Black Friday Sales Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Black Friday Sales - Analysis and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data loading, pre-processing and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the necessary modules\n",
    "from pyspark.sql import SQLContext, Row\n",
    "from pyspark import SparkConf, SparkContext\n",
    "conf = SparkConf().setMaster(\"local\").setAppName(\"BlackFriday\")\n",
    "sc = SparkContext.getOrCreate(conf = conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load in the data\n",
    "rdd = sc.textFile('BlackFriday.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[u'User_ID',\n",
       "  u'Product_ID',\n",
       "  u'Gender',\n",
       "  u'Age',\n",
       "  u'Occupation',\n",
       "  u'City_Category',\n",
       "  u'Stay_In_Current_City_Years',\n",
       "  u'Marital_Status',\n",
       "  u'Product_Category_1',\n",
       "  u'Product_Category_2',\n",
       "  u'Product_Category_3',\n",
       "  u'Purchase'],\n",
       " [u'1000001',\n",
       "  u'P00069042',\n",
       "  u'F',\n",
       "  u'0-17',\n",
       "  u'10',\n",
       "  u'A',\n",
       "  u'2',\n",
       "  u'0',\n",
       "  u'3',\n",
       "  u'',\n",
       "  u'',\n",
       "  u'8370']]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split lines on commas\n",
    "rdd = rdd.map(lambda line: line.split(\",\"))\n",
    "\n",
    "# Inspect the first 2 lines \n",
    "rdd.take(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[u'1000001',\n",
       "  u'P00069042',\n",
       "  u'F',\n",
       "  u'0-17',\n",
       "  u'10',\n",
       "  u'A',\n",
       "  u'2',\n",
       "  u'0',\n",
       "  u'3',\n",
       "  u'',\n",
       "  u'',\n",
       "  u'8370'],\n",
       " [u'1000001',\n",
       "  u'P00248942',\n",
       "  u'F',\n",
       "  u'0-17',\n",
       "  u'10',\n",
       "  u'A',\n",
       "  u'2',\n",
       "  u'0',\n",
       "  u'1',\n",
       "  u'6',\n",
       "  u'14',\n",
       "  u'15200']]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the header row from the data\n",
    "header = rdd.first()\n",
    "rdd = rdd.filter(lambda line: line != header)\n",
    "rdd.take(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-------------+------+--------------+----------+------------------+------------------+------------------+----------+--------+--------------------------+-------+\n",
      "| age|city_category|gender|marital_status|occupation|product_category_1|product_category_2|product_category_3|product_id|purchase|stay_in_current_city_years|user_id|\n",
      "+----+-------------+------+--------------+----------+------------------+------------------+------------------+----------+--------+--------------------------+-------+\n",
      "|0-17|            A|     F|             0|        10|                 3|                  |                  | P00069042|    8370|                         2|1000001|\n",
      "|0-17|            A|     F|             0|        10|                 1|                 6|                14| P00248942|   15200|                         2|1000001|\n",
      "+----+-------------+------+--------------+----------+------------------+------------------+------------------+----------+--------+--------------------------+-------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary modules \n",
    "from pyspark.sql import Row\n",
    "\n",
    "# Map the RDD to a DF\n",
    "df = rdd.map(lambda r1: Row(user_id=int(r1[0]),\n",
    "                              product_id=r1[1],\n",
    "                              gender=r1[2],\n",
    "                              age=r1[3],\n",
    "                              occupation=int(r1[4]),\n",
    "                              city_category=r1[5],\n",
    "                              stay_in_current_city_years=r1[6],\n",
    "                              marital_status=int(r1[7]),\n",
    "                              product_category_1=int(r1[8]),\n",
    "                              product_category_2=r1[9],\n",
    "                              product_category_3=r1[10],\n",
    "                              purchase=int(r1[11]))).toDF()\n",
    "\n",
    "# Show the top 2 rows \n",
    "df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>city_category</th>\n",
       "      <th>gender</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>product_category_1</th>\n",
       "      <th>product_category_2</th>\n",
       "      <th>product_category_3</th>\n",
       "      <th>product_id</th>\n",
       "      <th>purchase</th>\n",
       "      <th>stay_in_current_city_years</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8370</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>15200</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>P00087842</td>\n",
       "      <td>1422</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td>P00085442</td>\n",
       "      <td>1057</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55+</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>P00285442</td>\n",
       "      <td>7969</td>\n",
       "      <td>4+</td>\n",
       "      <td>1000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age city_category gender  marital_status  occupation  product_category_1  \\\n",
       "0  0-17             A      F               0          10                   3   \n",
       "1  0-17             A      F               0          10                   1   \n",
       "2  0-17             A      F               0          10                  12   \n",
       "3  0-17             A      F               0          10                  12   \n",
       "4   55+             C      M               0          16                   8   \n",
       "\n",
       "  product_category_2 product_category_3 product_id  purchase  \\\n",
       "0                                        P00069042      8370   \n",
       "1                  6                 14  P00248942     15200   \n",
       "2                                        P00087842      1422   \n",
       "3                 14                     P00085442      1057   \n",
       "4                                        P00285442      7969   \n",
       "\n",
       "  stay_in_current_city_years  user_id  \n",
       "0                          2  1000001  \n",
       "1                          2  1000001  \n",
       "2                          2  1000001  \n",
       "3                          2  1000001  \n",
       "4                         4+  1000002  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View it a better table format using pandas\n",
    "df.toPandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Looks like product category 2 and 3 have missing values. Lets check the dataframe schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: string (nullable = true)\n",
      " |-- city_category: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- marital_status: long (nullable = true)\n",
      " |-- occupation: long (nullable = true)\n",
      " |-- product_category_1: long (nullable = true)\n",
      " |-- product_category_2: string (nullable = true)\n",
      " |-- product_category_3: string (nullable = true)\n",
      " |-- product_id: string (nullable = true)\n",
      " |-- purchase: long (nullable = true)\n",
      " |-- stay_in_current_city_years: string (nullable = true)\n",
      " |-- user_id: long (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the schema of `df` \n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Casting data type of product category 2 and 3 from string to float\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "df = df.withColumn(\"product_category_2\", df[\"product_category_2\"].cast(FloatType())).withColumn(\"product_category_3\", df[\"product_category_3\"].cast(FloatType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: string (nullable = true)\n",
      " |-- city_category: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- marital_status: long (nullable = true)\n",
      " |-- occupation: long (nullable = true)\n",
      " |-- product_category_1: long (nullable = true)\n",
      " |-- product_category_2: float (nullable = true)\n",
      " |-- product_category_3: float (nullable = true)\n",
      " |-- product_id: string (nullable = true)\n",
      " |-- purchase: long (nullable = true)\n",
      " |-- stay_in_current_city_years: string (nullable = true)\n",
      " |-- user_id: long (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the schema of `df`\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>city_category</th>\n",
       "      <th>gender</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>product_category_1</th>\n",
       "      <th>product_category_2</th>\n",
       "      <th>product_category_3</th>\n",
       "      <th>product_id</th>\n",
       "      <th>purchase</th>\n",
       "      <th>stay_in_current_city_years</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8370</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>15200</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>1422</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>1057</td>\n",
       "      <td>2</td>\n",
       "      <td>1000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55+</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>7969</td>\n",
       "      <td>4+</td>\n",
       "      <td>1000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age city_category gender  marital_status  occupation  product_category_1  \\\n",
       "0  0-17             A      F               0          10                   3   \n",
       "1  0-17             A      F               0          10                   1   \n",
       "2  0-17             A      F               0          10                  12   \n",
       "3  0-17             A      F               0          10                  12   \n",
       "4   55+             C      M               0          16                   8   \n",
       "\n",
       "   product_category_2  product_category_3 product_id  purchase  \\\n",
       "0                 NaN                 NaN  P00069042      8370   \n",
       "1                 6.0                14.0  P00248942     15200   \n",
       "2                 NaN                 NaN  P00087842      1422   \n",
       "3                14.0                 NaN  P00085442      1057   \n",
       "4                 NaN                 NaN  P00285442      7969   \n",
       "\n",
       "  stay_in_current_city_years  user_id  \n",
       "0                          2  1000001  \n",
       "1                          2  1000001  \n",
       "2                          2  1000001  \n",
       "3                          2  1000001  \n",
       "4                         4+  1000002  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing it a better table format\n",
    "df.toPandas().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>city_category</th>\n",
       "      <th>gender</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>product_category_1</th>\n",
       "      <th>product_category_2</th>\n",
       "      <th>product_category_3</th>\n",
       "      <th>product_id</th>\n",
       "      <th>purchase</th>\n",
       "      <th>stay_in_current_city_years</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>166986</td>\n",
       "      <td>373299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  city_category  gender  marital_status  occupation  product_category_1  \\\n",
       "0    0              0       0               0           0                   0   \n",
       "\n",
       "   product_category_2  product_category_3  product_id  purchase  \\\n",
       "0              166986              373299           0         0   \n",
       "\n",
       "   stay_in_current_city_years  user_id  \n",
       "0                           0        0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the null values in the data\n",
    "# https://stackoverflow.com/questions/44413132/count-the-number-of-missing-values-in-a-dataframe-spark\n",
    "\n",
    "from pyspark.sql.functions import col,sum\n",
    "df.select(*(sum(col(c).isNull().cast(\"int\")).alias(c) for c in df.columns)).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|product_category_2|\n",
      "+------------------+\n",
      "|              18.0|\n",
      "|              17.0|\n",
      "|              16.0|\n",
      "|              15.0|\n",
      "|              14.0|\n",
      "|              13.0|\n",
      "|              12.0|\n",
      "|              11.0|\n",
      "|              10.0|\n",
      "|               9.0|\n",
      "|               8.0|\n",
      "|               7.0|\n",
      "|               6.0|\n",
      "|               5.0|\n",
      "|               4.0|\n",
      "|               3.0|\n",
      "|               2.0|\n",
      "|              null|\n",
      "+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get all the unique values in product category 2\n",
    "df.select(\"product_category_2\").distinct().sort(\"product_category_2\",ascending=False).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|product_category_3|\n",
      "+------------------+\n",
      "|              18.0|\n",
      "|              17.0|\n",
      "|              16.0|\n",
      "|              15.0|\n",
      "|              14.0|\n",
      "|              13.0|\n",
      "|              12.0|\n",
      "|              11.0|\n",
      "|              10.0|\n",
      "|               9.0|\n",
      "|               8.0|\n",
      "|               6.0|\n",
      "|               5.0|\n",
      "|               4.0|\n",
      "|               3.0|\n",
      "|              null|\n",
      "+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get all the unique values in product category 3\n",
    "df.select(\"product_category_3\").distinct().sort(\"product_category_3\",ascending=False).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Product category 2 have unique values ranging from 2.0 to 18.0. Product category 3 have unique values ranging from 3.0 to 18.0. Therefore, We can assign 0 for the null values in both the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get rid of the null values in all the columns and replace it by 0\n",
    "for x in df.columns:\n",
    "    df = df.na.fill(0,[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>city_category</th>\n",
       "      <th>gender</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>product_category_1</th>\n",
       "      <th>product_category_2</th>\n",
       "      <th>product_category_3</th>\n",
       "      <th>product_id</th>\n",
       "      <th>purchase</th>\n",
       "      <th>stay_in_current_city_years</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  city_category  gender  marital_status  occupation  product_category_1  \\\n",
       "0    0              0       0               0           0                   0   \n",
       "\n",
       "   product_category_2  product_category_3  product_id  purchase  \\\n",
       "0                   0                   0           0         0   \n",
       "\n",
       "   stay_in_current_city_years  user_id  \n",
       "0                           0        0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the null values in the data again\n",
    "from pyspark.sql.functions import col,sum\n",
    "df.select(*(sum(col(c).isNull().cast(\"int\")).alias(c) for c in df.columns)).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The data is now clean for analysis and visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing libraries to visualize the data\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df2=df.toPandas()\n",
    "df.registerTempTable('bFridayTable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537577"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total entries in the dataset\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('age', 7)\n",
      "('city_category', 3)\n",
      "('gender', 2)\n",
      "('marital_status', 2)\n",
      "('occupation', 21)\n",
      "('product_category_1', 18)\n",
      "('product_category_2', 18)\n",
      "('product_category_3', 16)\n",
      "('product_id', 3623)\n",
      "('purchase', 17959)\n",
      "('stay_in_current_city_years', 5)\n",
      "('user_id', 5891)\n"
     ]
    }
   ],
   "source": [
    "# Distinct values for each column\n",
    "for i in df.columns:\n",
    "    print(i, df.select(i).distinct().count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### From the above data, we come to know that during the black friday sale\n",
    "\n",
    "    Total 3623 products were for sale\n",
    "    Total 5891 shoppers participated in the sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>summary</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>537577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>9333.859852635065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stddev</td>\n",
       "      <td>4981.022132656451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max</td>\n",
       "      <td>23961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  summary           purchase\n",
       "0   count             537577\n",
       "1    mean  9333.859852635065\n",
       "2  stddev  4981.022132656451\n",
       "3     min                185\n",
       "4     max              23961"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Min and Max Purchase\n",
    "df.select('purchase').describe().toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Min purchase: 185\n",
    "- Max purchase: 23961"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Age group distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+------+\n",
      "|  age| count|\n",
      "+-----+------+\n",
      "| 0-17| 14707|\n",
      "|18-25| 97634|\n",
      "|26-35|214690|\n",
      "|36-45|107499|\n",
      "|46-50| 44526|\n",
      "|51-55| 37618|\n",
      "|  55+| 20903|\n",
      "+-----+------+\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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l6d5779WIESP03nvvSZJKSkoUHx+vUaNGKS4uTr/++qskac+ePRo5cqRGjhypZ5991nVf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\nADAcYQAAAMMRBgAAMBxhAAAAw/0/e5AbrTT/iYUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6695b7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count distribution\n",
    "df.groupBy(\"age\").count().sort(\"age\",ascending=True).show()\n",
    "sns.countplot(df2.age).set_title(\"Age count distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's quite apparent that the largest age group amongst the customers is 26-35. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UGLslIiKiOs/oAb9r1y78+uuvWLBgAQICAmBtbQ07Ozts3rwZn376KXr16lVq+oqOGFTl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd66e703ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distinct product distribution\n",
    "a_df = sqlContext.sql('select age, count( distinct product_id) as product_count from bFridayTable group by age').toPandas()\n",
    "a_df.columns=['Age','Distinct Products']\n",
    "sns.barplot(x='Age', y='Distinct Products', data=a_df).set_title(\"Age and distinct products distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, the distribution of product purchase, in terms of quantity, does not vary greatly amongst the age groups. This means that, though the 26-35 age group is the most popular, the other age groups purchase almost as many unique items as them. But does this mean that the amount of money spent amongst the age groups is the same? Let's see..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1haOjIxwdHeHq6go/Pz9IkoSQkBBDlUdERKQ4kqjOieunmM6p6a27arcQQxo13NgVEBHR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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd66e4a7810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total money spent distribution\n",
    "a_df=df.groupBy('age').sum('purchase').orderBy('sum(purchase)', ascending=False).toPandas()\n",
    "a_df.columns=['Age','Total Purchase']\n",
    "sns.barplot(x='Age', y='Total Purchase', data=a_df).set_title(\"Money spent per age group\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart clearly shows that the amount of money made from each age group correlates proportionally with the amount of customers within the age groups. This can be valuable information for the store, as it might want to add more products geared towards this age group in the future, or perhaps work on marketing different items to increase a broader diversity in the age groups of their customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gender group distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+------+\n",
      "|gender| count|\n",
      "+------+------+\n",
      "|     F|132197|\n",
      "|     M|405380|\n",
      "+------+------+\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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0Nrf7qF/qni1ktOiYID8/n2eeecb3eM2aNfz1r38953XvvPMOf/zjH3nhhRew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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd65505ac10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupBy(\"gender\").count().show()\n",
    "sns.countplot(df2.gender).set_title(\"Gender count distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of male shoppers exceeds the number of female shoppers by almost 4 times. Let's check the variety of products they shop for. Are female shoppers restricting themeselves to certain range of products?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jeQ49AAAXqdSAb9OmjbZt2+ZcDgoKKrHsKuDbtWunVq1aSZJq166tvLw8paam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\nAAAmRMADAGBCBDwAACb0/+mmH3U09LBiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd668616250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distinct product distribution\n",
    "g_df = sqlContext.sql('select gender, count( distinct product_id) as product_count from bFridayTable group by gender').toPandas()\n",
    "g_df.columns=['Gender','Distinct Products']\n",
    "sns.barplot(x='Gender', y='Distinct Products', data=g_df).set_title(\"Gender and distinct products distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't find a significant difference in the range of products.\n",
    "Let's check average purchase amount for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Average Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>9504.771713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>8809.761349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Average Purchase\n",
       "0      M       9504.771713\n",
       "1      F       8809.761349"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average Purchase\n",
    "gender_purchase=df.groupBy('gender').avg('purchase').orderBy('avg(purchase)', ascending=False).toPandas()\n",
    "gender_purchase.columns=['Gender','Average Purchase']\n",
    "gender_purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd65b906750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the above results on graph\n",
    "sns.barplot(x='Gender', y='Average Purchase', data=gender_purchase)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we don't see a major difference in average purchase for male and female shoppers. Let's now demonstrate the different tendencies of male and female shoppers for product category 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_1_gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>f_ratio</th>\n",
       "      <th>m_ratio</th>\n",
       "      <th>M/F</th>\n",
       "      <th>F/M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>41199</td>\n",
       "      <td>107393</td>\n",
       "      <td>0.311649</td>\n",
       "      <td>0.264919</td>\n",
       "      <td>0.850058</td>\n",
       "      <td>1.176390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1138</td>\n",
       "      <td>3894</td>\n",
       "      <td>0.008608</td>\n",
       "      <td>0.009606</td>\n",
       "      <td>1.115868</td>\n",
       "      <td>0.896163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>613</td>\n",
       "      <td>887</td>\n",
       "      <td>0.004637</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.471870</td>\n",
       "      <td>2.119227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24401</td>\n",
       "      <td>113952</td>\n",
       "      <td>0.184581</td>\n",
       "      <td>0.281099</td>\n",
       "      <td>1.522908</td>\n",
       "      <td>0.656639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4475</td>\n",
       "      <td>15689</td>\n",
       "      <td>0.033851</td>\n",
       "      <td>0.038702</td>\n",
       "      <td>1.143303</td>\n",
       "      <td>0.874658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>70</td>\n",
       "      <td>334</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>1.555993</td>\n",
       "      <td>0.642676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>1428</td>\n",
       "      <td>4012</td>\n",
       "      <td>0.010802</td>\n",
       "      <td>0.009897</td>\n",
       "      <td>0.916204</td>\n",
       "      <td>1.091460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>5573</td>\n",
       "      <td>17926</td>\n",
       "      <td>0.042157</td>\n",
       "      <td>0.044220</td>\n",
       "      <td>1.048947</td>\n",
       "      <td>0.953337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "      <td>506</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.001248</td>\n",
       "      <td>2.705079</td>\n",
       "      <td>0.369675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>1497</td>\n",
       "      <td>2378</td>\n",
       "      <td>0.011324</td>\n",
       "      <td>0.005866</td>\n",
       "      <td>0.518023</td>\n",
       "      <td>1.930415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>928</td>\n",
       "      <td>2740</td>\n",
       "      <td>0.007020</td>\n",
       "      <td>0.006759</td>\n",
       "      <td>0.962857</td>\n",
       "      <td>1.038576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>5874</td>\n",
       "      <td>13975</td>\n",
       "      <td>0.044434</td>\n",
       "      <td>0.034474</td>\n",
       "      <td>0.775849</td>\n",
       "      <td>1.288911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>376</td>\n",
       "      <td>2699</td>\n",
       "      <td>0.002844</td>\n",
       "      <td>0.006658</td>\n",
       "      <td>2.340854</td>\n",
       "      <td>0.427195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>2363</td>\n",
       "      <td>7334</td>\n",
       "      <td>0.017875</td>\n",
       "      <td>0.018092</td>\n",
       "      <td>1.012130</td>\n",
       "      <td>0.988015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>4659</td>\n",
       "      <td>19301</td>\n",
       "      <td>0.035243</td>\n",
       "      <td>0.047612</td>\n",
       "      <td>1.350972</td>\n",
       "      <td>0.740208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>32947</td>\n",
       "      <td>79185</td>\n",
       "      <td>0.249227</td>\n",
       "      <td>0.195335</td>\n",
       "      <td>0.783766</td>\n",
       "      <td>1.275891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>3572</td>\n",
       "      <td>7995</td>\n",
       "      <td>0.027020</td>\n",
       "      <td>0.019722</td>\n",
       "      <td>0.729905</td>\n",
       "      <td>1.370041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>1023</td>\n",
       "      <td>5180</td>\n",
       "      <td>0.007738</td>\n",
       "      <td>0.012778</td>\n",
       "      <td>1.651252</td>\n",
       "      <td>0.605601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_category_1_gender      F       M   f_ratio   m_ratio       M/F  \\\n",
       "0                          5  41199  107393  0.311649  0.264919  0.850058   \n",
       "1                         10   1138    3894  0.008608  0.009606  1.115868   \n",
       "2                         14    613     887  0.004637  0.002188  0.471870   \n",
       "3                          1  24401  113952  0.184581  0.281099  1.522908   \n",
       "4                          6   4475   15689  0.033851  0.038702  1.143303   \n",
       "5                          9     70     334  0.000530  0.000824  1.555993   \n",
       "6                         13   1428    4012  0.010802  0.009897  0.916204   \n",
       "7                          2   5573   17926  0.042157  0.044220  1.048947   \n",
       "8                         17     61     506  0.000461  0.001248  2.705079   \n",
       "9                         12   1497    2378  0.011324  0.005866  0.518023   \n",
       "10                         7    928    2740  0.007020  0.006759  0.962857   \n",
       "11                         3   5874   13975  0.044434  0.034474  0.775849   \n",
       "12                        18    376    2699  0.002844  0.006658  2.340854   \n",
       "13                        16   2363    7334  0.017875  0.018092  1.012130   \n",
       "14                        11   4659   19301  0.035243  0.047612  1.350972   \n",
       "15                         8  32947   79185  0.249227  0.195335  0.783766   \n",
       "16                         4   3572    7995  0.027020  0.019722  0.729905   \n",
       "17                        15   1023    5180  0.007738  0.012778  1.651252   \n",
       "\n",
       "         F/M  \n",
       "0   1.176390  \n",
       "1   0.896163  \n",
       "2   2.119227  \n",
       "3   0.656639  \n",
       "4   0.874658  \n",
       "5   0.642676  \n",
       "6   1.091460  \n",
       "7   0.953337  \n",
       "8   0.369675  \n",
       "9   1.930415  \n",
       "10  1.038576  \n",
       "11  1.288911  \n",
       "12  0.427195  \n",
       "13  0.988015  \n",
       "14  0.740208  \n",
       "15  1.275891  \n",
       "16  1.370041  \n",
       "17  0.605601  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From our previous results we know that total femal shoppers = 132197 and total male shoppers = 405380\n",
    "# https://www.analyticsvidhya.com/blog/2016/10/spark-dataframe-and-operations/\n",
    "prod1_gender = df.crosstab('product_category_1', 'gender')\n",
    "prod1_gender = prod1_gender.withColumn('f_ratio', prod1_gender.F / 132197)\n",
    "prod1_gender = prod1_gender.withColumn('m_ratio', prod1_gender.M / 405380)\n",
    "prod1_gender = prod1_gender.withColumn('M/F', prod1_gender.m_ratio / prod1_gender.f_ratio)\n",
    "prod1_gender = prod1_gender.withColumn('F/M', prod1_gender.f_ratio / prod1_gender.m_ratio)\n",
    "prod1_gender.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table tells us a lot about how likely a type of product is to be bought in regards of gender. For instance, men are almost 1.1 times as likely to buy an item in category 10, while women are almost 1.17 times as likely to buy a product in category 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To get a better understanding, we will now analyse the purchase habits across the different city categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JPIpGKP7fF+Dyxuu6YYNhCj5tmCxpfsppIOwQHYMsRpX5PSESC1ME8/Lt6hJ0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd65c9b9e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://pythonspot.com/matplotlib-pie-chart/\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cnt = df.groupBy(\"city_category\").count().toPandas()\n",
    "labels=cnt[\"city_category\"]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral']\n",
    "data=cnt[\"count\"]\n",
    "explode = (0, 0, 0)\n",
    "\n",
    "# Plot\n",
    "plt.pie(data, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's see the distribution of gender within each city category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IkAIC/FtfLFoIDw/2dgldSkftb/LQPsiD55AF30YWPOdy97VHGz6n06lbbrlFkjRw4ECV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\n+3/64buSllLC4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd658577750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://seaborn.pydata.org/generated/seaborn.FacetGrid.html\n",
    "purchase_city = sns.FacetGrid(df2,col=\"city_category\")\n",
    "purchase_city.map(sns.barplot, \"gender\", \"purchase\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly people from City_Category C are showing higher purchase capacity as compared to the other two cities on average.\n",
    "For City_categories B and C, Males tend to dominate the purchasing, whereas it is the opposite for City Category_C, where Females tend to puchase more than men. It is a useful insight, and it be useful to oserve which age group of females does higher purchasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: string (nullable = true)\n",
      " |-- city_category: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- marital_status: long (nullable = true)\n",
      " |-- occupation: long (nullable = true)\n",
      " |-- product_category_1: long (nullable = true)\n",
      " |-- product_category_2: float (nullable = false)\n",
      " |-- product_category_3: float (nullable = false)\n",
      " |-- purchase: long (nullable = true)\n",
      " |-- stay_in_current_city_years: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping unnecessary columns in the data\n",
    "df=df.drop('product_id')\n",
    "df=df.drop('user_id')\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Getting Numerical Columns from the Spark Dataframe\n",
    "num_cols=['marital_status','occupation','product_category_1','product_category_2','product_category_3','purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generating a list of Categorical columns\n",
    "categoricalcols=[]\n",
    "for col in df.columns:\n",
    "    if col in num_cols:\n",
    "        pass\n",
    "    else:\n",
    "        categoricalcols.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'city_category', 'gender', 'stay_in_current_city_years']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricalcols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ENCODING - Converting categorical columns to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.types import DoubleType\n",
    "from pyspark.ml.feature import StringIndexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Converting categorical columns to numeric using string indexer\n",
    "flag=0\n",
    "for col in categoricalcols:\n",
    "    if flag != 1:\n",
    "        indexer = StringIndexer(inputCol=col, outputCol=col+\"_categorical\").fit(df)\n",
    "        indexed_df = indexer.transform(df)\n",
    "        flag=1\n",
    "    else:\n",
    "        indexer = StringIndexer(inputCol=col, outputCol=col+\"_categorical\").fit(indexed_df)\n",
    "        indexed_df = indexer.transform(indexed_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>city_category</th>\n",
       "      <th>gender</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>product_category_1</th>\n",
       "      <th>product_category_2</th>\n",
       "      <th>product_category_3</th>\n",
       "      <th>purchase</th>\n",
       "      <th>stay_in_current_city_years</th>\n",
       "      <th>age_categorical</th>\n",
       "      <th>city_category_categorical</th>\n",
       "      <th>gender_categorical</th>\n",
       "      <th>stay_in_current_city_years_categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8370</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1422</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1057</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55+</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7969</td>\n",
       "      <td>4+</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15227</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>46-50</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19215</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46-50</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15854</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46-50</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15686</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7871</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5254</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3957</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6073</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15665</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>51-55</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5378</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>51-55</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2079</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>51-55</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13055</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>51-55</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8851</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36-45</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11788</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19614</td>\n",
       "      <td>4+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8584</td>\n",
       "      <td>4+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9872</td>\n",
       "      <td>4+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9743</td>\n",
       "      <td>4+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5982</td>\n",
       "      <td>4+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11927</td>\n",
       "      <td>4+</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16662</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5887</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6973</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5391</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>36-45</td>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16352</td>\n",
       "      <td>4+</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537547</th>\n",
       "      <td>18-25</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11543</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537548</th>\n",
       "      <td>51-55</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20924</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537549</th>\n",
       "      <td>51-55</td>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13082</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537550</th>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11658</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537551</th>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6863</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537552</th>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16415</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537553</th>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18526</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537554</th>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7099</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537555</th>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>578</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537556</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537557</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12724</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537558</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7796</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537559</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7770</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537560</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5940</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537561</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7834</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537562</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11508</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537563</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6074</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537564</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7084</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537565</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7934</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537566</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5350</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537567</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537568</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5930</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537569</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7042</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537570</th>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15491</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537571</th>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11852</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537572</th>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11664</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537573</th>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19196</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537574</th>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8043</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537575</th>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7172</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537576</th>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6875</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>537577 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age city_category gender  marital_status  occupation  \\\n",
       "0        0-17             A      F               0          10   \n",
       "1        0-17             A      F               0          10   \n",
       "2        0-17             A      F               0          10   \n",
       "3        0-17             A      F               0          10   \n",
       "4         55+             C      M               0          16   \n",
       "5       26-35             A      M               0          15   \n",
       "6       46-50             B      M               1           7   \n",
       "7       46-50             B      M               1           7   \n",
       "8       46-50             B      M               1           7   \n",
       "9       26-35             A      M               1          20   \n",
       "10      26-35             A      M               1          20   \n",
       "11      26-35             A      M               1          20   \n",
       "12      26-35             A      M               1          20   \n",
       "13      26-35             A      M               1          20   \n",
       "14      51-55             A      F               0           9   \n",
       "15      51-55             A      F               0           9   \n",
       "16      51-55             A      F               0           9   \n",
       "17      51-55             A      F               0           9   \n",
       "18      36-45             B      M               1           1   \n",
       "19      26-35             C      M               1          12   \n",
       "20      26-35             C      M               1          12   \n",
       "21      26-35             C      M               1          12   \n",
       "22      26-35             C      M               1          12   \n",
       "23      26-35             C      M               1          12   \n",
       "24      26-35             C      M               1          12   \n",
       "25      26-35             C      M               0          17   \n",
       "26      26-35             C      M               0          17   \n",
       "27      26-35             C      M               0          17   \n",
       "28      26-35             C      M               0          17   \n",
       "29      36-45             B      F               1           1   \n",
       "...       ...           ...    ...             ...         ...   \n",
       "537547  18-25             C      M               0          18   \n",
       "537548  51-55             B      M               1           1   \n",
       "537549  51-55             B      M               1           1   \n",
       "537550  46-50             C      M               0           3   \n",
       "537551  46-50             C      M               0           3   \n",
       "537552  46-50             C      M               0           3   \n",
       "537553  46-50             C      M               0           3   \n",
       "537554  46-50             C      M               0           3   \n",
       "537555  46-50             C      M               0           3   \n",
       "537556  18-25             A      M               1          20   \n",
       "537557  18-25             A      M               1          20   \n",
       "537558  18-25             A      M               1          20   \n",
       "537559  18-25             A      M               1          20   \n",
       "537560  18-25             A      M               1          20   \n",
       "537561  18-25             A      M               1          20   \n",
       "537562  18-25             A      M               1          20   \n",
       "537563  18-25             A      M               1          20   \n",
       "537564  18-25             A      M               1          20   \n",
       "537565  18-25             A      M               1          20   \n",
       "537566  18-25             A      M               1          20   \n",
       "537567  18-25             A      M               1          20   \n",
       "537568  18-25             A      M               1          20   \n",
       "537569  18-25             A      M               1          20   \n",
       "537570  18-25             A      M               1          20   \n",
       "537571  36-45             C      M               0          16   \n",
       "537572  36-45             C      M               0          16   \n",
       "537573  36-45             C      M               0          16   \n",
       "537574  36-45             C      M               0          16   \n",
       "537575  36-45             C      M               0          16   \n",
       "537576  36-45             C      M               0          16   \n",
       "\n",
       "        product_category_1  product_category_2  product_category_3  purchase  \\\n",
       "0                        3                 0.0                 0.0      8370   \n",
       "1                        1                 6.0                14.0     15200   \n",
       "2                       12                 0.0                 0.0      1422   \n",
       "3                       12                14.0                 0.0      1057   \n",
       "4                        8                 0.0                 0.0      7969   \n",
       "5                        1                 2.0                 0.0     15227   \n",
       "6                        1                 8.0                17.0     19215   \n",
       "7                        1                15.0                 0.0     15854   \n",
       "8                        1                16.0                 0.0     15686   \n",
       "9                        8                 0.0                 0.0      7871   \n",
       "10                       5                11.0                 0.0      5254   \n",
       "11                       8                 0.0                 0.0      3957   \n",
       "12                       8                 0.0                 0.0      6073   \n",
       "13                       1                 2.0                 5.0     15665   \n",
       "14                       5                 8.0                14.0      5378   \n",
       "15                       4                 5.0                 0.0      2079   \n",
       "16                       2                 3.0                 4.0     13055   \n",
       "17                       5                14.0                 0.0      8851   \n",
       "18                       1                14.0                16.0     11788   \n",
       "19                       1                 5.0                15.0     19614   \n",
       "20                       5                14.0                 0.0      8584   \n",
       "21                       8                 0.0                 0.0      9872   \n",
       "22                       8                 0.0                 0.0      9743   \n",
       "23                       8                 0.0                 0.0      5982   \n",
       "24                       1                 8.0                14.0     11927   \n",
       "25                       6                 8.0                 0.0     16662   \n",
       "26                       8                 0.0                 0.0      5887   \n",
       "27                       5                14.0                 0.0      6973   \n",
       "28                       5                 8.0                14.0      5391   \n",
       "29                       2                 4.0                 8.0     16352   \n",
       "...                    ...                 ...                 ...       ...   \n",
       "537547                   1                 2.0                15.0     11543   \n",
       "537548                  15                 0.0                 0.0     20924   \n",
       "537549                   2                 8.0                14.0     13082   \n",
       "537550                   1                 0.0                 0.0     11658   \n",
       "537551                   5                 6.0                 8.0      6863   \n",
       "537552                   6                 8.0                 0.0     16415   \n",
       "537553                  10                13.0                 0.0     18526   \n",
       "537554                   5                14.0                 0.0      7099   \n",
       "537555                  13                16.0                 0.0       578   \n",
       "537556                   8                14.0                 0.0      2183   \n",
       "537557                   2                 0.0                 0.0     12724   \n",
       "537558                   8                17.0                 0.0      7796   \n",
       "537559                   8                17.0                 0.0      7770   \n",
       "537560                   8                16.0                 0.0      5940   \n",
       "537561                   8                16.0                 0.0      7834   \n",
       "537562                   1                13.0                14.0     11508   \n",
       "537563                   8                 0.0                 0.0      6074   \n",
       "537564                   5                14.0                 0.0      7084   \n",
       "537565                   8                 0.0                 0.0      7934   \n",
       "537566                   5                 0.0                 0.0      5350   \n",
       "537567                   8                14.0                 0.0      1994   \n",
       "537568                  11                 0.0                 0.0      5930   \n",
       "537569                   5                14.0                 0.0      7042   \n",
       "537570                   1                16.0                 0.0     15491   \n",
       "537571                   1                 2.0                 5.0     11852   \n",
       "537572                   1                 2.0                 0.0     11664   \n",
       "537573                   1                15.0                16.0     19196   \n",
       "537574                   8                15.0                 0.0      8043   \n",
       "537575                   5                 0.0                 0.0      7172   \n",
       "537576                   5                 8.0                 0.0      6875   \n",
       "\n",
       "       stay_in_current_city_years  age_categorical  city_category_categorical  \\\n",
       "0                               2              6.0                        2.0   \n",
       "1                               2              6.0                        2.0   \n",
       "2                               2              6.0                        2.0   \n",
       "3                               2              6.0                        2.0   \n",
       "4                              4+              5.0                        1.0   \n",
       "5                               3              0.0                        2.0   \n",
       "6                               2              3.0                        0.0   \n",
       "7                               2              3.0                        0.0   \n",
       "8                               2              3.0                        0.0   \n",
       "9                               1              0.0                        2.0   \n",
       "10                              1              0.0                        2.0   \n",
       "11                              1              0.0                        2.0   \n",
       "12                              1              0.0                        2.0   \n",
       "13                              1              0.0                        2.0   \n",
       "14                              1              4.0                        2.0   \n",
       "15                              1              4.0                        2.0   \n",
       "16                              1              4.0                        2.0   \n",
       "17                              1              4.0                        2.0   \n",
       "18                              1              1.0                        0.0   \n",
       "19                             4+              0.0                        1.0   \n",
       "20                             4+              0.0                        1.0   \n",
       "21                             4+              0.0                        1.0   \n",
       "22                             4+              0.0                        1.0   \n",
       "23                             4+              0.0                        1.0   \n",
       "24                             4+              0.0                        1.0   \n",
       "25                              0              0.0                        1.0   \n",
       "26                              0              0.0                        1.0   \n",
       "27                              0              0.0                        1.0   \n",
       "28                              0              0.0                        1.0   \n",
       "29                             4+              1.0                        0.0   \n",
       "...                           ...              ...                        ...   \n",
       "537547                          1              2.0                        1.0   \n",
       "537548                          1              4.0                        0.0   \n",
       "537549                          1              4.0                        0.0   \n",
       "537550                          3              3.0                        1.0   \n",
       "537551                          3              3.0                        1.0   \n",
       "537552                          3              3.0                        1.0   \n",
       "537553                          3              3.0                        1.0   \n",
       "537554                          3              3.0                        1.0   \n",
       "537555                          3              3.0                        1.0   \n",
       "537556                          1              2.0                        2.0   \n",
       "537557                          1              2.0                        2.0   \n",
       "537558                          1              2.0                        2.0   \n",
       "537559                          1              2.0                        2.0   \n",
       "537560                          1              2.0                        2.0   \n",
       "537561                          1              2.0                        2.0   \n",
       "537562                          1              2.0                        2.0   \n",
       "537563                          1              2.0                        2.0   \n",
       "537564                          1              2.0                        2.0   \n",
       "537565                          1              2.0                        2.0   \n",
       "537566                          1              2.0                        2.0   \n",
       "537567                          1              2.0                        2.0   \n",
       "537568                          1              2.0                        2.0   \n",
       "537569                          1              2.0                        2.0   \n",
       "537570                          1              2.0                        2.0   \n",
       "537571                          1              1.0                        1.0   \n",
       "537572                          1              1.0                        1.0   \n",
       "537573                          1              1.0                        1.0   \n",
       "537574                          1              1.0                        1.0   \n",
       "537575                          1              1.0                        1.0   \n",
       "537576                          1              1.0                        1.0   \n",
       "\n",
       "        gender_categorical  stay_in_current_city_years_categorical  \n",
       "0                      1.0                                     1.0  \n",
       "1                      1.0                                     1.0  \n",
       "2                      1.0                                     1.0  \n",
       "3                      1.0                                     1.0  \n",
       "4                      0.0                                     3.0  \n",
       "5                      0.0                                     2.0  \n",
       "6                      0.0                                     1.0  \n",
       "7                      0.0                                     1.0  \n",
       "8                      0.0                                     1.0  \n",
       "9                      0.0                                     0.0  \n",
       "10                     0.0                                     0.0  \n",
       "11                     0.0                                     0.0  \n",
       "12                     0.0                                     0.0  \n",
       "13                     0.0                                     0.0  \n",
       "14                     1.0                                     0.0  \n",
       "15                     1.0                                     0.0  \n",
       "16                     1.0                                     0.0  \n",
       "17                     1.0                                     0.0  \n",
       "18                     0.0                                     0.0  \n",
       "19                     0.0                                     3.0  \n",
       "20                     0.0                                     3.0  \n",
       "21                     0.0                                     3.0  \n",
       "22                     0.0                                     3.0  \n",
       "23                     0.0                                     3.0  \n",
       "24                     0.0                                     3.0  \n",
       "25                     0.0                                     4.0  \n",
       "26                     0.0                                     4.0  \n",
       "27                     0.0                                     4.0  \n",
       "28                     0.0                                     4.0  \n",
       "29                     1.0                                     3.0  \n",
       "...                    ...                                     ...  \n",
       "537547                 0.0                                     0.0  \n",
       "537548                 0.0                                     0.0  \n",
       "537549                 0.0                                     0.0  \n",
       "537550                 0.0                                     2.0  \n",
       "537551                 0.0                                     2.0  \n",
       "537552                 0.0                                     2.0  \n",
       "537553                 0.0                                     2.0  \n",
       "537554                 0.0                                     2.0  \n",
       "537555                 0.0                                     2.0  \n",
       "537556                 0.0                                     0.0  \n",
       "537557                 0.0                                     0.0  \n",
       "537558                 0.0                                     0.0  \n",
       "537559                 0.0                                     0.0  \n",
       "537560                 0.0                                     0.0  \n",
       "537561                 0.0                                     0.0  \n",
       "537562                 0.0                                     0.0  \n",
       "537563                 0.0                                     0.0  \n",
       "537564                 0.0                                     0.0  \n",
       "537565                 0.0                                     0.0  \n",
       "537566                 0.0                                     0.0  \n",
       "537567                 0.0                                     0.0  \n",
       "537568                 0.0                                     0.0  \n",
       "537569                 0.0                                     0.0  \n",
       "537570                 0.0                                     0.0  \n",
       "537571                 0.0                                     0.0  \n",
       "537572                 0.0                                     0.0  \n",
       "537573                 0.0                                     0.0  \n",
       "537574                 0.0                                     0.0  \n",
       "537575                 0.0                                     0.0  \n",
       "537576                 0.0                                     0.0  \n",
       "\n",
       "[537577 rows x 14 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexed_df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+---------------+\n",
      "|  age|age_categorical|\n",
      "+-----+---------------+\n",
      "| 0-17|            6.0|\n",
      "| 0-17|            6.0|\n",
      "| 0-17|            6.0|\n",
      "| 0-17|            6.0|\n",
      "|  55+|            5.0|\n",
      "|26-35|            0.0|\n",
      "|46-50|            3.0|\n",
      "|46-50|            3.0|\n",
      "|46-50|            3.0|\n",
      "|26-35|            0.0|\n",
      "|26-35|            0.0|\n",
      "|26-35|            0.0|\n",
      "|26-35|            0.0|\n",
      "|26-35|            0.0|\n",
      "|51-55|            4.0|\n",
      "+-----+---------------+\n",
      "only showing top 15 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets see how age categiry is mapped to integer values\n",
    "indexed_df.select('age','age_categorical').show(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: string (nullable = true)\n",
      " |-- city_category: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- marital_status: long (nullable = true)\n",
      " |-- occupation: long (nullable = true)\n",
      " |-- product_category_1: long (nullable = true)\n",
      " |-- product_category_2: float (nullable = false)\n",
      " |-- product_category_3: float (nullable = false)\n",
      " |-- purchase: long (nullable = true)\n",
      " |-- stay_in_current_city_years: string (nullable = true)\n",
      " |-- age_categorical: double (nullable = false)\n",
      " |-- city_category_categorical: double (nullable = false)\n",
      " |-- gender_categorical: double (nullable = false)\n",
      " |-- stay_in_current_city_years_categorical: double (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "indexed_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Getting total number of string columns\n",
    "strcols=[]\n",
    "for i in range(0,len(indexed_df.columns)):\n",
    "    if (indexed_df.dtypes[i][1]=='string'):\n",
    "        strcols.append(indexed_df.dtypes[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(strcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[age: string, city_category: string, gender: string, stay_in_current_city_years: string]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexed_df.select(strcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop all the string columns which are converted to ca\n",
    "for i in strcols:\n",
    "    indexed_df= indexed_df.drop(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- marital_status: long (nullable = true)\n",
      " |-- occupation: long (nullable = true)\n",
      " |-- product_category_1: long (nullable = true)\n",
      " |-- product_category_2: float (nullable = false)\n",
      " |-- product_category_3: float (nullable = false)\n",
      " |-- purchase: long (nullable = true)\n",
      " |-- age_categorical: double (nullable = false)\n",
      " |-- city_category_categorical: double (nullable = false)\n",
      " |-- gender_categorical: double (nullable = false)\n",
      " |-- stay_in_current_city_years_categorical: double (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To verify that the indexed_df data frame has all numeric values\n",
    "indexed_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>count</td>\n",
       "      <td>mean</td>\n",
       "      <td>stddev</td>\n",
       "      <td>min</td>\n",
       "      <td>max</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_status</th>\n",
       "      <td>537577</td>\n",
       "      <td>0.4087972513705013</td>\n",
       "      <td>0.49161215222967514</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <td>537577</td>\n",
       "      <td>8.082710011775058</td>\n",
       "      <td>6.524119769935539</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category_1</th>\n",
       "      <td>537577</td>\n",
       "      <td>5.295546498455105</td>\n",
       "      <td>3.7507009498798096</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category_2</th>\n",
       "      <td>537577</td>\n",
       "      <td>6.784907092379324</td>\n",
       "      <td>6.21161846455343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category_3</th>\n",
       "      <td>537577</td>\n",
       "      <td>3.871772787898292</td>\n",
       "      <td>6.265963443383427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase</th>\n",
       "      <td>537577</td>\n",
       "      <td>9333.859852635065</td>\n",
       "      <td>4981.022132656451</td>\n",
       "      <td>185</td>\n",
       "      <td>23961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_categorical</th>\n",
       "      <td>537577</td>\n",
       "      <td>1.450162488350506</td>\n",
       "      <td>1.62650944151932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_category_categorical</th>\n",
       "      <td>537577</td>\n",
       "      <td>0.8477334409768275</td>\n",
       "      <td>0.816819144371732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_categorical</th>\n",
       "      <td>537577</td>\n",
       "      <td>0.2459126785558162</td>\n",
       "      <td>0.4306274236930542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stay_in_current_city_years_categorical</th>\n",
       "      <td>537577</td>\n",
       "      <td>1.5358730005189953</td>\n",
       "      <td>1.4396749857068285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             0                   1  \\\n",
       "summary                                  count                mean   \n",
       "marital_status                          537577  0.4087972513705013   \n",
       "occupation                              537577   8.082710011775058   \n",
       "product_category_1                      537577   5.295546498455105   \n",
       "product_category_2                      537577   6.784907092379324   \n",
       "product_category_3                      537577   3.871772787898292   \n",
       "purchase                                537577   9333.859852635065   \n",
       "age_categorical                         537577   1.450162488350506   \n",
       "city_category_categorical               537577  0.8477334409768275   \n",
       "gender_categorical                      537577  0.2459126785558162   \n",
       "stay_in_current_city_years_categorical  537577  1.5358730005189953   \n",
       "\n",
       "                                                          2    3      4  \n",
       "summary                                              stddev  min    max  \n",
       "marital_status                          0.49161215222967514    0      1  \n",
       "occupation                                6.524119769935539    0     20  \n",
       "product_category_1                       3.7507009498798096    1     18  \n",
       "product_category_2                         6.21161846455343  0.0   18.0  \n",
       "product_category_3                        6.265963443383427  0.0   18.0  \n",
       "purchase                                  4981.022132656451  185  23961  \n",
       "age_categorical                            1.62650944151932  0.0    6.0  \n",
       "city_category_categorical                 0.816819144371732  0.0    2.0  \n",
       "gender_categorical                       0.4306274236930542  0.0    1.0  \n",
       "stay_in_current_city_years_categorical   1.4396749857068285  0.0    4.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Common statistics of all the columns\n",
    "indexed_df.describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dropping purchase (target dependent variable)\n",
    "model_df = indexed_df.drop('purchase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.regression import LinearRegression\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# Preparing a list of all the input columns\n",
    "reg=[]\n",
    "for col in indexed_df.columns:\n",
    "    if col=='purchase':\n",
    "        continue\n",
    "    else:\n",
    "        reg.append(col)\n",
    "        \n",
    "vectorAssembler = VectorAssembler(inputCols = reg, outputCol = 'Features')\n",
    "bfriday_df = vectorAssembler.transform(indexed_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bfriday_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.0, 10.0, 3.0, 0.0, 0.0, 6.0, 2.0, 1.0, 1.0]</td>\n",
       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0, 10.0, 1.0, 6.0, 14.0, 6.0, 2.0, 1.0, 1.0]</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.0, 10.0, 12.0, 0.0, 0.0, 6.0, 2.0, 1.0, 1.0]</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.0, 10.0, 12.0, 14.0, 0.0, 6.0, 2.0, 1.0, 1.0]</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.0, 16.0, 8.0, 0.0, 0.0, 5.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.0, 15.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0, 2.0]</td>\n",
       "      <td>15227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[1.0, 7.0, 1.0, 8.0, 17.0, 3.0, 0.0, 0.0, 1.0]</td>\n",
       "      <td>19215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[1.0, 7.0, 1.0, 15.0, 0.0, 3.0, 0.0, 0.0, 1.0]</td>\n",
       "      <td>15854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[1.0, 7.0, 1.0, 16.0, 0.0, 3.0, 0.0, 0.0, 1.0]</td>\n",
       "      <td>15686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(1.0, 20.0, 8.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)</td>\n",
       "      <td>7871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[1.0, 20.0, 5.0, 11.0, 0.0, 0.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>5254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(1.0, 20.0, 8.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)</td>\n",
       "      <td>3957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(1.0, 20.0, 8.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)</td>\n",
       "      <td>6073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[1.0, 20.0, 1.0, 2.0, 5.0, 0.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>15665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.0, 9.0, 5.0, 8.0, 14.0, 4.0, 2.0, 1.0, 0.0]</td>\n",
       "      <td>5378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.0, 9.0, 4.0, 5.0, 0.0, 4.0, 2.0, 1.0, 0.0]</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[0.0, 9.0, 2.0, 3.0, 4.0, 4.0, 2.0, 1.0, 0.0]</td>\n",
       "      <td>13055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[0.0, 9.0, 5.0, 14.0, 0.0, 4.0, 2.0, 1.0, 0.0]</td>\n",
       "      <td>8851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[1.0, 1.0, 1.0, 14.0, 16.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>11788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[1.0, 12.0, 1.0, 5.0, 15.0, 0.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>19614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[1.0, 12.0, 5.0, 14.0, 0.0, 0.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>8584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[1.0, 12.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>9872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[1.0, 12.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>9743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[1.0, 12.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>5982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[1.0, 12.0, 1.0, 8.0, 14.0, 0.0, 1.0, 0.0, 3.0]</td>\n",
       "      <td>11927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[0.0, 17.0, 6.0, 8.0, 0.0, 0.0, 1.0, 0.0, 4.0]</td>\n",
       "      <td>16662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(0.0, 17.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.0)</td>\n",
       "      <td>5887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[0.0, 17.0, 5.0, 14.0, 0.0, 0.0, 1.0, 0.0, 4.0]</td>\n",
       "      <td>6973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[0.0, 17.0, 5.0, 8.0, 14.0, 0.0, 1.0, 0.0, 4.0]</td>\n",
       "      <td>5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[1.0, 1.0, 2.0, 4.0, 8.0, 1.0, 0.0, 1.0, 3.0]</td>\n",
       "      <td>16352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537547</th>\n",
       "      <td>[0.0, 18.0, 1.0, 2.0, 15.0, 2.0, 1.0, 0.0, 0.0]</td>\n",
       "      <td>11543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537548</th>\n",
       "      <td>(1.0, 1.0, 15.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>20924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537549</th>\n",
       "      <td>[1.0, 1.0, 2.0, 8.0, 14.0, 4.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>13082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537550</th>\n",
       "      <td>[0.0, 3.0, 1.0, 0.0, 0.0, 3.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>11658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537551</th>\n",
       "      <td>[0.0, 3.0, 5.0, 6.0, 8.0, 3.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>6863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537552</th>\n",
       "      <td>[0.0, 3.0, 6.0, 8.0, 0.0, 3.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>16415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537553</th>\n",
       "      <td>[0.0, 3.0, 10.0, 13.0, 0.0, 3.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>18526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537554</th>\n",
       "      <td>[0.0, 3.0, 5.0, 14.0, 0.0, 3.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>7099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537555</th>\n",
       "      <td>[0.0, 3.0, 13.0, 16.0, 0.0, 3.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537556</th>\n",
       "      <td>[1.0, 20.0, 8.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537557</th>\n",
       "      <td>[1.0, 20.0, 2.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>12724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537558</th>\n",
       "      <td>[1.0, 20.0, 8.0, 17.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>7796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537559</th>\n",
       "      <td>[1.0, 20.0, 8.0, 17.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>7770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537560</th>\n",
       "      <td>[1.0, 20.0, 8.0, 16.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>5940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537561</th>\n",
       "      <td>[1.0, 20.0, 8.0, 16.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>7834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537562</th>\n",
       "      <td>[1.0, 20.0, 1.0, 13.0, 14.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>11508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537563</th>\n",
       "      <td>[1.0, 20.0, 8.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>6074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537564</th>\n",
       "      <td>[1.0, 20.0, 5.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>7084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537565</th>\n",
       "      <td>[1.0, 20.0, 8.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>7934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537566</th>\n",
       "      <td>[1.0, 20.0, 5.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>5350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537567</th>\n",
       "      <td>[1.0, 20.0, 8.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537568</th>\n",
       "      <td>[1.0, 20.0, 11.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>5930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537569</th>\n",
       "      <td>[1.0, 20.0, 5.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537570</th>\n",
       "      <td>[1.0, 20.0, 1.0, 16.0, 0.0, 2.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>15491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537571</th>\n",
       "      <td>[0.0, 16.0, 1.0, 2.0, 5.0, 1.0, 1.0, 0.0, 0.0]</td>\n",
       "      <td>11852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537572</th>\n",
       "      <td>[0.0, 16.0, 1.0, 2.0, 0.0, 1.0, 1.0, 0.0, 0.0]</td>\n",
       "      <td>11664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537573</th>\n",
       "      <td>[0.0, 16.0, 1.0, 15.0, 16.0, 1.0, 1.0, 0.0, 0.0]</td>\n",
       "      <td>19196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537574</th>\n",
       "      <td>[0.0, 16.0, 8.0, 15.0, 0.0, 1.0, 1.0, 0.0, 0.0]</td>\n",
       "      <td>8043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537575</th>\n",
       "      <td>(0.0, 16.0, 5.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0)</td>\n",
       "      <td>7172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537576</th>\n",
       "      <td>[0.0, 16.0, 5.0, 8.0, 0.0, 1.0, 1.0, 0.0, 0.0]</td>\n",
       "      <td>6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>537577 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Features  purchase\n",
       "0         [0.0, 10.0, 3.0, 0.0, 0.0, 6.0, 2.0, 1.0, 1.0]      8370\n",
       "1        [0.0, 10.0, 1.0, 6.0, 14.0, 6.0, 2.0, 1.0, 1.0]     15200\n",
       "2        [0.0, 10.0, 12.0, 0.0, 0.0, 6.0, 2.0, 1.0, 1.0]      1422\n",
       "3       [0.0, 10.0, 12.0, 14.0, 0.0, 6.0, 2.0, 1.0, 1.0]      1057\n",
       "4         [0.0, 16.0, 8.0, 0.0, 0.0, 5.0, 1.0, 0.0, 3.0]      7969\n",
       "5         [0.0, 15.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0, 2.0]     15227\n",
       "6         [1.0, 7.0, 1.0, 8.0, 17.0, 3.0, 0.0, 0.0, 1.0]     19215\n",
       "7         [1.0, 7.0, 1.0, 15.0, 0.0, 3.0, 0.0, 0.0, 1.0]     15854\n",
       "8         [1.0, 7.0, 1.0, 16.0, 0.0, 3.0, 0.0, 0.0, 1.0]     15686\n",
       "9         (1.0, 20.0, 8.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)      7871\n",
       "10       [1.0, 20.0, 5.0, 11.0, 0.0, 0.0, 2.0, 0.0, 0.0]      5254\n",
       "11        (1.0, 20.0, 8.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)      3957\n",
       "12        (1.0, 20.0, 8.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)      6073\n",
       "13        [1.0, 20.0, 1.0, 2.0, 5.0, 0.0, 2.0, 0.0, 0.0]     15665\n",
       "14        [0.0, 9.0, 5.0, 8.0, 14.0, 4.0, 2.0, 1.0, 0.0]      5378\n",
       "15         [0.0, 9.0, 4.0, 5.0, 0.0, 4.0, 2.0, 1.0, 0.0]      2079\n",
       "16         [0.0, 9.0, 2.0, 3.0, 4.0, 4.0, 2.0, 1.0, 0.0]     13055\n",
       "17        [0.0, 9.0, 5.0, 14.0, 0.0, 4.0, 2.0, 1.0, 0.0]      8851\n",
       "18       [1.0, 1.0, 1.0, 14.0, 16.0, 1.0, 0.0, 0.0, 0.0]     11788\n",
       "19       [1.0, 12.0, 1.0, 5.0, 15.0, 0.0, 1.0, 0.0, 3.0]     19614\n",
       "20       [1.0, 12.0, 5.0, 14.0, 0.0, 0.0, 1.0, 0.0, 3.0]      8584\n",
       "21        [1.0, 12.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0]      9872\n",
       "22        [1.0, 12.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0]      9743\n",
       "23        [1.0, 12.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0]      5982\n",
       "24       [1.0, 12.0, 1.0, 8.0, 14.0, 0.0, 1.0, 0.0, 3.0]     11927\n",
       "25        [0.0, 17.0, 6.0, 8.0, 0.0, 0.0, 1.0, 0.0, 4.0]     16662\n",
       "26        (0.0, 17.0, 8.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.0)      5887\n",
       "27       [0.0, 17.0, 5.0, 14.0, 0.0, 0.0, 1.0, 0.0, 4.0]      6973\n",
       "28       [0.0, 17.0, 5.0, 8.0, 14.0, 0.0, 1.0, 0.0, 4.0]      5391\n",
       "29         [1.0, 1.0, 2.0, 4.0, 8.0, 1.0, 0.0, 1.0, 3.0]     16352\n",
       "...                                                  ...       ...\n",
       "537547   [0.0, 18.0, 1.0, 2.0, 15.0, 2.0, 1.0, 0.0, 0.0]     11543\n",
       "537548    (1.0, 1.0, 15.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)     20924\n",
       "537549    [1.0, 1.0, 2.0, 8.0, 14.0, 4.0, 0.0, 0.0, 0.0]     13082\n",
       "537550     [0.0, 3.0, 1.0, 0.0, 0.0, 3.0, 1.0, 0.0, 2.0]     11658\n",
       "537551     [0.0, 3.0, 5.0, 6.0, 8.0, 3.0, 1.0, 0.0, 2.0]      6863\n",
       "537552     [0.0, 3.0, 6.0, 8.0, 0.0, 3.0, 1.0, 0.0, 2.0]     16415\n",
       "537553   [0.0, 3.0, 10.0, 13.0, 0.0, 3.0, 1.0, 0.0, 2.0]     18526\n",
       "537554    [0.0, 3.0, 5.0, 14.0, 0.0, 3.0, 1.0, 0.0, 2.0]      7099\n",
       "537555   [0.0, 3.0, 13.0, 16.0, 0.0, 3.0, 1.0, 0.0, 2.0]       578\n",
       "537556   [1.0, 20.0, 8.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]      2183\n",
       "537557    [1.0, 20.0, 2.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]     12724\n",
       "537558   [1.0, 20.0, 8.0, 17.0, 0.0, 2.0, 2.0, 0.0, 0.0]      7796\n",
       "537559   [1.0, 20.0, 8.0, 17.0, 0.0, 2.0, 2.0, 0.0, 0.0]      7770\n",
       "537560   [1.0, 20.0, 8.0, 16.0, 0.0, 2.0, 2.0, 0.0, 0.0]      5940\n",
       "537561   [1.0, 20.0, 8.0, 16.0, 0.0, 2.0, 2.0, 0.0, 0.0]      7834\n",
       "537562  [1.0, 20.0, 1.0, 13.0, 14.0, 2.0, 2.0, 0.0, 0.0]     11508\n",
       "537563    [1.0, 20.0, 8.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]      6074\n",
       "537564   [1.0, 20.0, 5.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]      7084\n",
       "537565    [1.0, 20.0, 8.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]      7934\n",
       "537566    [1.0, 20.0, 5.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]      5350\n",
       "537567   [1.0, 20.0, 8.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]      1994\n",
       "537568   [1.0, 20.0, 11.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0]      5930\n",
       "537569   [1.0, 20.0, 5.0, 14.0, 0.0, 2.0, 2.0, 0.0, 0.0]      7042\n",
       "537570   [1.0, 20.0, 1.0, 16.0, 0.0, 2.0, 2.0, 0.0, 0.0]     15491\n",
       "537571    [0.0, 16.0, 1.0, 2.0, 5.0, 1.0, 1.0, 0.0, 0.0]     11852\n",
       "537572    [0.0, 16.0, 1.0, 2.0, 0.0, 1.0, 1.0, 0.0, 0.0]     11664\n",
       "537573  [0.0, 16.0, 1.0, 15.0, 16.0, 1.0, 1.0, 0.0, 0.0]     19196\n",
       "537574   [0.0, 16.0, 8.0, 15.0, 0.0, 1.0, 1.0, 0.0, 0.0]      8043\n",
       "537575    (0.0, 16.0, 5.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0)      7172\n",
       "537576    [0.0, 16.0, 5.0, 8.0, 0.0, 1.0, 1.0, 0.0, 0.0]      6875\n",
       "\n",
       "[537577 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfriday_df.select(['Features','purchase']).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Preparing train and test data set\n",
    "splits = bfriday_df.randomSplit([0.8, 0.2],seed='2019')\n",
    "train_df = splits[0]\n",
    "test_df = splits[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a Linear Regression Model object\n",
    "linearReg = LinearRegression(featuresCol = 'Features', labelCol='purchase', maxIter=10, regParam=0.3, elasticNetParam=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fit the model to the data and call this model lrModel\n",
    "lr_model = linearReg.fit(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [44.7400771573,9.46128471439,-318.985636974,9.65183461725,148.566171474,27.6348835321,-32.820223467,-478.158013881,-8.3515874888]\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficients: \" + str(lr_model.coefficients))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 10404.4318092\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept: \" + str(lr_model.intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>Features</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10019.805725</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)</td>\n",
       "      <td>4010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10035.922774</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 2.0)</td>\n",
       "      <td>4276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10066.723905</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 1.0)</td>\n",
       "      <td>4340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003.102550</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0)</td>\n",
       "      <td>4446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10041.669142</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 4.0)</td>\n",
       "      <td>7635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10195.985706</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>7727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10195.985706</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>7821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10077.094585</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0)</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10085.446172</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9773.097460</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 6.0, 0.0, 1.0, 0.0)</td>\n",
       "      <td>7973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10003.102550</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0)</td>\n",
       "      <td>8012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10242.903886</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0, 1.0)</td>\n",
       "      <td>8155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9986.399375</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)</td>\n",
       "      <td>8231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10058.372317</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 2.0)</td>\n",
       "      <td>11401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10052.625949</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0)</td>\n",
       "      <td>11412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9986.399375</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)</td>\n",
       "      <td>11524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9541.647711</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 1.0, 0.0)</td>\n",
       "      <td>11550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10052.625949</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0)</td>\n",
       "      <td>11554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10085.446172</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>11583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10060.391410</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0)</td>\n",
       "      <td>11585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9986.399375</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)</td>\n",
       "      <td>11595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10085.446172</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>11716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9986.399375</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)</td>\n",
       "      <td>11754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10096.377881</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 2.0)</td>\n",
       "      <td>11757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10107.895716</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 0.0, 0.0)</td>\n",
       "      <td>11760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10052.625949</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0)</td>\n",
       "      <td>11826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10027.571186</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0)</td>\n",
       "      <td>11843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10132.364352</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 1.0)</td>\n",
       "      <td>11975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10099.544128</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 0.0, 1.0)</td>\n",
       "      <td>12036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10041.669142</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 4.0)</td>\n",
       "      <td>12049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107707</th>\n",
       "      <td>5468.986942</td>\n",
       "      <td>(1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)</td>\n",
       "      <td>12248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107708</th>\n",
       "      <td>5496.621826</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>12351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107709</th>\n",
       "      <td>5452.283767</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]</td>\n",
       "      <td>12420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107710</th>\n",
       "      <td>5562.262273</td>\n",
       "      <td>(1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>12820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107711</th>\n",
       "      <td>5562.262273</td>\n",
       "      <td>(1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>16224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107712</th>\n",
       "      <td>5485.103991</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>16351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107713</th>\n",
       "      <td>5452.283767</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]</td>\n",
       "      <td>16433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107714</th>\n",
       "      <td>5463.215476</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 2.0, 0.0, 4.0]</td>\n",
       "      <td>16555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107715</th>\n",
       "      <td>5034.580860</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0]</td>\n",
       "      <td>16579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107716</th>\n",
       "      <td>5067.401084</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 0.0, 1.0, 2.0]</td>\n",
       "      <td>16580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107717</th>\n",
       "      <td>5517.924214</td>\n",
       "      <td>(1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0)</td>\n",
       "      <td>16615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107718</th>\n",
       "      <td>5452.283767</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]</td>\n",
       "      <td>16643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107719</th>\n",
       "      <td>5452.283767</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]</td>\n",
       "      <td>16681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107720</th>\n",
       "      <td>5452.283767</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]</td>\n",
       "      <td>16721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107721</th>\n",
       "      <td>5468.986942</td>\n",
       "      <td>(1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)</td>\n",
       "      <td>16821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107722</th>\n",
       "      <td>5463.215476</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 2.0, 0.0, 4.0]</td>\n",
       "      <td>16912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107723</th>\n",
       "      <td>5153.471982</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 5.0, 1.0, 1.0, 1.0]</td>\n",
       "      <td>20307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107724</th>\n",
       "      <td>5564.842394</td>\n",
       "      <td>[1.0, 20.0, 16.0, 0.0, 0.0, 2.0, 0.0, 0.0, 3.0]</td>\n",
       "      <td>20796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107725</th>\n",
       "      <td>5326.181286</td>\n",
       "      <td>(1.0, 20.0, 17.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>12817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107726</th>\n",
       "      <td>5235.486077</td>\n",
       "      <td>[1.0, 20.0, 17.0, 0.0, 0.0, 4.0, 2.0, 0.0, 3.0]</td>\n",
       "      <td>13040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107727</th>\n",
       "      <td>4861.230673</td>\n",
       "      <td>[1.0, 20.0, 18.0, 0.0, 0.0, 2.0, 2.0, 0.0, 3.0]</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107728</th>\n",
       "      <td>4858.650552</td>\n",
       "      <td>[1.0, 20.0, 18.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107729</th>\n",
       "      <td>5007.195649</td>\n",
       "      <td>(1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>2307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107730</th>\n",
       "      <td>4858.650552</td>\n",
       "      <td>[1.0, 20.0, 18.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0]</td>\n",
       "      <td>3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107731</th>\n",
       "      <td>5007.195649</td>\n",
       "      <td>(1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>3126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107732</th>\n",
       "      <td>5007.195649</td>\n",
       "      <td>(1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>3137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107733</th>\n",
       "      <td>5007.195649</td>\n",
       "      <td>(1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)</td>\n",
       "      <td>3815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107734</th>\n",
       "      <td>4985.307134</td>\n",
       "      <td>[1.0, 20.0, 18.0, 0.0, 0.0, 5.0, 1.0, 0.0, 2.0]</td>\n",
       "      <td>3833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107735</th>\n",
       "      <td>4973.789299</td>\n",
       "      <td>[1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 4.0]</td>\n",
       "      <td>3849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107736</th>\n",
       "      <td>4418.498101</td>\n",
       "      <td>(1.0, 20.0, 18.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0)</td>\n",
       "      <td>3890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107737 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          prediction                                         Features  \\\n",
       "0       10019.805725    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)   \n",
       "1       10035.922774    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 2.0)   \n",
       "2       10066.723905    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 1.0)   \n",
       "3       10003.102550    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0)   \n",
       "4       10041.669142    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 4.0)   \n",
       "5       10195.985706    (0.0, 0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "6       10195.985706    (0.0, 0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "7       10077.094585    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0)   \n",
       "8       10085.446172    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)   \n",
       "9        9773.097460    (0.0, 0.0, 1.0, 0.0, 0.0, 6.0, 0.0, 1.0, 0.0)   \n",
       "10      10003.102550    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0)   \n",
       "11      10242.903886    (0.0, 0.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0, 1.0)   \n",
       "12       9986.399375    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)   \n",
       "13      10058.372317    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 2.0)   \n",
       "14      10052.625949    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0)   \n",
       "15       9986.399375    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)   \n",
       "16       9541.647711    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 1.0, 0.0)   \n",
       "17      10052.625949    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0)   \n",
       "18      10085.446172    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)   \n",
       "19      10060.391410    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0)   \n",
       "20       9986.399375    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)   \n",
       "21      10085.446172    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)   \n",
       "22       9986.399375    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 4.0)   \n",
       "23      10096.377881    (0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 2.0)   \n",
       "24      10107.895716    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 0.0, 0.0)   \n",
       "25      10052.625949    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0)   \n",
       "26      10027.571186    (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 3.0)   \n",
       "27      10132.364352    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 1.0)   \n",
       "28      10099.544128    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 0.0, 1.0)   \n",
       "29      10041.669142    (0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 0.0, 4.0)   \n",
       "...              ...                                              ...   \n",
       "107707   5468.986942  (1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)   \n",
       "107708   5496.621826  [1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0]   \n",
       "107709   5452.283767  [1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]   \n",
       "107710   5562.262273  (1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0)   \n",
       "107711   5562.262273  (1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0)   \n",
       "107712   5485.103991  [1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 1.0, 0.0, 2.0]   \n",
       "107713   5452.283767  [1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]   \n",
       "107714   5463.215476  [1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 2.0, 0.0, 4.0]   \n",
       "107715   5034.580860  [1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0]   \n",
       "107716   5067.401084  [1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 0.0, 1.0, 2.0]   \n",
       "107717   5517.924214  (1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0)   \n",
       "107718   5452.283767  [1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]   \n",
       "107719   5452.283767  [1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]   \n",
       "107720   5452.283767  [1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 2.0]   \n",
       "107721   5468.986942  (1.0, 20.0, 16.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0)   \n",
       "107722   5463.215476  [1.0, 20.0, 16.0, 0.0, 0.0, 1.0, 2.0, 0.0, 4.0]   \n",
       "107723   5153.471982  [1.0, 20.0, 16.0, 0.0, 0.0, 5.0, 1.0, 1.0, 1.0]   \n",
       "107724   5564.842394  [1.0, 20.0, 16.0, 0.0, 0.0, 2.0, 0.0, 0.0, 3.0]   \n",
       "107725   5326.181286  (1.0, 20.0, 17.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "107726   5235.486077  [1.0, 20.0, 17.0, 0.0, 0.0, 4.0, 2.0, 0.0, 3.0]   \n",
       "107727   4861.230673  [1.0, 20.0, 18.0, 0.0, 0.0, 2.0, 2.0, 0.0, 3.0]   \n",
       "107728   4858.650552  [1.0, 20.0, 18.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0]   \n",
       "107729   5007.195649  (1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "107730   4858.650552  [1.0, 20.0, 18.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0]   \n",
       "107731   5007.195649  (1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "107732   5007.195649  (1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "107733   5007.195649  (1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0)   \n",
       "107734   4985.307134  [1.0, 20.0, 18.0, 0.0, 0.0, 5.0, 1.0, 0.0, 2.0]   \n",
       "107735   4973.789299  [1.0, 20.0, 18.0, 0.0, 0.0, 4.0, 0.0, 0.0, 4.0]   \n",
       "107736   4418.498101  (1.0, 20.0, 18.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0)   \n",
       "\n",
       "        purchase  \n",
       "0           4010  \n",
       "1           4276  \n",
       "2           4340  \n",
       "3           4446  \n",
       "4           7635  \n",
       "5           7727  \n",
       "6           7821  \n",
       "7           7844  \n",
       "8           7969  \n",
       "9           7973  \n",
       "10          8012  \n",
       "11          8155  \n",
       "12          8231  \n",
       "13         11401  \n",
       "14         11412  \n",
       "15         11524  \n",
       "16         11550  \n",
       "17         11554  \n",
       "18         11583  \n",
       "19         11585  \n",
       "20         11595  \n",
       "21         11716  \n",
       "22         11754  \n",
       "23         11757  \n",
       "24         11760  \n",
       "25         11826  \n",
       "26         11843  \n",
       "27         11975  \n",
       "28         12036  \n",
       "29         12049  \n",
       "...          ...  \n",
       "107707     12248  \n",
       "107708     12351  \n",
       "107709     12420  \n",
       "107710     12820  \n",
       "107711     16224  \n",
       "107712     16351  \n",
       "107713     16433  \n",
       "107714     16555  \n",
       "107715     16579  \n",
       "107716     16580  \n",
       "107717     16615  \n",
       "107718     16643  \n",
       "107719     16681  \n",
       "107720     16721  \n",
       "107721     16821  \n",
       "107722     16912  \n",
       "107723     20307  \n",
       "107724     20796  \n",
       "107725     12817  \n",
       "107726     13040  \n",
       "107727       842  \n",
       "107728      2268  \n",
       "107729      2307  \n",
       "107730      3076  \n",
       "107731      3126  \n",
       "107732      3137  \n",
       "107733      3815  \n",
       "107734      3833  \n",
       "107735      3849  \n",
       "107736      3890  \n",
       "\n",
       "[107737 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_predictions=lr_model.transform(test_df)\n",
    "lr_predictions.select(['prediction','Features','purchase']).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+----------+--------+\n",
      "|        prediction|occupation|purchase|\n",
      "+------------------+----------+--------+\n",
      "|10019.805725278557|         0|    4010|\n",
      "| 10035.92277376795|         0|    4276|\n",
      "|10066.723904853918|         0|    4340|\n",
      "|10003.102550300948|         0|    4446|\n",
      "|10041.669142387504|         0|    7635|\n",
      "| 10195.98570634089|         0|    7727|\n",
      "| 10195.98570634089|         0|    7821|\n",
      "|10077.094584723756|         0|    7844|\n",
      "| 10085.44617221256|         0|    7969|\n",
      "| 9773.097459524091|         0|    7973|\n",
      "+------------------+----------+--------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr_predictions.registerTempTable('pred_table')\n",
    "sqlContext.sql('select prediction, occupation, purchase from pred_table where occupation = 0').show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared (R2) on test data = 0.130943\n"
     ]
    }
   ],
   "source": [
    "#Evaluating R squared on test data for the Mean Model.\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "lr_evaluator = RegressionEvaluator(predictionCol=\"prediction\",labelCol=\"purchase\",metricName=\"r2\")\n",
    "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Creating a Correlation Dictionary to select the best positively and negatively Correlated values.\n",
    "correlation_dict={}\n",
    "\n",
    "for col in indexed_df.columns:\n",
    "    correlation_dict[col]=indexed_df.corr(col1='purchase',col2=col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/613183/how-do-i-sort-a-dictionary-by-value\n",
    "import operator\n",
    "sorted_dict = sorted(correlation_dict.items(), key=operator.itemgetter(1),reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'purchase'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_dict[0:15][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('marital_status', 0.00012901814909541888),\n",
       " ('stay_in_current_city_years_categorical', -0.0032702991465657183),\n",
       " ('city_category_categorical', -0.010854036609876973),\n",
       " ('gender_categorical', -0.0600861659869464),\n",
       " ('product_category_1', -0.3141247354708834)]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_dict[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating new_col_list which joins the slices created above.\n",
    "new_col_list=sorted_dict[0:15]+sorted_dict[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "collist=[]\n",
    "for item in new_col_list:\n",
    "    if item[0]=='purchase':\n",
    "        continue\n",
    "    else:\n",
    "        collist.append(item[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Getting Correlation within the columns of collist. \n",
    "f=indexed_df.select(collist).toPandas().corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CfnYOdZpNkpqaisPh4LvvvuPixYvcfvvtrse6devGjh07ANi1axdeXl54eHiQnp6O0+kk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\nBRfNRERERERPwUUzEREREdFT/D/8mieJDV4LWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd60d14c910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating heatmap for visualizing the correlated columns so that we can eliminate auto-correlation.\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "sns.heatmap(data=f)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared (R2) on test data = 0.130744\n"
     ]
    }
   ],
   "source": [
    "# Let's improve the fit by removing the variables which are highly correlated and do the linear regression again\n",
    "new_indexed_df=indexed_df.drop('age_categorical')\n",
    "new_indexed_df=new_indexed_df.drop('product_category_2')\n",
    "#Running the Feature selected model and checking for R squared Value.\n",
    "\n",
    "reg=[]\n",
    "for col in new_indexed_df.columns:\n",
    "    if col=='purchase':\n",
    "        continue\n",
    "    else:\n",
    "        reg.append(col)\n",
    "\n",
    "vectorAssembler = VectorAssembler(inputCols = reg, outputCol = 'Features')\n",
    "bfriday_df = vectorAssembler.transform(indexed_df)\n",
    "\n",
    "splits = bfriday_df.randomSplit([0.8, 0.2],seed='2019')\n",
    "train_df = splits[0]\n",
    "test_df = splits[1]\n",
    "\n",
    "linearReg = LinearRegression(featuresCol = 'Features', labelCol='purchase', maxIter=10, regParam=0.3, elasticNetParam=0.8)\n",
    "\n",
    "lr_model = linearReg.fit(train_df)\n",
    "\n",
    "lr_predictions=lr_model.transform(test_df)\n",
    "lr_evaluator = RegressionEvaluator(predictionCol=\"prediction\",labelCol=\"purchase\",metricName=\"r2\")\n",
    "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't get a significant difference in the r squared value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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