Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The goal of this notebook is to help Adventure works predict customer average monthly spend given the customer info, bike buyer and available average month spend data. This is a regression problem and we will explore various ML Regressors. We shall first perform needed data clean up and visualization, tuning, data balance and analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# perform necessary import\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import numpy.random as nr\n",
    "import math\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load datasets\n",
    "customer_info = pd.read_csv('data/AdvWorksCusts.csv')\n",
    "customer_spending = pd.read_csv('data/AW_AveMonthSpend.csv')\n",
    "customer_has_bike= pd.read_csv('data/AW_BikeBuyer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16519, 23)\n",
      "(16404,)\n",
      "(16404, 23)\n",
      "(16404,)\n"
     ]
    }
   ],
   "source": [
    "# remove duplicates\n",
    "print(customer_info.shape)\n",
    "print(customer_info.CustomerID.unique().shape)\n",
    "customer_info.drop_duplicates(subset = 'CustomerID', keep = 'last', inplace = True)\n",
    "print(customer_info.shape)\n",
    "print(customer_info.CustomerID.unique().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16519, 2)\n",
      "(16404,)\n",
      "(16404, 2)\n",
      "(16404,)\n"
     ]
    }
   ],
   "source": [
    "print(customer_spending.shape)\n",
    "print(customer_spending.CustomerID.unique().shape)\n",
    "customer_spending.drop_duplicates(subset = 'CustomerID', keep = 'last', inplace = True)\n",
    "print(customer_spending.shape)\n",
    "print(customer_spending.CustomerID.unique().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16519, 2)\n",
      "(16404,)\n",
      "(16404, 2)\n",
      "(16404,)\n"
     ]
    }
   ],
   "source": [
    "print(customer_has_bike.shape)\n",
    "print(customer_has_bike.CustomerID.unique().shape)\n",
    "customer_has_bike.drop_duplicates(subset = 'CustomerID', keep = 'last', inplace = True)\n",
    "print(customer_has_bike.shape)\n",
    "print(customer_has_bike.CustomerID.unique().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID    False\n",
       "BikeBuyer     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "(customer_has_bike.astype(np.object).isnull()).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID       False\n",
       "AveMonthSpend    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(customer_spending.astype(np.object).isnull()).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID              False\n",
       "Title                    True\n",
       "FirstName               False\n",
       "MiddleName               True\n",
       "LastName                False\n",
       "Suffix                   True\n",
       "AddressLine1            False\n",
       "AddressLine2             True\n",
       "City                    False\n",
       "StateProvinceName       False\n",
       "CountryRegionName       False\n",
       "PostalCode              False\n",
       "PhoneNumber             False\n",
       "BirthDate               False\n",
       "Education               False\n",
       "Occupation              False\n",
       "Gender                  False\n",
       "MaritalStatus           False\n",
       "HomeOwnerFlag           False\n",
       "NumberCarsOwned         False\n",
       "NumberChildrenAtHome    False\n",
       "TotalChildren           False\n",
       "YearlyIncome            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(customer_info.astype(np.object).isnull()).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is cleaned and fine with missing values check. Will now perform exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>HomeOwnerFlag</th>\n",
       "      <th>NumberCarsOwned</th>\n",
       "      <th>NumberChildrenAtHome</th>\n",
       "      <th>TotalChildren</th>\n",
       "      <th>YearlyIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20240.143136</td>\n",
       "      <td>0.674104</td>\n",
       "      <td>1.499756</td>\n",
       "      <td>0.991100</td>\n",
       "      <td>2.004511</td>\n",
       "      <td>78129.672397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5340.370705</td>\n",
       "      <td>0.468723</td>\n",
       "      <td>1.139135</td>\n",
       "      <td>1.514278</td>\n",
       "      <td>1.682318</td>\n",
       "      <td>39728.383493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9482.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15617.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47808.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20231.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>76125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24862.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>105211.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29482.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>196511.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID  HomeOwnerFlag  NumberCarsOwned  NumberChildrenAtHome  \\\n",
       "count  16404.000000   16404.000000     16404.000000          16404.000000   \n",
       "mean   20240.143136       0.674104         1.499756              0.991100   \n",
       "std     5340.370705       0.468723         1.139135              1.514278   \n",
       "min    11000.000000       0.000000         0.000000              0.000000   \n",
       "25%    15617.750000       0.000000         1.000000              0.000000   \n",
       "50%    20231.500000       1.000000         2.000000              0.000000   \n",
       "75%    24862.250000       1.000000         2.000000              2.000000   \n",
       "max    29482.000000       1.000000         4.000000              5.000000   \n",
       "\n",
       "       TotalChildren   YearlyIncome  \n",
       "count   16404.000000   16404.000000  \n",
       "mean        2.004511   78129.672397  \n",
       "std         1.682318   39728.383493  \n",
       "min         0.000000    9482.000000  \n",
       "25%         0.000000   47808.750000  \n",
       "50%         2.000000   76125.000000  \n",
       "75%         3.000000  105211.750000  \n",
       "max         5.000000  196511.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_info.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>CustomerID</th>\n",
       "      <th>AveMonthSpend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20240.143136</td>\n",
       "      <td>72.391002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5340.370705</td>\n",
       "      <td>27.269921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11000.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15617.750000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20231.500000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24862.250000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29482.000000</td>\n",
       "      <td>176.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID  AveMonthSpend\n",
       "count  16404.000000   16404.000000\n",
       "mean   20240.143136      72.391002\n",
       "std     5340.370705      27.269921\n",
       "min    11000.000000      22.000000\n",
       "25%    15617.750000      52.000000\n",
       "50%    20231.500000      68.000000\n",
       "75%    24862.250000      84.000000\n",
       "max    29482.000000     176.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_spending.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>CustomerID</th>\n",
       "      <th>BikeBuyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16404.000000</td>\n",
       "      <td>16404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20240.143136</td>\n",
       "      <td>0.332541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5340.370705</td>\n",
       "      <td>0.471138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15617.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20231.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24862.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29482.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID     BikeBuyer\n",
       "count  16404.000000  16404.000000\n",
       "mean   20240.143136      0.332541\n",
       "std     5340.370705      0.471138\n",
       "min    11000.000000      0.000000\n",
       "25%    15617.750000      0.000000\n",
       "50%    20231.500000      0.000000\n",
       "75%    24862.250000      1.000000\n",
       "max    29482.000000      1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_has_bike.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge data for analysis. We will like to predict customer average monthly spend hence this is the label in question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Title</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>Suffix</th>\n",
       "      <th>AddressLine1</th>\n",
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       "      <th>City</th>\n",
       "      <th>StateProvinceName</th>\n",
       "      <th>...</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Gender</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>HomeOwnerFlag</th>\n",
       "      <th>NumberCarsOwned</th>\n",
       "      <th>NumberChildrenAtHome</th>\n",
       "      <th>TotalChildren</th>\n",
       "      <th>YearlyIncome</th>\n",
       "      <th>AveMonthSpend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jon</td>\n",
       "      <td>V</td>\n",
       "      <td>Yang</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3761 N. 14th St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rockhampton</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>137947</td>\n",
       "      <td>89</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eugene</td>\n",
       "      <td>L</td>\n",
       "      <td>Huang</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2243 W St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Seaford</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>101141</td>\n",
       "      <td>117</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ruben</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Torres</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5844 Linden Land</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hobart</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>91945</td>\n",
       "      <td>123</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Christy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zhu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1825 Village Pl.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Ryde</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86688</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7553 Harness Circle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wollongong</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>92771</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID Title  FirstName MiddleName LastName Suffix  \\\n",
       "0       11000   NaN        Jon          V     Yang    NaN   \n",
       "1       11001   NaN     Eugene          L    Huang    NaN   \n",
       "2       11002   NaN      Ruben        NaN   Torres    NaN   \n",
       "3       11003   NaN    Christy        NaN      Zhu    NaN   \n",
       "4       11004   NaN  Elizabeth        NaN  Johnson    NaN   \n",
       "\n",
       "          AddressLine1 AddressLine2         City StateProvinceName  \\\n",
       "0      3761 N. 14th St          NaN  Rockhampton        Queensland   \n",
       "1           2243 W St.          NaN      Seaford          Victoria   \n",
       "2     5844 Linden Land          NaN       Hobart          Tasmania   \n",
       "3     1825 Village Pl.          NaN   North Ryde   New South Wales   \n",
       "4  7553 Harness Circle          NaN   Wollongong   New South Wales   \n",
       "\n",
       "       ...         Education    Occupation Gender MaritalStatus HomeOwnerFlag  \\\n",
       "0      ...        Bachelors   Professional      M             M             1   \n",
       "1      ...        Bachelors   Professional      M             S             0   \n",
       "2      ...        Bachelors   Professional      M             M             1   \n",
       "3      ...        Bachelors   Professional      F             S             0   \n",
       "4      ...        Bachelors   Professional      F             S             1   \n",
       "\n",
       "  NumberCarsOwned NumberChildrenAtHome TotalChildren  YearlyIncome  \\\n",
       "0               0                    0             2        137947   \n",
       "1               1                    3             3        101141   \n",
       "2               1                    3             3         91945   \n",
       "3               1                    0             0         86688   \n",
       "4               4                    5             5         92771   \n",
       "\n",
       "   AveMonthSpend  \n",
       "0             89  \n",
       "1            117  \n",
       "2            123  \n",
       "3             50  \n",
       "4             95  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = customer_info\n",
    "combined = customer_info.merge(customer_spending, on='CustomerID', how='left')\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# generate Age column since we are given birthdate\n",
    "from datetime import datetime\n",
    "from dateutil.parser import parse \n",
    "\n",
    "def generate_age(customer_info, format):\n",
    "    collection_day = birthday = datetime(1998, 1, 1, 0, 0, 0)\n",
    "    age = []\n",
    "    for index, row in customer_info.iterrows():\n",
    "        conv = datetime.strptime(row['BirthDate'], format)\n",
    "        age.append(int((collection_day - conv).days/365))\n",
    "    return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined['Age'] = generate_age(combined, '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1966-04-08</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-05-14</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965-08-12</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1968-02-15</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1968-08-08</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BirthDate  Age\n",
       "0  1966-04-08   31\n",
       "1  1965-05-14   32\n",
       "2  1965-08-12   32\n",
       "3  1968-02-15   29\n",
       "4  1968-08-08   29"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[['BirthDate','Age']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting scatter plots to see relationships between other numeric features and AveMonthSpend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_scatter(auto_prices, cols, col_y = 'AveMonthSpend'):\n",
    "    for col in cols:\n",
    "        fig = plt.figure(figsize=(7,6)) # define plot area\n",
    "        ax = fig.gca() # define axis   \n",
    "        auto_prices.plot.scatter(x = col, y = col_y, ax = ax)\n",
    "        ax.set_title('Scatter plot of ' + col_y + ' vs. ' + col) # Give the plot a main title\n",
    "        ax.set_xlabel(col) # Set text for the x axis\n",
    "        ax.set_ylabel(col_y)# Set text for y axis\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10ea3b9e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ajUpkMhn5gpx8ocjScoKmBjW7+6y88u5slSW4koUYjmfZK1jX7idl1qDTKFn0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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea3b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['AveMonthSpend','YearlyIncome','Age']\n",
    "sns.pairplot(combined[cols],  palette=\"Set2\", diag_kind=\"kde\", size=2).map_upper(sns.kdeplot, cmap=\"Blues_d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter plots for YearlyIncome and Age there is some relationship with AveMonthSpend. These might be good features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will make use of box plots to take a look at the categorical variables to AveMonthSpend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_box(auto_prices, cols, col_y = 'AveMonthSpend'):\n",
    "    for col in cols:\n",
    "        sns.set_style(\"whitegrid\")\n",
    "        sns.boxplot(col, col_y, data=auto_prices)\n",
    "        plt.xlabel(col) # Set text for the x axis\n",
    "        plt.ylabel(col_y)# Set text for y axis\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KiB1xdHQssl0bhYWFFdmujoo9MjCZTLRo0QKAbdu20bNnTwDc3Nw0+JRILTJ06NBy76kH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\ngsJARERQGIiICAoDERFBYSAiIsD/A1RK4TdaHC1tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8d9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WW27hyJEjJCcn88477wA/TQG+fPlyKisrufXWWxk9ejRvvfUWAO+99x7ffvut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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d58d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ANddcA8Dy5cv5/e9/z6hRo0hISGDfvn0AxMXFMXXqVOLj4ykuLiYuLo7PPvuM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\nIsD/A45ON4I0yIWnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aac0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CcnIyZrMZq9VKeno6LpeLzMxM918DIiJiHI/XGbRq1YoHHniAI0eONBitdNGiRR5nHhkZ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\nf8opZRSqbJ1CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ef56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['Occupation', 'Gender', 'MaritalStatus', 'HomeOwnerFlag']\n",
    "plot_box(combined, cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BgwZ44YUXVL+Ezp07h99++63Keasaz5KSEnh7eyM/Px+jR4/G3LlzcenSpWo9zweee+45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\nxqAjIiJJ+3/QKxMbVD1rMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a896828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qpk0b/Pbbb1CpVABK/oE//Bfig9re3h7u7u5YvXo1gJIv+WHDhiEhIeGxet2+fTvUajWy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\nERGZNAYdERGZtP8DU8eJ8Ob6LwMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ee6ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k8+fPx8svvwwA6NixI1avXo3S0lIEBwfD09MTSqUS/fv3x1dffYXa2lr069cPmzdvxuzZ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\njUFHRESyxqAjIiJZ+z+8QrX1zRX7EAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4d9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(combined, ['NumberChildrenAtHome', 'NumberCarsOwned', 'TotalChildren'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical features such as occupation, gender, marital status and home owner flag have distinct relationships with average month spend. The quartiles are of different levels. It seems that Males spend more on average than females same for married and homeowners.\n",
    "Numeric features of interest would be Number of children at home, age and yearly income(as seen earlier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categorical_features = ['Gender','MaritalStatus','HomeOwnerFlag', 'Occupation']\n",
    "numeric_features = ['Age', 'YearlyIncome','NumberChildrenAtHome']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is time to prepare the data for training.\n",
    "1. We will be doing one-hot encoding for categorical variables.\n",
    "2. Converting features to numpy arrays for scikit learn ML library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define encoder for categorical variables\n",
    "from sklearn import preprocessing\n",
    "import sklearn.model_selection as ms\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as sklm\n",
    "\n",
    "def encode_string(cat_features):\n",
    "    enc = preprocessing.LabelEncoder()\n",
    "    enc.fit(cat_features)\n",
    "    enc_cat_features = enc.transform(cat_features)\n",
    "    ohe = preprocessing.OneHotEncoder()\n",
    "    encoded = ohe.fit(enc_cat_features.reshape(-1,1))\n",
    "    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def encode_cat_features(features):\n",
    "    categorical_features = ['Gender','MaritalStatus','HomeOwnerFlag']\n",
    "    f = encode_string(features['Occupation'])\n",
    "    for cat in categorical_features:\n",
    "        enc = encode_string(features[cat])\n",
    "        f = np.concatenate([f,enc],1)\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 89 117 123 ...,  79  65  68]\n"
     ]
    }
   ],
   "source": [
    "labels = np.array(combined.AveMonthSpend)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>YearlyIncome</th>\n",
       "      <th>NumberChildrenAtHome</th>\n",
       "      <th>Gender</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>HomeOwnerFlag</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>137947</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Professional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>101141</td>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Professional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>91945</td>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Professional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>86688</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Professional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>92771</td>\n",
       "      <td>5</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Professional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  YearlyIncome  NumberChildrenAtHome Gender MaritalStatus  \\\n",
       "0   31        137947                     0      M             M   \n",
       "1   32        101141                     3      M             S   \n",
       "2   32         91945                     3      M             M   \n",
       "3   29         86688                     0      F             S   \n",
       "4   29         92771                     5      F             S   \n",
       "\n",
       "   HomeOwnerFlag    Occupation  \n",
       "0              1  Professional  \n",
       "1              0  Professional  \n",
       "2              1  Professional  \n",
       "3              0  Professional  \n",
       "4              1  Professional  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected = numeric_features + categorical_features\n",
    "features = combined[selected]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       ..., \n",
       "       [ 0.,  1.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  0.,  1.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = encode_cat_features(features)\n",
    "encoded[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    31, 137947,      0],\n",
       "       [    32, 101141,      3],\n",
       "       [    32,  91945,      3],\n",
       "       ..., \n",
       "       [    58, 133053,      0],\n",
       "       [    51,  31930,      0],\n",
       "       [    52,  59382,      0]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = np.array(combined[numeric_features])\n",
    "numeric_features[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16404, 14)\n",
      "[[  0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   3.10000000e+01\n",
      "    1.37947000e+05   0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "features = np.concatenate([encoded,numeric_features], 1)\n",
    "print(features.shape)\n",
    "print(features[:1,:14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.50000000e+01,   3.97160000e+04],\n",
       "       [  3.00000000e+01,   1.72468000e+05]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time to split and train\n",
    "nr.seed(9988)\n",
    "indx = range(features.shape[0])\n",
    "indx = ms.train_test_split(indx, test_size = 300)\n",
    "X_train = features[indx[0],:]\n",
    "y_train = np.ravel(labels[indx[0]])\n",
    "X_test = features[indx[1],:]\n",
    "y_test = np.ravel(labels[indx[1]])\n",
    "X_train[:2,11:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ,  0.        ,  0.        ,  1.        ,  1.        ,\n",
       "         0.        , -0.04218537, -0.96803832,  0.        ],\n",
       "       [ 0.        ,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  1.        ,  0.        ,  1.        ,  1.        ,\n",
       "         0.        , -0.48707941,  2.37320265,  0.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time to scale, this would most especially be useful in yearly income\n",
    "scaler = preprocessing.StandardScaler().fit(X_train[:,11:13])\n",
    "X_train[:,11:13] = scaler.transform(X_train[:,11:13])\n",
    "X_test[:,11:13] = scaler.transform(X_test[:,11:13])\n",
    "X_train[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feautres are now prepared."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of lets train with a simple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.8437032326\n",
      "[  6.62462745e-01  -2.17174555e+00  -1.79238414e-01   8.84109472e-01\n",
      "   8.04411670e-01  -1.40857877e+01   1.40857877e+01   3.09843700e+00\n",
      "  -3.09843700e+00   3.72134830e-03  -3.72134822e-03  -1.24172121e+00\n",
      "   8.20153712e+00   1.10719328e+01]\n"
     ]
    }
   ],
   "source": [
    "## define and fit the linear regression model\n",
    "lin_mod = linear_model.Ridge(alpha = 0.04)\n",
    "lin_mod.fit(X_train, y_train)\n",
    "print(lin_mod.intercept_)\n",
    "print(lin_mod.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error      = 34.3036755262\n",
      "Root Mean Square Error = 5.856933969765431\n",
      "Mean Absolute Error    = 4.39489932329\n",
      "Median Absolute Error  = 3.39298386146\n",
      "R^2                    = 0.952262520316\n",
      "Adjusted R^2           = 0.950092634876\n"
     ]
    }
   ],
   "source": [
    "def print_metrics(y_true, y_predicted, n_parameters):\n",
    "    ## First compute R^2 and the adjusted R^2\n",
    "    r2 = sklm.r2_score(y_true, y_predicted)\n",
    "    r2_adj = r2 - (n_parameters - 1)/(y_true.shape[0] - n_parameters) * (1 - r2)\n",
    "    \n",
    "    ## Print the usual metrics and the R^2 values\n",
    "    print('Mean Square Error      = ' + str(sklm.mean_squared_error(y_true, y_predicted)))\n",
    "    print('Root Mean Square Error = ' + str(math.sqrt(sklm.mean_squared_error(y_true, y_predicted))))\n",
    "    print('Mean Absolute Error    = ' + str(sklm.mean_absolute_error(y_true, y_predicted)))\n",
    "    print('Median Absolute Error  = ' + str(sklm.median_absolute_error(y_true, y_predicted)))\n",
    "    print('R^2                    = ' + str(r2))\n",
    "    print('Adjusted R^2           = ' + str(r2_adj))\n",
    "   \n",
    "y_score = lin_mod.predict(X_test) \n",
    "print_metrics(y_test, y_score, 14) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ply4k0epIKIg2qbyqjqKyGtpHBF7xuQlXS6fT0qVjCMVlNRzNkXsWROsioSDapL1HiwCI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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ad6ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def hist_resids(y_test, y_score):\n",
    "    ## first compute vector of residuals. \n",
    "    resids = np.subtract(y_test.reshape(-1,1), y_score.reshape(-1,1))\n",
    "    ## now make the residual plots\n",
    "    sns.distplot(resids)\n",
    "    plt.title('Histogram of residuals')\n",
    "    plt.xlabel('Residual value')\n",
    "    plt.ylabel('count')\n",
    "    \n",
    "hist_resids(y_test, y_score)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51\n",
      "44.3796127147\n",
      "       CustomerID Title FirstName MiddleName LastName Suffix  \\\n",
      "10653       14328   NaN    Andrea          L   Rogers    NaN   \n",
      "\n",
      "           AddressLine1 AddressLine2     City StateProvinceName ...   \\\n",
      "10653  9669 Cynthia Dr.          NaN  Shawnee  British Columbia ...    \n",
      "\n",
      "           Occupation Gender MaritalStatus HomeOwnerFlag NumberCarsOwned  \\\n",
      "10653  Skilled Manual      F             S             0               2   \n",
      "\n",
      "      NumberChildrenAtHome TotalChildren YearlyIncome  AveMonthSpend  Age  \n",
      "10653                    0             0        51443             43   21  \n",
      "\n",
      "[1 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "print(y_test[14])\n",
    "print(y_score[14])\n",
    "start = indx[1][14] -1\n",
    "print(combined[start:indx[1][14]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dgLBS1x5fAPsP1+OOFzaib7A43cYdVcHFwYTaBhcAMStZxzFBAcAEM0nVwqB3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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116dfc828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def resid_plot(y_test, y_score):\n",
    "    ## first compute vector of residuals. \n",
    "    resids = np.subtract(y_test.reshape(-1,1), y_score.reshape(-1,1))\n",
    "    ## now make the residual plots\n",
    "    sns.regplot(y_score, resids, fit_reg=False)\n",
    "    plt.title('Residuals vs. predicted values')\n",
    "    plt.xlabel('Predicted values')\n",
    "    plt.ylabel('Residual')\n",
    "    plt.show()\n",
    "resid_plot(y_test, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residual vs predicted values does not look great, there seems to be a pattern for lower AveMonthSpend even if residuals are of deceptively normal distribution. This signals that the model might not generalize well. The RMSE is also not great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error      = 8.00760789497\n",
      "Root Mean Square Error = 2.829771703684563\n",
      "Mean Absolute Error    = 2.28914160808\n",
      "Median Absolute Error  = 2.06052434444\n",
      "R^2                    = 0.98885649968\n",
      "Adjusted R^2           = 0.988349976938\n"
     ]
    }
   ],
   "source": [
    "# trying polynomial regression \n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly_model = make_pipeline(PolynomialFeatures(4),\n",
    "                           linear_model.LinearRegression())\n",
    "poly_model.fit(X_train,y_train)\n",
    "scores = poly_model.predict(X_test) \n",
    "print_metrics(y_test, scores, 14) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jwoQJQd85nU5ccMEFWL16NSwWC7777jvMmDEjSyNNnVhhskMH2rFrf0vY90MG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\nOCTFQgghhENSLIQQQjgkxUIIIYRD/wGCTyG9P5M/WgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4c5940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid_plot(y_test, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJ0/mhx9+cEceIa5JfaONI/lV9O8TgtHXS+04XVK3IB+Mfl7kl9TicCpqxxHX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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4d9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_resids(y_test, scores)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 88  47  47 101  79  47  74  76  32  49  44  65  61  48  51  76  64  51\n",
      "  99  58  54  51  51  86 121  49 148 137  55  40  56  77  61  68  77  57\n",
      "  48 164  48 112  37  90  46  90  69  57  74  61  52  99  87  75  39 102\n",
      "  63  48  76  54  77  60  70  48  59 103 163  64  82  59  46  50  90  71\n",
      "  55  57  62  43  45  49  59  50  70  77  85  43  77  66  56  43  47  60\n",
      "  66  49  40  39  64 146  46  45  63  82  81  49  74  59  45  81  48 109\n",
      "  48  72  66  67  82 150 114  64  55  49  39  79  75  85  81  50  83  67\n",
      "  80  45 174  52  66  51 133  51  89  45  47 145  39  49  57  72  80  78\n",
      " 120  78  80  57  80  79  51 131  90  91  45  71  44  42  41  43  43  51\n",
      "  92  40  59 118  52  75  82  50  63  83  49  50  93  77  46  39  54  64\n",
      "  78  36  39  83  68  72  53  49  47  64  67 123  71  67  47  83  70  84\n",
      "  58  87  49  44  43  64  76  75  78 106  76  99  63  59  42  81  45  67\n",
      "  75  52  57  56  91  42  44  38 134  52  38  38  45 152 100  57  48  64\n",
      "  58  77  79 157  84  53  73  82  51  64  51  76 108  46 110  77  68 104\n",
      "  75  49 124 102  52  51  65  82  64 119  78  65 117  55  48  64  76  58\n",
      " 156  64 139 140  46  57  79  82  61  53  96  74  39  54  61  34  64  40\n",
      "  45  89  55  84  87  56  45  85  62  85  63  47]\n",
      "[  85.8333025    52.47156632   49.25712264  102.47343266   80.91409159\n",
      "   48.50467062   74.81081653   73.07232344   37.59766841   48.995561\n",
      "   44.49851906   66.44286072   62.24592888   50.28997123   48.57802045\n",
      "   74.39616501   63.7101593    51.95296717   96.32060349   57.10289204\n",
      "   59.61490357   51.23262012   48.54850781   87.84695685  128.918378\n",
      "   50.68073905  147.36356843  139.72517121   51.53610861   42.7335397\n",
      "   55.04069865   75.55576003   62.91887355   68.89062226   73.24360859\n",
      "   57.73063862   46.97685707  162.55061066   48.59870827  106.64166176\n",
      "   41.26606286   84.33164251   46.82289803   86.45821869   65.89819539\n",
      "   60.18923438   71.36607575   60.54458201   52.08237135  101.06035912\n",
      "   87.71573699   80.05046177   41.09929287  100.61799347   67.91438973\n",
      "   48.13525212   76.25381958   54.45942414   76.04430258   60.31627738\n",
      "   70.80196071   47.77676284   65.60608923  102.57816803  166.0569278\n",
      "   66.30299485   79.75268912   62.01368785   41.54275      48.74416792\n",
      "   93.01478302   74.65921056   52.92989635   55.6978265    65.44904435\n",
      "   42.39990819   45.93442333   51.06229377   56.67164195   52.56847727\n",
      "   69.72779191   72.91384614   87.89248192   39.37993777   77.54348099\n",
      "   62.68336594   54.96695709   40.12710321   47.53010213   63.3523165\n",
      "   68.11756623   51.25643909   43.19231796   38.43839943   63.13981354\n",
      "  148.82206452   41.55655873   44.90795863   59.76030838   80.92735016\n",
      "   78.15995181   50.11469078   72.09510911   61.18469727   48.8282901\n",
      "   81.10223091   47.75308287  110.60836709   43.67254651   70.89349627\n",
      "   65.87977564   68.87426078   81.15180814  153.2171756   111.73113739\n",
      "   60.37495244   54.61393464   50.71927607   41.74683952   78.89044952\n",
      "   70.74830496   83.74444711   83.76677787   47.17043376   83.7194556\n",
      "   60.76057327   79.49418223   48.56681621  170.87308991   48.67314863\n",
      "   64.13655782   46.83011973  129.43604767   51.31629479   92.87841618\n",
      "   50.34913218   47.06556475  141.76331818   38.87394226   47.01325691\n",
      "   54.53350604   70.64403117   82.43978131   75.17502213  120.5963794\n",
      "   76.72058928   78.27527916   54.39659703   80.67838538   80.30643284\n",
      "   50.06212544  137.23521149   92.38557947   92.28048432   46.24916184\n",
      "   67.79491436   49.53701317   40.75719941   41.71774364   49.01428235\n",
      "   41.12782967   52.58848011   92.51813543   39.38339841   57.74673712\n",
      "  118.15009797   47.65637124   72.91256821   77.24972582   51.87477982\n",
      "   58.73615944   81.72367585   51.3520627    52.63884842   90.19554818\n",
      "   76.03824377   48.19086754   48.34340012   55.00432432   60.70440352\n",
      "   76.3326695    32.99933732   41.49174011   84.28407204   68.58859742\n",
      "   68.53953826   51.50370455   47.17499459   44.74144387   61.96381104\n",
      "   69.06068957  118.73602021   73.72582316   68.14975083   50.53679574\n",
      "   85.27848542   69.12473452   84.01916063   56.61774933   90.62538481\n",
      "   49.62660861   49.96053576   44.67877209   60.1645056    76.233693\n",
      "   74.82425416   77.16344774  101.06623566   79.80501854   91.51675141\n",
      "   62.60248756   63.91190827   40.08921969   78.60454023   44.16790569\n",
      "   65.00499427   73.12344396   53.88598883   52.34744358   59.00365031\n",
      "   86.8222729    40.31704628   43.56396973   40.3464452   130.91966736\n",
      "   50.35520434   36.00468743   41.12223208   47.84106624  152.50318635\n",
      "   97.38981354   53.78273368   43.20436633   70.08652985   56.41406035\n",
      "   79.11078393   79.14631426  157.77756989   77.53126705   54.12508893\n",
      "   72.9077096    86.96625245   50.44923723   63.82651055   54.76783335\n",
      "   79.57277215  102.22258008   49.99994779  111.25771821   76.24040711\n",
      "   69.48420823  106.95106232   76.31819451   49.98647606  120.82349885\n",
      "   99.26279175   50.30836844   47.20088351   64.38041389   85.30537069\n",
      "   64.42109406  124.36034882   75.54543221   66.38857067  115.66042054\n",
      "   54.66668046   47.87846982   64.80472076   76.36294639   62.57107162\n",
      "  159.1801883    60.72566915  140.05201447  137.06397736   45.51560318\n",
      "   50.52450097   82.25800812   79.99757302   63.77723801   53.10380661\n",
      "   92.93640006   73.12691605   42.48221362   55.1639787    64.18177187\n",
      "   39.34770119   67.43121445   40.17654049   47.31727469   90.43840849\n",
      "   51.71867669   82.48392546   87.18730652   53.48353112   46.06756413\n",
      "   81.01831257   65.63656902   87.79543793   61.36096084   50.61000443]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using polynomial features performs much better than linear regression. The RMSE is down by an order of a magnitude. The R2 and Adjusted R2 signify a good residual distribution as evidenced on the residual scatter plot and histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error      = 8.44310077901\n",
      "Root Mean Square Error = 2.905701426335018\n",
      "Mean Absolute Error    = 2.36058018967\n",
      "Median Absolute Error  = 2.19996959942\n",
      "R^2                    = 0.988250461628\n",
      "Adjusted R^2           = 0.987716391702\n"
     ]
    }
   ],
   "source": [
    "# trying Neural network \n",
    "from sklearn.neural_network import MLPRegressor\n",
    "regressor = MLPRegressor(hidden_layer_sizes=(100,),  \n",
    "                                    activation='logistic',\n",
    "                                       learning_rate='adaptive',\n",
    "                                       max_iter=1000,\n",
    "                                       learning_rate_init=0.001)\n",
    "regressor.fit(X_train,y_train)\n",
    "scores = regressor.predict(X_test)\n",
    "print_metrics(y_test, scores, 14) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error      = 11.5412833333\n",
      "Root Mean Square Error = 3.3972464340011204\n",
      "Mean Absolute Error    = 2.743\n",
      "Median Absolute Error  = 2.4375\n",
      "R^2                    = 0.983938986998\n",
      "Adjusted R^2           = 0.983208940952\n"
     ]
    }
   ],
   "source": [
    "# trying Random Forest\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rand_regressor = RandomForestRegressor(n_estimators=40)\n",
    "rand_regressor.fit(X_train, y_train)\n",
    "scores = rand_regressor.predict(X_test)\n",
    "print_metrics(y_test, scores, 14) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest even if better in RMSE than LinearRegression the RandomForestR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 23)\n",
      "(500,)\n"
     ]
    }
   ],
   "source": [
    "# test on final test data\n",
    "# import final test data\n",
    "final = pd.read_csv('data/AW_test.csv')\n",
    "print(final.shape)\n",
    "print(final.CustomerID.unique().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>BirthDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53</td>\n",
       "      <td>1/5/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>10/4/1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64</td>\n",
       "      <td>1/12/1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>9/22/1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>3/19/1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  BirthDate\n",
       "0   53   1/5/1945\n",
       "1   33  10/4/1964\n",
       "2   64  1/12/1934\n",
       "3   39  9/22/1958\n",
       "4   32  3/19/1965"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['Age'] = generate_age(final,'%m/%d/%Y')\n",
    "final[['Age','BirthDate']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0., ...,  1.,  1.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  1.,  0., ...,  0.,  0.,  1.],\n",
       "       ..., \n",
       "       [ 0.,  1.,  0., ...,  1.,  1.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = encode_cat_features(final)\n",
    "encoded[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   53, 86931,     0]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_final_features = np.array(final[['Age','YearlyIncome', 'NumberChildrenAtHome']])\n",
    "numeric_final_features[:1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ,  0.        ,  0.        ,  1.        ,  1.        ,\n",
       "         0.        ,  1.55943317,  0.2203181 ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  1.        ,\n",
       "         0.        ,  1.        ,  1.        ,  0.        ,  0.        ,\n",
       "         1.        , -0.22014298,  0.55239847,  2.        ],\n",
       "       [ 0.        ,  1.        ,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "         1.        ,  2.53820005,  0.64955098,  0.        ]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test = np.concatenate([encoded,numeric_final_features], 1)\n",
    "final_test[:,11:13]= scaler.transform(final_test[:,11:13])\n",
    "final_test[:3,:14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_reg_scores = rand_regressor.predict(final_test)\n",
    "np.savetxt('final_regression_answers.csv', final_reg_scores, delimiter=',',fmt='%i')"
   ]
  }
 ],
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