Predictive Analytics, Task-2 @ANZ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c13bea48",
   "metadata": {},
   "source": [
    "# Import libaries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c0b14ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data wrangling \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statistics import mode\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c84eaf",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d0c9ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>card_present_flag</th>\n",
       "      <th>bpay_biller_code</th>\n",
       "      <th>account</th>\n",
       "      <th>currency</th>\n",
       "      <th>long_lat</th>\n",
       "      <th>txn_description</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>merchant_code</th>\n",
       "      <th>first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>age</th>\n",
       "      <th>merchant_suburb</th>\n",
       "      <th>merchant_state</th>\n",
       "      <th>extraction</th>\n",
       "      <th>amount</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>country</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>merchant_long_lat</th>\n",
       "      <th>movement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>POS</td>\n",
       "      <td>81c48296-73be-44a7-befa-d053f48ce7cd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diana</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>Ashmore</td>\n",
       "      <td>QLD</td>\n",
       "      <td>2018-08-01T01:01:15.000+0000</td>\n",
       "      <td>16.25</td>\n",
       "      <td>a623070bfead4541a6b0fff8a09e706c</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>153.38 -27.99</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>authorized</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>830a451c-316e-4a6a-bf25-e37caedca49e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diana</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2018-08-01T01:13:45.000+0000</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13270a2a902145da9db4c951e04b51b9</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1222300524</td>\n",
       "      <td>AUD</td>\n",
       "      <td>151.23 -33.94</td>\n",
       "      <td>POS</td>\n",
       "      <td>835c231d-8cdf-4e96-859d-e9d571760cf0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michael</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2018-08-01T01:26:15.000+0000</td>\n",
       "      <td>6.42</td>\n",
       "      <td>feb79e7ecd7048a5a36ec889d1a94270</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2142601169</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1037050564</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.10 -27.66</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>48514682-c78a-4a88-b0da-2d6302e64673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhonda</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>Buderim</td>\n",
       "      <td>QLD</td>\n",
       "      <td>2018-08-01T01:38:45.000+0000</td>\n",
       "      <td>40.90</td>\n",
       "      <td>2698170da3704fd981b15e64a006079e</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-1614226872</td>\n",
       "      <td>153.05 -26.68</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>b4e02c10-0852-4273-b8fd-7b3395e32eb0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diana</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>Mermaid Beach</td>\n",
       "      <td>QLD</td>\n",
       "      <td>2018-08-01T01:51:15.000+0000</td>\n",
       "      <td>3.25</td>\n",
       "      <td>329adf79878c4cf0aeb4188b4691c266</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>153.44 -28.06</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  card_present_flag bpay_biller_code         account currency  \\\n",
       "0  authorized                1.0              NaN  ACC-1598451071      AUD   \n",
       "1  authorized                0.0              NaN  ACC-1598451071      AUD   \n",
       "2  authorized                1.0              NaN  ACC-1222300524      AUD   \n",
       "3  authorized                1.0              NaN  ACC-1037050564      AUD   \n",
       "4  authorized                1.0              NaN  ACC-1598451071      AUD   \n",
       "\n",
       "        long_lat txn_description                           merchant_id  \\\n",
       "0  153.41 -27.95             POS  81c48296-73be-44a7-befa-d053f48ce7cd   \n",
       "1  153.41 -27.95       SALES-POS  830a451c-316e-4a6a-bf25-e37caedca49e   \n",
       "2  151.23 -33.94             POS  835c231d-8cdf-4e96-859d-e9d571760cf0   \n",
       "3  153.10 -27.66       SALES-POS  48514682-c78a-4a88-b0da-2d6302e64673   \n",
       "4  153.41 -27.95       SALES-POS  b4e02c10-0852-4273-b8fd-7b3395e32eb0   \n",
       "\n",
       "   merchant_code first_name  ...  age merchant_suburb merchant_state  \\\n",
       "0            NaN      Diana  ...   26         Ashmore            QLD   \n",
       "1            NaN      Diana  ...   26          Sydney            NSW   \n",
       "2            NaN    Michael  ...   38          Sydney            NSW   \n",
       "3            NaN     Rhonda  ...   40         Buderim            QLD   \n",
       "4            NaN      Diana  ...   26   Mermaid Beach            QLD   \n",
       "\n",
       "                     extraction amount                    transaction_id  \\\n",
       "0  2018-08-01T01:01:15.000+0000  16.25  a623070bfead4541a6b0fff8a09e706c   \n",
       "1  2018-08-01T01:13:45.000+0000  14.19  13270a2a902145da9db4c951e04b51b9   \n",
       "2  2018-08-01T01:26:15.000+0000   6.42  feb79e7ecd7048a5a36ec889d1a94270   \n",
       "3  2018-08-01T01:38:45.000+0000  40.90  2698170da3704fd981b15e64a006079e   \n",
       "4  2018-08-01T01:51:15.000+0000   3.25  329adf79878c4cf0aeb4188b4691c266   \n",
       "\n",
       "     country     customer_id merchant_long_lat movement  \n",
       "0  Australia  CUS-2487424745     153.38 -27.99    debit  \n",
       "1  Australia  CUS-2487424745     151.21 -33.87    debit  \n",
       "2  Australia  CUS-2142601169     151.21 -33.87    debit  \n",
       "3  Australia  CUS-1614226872     153.05 -26.68    debit  \n",
       "4  Australia  CUS-2487424745     153.44 -28.06    debit  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"C:\\\\Users\\\\ASUS\\\\Downloads\\\\ANZ synthesised transaction dataset.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a861f34",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>bpay_biller_code</td>\n",
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       "      <th>3</th>\n",
       "      <td>account</td>\n",
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       "      <th>4</th>\n",
       "      <td>currency</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>long_lat</td>\n",
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       "      <th>6</th>\n",
       "      <td>txn_description</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>merchant_id</td>\n",
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       "      <th>8</th>\n",
       "      <td>merchant_code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>first_name</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>date</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gender</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>age</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>merchant_suburb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>merchant_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>extraction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>amount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>transaction_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>customer_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>merchant_long_lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>movement</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              Columns\n",
       "0              status\n",
       "1   card_present_flag\n",
       "2    bpay_biller_code\n",
       "3             account\n",
       "4            currency\n",
       "5            long_lat\n",
       "6     txn_description\n",
       "7         merchant_id\n",
       "8       merchant_code\n",
       "9          first_name\n",
       "10            balance\n",
       "11               date\n",
       "12             gender\n",
       "13                age\n",
       "14    merchant_suburb\n",
       "15     merchant_state\n",
       "16         extraction\n",
       "17             amount\n",
       "18     transaction_id\n",
       "19            country\n",
       "20        customer_id\n",
       "21  merchant_long_lat\n",
       "22           movement"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe columns\n",
    "pd.DataFrame({\"Columns\": df.columns})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12885c6",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "raw",
   "id": "42fd75c2",
   "metadata": {},
   "source": [
    "For moddel annula salary, We first need to compute the annual salary for each customer as well as create features that can help to predict the those salaries "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102588e0",
   "metadata": {},
   "source": [
    "# Target variables (Customers and annual salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a3baaf3",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>CUS-2487424745</td>\n",
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       "      <th>3</th>\n",
       "      <td>CUS-2688605418</td>\n",
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       "      <th>4</th>\n",
       "      <td>CUS-4123612273</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      customer_id\n",
       "0  CUS-2487424745\n",
       "1  CUS-2142601169\n",
       "2  CUS-1614226872\n",
       "3  CUS-2688605418\n",
       "4  CUS-4123612273"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#salary Payment of frequnecy of each customer\n",
    "salary_df =  pd.DataFrame({\"customer_id\": df.customer_id.unique()}) #for refrence\n",
    "salary_df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af1d70f",
   "metadata": {},
   "source": [
    "# Modifying data to obtain salaries for each customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a06ab5cc",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>card_present_flag</th>\n",
       "      <th>merchant_code</th>\n",
       "      <th>balance</th>\n",
       "      <th>age</th>\n",
       "      <th>amount</th>\n",
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       "      <th>customer_id</th>\n",
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       "      <th>CUS-1005756958</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4718.665385</td>\n",
       "      <td>53.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5841.720000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1916.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1147642491</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8813.467692</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1711.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1196156254</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23845.717143</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3903.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                card_present_flag  merchant_code       balance   age   amount\n",
       "customer_id                                                                  \n",
       "CUS-1005756958                NaN            0.0   4718.665385  53.0   970.47\n",
       "CUS-1117979751                NaN            0.0  11957.202857  21.0  3578.65\n",
       "CUS-1140341822                NaN            0.0   5841.720000  28.0  1916.51\n",
       "CUS-1147642491                NaN            0.0   8813.467692  34.0  1711.39\n",
       "CUS-1196156254                NaN            0.0  23845.717143  34.0  3903.73"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salaries =  df[df[\"txn_description\"]==\"PAY/SALARY\"].groupby(\"customer_id\").mean()\n",
    "df_salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c3f479d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "salaries = []\n",
    "for customer_id in df[\"customer_id\"]:\n",
    "    salaries.append(int(df_salaries.loc[customer_id][\"amount\"]))\n",
    "df[\"annual_salary\"] = salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2cf1bd49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>card_present_flag</th>\n",
       "      <th>merchant_code</th>\n",
       "      <th>balance</th>\n",
       "      <th>age</th>\n",
       "      <th>amount</th>\n",
       "      <th>annual_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CUS-1005756958</th>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2275.852055</td>\n",
       "      <td>53.0</td>\n",
       "      <td>222.862603</td>\n",
       "      <td>970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1117979751</th>\n",
       "      <td>0.826923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9829.929000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>339.843700</td>\n",
       "      <td>3578.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1140341822</th>\n",
       "      <td>0.815385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5699.212250</td>\n",
       "      <td>28.0</td>\n",
       "      <td>212.632500</td>\n",
       "      <td>1916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1147642491</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9032.841186</td>\n",
       "      <td>34.0</td>\n",
       "      <td>245.600169</td>\n",
       "      <td>1711.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1196156254</th>\n",
       "      <td>0.785276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22272.433755</td>\n",
       "      <td>34.0</td>\n",
       "      <td>147.145796</td>\n",
       "      <td>3903.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                card_present_flag  merchant_code       balance   age  \\\n",
       "customer_id                                                            \n",
       "CUS-1005756958           0.812500            0.0   2275.852055  53.0   \n",
       "CUS-1117979751           0.826923            0.0   9829.929000  21.0   \n",
       "CUS-1140341822           0.815385            0.0   5699.212250  28.0   \n",
       "CUS-1147642491           0.750000            0.0   9032.841186  34.0   \n",
       "CUS-1196156254           0.785276            0.0  22272.433755  34.0   \n",
       "\n",
       "                    amount  annual_salary  \n",
       "customer_id                                \n",
       "CUS-1005756958  222.862603          970.0  \n",
       "CUS-1117979751  339.843700         3578.0  \n",
       "CUS-1140341822  212.632500         1916.0  \n",
       "CUS-1147642491  245.600169         1711.0  \n",
       "CUS-1196156254  147.145796         3903.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer = df.groupby(\"customer_id\").mean()\n",
    "df_customer.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54632b9b",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf0a6242",
   "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>card_present_flag</th>\n",
       "      <th>balance</th>\n",
       "      <th>age</th>\n",
       "      <th>amount</th>\n",
       "      <th>annual_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>card_present_flag</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>-0.008405</td>\n",
       "      <td>-0.002074</td>\n",
       "      <td>-0.003895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.005925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199329</td>\n",
       "      <td>0.059178</td>\n",
       "      <td>0.258148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.008405</td>\n",
       "      <td>0.199329</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029980</td>\n",
       "      <td>-0.083823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>-0.002074</td>\n",
       "      <td>0.059178</td>\n",
       "      <td>0.029980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_salary</th>\n",
       "      <td>-0.003895</td>\n",
       "      <td>0.258148</td>\n",
       "      <td>-0.083823</td>\n",
       "      <td>0.056421</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   card_present_flag   balance       age    amount  \\\n",
       "card_present_flag           1.000000  0.005925 -0.008405 -0.002074   \n",
       "balance                     0.005925  1.000000  0.199329  0.059178   \n",
       "age                        -0.008405  0.199329  1.000000  0.029980   \n",
       "amount                     -0.002074  0.059178  0.029980  1.000000   \n",
       "annual_salary              -0.003895  0.258148 -0.083823  0.056421   \n",
       "\n",
       "                   annual_salary  \n",
       "card_present_flag      -0.003895  \n",
       "balance                 0.258148  \n",
       "age                    -0.083823  \n",
       "amount                  0.056421  \n",
       "annual_salary           1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here we are dropping marchent_code, as it doesnot effect the dataset for correlation\n",
    "#it is no longer needed for correlation\n",
    "df = df.drop(\"merchant_code\", axis=1)\n",
    "cor = df.corr(method = 'pearson') #pearson correlation\n",
    "cor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d77e647",
   "metadata": {},
   "source": [
    "# Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2a1f9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cor, annot=True) #it shows correlation between target variables \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121ac9b1",
   "metadata": {},
   "source": [
    "# Predictive Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526b03b9",
   "metadata": {},
   "source": [
    "Linear Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7f45e49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_train = int(len(df_customer)*0.8)\n",
    "X_train = df_customer.drop(\"annual_salary\", axis=1).iloc[:N_train]\n",
    "Y_train = df_customer[\"annual_salary\"].iloc[:N_train]\n",
    "X_test = df_customer.drop(\"annual_salary\", axis=1).iloc[N_train:]\n",
    "Y_test = df_customer[\"annual_salary\"].iloc[N_train:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4bfdfaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg =  LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "69e49e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b828cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23295376366257836"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train, Y_train) #regression score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3fcb96cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1993.98473311, 2867.39066481, 1944.95959591, 1806.85984885,\n",
       "       2226.35045442, 2075.34697175, 1813.02987337, 5388.67435983,\n",
       "       1902.35351608, 2191.90445145, 1713.48134178, 2854.40519949,\n",
       "       2094.77781158, 3815.34342881, 2249.92922822, 1768.80816189,\n",
       "       2095.02988288, 1515.18425875, 1782.72752537, 2481.2898546 ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(X_test) #Predicted values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f4e05c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3169423498074737"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ea4be6",
   "metadata": {},
   "source": [
    "# Decision Tree - Classification and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "55a8b2b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dec = df[[\"txn_description\", \"gender\", \"age\", \"merchant_state\", \"movement\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "07b7549a",
   "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>age</th>\n",
       "      <th>txn_description_INTER BANK</th>\n",
       "      <th>txn_description_PAY/SALARY</th>\n",
       "      <th>txn_description_PAYMENT</th>\n",
       "      <th>txn_description_PHONE BANK</th>\n",
       "      <th>txn_description_POS</th>\n",
       "      <th>txn_description_SALES-POS</th>\n",
       "      <th>gender_F</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>merchant_state_ACT</th>\n",
       "      <th>merchant_state_NSW</th>\n",
       "      <th>merchant_state_NT</th>\n",
       "      <th>merchant_state_QLD</th>\n",
       "      <th>merchant_state_SA</th>\n",
       "      <th>merchant_state_TAS</th>\n",
       "      <th>merchant_state_VIC</th>\n",
       "      <th>merchant_state_WA</th>\n",
       "      <th>movement_credit</th>\n",
       "      <th>movement_debit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  txn_description_INTER BANK  txn_description_PAY/SALARY  \\\n",
       "0   26                           0                           0   \n",
       "1   26                           0                           0   \n",
       "2   38                           0                           0   \n",
       "3   40                           0                           0   \n",
       "4   26                           0                           0   \n",
       "\n",
       "   txn_description_PAYMENT  txn_description_PHONE BANK  txn_description_POS  \\\n",
       "0                        0                           0                    1   \n",
       "1                        0                           0                    0   \n",
       "2                        0                           0                    1   \n",
       "3                        0                           0                    0   \n",
       "4                        0                           0                    0   \n",
       "\n",
       "   txn_description_SALES-POS  gender_F  gender_M  merchant_state_ACT  \\\n",
       "0                          0         1         0                   0   \n",
       "1                          1         1         0                   0   \n",
       "2                          0         0         1                   0   \n",
       "3                          1         1         0                   0   \n",
       "4                          1         1         0                   0   \n",
       "\n",
       "   merchant_state_NSW  merchant_state_NT  merchant_state_QLD  \\\n",
       "0                   0                  0                   1   \n",
       "1                   1                  0                   0   \n",
       "2                   1                  0                   0   \n",
       "3                   0                  0                   1   \n",
       "4                   0                  0                   1   \n",
       "\n",
       "   merchant_state_SA  merchant_state_TAS  merchant_state_VIC  \\\n",
       "0                  0                   0                   0   \n",
       "1                  0                   0                   0   \n",
       "2                  0                   0                   0   \n",
       "3                  0                   0                   0   \n",
       "4                  0                   0                   0   \n",
       "\n",
       "   merchant_state_WA  movement_credit  movement_debit  \n",
       "0                  0                0               1  \n",
       "1                  0                0               1  \n",
       "2                  0                0               1  \n",
       "3                  0                0               1  \n",
       "4                  0                0               1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df_dec).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3346355a",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_train = int(len(df)*0.8)\n",
    "X_train = pd.get_dummies(df_dec).iloc[:N_train]\n",
    "Y_train = df[\"annual_salary\"].iloc[:N_train]\n",
    "X_test = pd.get_dummies(df_dec).iloc[N_train:]\n",
    "Y_test = df[\"annual_salary\"].iloc[N_train:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "33151ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#decision tree \n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "decision_tree_class = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "04e7ce44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7882499481004774"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_class.fit(X_train, Y_train)\n",
    "decision_tree_class.score(X_train, Y_train) #decision tree score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "980c73b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1013, 1043, 4132, ..., 4054, 1043,  996], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_class.predict(X_test) #Predicated Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a1dbc022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.755085097550851"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_class.score(X_test, Y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23cec2df",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eb355360",
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_tree_reg = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b775b107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7468978726536879"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_reg.fit(X_train, Y_train)\n",
    "decision_tree_reg.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e9179c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1226.42857143, 1043.        , 4132.        , ..., 3345.04761905,\n",
       "       1043.        , 1626.        ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cc6f9c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.673099285463798"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_reg.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e49c248",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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