SparkSQL_Game_Sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://10f59d7a1725:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.4.4</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>RDD Exercise</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7efbd916d128>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import desc, col\n",
    "import pandas as pd\n",
    "\n",
    "spark = SparkSession.builder \\\n",
    "    .master('local[*]') \\\n",
    "    .appName('RDD Exercise') \\\n",
    "    .getOrCreate()\n",
    "    \n",
    "spark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = spark.read \\\n",
    "  .csv('vgsales.csv', header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.rdd.getNumPartitions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>709.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name Platform Year_of_Release         Genre Publisher  \\\n",
       "0                Wii Sports      Wii            2006        Sports  Nintendo   \n",
       "1         Super Mario Bros.      NES            1985      Platform  Nintendo   \n",
       "2            Mario Kart Wii      Wii            2008        Racing  Nintendo   \n",
       "3         Wii Sports Resort      Wii            2009        Sports  Nintendo   \n",
       "4  Pokemon Red/Pokemon Blue       GB            1996  Role-Playing  Nintendo   \n",
       "\n",
       "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
       "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
       "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
       "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
       "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
       "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
       "\n",
       "   Critic_Count User_Score  User_Count Developer Rating  \n",
       "0          51.0          8       322.0  Nintendo      E  \n",
       "1           NaN       None         NaN      None   None  \n",
       "2          73.0        8.3       709.0  Nintendo      E  \n",
       "3          73.0          8       192.0  Nintendo      E  \n",
       "4           NaN       None         NaN      None   None  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save as parquet with partitioning by type of housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data.write.partitionBy('Platform').parquet('salesparquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reload the data from parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = spark.read.parquet('salesparquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repartition the data (e.g., use 10 partitions) - in this sample dataset not really worth doing, but definitely important when it comes to performance tuning in Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = sales_data.repartition(10).cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.rdd.getNumPartitions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Take a short look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14565</td>\n",
       "      <td>Worms</td>\n",
       "      <td>2009</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>Sony Computer Entertainment</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>PS3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14703</td>\n",
       "      <td>MechAssault: Phantom War</td>\n",
       "      <td>2006</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>Majesco Entertainment</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>2008</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.88</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.31</td>\n",
       "      <td>35.82</td>\n",
       "      <td>Wii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5105</td>\n",
       "      <td>Mini Ninjas</td>\n",
       "      <td>2009</td>\n",
       "      <td>Action</td>\n",
       "      <td>Eidos Interactive</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>Wii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1385</td>\n",
       "      <td>Far Cry 2</td>\n",
       "      <td>2008</td>\n",
       "      <td>Action</td>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.40</td>\n",
       "      <td>PS3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank                      Name  Year       Genre  \\\n",
       "0  14565                     Worms  2009    Strategy   \n",
       "1  14703  MechAssault: Phantom War  2006  Simulation   \n",
       "2      3            Mario Kart Wii  2008      Racing   \n",
       "3   5105               Mini Ninjas  2009      Action   \n",
       "4   1385                 Far Cry 2  2008      Action   \n",
       "\n",
       "                     Publisher  NA_Sales  EU_Sales  JP_Sales  Other_Sales  \\\n",
       "0  Sony Computer Entertainment      0.00      0.02      0.00         0.01   \n",
       "1        Majesco Entertainment      0.03      0.00      0.00         0.00   \n",
       "2                     Nintendo     15.85     12.88      3.79         3.31   \n",
       "3            Eidos Interactive      0.17      0.17      0.00         0.04   \n",
       "4                      Ubisoft      0.43      0.70      0.01         0.27   \n",
       "\n",
       "   Global_Sales Platform  \n",
       "0          0.03      PS3  \n",
       "1          0.03       DS  \n",
       "2         35.82      Wii  \n",
       "3          0.37      Wii  \n",
       "4          1.40      PS3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What does the dataset's schema look like (based on Spark's schema inference)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Name: string (nullable = true)\n",
      " |-- Year_of_Release: string (nullable = true)\n",
      " |-- Genre: string (nullable = true)\n",
      " |-- Publisher: string (nullable = true)\n",
      " |-- NA_Sales: double (nullable = true)\n",
      " |-- EU_Sales: double (nullable = true)\n",
      " |-- JP_Sales: double (nullable = true)\n",
      " |-- Other_Sales: double (nullable = true)\n",
      " |-- Global_Sales: double (nullable = true)\n",
      " |-- Critic_Score: integer (nullable = true)\n",
      " |-- Critic_Count: integer (nullable = true)\n",
      " |-- User_Score: string (nullable = true)\n",
      " |-- User_Count: integer (nullable = true)\n",
      " |-- Developer: string (nullable = true)\n",
      " |-- Rating: string (nullable = true)\n",
      " |-- Platform: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sales_data.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get summary statistics for your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>summary</th>\n",
       "      <th>Name</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>16717</td>\n",
       "      <td>16719</td>\n",
       "      <td>16717</td>\n",
       "      <td>16719</td>\n",
       "      <td>16719</td>\n",
       "      <td>16719</td>\n",
       "      <td>16719</td>\n",
       "      <td>16719</td>\n",
       "      <td>16719</td>\n",
       "      <td>8137</td>\n",
       "      <td>8137</td>\n",
       "      <td>10015</td>\n",
       "      <td>7590</td>\n",
       "      <td>10096</td>\n",
       "      <td>9950</td>\n",
       "      <td>16719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>1942.0</td>\n",
       "      <td>2006.4873556231003</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.26333034272384664</td>\n",
       "      <td>0.1450248220587358</td>\n",
       "      <td>0.07760212931395424</td>\n",
       "      <td>0.047331778216400736</td>\n",
       "      <td>0.5335426759973656</td>\n",
       "      <td>68.96767850559173</td>\n",
       "      <td>26.360820941378886</td>\n",
       "      <td>7.12504611330699</td>\n",
       "      <td>162.22990777338603</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stddev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.878994768348372</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.8135138347516192</td>\n",
       "      <td>0.5032832960942157</td>\n",
       "      <td>0.30881808051463777</td>\n",
       "      <td>0.18670989698947893</td>\n",
       "      <td>1.5479351247177673</td>\n",
       "      <td>13.938164552843212</td>\n",
       "      <td>18.98049453458207</td>\n",
       "      <td>1.5000060936257982</td>\n",
       "      <td>561.2823262473771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>Beyblade Burst</td>\n",
       "      <td>1980</td>\n",
       "      <td>Action</td>\n",
       "      <td>10TACLE Studios</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10tacle Studios</td>\n",
       "      <td>AO</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max</td>\n",
       "      <td>¡Shin Chan Flipa en colores!</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>responDESIGN</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>10.22</td>\n",
       "      <td>10.57</td>\n",
       "      <td>82.53</td>\n",
       "      <td>98</td>\n",
       "      <td>113</td>\n",
       "      <td>tbd</td>\n",
       "      <td>10665</td>\n",
       "      <td>zSlide</td>\n",
       "      <td>T</td>\n",
       "      <td>XOne</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  summary                          Name     Year_of_Release     Genre  \\\n",
       "0   count                         16717               16719     16717   \n",
       "1    mean                        1942.0  2006.4873556231003      None   \n",
       "2  stddev                           NaN   5.878994768348372      None   \n",
       "3     min                Beyblade Burst                1980    Action   \n",
       "4     max  ¡Shin Chan Flipa en colores!                 N/A  Strategy   \n",
       "\n",
       "         Publisher             NA_Sales            EU_Sales  \\\n",
       "0            16719                16719               16719   \n",
       "1             None  0.26333034272384664  0.1450248220587358   \n",
       "2             None   0.8135138347516192  0.5032832960942157   \n",
       "3  10TACLE Studios                  0.0                 0.0   \n",
       "4     responDESIGN                41.36               28.96   \n",
       "\n",
       "              JP_Sales           Other_Sales        Global_Sales  \\\n",
       "0                16719                 16719               16719   \n",
       "1  0.07760212931395424  0.047331778216400736  0.5335426759973656   \n",
       "2  0.30881808051463777   0.18670989698947893  1.5479351247177673   \n",
       "3                  0.0                   0.0                0.01   \n",
       "4                10.22                 10.57               82.53   \n",
       "\n",
       "         Critic_Score        Critic_Count          User_Score  \\\n",
       "0                8137                8137               10015   \n",
       "1   68.96767850559173  26.360820941378886    7.12504611330699   \n",
       "2  13.938164552843212   18.98049453458207  1.5000060936257982   \n",
       "3                  13                   3                   0   \n",
       "4                  98                 113                 tbd   \n",
       "\n",
       "           User_Count        Developer Rating Platform  \n",
       "0                7590            10096   9950    16719  \n",
       "1  162.22990777338603           2015.0   None   2600.0  \n",
       "2   561.2823262473771              NaN   None      0.0  \n",
       "3                   4  10tacle Studios     AO     2600  \n",
       "4               10665           zSlide      T     XOne  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.describe().toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect Data grouped by interesting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data.createOrReplaceTempView('sales_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd333c9e8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# enable matplotlib inline to work with jupyter notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# increase plot size to be able to actually see something\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "\n",
    "sales_data.groupBy(\"Platform\").count().sort('Platform').toPandas().plot.bar(x='Platform',y='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd34ecba8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spark.sql('SELECT Year_of_Release as Year, COUNT(*) ' + \\\n",
    "          'AS count FROM sales_data ' + \\\n",
    "          'GROUP BY Year ' + \\\n",
    "          'ORDER BY Year') \\\n",
    ".toPandas().plot.bar(x='Year',y='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Games released in 2008 and 2009 have the highest sale count, this could be because of the release of the Nintendo Wii, lets take a look at this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd3168da0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_data.groupBy(\"Genre\").count().sort('Genre').toPandas().plot.bar(x='Genre',y='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Action Genre is the most saled one, followed by the Sports Genre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbc85a7358>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nr_publishers = sales_data.select(\"Publisher\").distinct().count()\n",
    "nr_top_publishers = 15\n",
    "\n",
    "top_pubs = sales_data.groupBy(\"Publisher\").count().sort(desc('count')).limit(nr_top_publishers).toPandas()\n",
    "\n",
    "top_pubs_list = top_pubs[\"Publisher\"].values.tolist()\n",
    "\n",
    "# group remaining publishers, would be to many for a graph\n",
    "top_pubs = top_pubs.append({\n",
    "    \"Publisher\": \"Others ({count})\".format(count = str(nr_publishers - nr_top_publishers)),\n",
    "     \"count\": sales_data.filter(~col(\"Publisher\").isin(top_pubs_list)).count()\n",
    "    }, ignore_index=True)\n",
    "\n",
    "top_pubs.plot.barh(x = \"Publisher\", y = \"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the number of Publishers is high and the distribution of sales among them is not very outstanding, this features is among the less intersting ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select columns of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = sales_data[['Publisher', 'Platform',\n",
    "                         'Genre', 'Critic_Score',\n",
    "                         'User_Score', 'Global_Sales',\n",
    "                         'Developer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = sales_data.filter(\n",
    "    \"CAST(User_Score AS DECIMAL) IS NOT NULL AND CAST(Critic_Score AS DECIMAL) IS NOT NULL\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression based on the most impacting features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Developer</th>\n",
       "      <th>platform_index</th>\n",
       "      <th>genre_index</th>\n",
       "      <th>dev_index</th>\n",
       "      <th>publisher_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Universal Interactive</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>75</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Terminal Reality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nippon Ichi Software</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>69</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Gust</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Activision</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Action</td>\n",
       "      <td>77</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Toys for Bob</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sega</td>\n",
       "      <td>Wii</td>\n",
       "      <td>Platform</td>\n",
       "      <td>54</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.09</td>\n",
       "      <td>Sonic Team</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Majesco Entertainment</td>\n",
       "      <td>DS</td>\n",
       "      <td>Platform</td>\n",
       "      <td>79</td>\n",
       "      <td>8</td>\n",
       "      <td>0.06</td>\n",
       "      <td>DreamRift</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sega</td>\n",
       "      <td>Wii</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>58</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>Polygon Magic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SouthPeak Games</td>\n",
       "      <td>DS</td>\n",
       "      <td>Action</td>\n",
       "      <td>45</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Red Fly Studio</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Namco Bandai Games</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Action</td>\n",
       "      <td>71</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.66</td>\n",
       "      <td>Omega Force</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>71</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Game Arts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Konami Digital Entertainment</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Racing</td>\n",
       "      <td>67</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.08</td>\n",
       "      <td>Isopod Labs</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Publisher Platform         Genre  Critic_Score  \\\n",
       "0         Universal Interactive      PS2       Shooter            75   \n",
       "1          Nippon Ichi Software      PS2  Role-Playing            69   \n",
       "2                    Activision      PS3        Action            77   \n",
       "3                          Sega      Wii      Platform            54   \n",
       "4         Majesco Entertainment       DS      Platform            79   \n",
       "5                          Sega      Wii      Fighting            58   \n",
       "6               SouthPeak Games       DS        Action            45   \n",
       "7            Namco Bandai Games      PS3        Action            71   \n",
       "8                       Ubisoft      PS2  Role-Playing            71   \n",
       "9  Konami Digital Entertainment      PS3        Racing            67   \n",
       "\n",
       "  User_Score  Global_Sales         Developer  platform_index  genre_index  \\\n",
       "0        8.5          0.42  Terminal Reality             0.0          2.0   \n",
       "1        7.5          0.06              Gust             0.0          3.0   \n",
       "2        6.3          1.06      Toys for Bob             2.0          0.0   \n",
       "3        6.9          1.09        Sonic Team             5.0          5.0   \n",
       "4          8          0.06         DreamRift             6.0          5.0   \n",
       "5        7.5          0.32     Polygon Magic             5.0          7.0   \n",
       "6        7.3          0.11    Red Fly Studio             6.0          0.0   \n",
       "7        7.8          0.66       Omega Force             2.0          0.0   \n",
       "8        8.6          0.29         Game Arts             0.0          3.0   \n",
       "9        6.8          0.08       Isopod Labs             2.0          4.0   \n",
       "\n",
       "   dev_index  publisher_index  \n",
       "0       53.0             41.0  \n",
       "1      101.0             26.0  \n",
       "2       83.0              2.0  \n",
       "3       24.0              6.0  \n",
       "4      911.0             37.0  \n",
       "5      425.0              6.0  \n",
       "6      245.0             38.0  \n",
       "7        7.0              9.0  \n",
       "8      131.0              1.0  \n",
       "9      743.0              8.0  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.ml.feature import StringIndexer\n",
    "\n",
    "indexer = StringIndexer(inputCol='Platform', outputCol='platform_index', handleInvalid=\"skip\")\n",
    "sales_data = indexer.fit(sales_data).transform(sales_data)\n",
    "\n",
    "indexer = StringIndexer(inputCol='Genre', outputCol='genre_index', handleInvalid=\"skip\")\n",
    "sales_data = indexer.fit(sales_data).transform(sales_data)\n",
    "\n",
    "indexer = StringIndexer(inputCol='Developer', outputCol='dev_index', handleInvalid=\"skip\")\n",
    "sales_data = indexer.fit(sales_data).transform(sales_data)\n",
    "\n",
    "indexer = StringIndexer(inputCol='Publisher', outputCol='publisher_index', handleInvalid=\"skip\")\n",
    "sales_data = indexer.fit(sales_data).transform(sales_data)\n",
    "\n",
    "sales_data.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select the relevant features from the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assemble the training features into a vector (needed for MLlib in PySpark) using the *VectorAssembler*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# User_Score wont work, strings wont dissapear\n",
    "\n",
    "assembler = VectorAssembler(inputCols=['platform_index', 'genre_index',\n",
    "                                       'dev_index', 'publisher_index',\n",
    "                                       'Critic_Score'],\n",
    "                            outputCol='features')\n",
    "prep_sales_data = assembler.transform(sales_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Developer</th>\n",
       "      <th>platform_index</th>\n",
       "      <th>genre_index</th>\n",
       "      <th>dev_index</th>\n",
       "      <th>publisher_index</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Universal Interactive</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>75</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Terminal Reality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>[0.0, 2.0, 53.0, 41.0, 75.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nippon Ichi Software</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>69</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Gust</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>[0.0, 3.0, 101.0, 26.0, 69.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Activision</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Action</td>\n",
       "      <td>77</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Toys for Bob</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[2.0, 0.0, 83.0, 2.0, 77.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sega</td>\n",
       "      <td>Wii</td>\n",
       "      <td>Platform</td>\n",
       "      <td>54</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.09</td>\n",
       "      <td>Sonic Team</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[5.0, 5.0, 24.0, 6.0, 54.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Majesco Entertainment</td>\n",
       "      <td>DS</td>\n",
       "      <td>Platform</td>\n",
       "      <td>79</td>\n",
       "      <td>8</td>\n",
       "      <td>0.06</td>\n",
       "      <td>DreamRift</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>[6.0, 5.0, 911.0, 37.0, 79.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sega</td>\n",
       "      <td>Wii</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>58</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>Polygon Magic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[5.0, 7.0, 425.0, 6.0, 58.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SouthPeak Games</td>\n",
       "      <td>DS</td>\n",
       "      <td>Action</td>\n",
       "      <td>45</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Red Fly Studio</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[6.0, 0.0, 245.0, 38.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Namco Bandai Games</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Action</td>\n",
       "      <td>71</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.66</td>\n",
       "      <td>Omega Force</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>[2.0, 0.0, 7.0, 9.0, 71.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>71</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Game Arts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[0.0, 3.0, 131.0, 1.0, 71.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Konami Digital Entertainment</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Racing</td>\n",
       "      <td>67</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.08</td>\n",
       "      <td>Isopod Labs</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2.0, 4.0, 743.0, 8.0, 67.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Publisher Platform         Genre  Critic_Score  \\\n",
       "0         Universal Interactive      PS2       Shooter            75   \n",
       "1          Nippon Ichi Software      PS2  Role-Playing            69   \n",
       "2                    Activision      PS3        Action            77   \n",
       "3                          Sega      Wii      Platform            54   \n",
       "4         Majesco Entertainment       DS      Platform            79   \n",
       "5                          Sega      Wii      Fighting            58   \n",
       "6               SouthPeak Games       DS        Action            45   \n",
       "7            Namco Bandai Games      PS3        Action            71   \n",
       "8                       Ubisoft      PS2  Role-Playing            71   \n",
       "9  Konami Digital Entertainment      PS3        Racing            67   \n",
       "\n",
       "  User_Score  Global_Sales         Developer  platform_index  genre_index  \\\n",
       "0        8.5          0.42  Terminal Reality             0.0          2.0   \n",
       "1        7.5          0.06              Gust             0.0          3.0   \n",
       "2        6.3          1.06      Toys for Bob             2.0          0.0   \n",
       "3        6.9          1.09        Sonic Team             5.0          5.0   \n",
       "4          8          0.06         DreamRift             6.0          5.0   \n",
       "5        7.5          0.32     Polygon Magic             5.0          7.0   \n",
       "6        7.3          0.11    Red Fly Studio             6.0          0.0   \n",
       "7        7.8          0.66       Omega Force             2.0          0.0   \n",
       "8        8.6          0.29         Game Arts             0.0          3.0   \n",
       "9        6.8          0.08       Isopod Labs             2.0          4.0   \n",
       "\n",
       "   dev_index  publisher_index                       features  \n",
       "0       53.0             41.0   [0.0, 2.0, 53.0, 41.0, 75.0]  \n",
       "1      101.0             26.0  [0.0, 3.0, 101.0, 26.0, 69.0]  \n",
       "2       83.0              2.0    [2.0, 0.0, 83.0, 2.0, 77.0]  \n",
       "3       24.0              6.0    [5.0, 5.0, 24.0, 6.0, 54.0]  \n",
       "4      911.0             37.0  [6.0, 5.0, 911.0, 37.0, 79.0]  \n",
       "5      425.0              6.0   [5.0, 7.0, 425.0, 6.0, 58.0]  \n",
       "6      245.0             38.0  [6.0, 0.0, 245.0, 38.0, 45.0]  \n",
       "7        7.0              9.0     [2.0, 0.0, 7.0, 9.0, 71.0]  \n",
       "8      131.0              1.0   [0.0, 3.0, 131.0, 1.0, 71.0]  \n",
       "9      743.0              8.0   [2.0, 4.0, 743.0, 8.0, 67.0]  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep_sales_data.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>Global_Sales</th>\n",
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       "      <th>genre_index</th>\n",
       "      <th>dev_index</th>\n",
       "      <th>publisher_index</th>\n",
       "      <th>features</th>\n",
       "      <th>scaledFeatures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Universal Interactive</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>75</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>Terminal Reality</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>[0.0, 2.0, 53.0, 41.0, 75.0]</td>\n",
       "      <td>[0.0, 0.635021193711274, 0.17578230141939521, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nippon Ichi Software</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>69</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Gust</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>[0.0, 3.0, 101.0, 26.0, 69.0]</td>\n",
       "      <td>[0.0, 0.952531790566911, 0.33498136685582863, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Activision</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Action</td>\n",
       "      <td>77</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Toys for Bob</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[2.0, 0.0, 83.0, 2.0, 77.0]</td>\n",
       "      <td>[0.5031257911541318, 0.0, 0.2752817173171661, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sega</td>\n",
       "      <td>Wii</td>\n",
       "      <td>Platform</td>\n",
       "      <td>54</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.09</td>\n",
       "      <td>Sonic Team</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[5.0, 5.0, 24.0, 6.0, 54.0]</td>\n",
       "      <td>[1.2578144778853295, 1.5875529842781848, 0.079...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Majesco Entertainment</td>\n",
       "      <td>DS</td>\n",
       "      <td>Platform</td>\n",
       "      <td>79</td>\n",
       "      <td>8</td>\n",
       "      <td>0.06</td>\n",
       "      <td>DreamRift</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>[6.0, 5.0, 911.0, 37.0, 79.0]</td>\n",
       "      <td>[1.5093773734623954, 1.5875529842781848, 3.021...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sega</td>\n",
       "      <td>Wii</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>58</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>Polygon Magic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[5.0, 7.0, 425.0, 6.0, 58.0]</td>\n",
       "      <td>[1.2578144778853295, 2.222574177989459, 1.4095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SouthPeak Games</td>\n",
       "      <td>DS</td>\n",
       "      <td>Action</td>\n",
       "      <td>45</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Red Fly Studio</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[6.0, 0.0, 245.0, 38.0, 45.0]</td>\n",
       "      <td>[1.5093773734623954, 0.0, 0.8125785631651288, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Namco Bandai Games</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Action</td>\n",
       "      <td>71</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.66</td>\n",
       "      <td>Omega Force</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>[2.0, 0.0, 7.0, 9.0, 71.0]</td>\n",
       "      <td>[0.5031257911541318, 0.0, 0.023216530376146537...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ubisoft</td>\n",
       "      <td>PS2</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>71</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Game Arts</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[0.0, 3.0, 131.0, 1.0, 71.0]</td>\n",
       "      <td>[0.0, 0.952531790566911, 0.43448078275359947, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Konami Digital Entertainment</td>\n",
       "      <td>PS3</td>\n",
       "      <td>Racing</td>\n",
       "      <td>67</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.08</td>\n",
       "      <td>Isopod Labs</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[2.0, 4.0, 743.0, 8.0, 67.0]</td>\n",
       "      <td>[0.5031257911541318, 1.270042387422548, 2.4642...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Publisher Platform         Genre  Critic_Score  \\\n",
       "0         Universal Interactive      PS2       Shooter            75   \n",
       "1          Nippon Ichi Software      PS2  Role-Playing            69   \n",
       "2                    Activision      PS3        Action            77   \n",
       "3                          Sega      Wii      Platform            54   \n",
       "4         Majesco Entertainment       DS      Platform            79   \n",
       "5                          Sega      Wii      Fighting            58   \n",
       "6               SouthPeak Games       DS        Action            45   \n",
       "7            Namco Bandai Games      PS3        Action            71   \n",
       "8                       Ubisoft      PS2  Role-Playing            71   \n",
       "9  Konami Digital Entertainment      PS3        Racing            67   \n",
       "\n",
       "  User_Score  Global_Sales         Developer  platform_index  genre_index  \\\n",
       "0        8.5          0.42  Terminal Reality             0.0          2.0   \n",
       "1        7.5          0.06              Gust             0.0          3.0   \n",
       "2        6.3          1.06      Toys for Bob             2.0          0.0   \n",
       "3        6.9          1.09        Sonic Team             5.0          5.0   \n",
       "4          8          0.06         DreamRift             6.0          5.0   \n",
       "5        7.5          0.32     Polygon Magic             5.0          7.0   \n",
       "6        7.3          0.11    Red Fly Studio             6.0          0.0   \n",
       "7        7.8          0.66       Omega Force             2.0          0.0   \n",
       "8        8.6          0.29         Game Arts             0.0          3.0   \n",
       "9        6.8          0.08       Isopod Labs             2.0          4.0   \n",
       "\n",
       "   dev_index  publisher_index                       features  \\\n",
       "0       53.0             41.0   [0.0, 2.0, 53.0, 41.0, 75.0]   \n",
       "1      101.0             26.0  [0.0, 3.0, 101.0, 26.0, 69.0]   \n",
       "2       83.0              2.0    [2.0, 0.0, 83.0, 2.0, 77.0]   \n",
       "3       24.0              6.0    [5.0, 5.0, 24.0, 6.0, 54.0]   \n",
       "4      911.0             37.0  [6.0, 5.0, 911.0, 37.0, 79.0]   \n",
       "5      425.0              6.0   [5.0, 7.0, 425.0, 6.0, 58.0]   \n",
       "6      245.0             38.0  [6.0, 0.0, 245.0, 38.0, 45.0]   \n",
       "7        7.0              9.0     [2.0, 0.0, 7.0, 9.0, 71.0]   \n",
       "8      131.0              1.0   [0.0, 3.0, 131.0, 1.0, 71.0]   \n",
       "9      743.0              8.0   [2.0, 4.0, 743.0, 8.0, 67.0]   \n",
       "\n",
       "                                      scaledFeatures  \n",
       "0  [0.0, 0.635021193711274, 0.17578230141939521, ...  \n",
       "1  [0.0, 0.952531790566911, 0.33498136685582863, ...  \n",
       "2  [0.5031257911541318, 0.0, 0.2752817173171661, ...  \n",
       "3  [1.2578144778853295, 1.5875529842781848, 0.079...  \n",
       "4  [1.5093773734623954, 1.5875529842781848, 3.021...  \n",
       "5  [1.2578144778853295, 2.222574177989459, 1.4095...  \n",
       "6  [1.5093773734623954, 0.0, 0.8125785631651288, ...  \n",
       "7  [0.5031257911541318, 0.0, 0.023216530376146537...  \n",
       "8  [0.0, 0.952531790566911, 0.43448078275359947, ...  \n",
       "9  [0.5031257911541318, 1.270042387422548, 2.4642...  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.ml.feature import StandardScaler\n",
    "\n",
    "scaler = StandardScaler(inputCol=\"features\", outputCol=\"scaledFeatures\")\n",
    "prep_sales_data = scaler.fit(prep_sales_data).transform(prep_sales_data)\n",
    "prep_sales_data.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now build the regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-0.039236268796005246,-0.05462895697496037,-0.03154225497667445,-0.12689658291430533,0.43303113836918566]\n",
      "Intercept: -1.2377963184125798\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.regression import LinearRegression\n",
    "\n",
    "train, test = prep_sales_data.randomSplit([.8,.2], seed=0)\n",
    "lr = LinearRegression(featuresCol='scaledFeatures', labelCol='Global_Sales')\n",
    "\n",
    "lrModel = lr.fit(train)\n",
    "\n",
    "print(\"Coefficients: %s\" % str(lrModel.coefficients))\n",
    "print(\"Intercept: %s\" % str(lrModel.intercept))\n",
    "trainingSummary = lrModel.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numIterations: 1\n",
      "objectiveHistory: [0.0]\n",
      "RMSE: 1.901000\n",
      "r2: 0.060346\n"
     ]
    }
   ],
   "source": [
    "print(\"numIterations: %d\" % trainingSummary.totalIterations)\n",
    "print(\"objectiveHistory: %s\" % str(trainingSummary.objectiveHistory))\n",
    "print(\"RMSE: %f\" % trainingSummary.rootMeanSquaredError)\n",
    "print(\"r2: %f\" % trainingSummary.r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>scaledFeatures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.440403</td>\n",
       "      <td>0.04</td>\n",
       "      <td>[0.7546886867311977, 3.492616565412007, 3.5488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.941585</td>\n",
       "      <td>0.03</td>\n",
       "      <td>[0.7546886867311977, 2.8575953717007327, 1.913...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.807613</td>\n",
       "      <td>0.10</td>\n",
       "      <td>[0.0, 2.222574177989459, 0.2852316589069432, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.010827</td>\n",
       "      <td>0.33</td>\n",
       "      <td>[2.0125031646165272, 0.635021193711274, 0.0696...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.021686</td>\n",
       "      <td>0.12</td>\n",
       "      <td>[2.0125031646165272, 0.317510596855637, 0.2752...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.025869</td>\n",
       "      <td>0.03</td>\n",
       "      <td>[0.7546886867311977, 0.0, 0.27859836451375847,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.094346</td>\n",
       "      <td>0.11</td>\n",
       "      <td>[0.7546886867311977, 0.0, 0.08623282711140143,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.412898</td>\n",
       "      <td>5.14</td>\n",
       "      <td>[0.7546886867311977, 0.952531790566911, 0.2089...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.501036</td>\n",
       "      <td>0.46</td>\n",
       "      <td>[3.2703176425018565, 1.270042387422548, 0.7064...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.861644</td>\n",
       "      <td>1.86</td>\n",
       "      <td>[0.0, 1.5875529842781848, 0.7064458528741732, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prediction  Global_Sales                                     scaledFeatures\n",
       "0    0.440403          0.04  [0.7546886867311977, 3.492616565412007, 3.5488...\n",
       "1    0.941585          0.03  [0.7546886867311977, 2.8575953717007327, 1.913...\n",
       "2    0.807613          0.10  [0.0, 2.222574177989459, 0.2852316589069432, 0...\n",
       "3    1.010827          0.33  [2.0125031646165272, 0.635021193711274, 0.0696...\n",
       "4    1.021686          0.12  [2.0125031646165272, 0.317510596855637, 0.2752...\n",
       "5    1.025869          0.03  [0.7546886867311977, 0.0, 0.27859836451375847,...\n",
       "6    1.094346          0.11  [0.7546886867311977, 0.0, 0.08623282711140143,...\n",
       "7    1.412898          5.14  [0.7546886867311977, 0.952531790566911, 0.2089...\n",
       "8    0.501036          0.46  [3.2703176425018565, 1.270042387422548, 0.7064...\n",
       "9    0.861644          1.86  [0.0, 1.5875529842781848, 0.7064458528741732, ..."
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_predictions = lrModel.transform(test)\n",
    "lr_predictions.select(['prediction', 'Global_Sales', 'scaledFeatures']).limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the model's performance on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared (R2) on test data = 0.0751742\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "\n",
    "lr_evaluator = RegressionEvaluator(predictionCol=\"prediction\", \\\n",
    "                 labelCol=\"Global_Sales\",metricName=\"r2\")\n",
    "\n",
    "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very bad performance, try with less features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Squared (R2) on test data = 0.0698762\n"
     ]
    }
   ],
   "source": [
    "assembler = VectorAssembler(inputCols=['platform_index', 'genre_index',\n",
    "                                       'Critic_Score'],\n",
    "                            outputCol='features')\n",
    "prep_sales_data = assembler.transform(sales_data)\n",
    "\n",
    "scaler = StandardScaler(inputCol=\"features\", outputCol=\"scaledFeatures\")\n",
    "prep_sales_data = scaler.fit(prep_sales_data).transform(prep_sales_data)\n",
    "prep_sales_data.limit(10).toPandas()\n",
    "\n",
    "train, test = prep_sales_data.randomSplit([.8,.2], seed=0)\n",
    "lr = LinearRegression(featuresCol='scaledFeatures', labelCol='Global_Sales')\n",
    "\n",
    "lrModel = lr.fit(train)\n",
    "\n",
    "lr_evaluator = RegressionEvaluator(predictionCol=\"prediction\", \\\n",
    "                 labelCol=\"Global_Sales\",metricName=\"r2\")\n",
    "\n",
    "print(\"R Squared (R2) on test data = %g\" % lr_evaluator.evaluate(lr_predictions))"
   ]
  }
 ],
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