouped['credit_limit']).astype(int)\n",
    "grouped_sum = grouped.groupby('credit_card')['exceed'].sum().reset_index()\n",
    "\n",
    "lists = grouped_sum[grouped_sum['exceed'] == 0][['credit_card']]\n",
    "\n",
    "print('Valid users:\\t', len(lists))\n",
    "lists.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42dbd03b",
   "metadata": {},
   "source": [
    "We can see from the analysis, there are 862 people in total who never exceed their credit limit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37acc682",
   "metadata": {},
   "source": [
    "### Part 2: We assume here that at the beginning of the new month, user total money spent gets reset to zero (i.e. she pays the card fully at the end of each month). Build a function that for each day, returns a list of users who went above their credit card monthly limit on that day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc1831c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def alert(data, month, day):\n",
    "    \"\"\" function to return the list of credit cards that exceed the limit \"\"\"\n",
    "    data = data[(data['month'] == month) & (data['day'] <= day)]\n",
    "    limit = data.groupby('credit_card')['credit_card_limit'].min().reset_index()\n",
    "    transaction = data.groupby('credit_card')['transaction_dollar_amount'].sum().reset_index()\n",
    "    \n",
    "    merged = pd.merge(left=transaction, right=limit, on='credit_card', how='left')\n",
    "    result = merged[merged['transaction_dollar_amount'] > merged['credit_card_limit']]\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae98a8b",
   "metadata": {},
   "source": [
    "after we built the function, we try to see the data on August 20th:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92f1e349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>credit_card</th>\n",
       "      <th>transaction_dollar_amount</th>\n",
       "      <th>credit_card_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>194</th>\n",
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       "      <td>4012.81</td>\n",
       "      <td>4000</td>\n",
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       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>3936887050542904</td>\n",
       "      <td>5352.07</td>\n",
       "      <td>4000</td>\n",
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       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>4118286032166087</td>\n",
       "      <td>3416.52</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>4462290711836916</td>\n",
       "      <td>3119.53</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>5723635641134781</td>\n",
       "      <td>2161.30</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>7299183791723634</td>\n",
       "      <td>7074.49</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>7850942767136368</td>\n",
       "      <td>5704.00</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>8522875529951473</td>\n",
       "      <td>5860.89</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>8972201384562696</td>\n",
       "      <td>5266.08</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          credit_card  transaction_dollar_amount  credit_card_limit\n",
       "194  2850146878241916                    4012.81               4000\n",
       "319  3936887050542904                    5352.07               4000\n",
       "341  4118286032166087                    3416.52               3000\n",
       "383  4462290711836916                    3119.53               2000\n",
       "525  5723635641134781                    2161.30               2000\n",
       "698  7299183791723634                    7074.49               7000\n",
       "772  7850942767136368                    5704.00               4000\n",
       "834  8522875529951473                    5860.89               5000\n",
       "874  8972201384562696                    5266.08               5000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alert(data, month=8, day=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390c2caa",
   "metadata": {},
   "source": [
    "### Part 3: an unsupervised algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d963be6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit_card</th>\n",
       "      <th>transaction_dollar_amount</th>\n",
       "      <th>Long</th>\n",
       "      <th>Lat</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>credit_card_limit</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>Long_median</th>\n",
       "      <th>Lat_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>43.78</td>\n",
       "      <td>-80.174132</td>\n",
       "      <td>40.267370</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>39.610299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>103.15</td>\n",
       "      <td>-80.194240</td>\n",
       "      <td>40.180114</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>48.55</td>\n",
       "      <td>-80.211033</td>\n",
       "      <td>40.313004</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>136.18</td>\n",
       "      <td>-80.174138</td>\n",
       "      <td>40.290895</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>71.82</td>\n",
       "      <td>-80.238720</td>\n",
       "      <td>40.166719</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        credit_card  transaction_dollar_amount       Long        Lat     city  \\\n",
       "0  1003715054175576                      43.78 -80.174132  40.267370  Houston   \n",
       "1  1003715054175576                     103.15 -80.194240  40.180114  Houston   \n",
       "2  1003715054175576                      48.55 -80.211033  40.313004  Houston   \n",
       "3  1003715054175576                     136.18 -80.174138  40.290895  Houston   \n",
       "4  1003715054175576                      71.82 -80.238720  40.166719  Houston   \n",
       "\n",
       "  state  zipcode  credit_card_limit  month  day  weekday  hour  Long_median  \\\n",
       "0    PA    15342              20000      9   11        4     0   -77.583829   \n",
       "1    PA    15342              20000     10   24        5    22   -77.583829   \n",
       "2    PA    15342              20000     10   26        0    18   -77.583829   \n",
       "3    PA    15342              20000     10   22        3    19   -77.583829   \n",
       "4    PA    15342              20000     10   26        0    20   -77.583829   \n",
       "\n",
       "   Lat_median  \n",
       "0   39.610299  \n",
       "1   39.610299  \n",
       "2   39.610299  \n",
       "3   39.610299  \n",
       "4   39.610299  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# calculate the median for Longitude and Latitude\n",
    "address = data.groupby('credit_card')['Long', 'Lat'].mean().reset_index()\n",
    "address = address.rename(columns={'Long': 'Long_median', 'Lat': 'Lat_median'})\n",
    "data = pd.merge(left=data, right=address, on='credit_card', how='left')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2ffd155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the distance\n",
    "data['distance'] = np.sqrt((data['Long'] - data['Long_median'])**2 + (data['Lat'] - data['Lat_median'])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "339363df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate user transaction median\n",
    "median = data.groupby('credit_card')['transaction_dollar_amount'].median().reset_index()\n",
    "median = median.rename(columns={'transaction_dollar_amount': 'transaction_median'})\n",
    "data = pd.merge(left=data, right=median, on='credit_card', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19656cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Long</th>\n",
       "      <th>Lat</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
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       "      <th>hour</th>\n",
       "      <th>Long_median</th>\n",
       "      <th>Lat_median</th>\n",
       "      <th>distance</th>\n",
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       "      <th>transaction_to_median</th>\n",
       "      <th>transaction_to_limit</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>43.78</td>\n",
       "      <td>-80.174132</td>\n",
       "      <td>40.267370</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>9</td>\n",
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       "      <td>39.610299</td>\n",
       "      <td>2.672342</td>\n",
       "      <td>93.73</td>\n",
       "      <td>0.467086</td>\n",
       "      <td>0.002189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>103.15</td>\n",
       "      <td>-80.194240</td>\n",
       "      <td>40.180114</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "      <td>2.671878</td>\n",
       "      <td>93.73</td>\n",
       "      <td>1.100501</td>\n",
       "      <td>0.005158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>48.55</td>\n",
       "      <td>-80.211033</td>\n",
       "      <td>40.313004</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "      <td>2.719557</td>\n",
       "      <td>93.73</td>\n",
       "      <td>0.517977</td>\n",
       "      <td>0.002427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>136.18</td>\n",
       "      <td>-80.174138</td>\n",
       "      <td>40.290895</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "      <td>2.678229</td>\n",
       "      <td>93.73</td>\n",
       "      <td>1.452897</td>\n",
       "      <td>0.006809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>71.82</td>\n",
       "      <td>-80.238720</td>\n",
       "      <td>40.166719</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>-77.583829</td>\n",
       "      <td>39.610299</td>\n",
       "      <td>2.712572</td>\n",
       "      <td>93.73</td>\n",
       "      <td>0.766243</td>\n",
       "      <td>0.003591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        credit_card  transaction_dollar_amount       Long        Lat     city  \\\n",
       "0  1003715054175576                      43.78 -80.174132  40.267370  Houston   \n",
       "1  1003715054175576                     103.15 -80.194240  40.180114  Houston   \n",
       "2  1003715054175576                      48.55 -80.211033  40.313004  Houston   \n",
       "3  1003715054175576                     136.18 -80.174138  40.290895  Houston   \n",
       "4  1003715054175576                      71.82 -80.238720  40.166719  Houston   \n",
       "\n",
       "  state  zipcode  credit_card_limit  month  day  weekday  hour  Long_median  \\\n",
       "0    PA    15342              20000      9   11        4     0   -77.583829   \n",
       "1    PA    15342              20000     10   24        5    22   -77.583829   \n",
       "2    PA    15342              20000     10   26        0    18   -77.583829   \n",
       "3    PA    15342              20000     10   22        3    19   -77.583829   \n",
       "4    PA    15342              20000     10   26        0    20   -77.583829   \n",
       "\n",
       "   Lat_median  distance  transaction_median  transaction_to_median  \\\n",
       "0   39.610299  2.672342               93.73               0.467086   \n",
       "1   39.610299  2.671878               93.73               1.100501   \n",
       "2   39.610299  2.719557               93.73               0.517977   \n",
       "3   39.610299  2.678229               93.73               1.452897   \n",
       "4   39.610299  2.712572               93.73               0.766243   \n",
       "\n",
       "   transaction_to_limit  \n",
       "0              0.002189  \n",
       "1              0.005158  \n",
       "2              0.002427  \n",
       "3              0.006809  \n",
       "4              0.003591  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract useful information\n",
    "data['transaction_to_median'] = data['transaction_dollar_amount'] / data['transaction_median']\n",
    "data['transaction_to_limit'] = data['transaction_dollar_amount'] / data['credit_card_limit']\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a91bf4b1",
   "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>credit_card</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>distance</th>\n",
       "      <th>transaction_to_median</th>\n",
       "      <th>transaction_to_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2.672342</td>\n",
       "      <td>0.467086</td>\n",
       "      <td>0.002189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>2.671878</td>\n",
       "      <td>1.100501</td>\n",
       "      <td>0.005158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2.719557</td>\n",
       "      <td>0.517977</td>\n",
       "      <td>0.002427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>2.678229</td>\n",
       "      <td>1.452897</td>\n",
       "      <td>0.006809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003715054175576</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2.712572</td>\n",
       "      <td>0.766243</td>\n",
       "      <td>0.003591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        credit_card  weekday  hour  distance  transaction_to_median  \\\n",
       "0  1003715054175576        4     0  2.672342               0.467086   \n",
       "1  1003715054175576        5    22  2.671878               1.100501   \n",
       "2  1003715054175576        0    18  2.719557               0.517977   \n",
       "3  1003715054175576        3    19  2.678229               1.452897   \n",
       "4  1003715054175576        0    20  2.712572               0.766243   \n",
       "\n",
       "   transaction_to_limit  \n",
       "0              0.002189  \n",
       "1              0.005158  \n",
       "2              0.002427  \n",
       "3              0.006809  \n",
       "4              0.003591  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop useless features\n",
    "labels = ['transaction_dollar_amount', 'Long', 'Lat', 'city', 'state', 'zipcode', 'credit_card_limit', \n",
    "          'month', 'day', 'Long_median', 'Lat_median', 'transaction_median']\n",
    "data = data.drop(labels=labels, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d53f842",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "# normalize the data\n",
    "x = data[['weekday', 'hour', 'distance', 'transaction_to_median', 'transaction_to_limit']]\n",
    "# x = data[['distance', 'transaction_to_median', 'transaction_to_limit']]\n",
    "scaler = StandardScaler()\n",
    "norm_x = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04bf70ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apply PCA\n",
    "pca = PCA(n_components=2, random_state=42)\n",
    "pca_x = pca.fit_transform(norm_x)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.plot(pca_x[:, 0], pca_x[:, 1], '.', markersize=1)\n",
    "ax.set_xlabel('PCA Component 1', fontsize=12)\n",
    "ax.set_ylabel('PCA Component 2', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f507928",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build isolation forest model\n",
    "forest = IsolationForest(n_estimators=100, max_samples='auto', contamination=0.1, \n",
    "                         max_features=1.0, bootstrap=False, n_jobs=-1, random_state=42)\n",
    "forest = forest.fit(norm_x)\n",
    "score = forest.decision_function(norm_x)\n",
    "label = forest.predict(norm_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "29166c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "inlier = pca_x[label == 1]\n",
    "outlier = pca_x[label == -1]\n",
    "xmin = pca_x[:, 0].min() - 1\n",
    "xmax = pca_x[:, 0].max() + 1\n",
    "ymin = pca_x[:, 1].min() - 1\n",
    "ymax = pca_x[:, 1].max() + 1\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 7))\n",
    "ax[0].plot(inlier[:, 0], inlier[:, 1], 'b.', markersize=2, label='Inlier')\n",
    "ax[0].set_xlabel('PCA Component 1', fontsize=14)\n",
    "ax[0].set_ylabel('PCA Component 2', fontsize=14)\n",
    "ax[0].legend(fontsize=14)\n",
    "ax[0].set_xlim([xmin, xmax])\n",
    "ax[0].set_ylim([ymin, ymax])\n",
    "ax[1].plot(outlier[:, 0], outlier[:, 1], 'r.', markersize=2, label='Outlier')\n",
    "ax[1].set_xlabel('PCA Component 1', fontsize=14)\n",
    "ax[1].set_ylabel('PCA Component 2', fontsize=14)\n",
    "ax[1].legend(fontsize=14)\n",
    "ax[1].set_xlim([xmin, xmax])\n",
    "ax[1].set_ylim([ymin, ymax])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab95366",
   "metadata": {},
   "source": [
    "There are different methods for unsupervised anomaly detection, such as Isolation Forest, One-Class SVM, DBSCAN and so on.\n",
    "\n",
    "Here, for simplicity, only DBSCAN method with default parameters is implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6d0d38f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}