Predicting football Results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Football Results using Team Performance using pySpark\n",
    "\n",
    "We are predicting  2020/21 football results of top 5 leagues using  2019/20 results and finding which model predicts the results with high accuracy. \n",
    "\n",
    "### Tools used\n",
    "\n",
    "- MongoDb for storing and retrieving the results of top 5 leagues\n",
    "- PySpark for applying various Machine Learning algorithms on our data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyspark in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (3.2.0)\n",
      "Requirement already satisfied: py4j==0.10.9.2 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from pyspark) (0.10.9.2)\n"
     ]
    }
   ],
   "source": [
    "#installing dependencies\n",
    "!pip install pyspark\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sparksql-magic in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (0.0.3)\n",
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      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from jedi>=0.16->ipython>=7.4.0->sparksql-magic) (0.8.2)\n",
      "Requirement already satisfied: wcwidth in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=7.4.0->sparksql-magic) (0.2.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install sparksql-magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pymongo in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (3.12.1)\n",
      "Requirement already satisfied: pandas in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (1.3.4)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from pandas) (1.21.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pymongo\n",
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = pymongo.MongoClient(\"mongodb://localhost:27017\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset and storing them in MongoDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = client[\"LeagueStats\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'LeagueStats')\n"
     ]
    }
   ],
   "source": [
    "print(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x23b495c5f48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.PL2021.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x23b490e27c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing the data in csv files to mongodb\n",
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\PL2020-21.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.PL2021.insert_many(data)\n",
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\PL2019-20.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.PL2020.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x23b496b9c88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\Bundesliga2020-21.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.Bundesliga2021.insert_many(data)\n",
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\Bundesliga2019-20.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.Bundesliga2020.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x23b49848fc8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\SerieA2020-21.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.SerieA2021.insert_many(data)\n",
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\SerieA2019-20.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.SerieA2020.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x23b49703908>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\LaLiga2020-21.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.LaLiga2021.insert_many(data)\n",
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\LaLiga2019-20.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.LaLiga2020.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.InsertManyResult at 0x23b4944f848>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\Ligue2020-21.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.Ligue2021.insert_many(data)\n",
    "df = pd.read_csv(r\"C:\\Users\\Aswath\\Desktop\\Big Data Pack\\Datas\\Ligue2019-20.csv\")\n",
    "data = df.to_dict(orient=\"records\")\n",
    "db.Ligue2020.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "my_spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName(\"myApp\") \\\n",
    "    .master('local')\\\n",
    "    .config(\"spark.mongodb.input.uri\", \"mongodb://127.0.0.1/LeagueStats.PL2020\") \\\n",
    "    .config(\"spark.mongodb.output.uri\", \"mongodb://127.0.0.1/LeagueStats.PL2020\") \\\n",
    "    .config(\"spark.jars.packages\", \"org.mongodb.spark:mongo-spark-connector_2.12:3.0.1\")\\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = my_spark.read.format(\"com.mongodb.spark.sql.DefaultSource\").load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you encounter problem while connecting pyspark with mongoDB\n",
    "- Download Java 8 jdk\n",
    "- In your Spark shell, run this command \"bin\\pyspark --packages org.mongodb.spark:mongo-spark-connector_2.11:2.2.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (3.4.3)\n",
      "Requirement already satisfied: numpy>=1.16 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from matplotlib) (1.21.3)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from matplotlib) (0.11.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from matplotlib) (1.3.2)\n",
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      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from matplotlib) (8.4.0)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aswath\\Anaconda3\\envs\\py38\\lib\\site-packages\\pyspark\\sql\\pandas\\conversion.py:202: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[column_name] = series\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='FTHG', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='FTHG'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='FTAG'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='HST'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='AST'>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install matplotlib\n",
    "#to find the relation between two variables we use Scatter matrix\n",
    "#It can be used to determine whether the variables are correlated and whether the correlation is positive or negative\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "#Premier League \n",
    "pandas_df = df.toPandas()\n",
    "scatter_matrix(pandas_df[['FTHG','FTAG','FTR','HST','AST']], figsize=(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aswath\\Anaconda3\\envs\\py38\\lib\\site-packages\\pyspark\\sql\\pandas\\conversion.py:202: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[column_name] = series\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='FTHG', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='FTHG'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='FTAG'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='HST'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='AST'>]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Laliga\n",
    "df= my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.LaLiga2020\").load()\n",
    "pandas_df = df.toPandas()\n",
    "scatter_matrix(pandas_df[['FTHG','FTAG','FTR','HST','AST']], figsize=(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='FTHG', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='FTHG'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='FTHG'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='FTAG'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='FTAG'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='HST'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='HST'>],\n",
       "       [<AxesSubplot:xlabel='FTHG', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='FTAG', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='HST', ylabel='AST'>,\n",
       "        <AxesSubplot:xlabel='AST', ylabel='AST'>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Serie-A\n",
    "df= my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.SerieA2020\").load()\n",
    "pandas_df = df.toPandas()\n",
    "scatter_matrix(pandas_df[['FTHG','FTAG','FTR','HST','AST']], figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Scatter Matrices,We can observe that\n",
    "- The Scatter Graph between HST (Home Shot on Target) and FTHG (Full Time Home Goal) has a positive slope (positive Correlation)\n",
    "- The Scatter Graph between AST (Away Shot on Target) and FTAG (Full Time Away Goal) has a positive slope (positive Correlation)\n",
    "- The Scatter Graph between AST (Away Shot on Target) and HST (Home Shot on Target) has a negative slope (negative Correlation)\n",
    "\n",
    "\n",
    "So, we can understand that HST , FTHG , AST and FTAG are some factors influencing a match's result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting scores through Poisson Regression\n",
    "\n",
    "Poisson distribution can be used to measure the probability of independent events occurring a certain number of times within a set period - such as the number of goals scored in a football match.\n",
    "\n",
    "We do not need information about division,date and referee from various companies for this method.We just need team's attacking strength and defensive strength for prediction.Poisson regression helps us to predict a fixture.We can simulate for all fixtures \n",
    "to get the points table for the season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating Home Attacking Strength,Home Defensive Strength,Away Attacking Strength,Away Defensive Strength of Each Team\n",
    "def calculate_AS_DS(df): \n",
    "    \n",
    "    no_of_teams = df.select('HomeTeam').distinct().count()\n",
    "    \n",
    "    avg_home_goal_scored = df.groupBy().sum('FTHG').collect()[0][0]/((no_of_teams-1)*no_of_teams)\n",
    "    avg_away_goal_scored = df.groupBy().sum('FTAG').collect()[0][0]/((no_of_teams-1)*no_of_teams)\n",
    "\n",
    "    avg_home_goal_conceded = avg_away_goal_scored\n",
    "    avg_away_goal_conceded = avg_home_goal_scored\n",
    "\n",
    "    #calculating home attacking strength\n",
    "    avg_has = df.groupBy('HomeTeam').sum('FTHG')\n",
    "    avg_has = avg_has.withColumnRenamed('sum(FTHG)','HGS')\n",
    "    avg_has = avg_has.withColumn('HAS',avg_has['HGS']/((no_of_teams-1)*avg_home_goal_scored))\n",
    "\n",
    "    #calculating home defensive strength\n",
    "    avg_hds = df.groupBy('HomeTeam').sum('FTAG')\n",
    "    avg_hds = avg_hds.withColumnRenamed('sum(FTAG)','HGC')\n",
    "    avg_hds = avg_hds.withColumnRenamed('HomeTeam','Team')\n",
    "    avg_hds = avg_hds.withColumn('HDS',avg_hds['HGC']/((no_of_teams-1)*avg_home_goal_conceded))\n",
    "\n",
    "    #appending avg_hds with avg_has\n",
    "    avg_has=avg_has.join(avg_hds,avg_has.HomeTeam == avg_hds.Team,\"inner\")\n",
    "    avg_has=avg_has.drop('Team')\n",
    "\n",
    "    #calculating away attacking strength\n",
    "    avg_aas = df.groupBy('AwayTeam').sum('FTAG')\n",
    "    avg_aas = avg_aas.withColumnRenamed('sum(FTAG)','AGS')\n",
    "    avg_aas = avg_aas.withColumn('AAS',avg_aas['AGS']/((no_of_teams-1)*avg_away_goal_scored))\n",
    "\n",
    "    #appending avg_aas with avg_has\n",
    "    avg_has=avg_has.join(avg_aas,avg_has.HomeTeam == avg_aas.AwayTeam,\"inner\")\n",
    "    avg_has=avg_has.drop('AwayTeam')\n",
    "\n",
    "    #calculating away defensive strength\n",
    "    avg_ads = df.groupBy('AwayTeam').sum('FTHG')\n",
    "    avg_ads = avg_ads.withColumnRenamed('sum(FTHG)','AGC')\n",
    "    avg_ads = avg_ads.withColumnRenamed('AwayTeam','Team')\n",
    "    avg_ads = avg_ads.withColumn('ADS',avg_ads['AGC']/((no_of_teams-1)*avg_away_goal_conceded))\n",
    "\n",
    "    #appending avg_ads with avg_has and renaming avg_has as Team_Stats\n",
    "    avg_has=avg_has.join(avg_ads,avg_has.HomeTeam == avg_ads.Team,\"inner\")\n",
    "    Team_Stats=avg_has.drop('Team')\n",
    "    Team_Stats = Team_Stats.withColumnRenamed('HomeTeam','Team')\n",
    "    return Team_Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (1.7.1)\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (from scipy) (1.21.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy\n",
    "import scipy.stats as spy\n",
    "def expected_goals(xG):\n",
    "    ret = []\n",
    "    for i in range(0,6):\n",
    "        pmf = spy.poisson.pmf(i,xG)\n",
    "        ret.append(pmf)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GoalsScored(df):\n",
    "    no_of_teams = df.select('HomeTeam').distinct().count()\n",
    "    avg_home_goal_scored = df.groupBy().sum('FTHG').collect()[0][0]/((no_of_teams-1)*no_of_teams)\n",
    "    avg_away_goal_scored = df.groupBy().sum('FTAG').collect()[0][0]/((no_of_teams-1)*no_of_teams)\n",
    "    return avg_home_goal_scored,avg_away_goal_scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabulate import tabulate\n",
    "def ComputePoissonDistribution(h_team,a_team,ts,ahgs,aags):\n",
    "    h_team_hs = ts.filter(ts['Team']==h_team).select([\"HAS\",\"HDS\"])\n",
    "    a_team_as = ts.filter(ts['Team']==a_team).select([\"AAS\",\"ADS\"])\n",
    "    \n",
    "    hxG = h_team_hs.collect()[0][0]*a_team_as.collect()[0][1]*ahgs\n",
    "    axG = a_team_as.collect()[0][0]*h_team_hs.collect()[0][1]*aags\n",
    "    \n",
    "    expected_hg = expected_goals(hxG)\n",
    "    expected_ag = expected_goals(axG)\n",
    "\n",
    "    lst=[]\n",
    "    for i in range(len(expected_hg)):\n",
    "        l=[i]\n",
    "        for j in range(len(expected_ag)):\n",
    "            l.append(expected_hg[i]*expected_ag[j])\n",
    "        lst.append(l)\n",
    "    print(\"Probabilites of Each Score(row headers are away team goals and column headers are home team goals)\\n\")     \n",
    "    print(tabulate(lst,headers = [\" \",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"],tablefmt='orgtbl'))\n",
    "    \n",
    "    drawPercent = 0\n",
    "    HomeWinPercent = 0\n",
    "    AwayWinPercent = 0\n",
    "\n",
    "    for i in range(0,len(lst)):\n",
    "        for j in range(1,len(lst[0])):\n",
    "            if(i==j):\n",
    "                drawPercent = drawPercent+lst[i][j]\n",
    "            elif i>j:\n",
    "                AwayWinPercent = AwayWinPercent+lst[i][j]\n",
    "            else:\n",
    "                HomeWinPercent = HomeWinPercent+lst[i][j]\n",
    "\n",
    "    print(\"\\nPercentage of  Draw : \",drawPercent*100,\"\\nPercentage of \",h_team,\" Win : \",HomeWinPercent*100,\"\\nPercentage of \",a_team,\" Win : \",AwayWinPercent*100)\n",
    "\n",
    "    print(\"\\n\")\n",
    "    if(drawPercent > max(HomeWinPercent,AwayWinPercent)):\n",
    "        print(\"Draw\")\n",
    "    elif (HomeWinPercent>AwayWinPercent):\n",
    "        print(h_team,\" wins\")\n",
    "    else:\n",
    "        print(a_team,\" wins\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilites of Each Score(row headers are away team goals and column headers are home team goals)\n",
      "\n",
      "|     |          0 |         1 |          2 |          3 |          4 |           5 |\n",
      "|-----+------------+-----------+------------+------------+------------+-------------|\n",
      "|   0 | 0.0366715  | 0.0501821 | 0.0343351  | 0.0156616  | 0.00535793 | 0.00146638  |\n",
      "|   1 | 0.071045   | 0.0972194 | 0.0665186  | 0.0303418  | 0.0103801  | 0.00284087  |\n",
      "|   2 | 0.0688189  | 0.0941733 | 0.0644343  | 0.0293911  | 0.0100548  | 0.00275185  |\n",
      "|   3 | 0.0444417  | 0.060815  | 0.0416103  | 0.0189801  | 0.0064932  | 0.00177709  |\n",
      "|   4 | 0.0215246  | 0.0294547 | 0.0201532  | 0.00919271 | 0.00314487 | 0.000860702 |\n",
      "|   5 | 0.00834007 | 0.0114127 | 0.00780871 | 0.00356187 | 0.00121853 | 0.000333493 |\n",
      "\n",
      "Percentage of  Draw :  21.72397383716124 \n",
      "Percentage of  Man City  Win :  48.91970531278666 \n",
      "Percentage of  Liverpool  Win :  27.633162314131525\n",
      "\n",
      "\n",
      "Man City  wins\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.PL2021\").load()\n",
    "ahgs,aags = GoalsScored(df)\n",
    "ComputePoissonDistribution(\"Man City\",\"Liverpool\",calculate_AS_DS(df),ahgs,aags)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilites of Each Score(row headers are away team goals and column headers are home team goals)\n",
      "\n",
      "|     |          0 |         1 |          2 |          3 |           4 |           5 |\n",
      "|-----+------------+-----------+------------+------------+-------------+-------------|\n",
      "|   0 | 0.082236   | 0.106556  | 0.069034   | 0.0298166  | 0.00965858  | 0.00250299  |\n",
      "|   1 | 0.0988829  | 0.128126  | 0.0830085  | 0.0358523  | 0.0116138   | 0.00300967  |\n",
      "|   2 | 0.0594498  | 0.0770312 | 0.0499059  | 0.0215549  | 0.00698236  | 0.00180946  |\n",
      "|   3 | 0.0238281  | 0.0308748 | 0.0200028  | 0.00863942 | 0.0027986   | 0.000725248 |\n",
      "|   4 | 0.00716289 | 0.0092812 | 0.00601298 | 0.00259707 | 0.000841279 | 0.000218015 |\n",
      "|   5 | 0.00172257 | 0.002232  | 0.00144604 | 0.00062456 | 0.000202316 | 5.24294e-05 |\n",
      "\n",
      "Percentage of  Draw :  19.871623301289087 \n",
      "Percentage of  Real Madrid  Win :  65.49417934745146 \n",
      "Percentage of  Barcelona  Win :  14.263499847547365\n",
      "\n",
      "\n",
      "Real Madrid  wins\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.LaLiga2021\").load()\n",
    "ahgs,aags = GoalsScored(df)\n",
    "ComputePoissonDistribution(\"Real Madrid\",\"Barcelona\",calculate_AS_DS(df),ahgs,aags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilites of Each Score(row headers are away team goals and column headers are home team goals)\n",
      "\n",
      "|     |          0 |          1 |          2 |           3 |           4 |           5 |\n",
      "|-----+------------+------------+------------+-------------+-------------+-------------|\n",
      "|   0 | 0.0892127  | 0.112274   | 0.0706481  | 0.0296368   | 0.00932446  | 0.00234696  |\n",
      "|   1 | 0.103329   | 0.13004    | 0.0818272  | 0.0343264   | 0.0107999   | 0.00271833  |\n",
      "|   2 | 0.0598398  | 0.0753082  | 0.0473876  | 0.019879    | 0.00625443  | 0.00157423  |\n",
      "|   3 | 0.0231029  | 0.0290749  | 0.0182953  | 0.00767487  | 0.0024147   | 0.000607778 |\n",
      "|   4 | 0.00668964 | 0.00841889 | 0.00529757 | 0.00222233  | 0.000699197 | 0.000175988 |\n",
      "|   5 | 0.00154964 | 0.00195021 | 0.00122717 | 0.000514795 | 0.000161967 | 4.0767e-05  |\n",
      "\n",
      "Percentage of  Draw :  19.93170981217693 \n",
      "Percentage of  Juventus  Win :  65.98629054138574 \n",
      "Percentage of  Inter  Win :  13.766545892425706\n",
      "\n",
      "\n",
      "Juventus  wins\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.SerieA2021\").load()\n",
    "ahgs,aags = GoalsScored(df)\n",
    "ComputePoissonDistribution(\"Juventus\",\"Inter\",calculate_AS_DS(df),ahgs,aags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilites of Each Score(row headers are away team goals and column headers are home team goals)\n",
      "\n",
      "|     |          0 |          1 |          2 |          3 |          4 |           5 |\n",
      "|-----+------------+------------+------------+------------+------------+-------------|\n",
      "|   0 | 0.00494324 | 0.00926989 | 0.00869175 | 0.00543312 | 0.00254713 | 0.000955311 |\n",
      "|   1 | 0.0169774  | 0.0318371  | 0.0298516  | 0.0186599  | 0.00874805 | 0.00328098  |\n",
      "|   2 | 0.0291542  | 0.0546718  | 0.0512621  | 0.0320434  | 0.0150225  | 0.00563422  |\n",
      "|   3 | 0.0333764  | 0.0625896  | 0.058686   | 0.036684   | 0.017198   | 0.00645018  |\n",
      "|   4 | 0.0286575  | 0.0537405  | 0.0503888  | 0.0314975  | 0.0147665  | 0.00553824  |\n",
      "|   5 | 0.0196847  | 0.036914   | 0.0346118  | 0.0216354  | 0.010143   | 0.00380418  |\n",
      "\n",
      "Percentage of  Draw :  17.197578515008736 \n",
      "Percentage of  Bayern Munich  Win :  31.26213142507584 \n",
      "Percentage of  Dortmund  Win :  37.07527741038135\n",
      "\n",
      "\n",
      "Dortmund  wins\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.Bundesliga2021\").load()\n",
    "ahgs,aags = GoalsScored(df)\n",
    "ComputePoissonDistribution(\"Bayern Munich\",\"Dortmund\",calculate_AS_DS(df),ahgs,aags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilites of Each Score(row headers are away team goals and column headers are home team goals)\n",
      "\n",
      "|     |          0 |          1 |           2 |           3 |           4 |           5 |\n",
      "|-----+------------+------------+-------------+-------------+-------------+-------------|\n",
      "|   0 | 0.126076   | 0.129125   | 0.0661239   | 0.0225743   | 0.00578005  | 0.00118396  |\n",
      "|   1 | 0.131962   | 0.135153   | 0.0692109   | 0.0236282   | 0.00604989  | 0.00123924  |\n",
      "|   2 | 0.0690614  | 0.0707315  | 0.036221    | 0.0123656   | 0.00316616  | 0.000648545 |\n",
      "|   3 | 0.0240952  | 0.0246779  | 0.0126373   | 0.0043143   | 0.00110466  | 0.000226274 |\n",
      "|   4 | 0.00630501 | 0.00645748 | 0.00330682  | 0.00112893  | 0.000289057 | 5.92094e-05 |\n",
      "|   5 | 0.00131987 | 0.00135179 | 0.000692239 | 0.000236326 | 6.05103e-05 | 1.23947e-05 |\n",
      "\n",
      "Percentage of  Draw :  21.652053768166212 \n",
      "Percentage of  Paris SG  Win :  64.45527147040131 \n",
      "Percentage of  Lille  Win :  13.750402118194813\n",
      "\n",
      "\n",
      "Paris SG  wins\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.Ligue2021\").load()\n",
    "ahgs,aags = GoalsScored(df)\n",
    "ComputePoissonDistribution(\"Paris SG\",\"Lille\",calculate_AS_DS(df),ahgs,aags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction of the final result using Classification Algorithms using Machine Learning \n",
    "\n",
    "Instead of trying to predict the scores in a given fixture, we can try to predict the final outcome(home win, draw or away win).The objective would be to compare a model between different leagues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#computing previous year points table for labelling the teams.Why Iam not going for other encoders is,Each year teams \n",
    "# in 18,19,20 places gets relegated and new Teams come in their position\n",
    "\n",
    "from pyspark.sql.types import StructType,StructField, StringType, IntegerType\n",
    "\n",
    "def ComputePointsTable(df):\n",
    "    Team_Names=df.select('HomeTeam').distinct().collect()\n",
    "    no_of_teams = len(Team_Names)\n",
    "    pointsTable = []\n",
    "    for team in Team_Names:\n",
    "        wins = df.filter(((df['HomeTeam']==team.HomeTeam)&(df['FTR']=='H'))|((df['AwayTeam']==team.HomeTeam)&(df['FTR']=='A'))).count() \n",
    "        draws = df.filter(((df['HomeTeam']==team.HomeTeam)&(df['FTR']=='D'))|((df['AwayTeam']==team.HomeTeam)&(df['FTR']=='D'))).count()\n",
    "        loses = 2*(no_of_teams-1)-wins-draws\n",
    "        points = wins*3+draws\n",
    "        tup = (team.HomeTeam,wins,draws,loses,points)\n",
    "        pointsTable.append(tup)\n",
    "\n",
    "    schema = StructType([ \\\n",
    "        StructField(\"TeamName\",StringType(),True), \\\n",
    "        StructField(\"Wins\",IntegerType(),True), \\\n",
    "        StructField(\"Draws\",IntegerType(),True), \\\n",
    "        StructField(\"Loses\",IntegerType(), True), \\\n",
    "        StructField(\"Points\",IntegerType(), True), \\\n",
    "      ])\n",
    "\n",
    "    Team_df = my_spark.createDataFrame(data=pointsTable,schema=schema)\n",
    "    Team_df = Team_df.sort(Team_df.Points.desc())\n",
    "    labelling_team = {}\n",
    "    lst=[]\n",
    "    k=1\n",
    "    for i in Team_df.collect():\n",
    "        labelling_team[i.TeamName]=k\n",
    "        k+=1\n",
    "    return Team_df,labelling_team"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Home Attacking Strength,Home Defensive Strength,Away Attacking Strength,Away Defensive Strength of Each Team\n",
    "can be done using calculate_AS_DS() declared above\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing our dataframe for performing ML Algorithms\n",
    "from pyspark.sql import functions as f\n",
    "from pyspark.sql.types import FloatType\n",
    "\n",
    "def preparingForTraining(Team_Stats,labelling_team,df):\n",
    "    \n",
    "    team_Dict={}\n",
    "    for i in Team_Stats.collect():\n",
    "        team_Dict[i[0]]=(i[2],i[4],i[6],i[8])\n",
    "    \n",
    "    data = []\n",
    "\n",
    "    for i in df.select(['HomeTeam','AwayTeam','HST','AST','FTR']).collect():\n",
    "        lst=[]\n",
    "        lst.append(labelling_team[i[0]])\n",
    "        lst.append(labelling_team[i[1]])\n",
    "        lst.append(team_Dict[i[0]][0])\n",
    "        lst.append(team_Dict[i[0]][1])\n",
    "        lst.append(team_Dict[i[1]][2])\n",
    "        lst.append(team_Dict[i[1]][3])\n",
    "        lst.append(i[2])\n",
    "        lst.append(i[3])\n",
    "        lst.append(i[4])\n",
    "        data.append(tuple(lst))\n",
    "\n",
    "\n",
    "    \n",
    "    schema = StructType([ \\\n",
    "        StructField(\"HT\",IntegerType(),True), \\\n",
    "        StructField(\"AT\",IntegerType(),True), \\\n",
    "        StructField(\"HAS\",FloatType(),True), \\\n",
    "        StructField(\"HDS\",FloatType(), True), \\\n",
    "        StructField(\"AAS\",FloatType(), True), \\\n",
    "        StructField(\"ADS\",FloatType(), True), \\\n",
    "        StructField(\"HST\",IntegerType(), True), \\\n",
    "        StructField(\"AST\",IntegerType(), True), \\\n",
    "        StructField(\"FTR\",StringType(), True), \\\n",
    "      ]) \n",
    "\n",
    "    Training_df = my_spark.createDataFrame(data=data,schema=schema) \n",
    "    Training_df=Training_df.withColumn('R',f.when((f.col('FTR') == 'H'),1).when((f.col('FTR') == 'A'),2).otherwise(0))\n",
    "    Training_df=Training_df.drop('FTR')\n",
    "    return Training_df\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "def VectorforTrainingSet(Training_df):\n",
    "    required_features = ['HT','AT','HAS','HDS','AAS','ADS','HST','AST']\n",
    "    vector_assembler = VectorAssembler(inputCols=required_features,outputCol = 'features')\n",
    "    vec_df= vector_assembler.transform(Training_df)\n",
    "    return vec_df,vector_assembler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vec_df data is trained using Decision tree classifier model\n",
    "from pyspark.ml.classification import DecisionTreeClassifier,RandomForestClassifier,NaiveBayes,LogisticRegression\n",
    "def retDecisionTreeModel(vec_df):\n",
    "    dt = DecisionTreeClassifier(featuresCol='features',labelCol = 'R',maxDepth = 4)\n",
    "    dt_Model = dt.fit(vec_df)\n",
    "    return dt_Model\n",
    "\n",
    "def retRandomForestModel(vec_df):\n",
    "    rf = RandomForestClassifier(featuresCol='features',labelCol = 'R')\n",
    "    rf_Model = rf.fit(vec_df)\n",
    "    return rf_Model\n",
    "\n",
    "def retNaiveBayesModel(vec_df):\n",
    "    nb = NaiveBayes()\n",
    "    vec_df=vec_df.withColumnRenamed('R','label')\n",
    "    Model = nb.fit(vec_df)\n",
    "    return Model\n",
    "\n",
    "def retLogisticRegressionModel(vec_df):\n",
    "    lr = LogisticRegression(featuresCol='features',labelCol='R')\n",
    "    lr_Model = lr.fit(vec_df)\n",
    "    return lr_Model\n",
    "\n",
    "#we need to generate test set(Next season) for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test set generation\n",
    "def generateTestSet(collection):\n",
    "    test_df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.\"+collection).load()\n",
    "    Team_Stats = calculate_AS_DS(test_df)\n",
    "    return test_df,Team_Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preparingForTesting(Team_Stats,test_df,labelling_team):\n",
    "    team_Dict={}\n",
    "    for i in Team_Stats.collect():\n",
    "        team_Dict[i[0]]=(i[2],i[4],i[6],i[8])\n",
    "    data = []\n",
    "\n",
    "    no_of_teams = test_df.select('HomeTeam').distinct().count()\n",
    "    k=0\n",
    "    for i in test_df.select(['HomeTeam','AwayTeam','HST','AST','FTR']).collect():\n",
    "        lst=[]\n",
    "        if labelling_team.get(i[0]) is not None:\n",
    "            lst.append(labelling_team[i[0]])\n",
    "        else:\n",
    "            labelling_team[i[0]]=(no_of_teams-2)+k\n",
    "            lst.append(labelling_team[i[0]])\n",
    "            k+=1\n",
    "        if labelling_team.get(i[1]) is not None:   \n",
    "            lst.append(labelling_team[i[1]])\n",
    "        else:\n",
    "            labelling_team[i[1]]=(no_of_teams-2)+k\n",
    "            lst.append(labelling_team[i[1]])\n",
    "            k+=1\n",
    "        lst.append(team_Dict[i[0]][0])\n",
    "        lst.append(team_Dict[i[0]][1])\n",
    "        lst.append(team_Dict[i[1]][2])\n",
    "        lst.append(team_Dict[i[1]][3])\n",
    "        lst.append(i[2])\n",
    "        lst.append(i[3])\n",
    "        data.append(tuple(lst))\n",
    "\n",
    "    \n",
    "    schema = StructType([ \\\n",
    "        StructField(\"HT\",IntegerType(),True), \\\n",
    "        StructField(\"AT\",IntegerType(),True), \\\n",
    "        StructField(\"HAS\",FloatType(),True), \\\n",
    "        StructField(\"HDS\",FloatType(), True), \\\n",
    "        StructField(\"AAS\",FloatType(), True), \\\n",
    "        StructField(\"ADS\",FloatType(), True), \\\n",
    "        StructField(\"HST\",IntegerType(), True), \\\n",
    "        StructField(\"AST\",IntegerType(), True), \\\n",
    "      ]) \n",
    "\n",
    "    Training_df = my_spark.createDataFrame(data=data,schema=schema) \n",
    "\n",
    "    return Training_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorForTesting(Training_df,vector_assembler):\n",
    "    vec_test_df= vector_assembler.transform(Training_df)\n",
    "    return vec_test_df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retPrediction(Model,vec_test_df):\n",
    "    y_pred_dt = Model.transform(vec_test_df)\n",
    "    y_pred_dt=y_pred_dt.withColumn('FTR',f.when(f.col('prediction') == 0 ,'D').when(f.col('prediction') ==1,'H').otherwise('A'))\n",
    "    return y_pred_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAccuracy(test_df,y_pred_dt):\n",
    "    Original_results=test_df.select('FTR').collect()\n",
    "    predicted_results = y_pred_dt.select('FTR').collect()\n",
    "    count=0\n",
    "    for i in range(len(predicted_results)):\n",
    "        if predicted_results[i][0] == Original_results[i][0]:\n",
    "            count+=1\n",
    "    Accuracy = count/380\n",
    "    return Accuracy\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcRMSE(actual,predicted):\n",
    "    rmse = 0\n",
    "    for i in range(len(actual)):\n",
    "        rmse += ((predicted[i][0]-actual[i][0])**2)\n",
    "    rmse = rmse/len(actual)\n",
    "    return (rmse**0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateActualTable(test_df):\n",
    "    #for calculating rmse first we need to compute PL2020-21 Points table\n",
    "    Team_Names=test_df.select('HomeTeam').distinct().collect()\n",
    "    no_of_teams = len(Team_Names)\n",
    "    pointsTable = []\n",
    "    for team in Team_Names:\n",
    "        wins = test_df.filter(((test_df['HomeTeam']==team.HomeTeam)&(test_df['FTR']=='H'))|((test_df['AwayTeam']==team.HomeTeam)&(test_df['FTR']=='A'))).count()\n",
    "        draws = test_df.filter(((test_df['HomeTeam']==team.HomeTeam)&(test_df['FTR']=='D'))|((test_df['AwayTeam']==team.HomeTeam)&(test_df['FTR']=='D'))).count()\n",
    "        loses = 2*(no_of_teams-1)-wins-draws\n",
    "        points = wins*3+draws\n",
    "        tup = (team.HomeTeam,wins,draws,loses,points)\n",
    "        pointsTable.append(tup)\n",
    "\n",
    "    schema = StructType([ \\\n",
    "        StructField(\"TeamName\",StringType(),True), \\\n",
    "        StructField(\"Wins\",IntegerType(),True), \\\n",
    "        StructField(\"Draws\",IntegerType(),True), \\\n",
    "        StructField(\"Loses\",IntegerType(), True), \\\n",
    "        StructField(\"Points\",IntegerType(), True), \\\n",
    "      ])\n",
    "\n",
    "    real_points_df = my_spark.createDataFrame(data=pointsTable,schema=schema)\n",
    "    return real_points_df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateRMSE(test_df,real_points_df,y_pred_dt,labelling_team):\n",
    "    \n",
    "    \n",
    "\n",
    "    #computing pl2020-21 table based on predicted results\n",
    "    pointsTable = []\n",
    "    Team_Names=test_df.select('HomeTeam').distinct().collect()\n",
    "    no_of_teams = len(Team_Names)\n",
    "    test_df = y_pred_dt.select(['HT','AT','FTR'])\n",
    "\n",
    "    for team in Team_Names:\n",
    "        team_no = labelling_team[team[0]]\n",
    "        wins = test_df.filter(((test_df['HT']== team_no)&(test_df['FTR']=='H'))|((test_df['AT']==team_no)&(test_df['FTR']=='A'))).count()    \n",
    "        draws = test_df.filter(((test_df['HT']==team_no)&(test_df['FTR']=='D'))|((test_df['AT']==team_no)&(test_df['FTR']=='D'))).count()\n",
    "        loses = 2*(no_of_teams-1)-wins-draws\n",
    "        points = wins*3+draws\n",
    "        tup = (team.HomeTeam,wins,draws,loses,points)\n",
    "        pointsTable.append(tup)\n",
    "    \n",
    "    schema = StructType([ \\\n",
    "        StructField(\"TeamName\",StringType(),True), \\\n",
    "        StructField(\"Wins\",IntegerType(),True), \\\n",
    "        StructField(\"Draws\",IntegerType(),True), \\\n",
    "        StructField(\"Loses\",IntegerType(), True), \\\n",
    "        StructField(\"Points\",IntegerType(), True), \\\n",
    "      ])\n",
    "    \n",
    "    predicted_points_df = my_spark.createDataFrame(data=pointsTable,schema=schema)\n",
    "    predicted_points_df.sort(predicted_points_df.Points.desc()).show()\n",
    "    \n",
    "    RMSE = calcRMSE(real_points_df.select('Points').collect(),predicted_points_df.select('Points').collect())\n",
    "\n",
    "    return RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeMLForLeagues(df,coll):\n",
    "    Team_df,labelling_team = ComputePointsTable(df)\n",
    "    Team_Stats = calculate_AS_DS(df)\n",
    "    Training_df = preparingForTraining(Team_Stats,labelling_team,df)\n",
    "    vec_df,vector_assembler = VectorforTrainingSet(Training_df)\n",
    "    DecisionTreeModel = retDecisionTreeModel(vec_df)\n",
    "    RandomForestModel = retRandomForestModel(vec_df)\n",
    "    NaiveBayesModel = retNaiveBayesModel(vec_df)\n",
    "    LogisticRegressionModel = retLogisticRegressionModel(vec_df)\n",
    "\n",
    "    test_df,Team_Stats = generateTestSet(coll)\n",
    "    Training_df = preparingForTesting(Team_Stats,test_df,labelling_team)\n",
    "    vec_test_df = vectorForTesting(Training_df,vector_assembler)\n",
    "    y_pred_dt = retPrediction(DecisionTreeModel,vec_test_df) \n",
    "    y_pred_rf = retPrediction(RandomForestModel,vec_test_df)\n",
    "    y_pred_nb = retPrediction(NaiveBayesModel,vec_test_df)\n",
    "    y_pred_lr = retPrediction(LogisticRegressionModel,vec_test_df)\n",
    "    Accuracy_dt = getAccuracy(test_df,y_pred_dt)\n",
    "    Accuracy_rf = getAccuracy(test_df,y_pred_rf)\n",
    "    Accuracy_nb = getAccuracy(test_df,y_pred_nb)\n",
    "    Accuracy_lr = getAccuracy(test_df,y_pred_lr)\n",
    "    actual_p_table = calculateActualTable(test_df)\n",
    "    print(\" 2020-21 Points table using DecisionTreeClassifier\")\n",
    "    rmse_dt = calculateRMSE(test_df,actual_p_table,y_pred_dt,labelling_team)\n",
    "    print(\" 2020-21 Points table using RandomForestClassifier\")\n",
    "    rmse_rf = calculateRMSE(test_df,actual_p_table,y_pred_rf,labelling_team)\n",
    "    print(\" 2020-21 Points table using NaiveBayesClassifier\")\n",
    "    rmse_nb = calculateRMSE(test_df,actual_p_table,y_pred_nb,labelling_team)\n",
    "    print(\" 2020-21 Points table using LogisticRegressionClassifier\")\n",
    "    rmse_lr = calculateRMSE(test_df,actual_p_table,y_pred_lr,labelling_team)\n",
    "    return Accuracy_dt,Accuracy_rf,Accuracy_nb,Accuracy_lr,rmse_dt,rmse_rf,rmse_nb,rmse_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Premier League\n",
    "\n",
    "### 2019-20 Points Table\n",
    "\n",
    "|        TeamName|Wins|Draws|Loses|Points| \n",
    "| --- | --- | --- | --- | --- |\n",
    "|       Liverpool|  32|    3|    3|    99|\n",
    "|        Man City|  26|    3|    9|    81| \n",
    "|      Man United|  18|   12|    8|    66| \n",
    "|         Chelsea|  20|    6|   12|    66| \n",
    "|       Leicester|  18|    8|   12|    62| \n",
    "|          Wolves|  15|   14|    9|    59| \n",
    "|       Tottenham|  16|   11|   11|    59| \n",
    "|         Arsenal|  14|   14|   10|    56| \n",
    "|Sheffield United|  14|   12|   12|    54| \n",
    "|         Burnley|  15|    9|   14|    54| \n",
    "|     Southampton|  15|    7|   16|    52| \n",
    "|         Everton|  13|   10|   15|    49| \n",
    "|       Newcastle|  11|   11|   16|    44| \n",
    "|  Crystal Palace|  11|   10|   17|    43| \n",
    "|        Brighton|   9|   14|   15|    41| \n",
    "|        West Ham|  10|    9|   19|    39| \n",
    "|     Aston Villa|   9|    8|   21|    35| \n",
    "|     Bournemouth|   9|    7|   22|    34| \n",
    "|         Watford|   8|   10|   20|    34| \n",
    "|         Norwich|   5|    6|   27|    21| \n",
    "\n",
    "### 2020-21 Actual Points Table\n",
    "\n",
    "\n",
    "|        TeamName|Wins|Draws|Loses|Points| \n",
    "| --- | --- | --- | --- | --- |\n",
    "|        Man City|  27|    5|    6|    86| \n",
    "|      Man United|  21|   11|    6|    74| \n",
    "|       Liverpool|  20|    9|    9|    69| \n",
    "|         Chelsea|  19|   10|    9|    67| \n",
    "|       Leicester|  20|    6|   12|    66| \n",
    "|        West Ham|  19|    8|   11|    65| \n",
    "|       Tottenham|  18|    8|   12|    62| \n",
    "|         Arsenal|  18|    7|   13|    61| \n",
    "|         Everton|  17|    8|   13|    59| \n",
    "|           Leeds|  18|    5|   15|    59| \n",
    "|     Aston Villa|  16|    7|   15|    55| \n",
    "|          Wolves|  12|    9|   17|    45| \n",
    "|       Newcastle|  12|    9|   17|    45| \n",
    "|  Crystal Palace|  12|    8|   18|    44| \n",
    "|     Southampton|  12|    7|   19|    43| \n",
    "|        Brighton|   9|   14|   15|    41| \n",
    "|         Burnley|  10|    9|   19|    39| \n",
    "|          Fulham|   5|   13|   20|    28| \n",
    "|       West Brom|   5|   11|   22|    26| \n",
    "|Sheffield United|   7|    2|   29|    23| "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2020-21 Points table using DecisionTreeClassifier\n",
      "+----------------+----+-----+-----+------+\n",
      "|        TeamName|Wins|Draws|Loses|Points|\n",
      "+----------------+----+-----+-----+------+\n",
      "|        Man City|  26|   10|    2|    88|\n",
      "|       Liverpool|  23|   12|    3|    81|\n",
      "|      Man United|  16|   20|    2|    68|\n",
      "|       Leicester|  16|   19|    3|    67|\n",
      "|         Chelsea|  16|   16|    6|    64|\n",
      "|         Everton|  11|   21|    6|    54|\n",
      "|         Arsenal|  10|   21|    7|    51|\n",
      "|     Southampton|  11|   18|    9|    51|\n",
      "|       Tottenham|  11|   17|   10|    50|\n",
      "|        Brighton|   8|   23|    7|    47|\n",
      "|          Wolves|   8|   23|    7|    47|\n",
      "|  Crystal Palace|   6|   24|    8|    42|\n",
      "|       Newcastle|   6|   22|   10|    40|\n",
      "|         Burnley|   7|   17|   14|    38|\n",
      "|        West Ham|   7|   16|   15|    37|\n",
      "|           Leeds|   8|   13|   17|    37|\n",
      "|     Aston Villa|   6|   13|   19|    31|\n",
      "|Sheffield United|   3|   21|   14|    30|\n",
      "|          Fulham|   5|   13|   20|    28|\n",
      "|       West Brom|   3|    7|   28|    16|\n",
      "+----------------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using RandomForestClassifier\n",
      "+----------------+----+-----+-----+------+\n",
      "|        TeamName|Wins|Draws|Loses|Points|\n",
      "+----------------+----+-----+-----+------+\n",
      "|        Man City|  33|    3|    2|   102|\n",
      "|      Man United|  28|    5|    5|    89|\n",
      "|       Liverpool|  29|    0|    9|    87|\n",
      "|         Chelsea|  26|    5|    7|    83|\n",
      "|       Leicester|  24|    6|    8|    78|\n",
      "|         Arsenal|  18|   11|    9|    65|\n",
      "|        West Ham|  19|    2|   17|    59|\n",
      "|       Tottenham|  17|    7|   14|    58|\n",
      "|         Everton|  16|    8|   14|    56|\n",
      "|          Wolves|  14|   11|   13|    53|\n",
      "|     Southampton|  15|    4|   19|    49|\n",
      "|         Burnley|  14|    7|   17|    49|\n",
      "|        Brighton|  13|    9|   16|    48|\n",
      "|           Leeds|  15|    3|   20|    48|\n",
      "|       Newcastle|  14|    2|   22|    44|\n",
      "|     Aston Villa|   9|    4|   25|    31|\n",
      "|  Crystal Palace|   8|    4|   26|    28|\n",
      "|Sheffield United|   7|    6|   25|    27|\n",
      "|          Fulham|   5|    7|   26|    22|\n",
      "|       West Brom|   4|    0|   34|    12|\n",
      "+----------------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using NaiveBayesClassifier\n",
      "+----------------+----+-----+-----+------+\n",
      "|        TeamName|Wins|Draws|Loses|Points|\n",
      "+----------------+----+-----+-----+------+\n",
      "|        Man City|  37|    0|    1|   111|\n",
      "|       Liverpool|  36|    0|    2|   108|\n",
      "|      Man United|  32|    0|    6|    96|\n",
      "|         Chelsea|  32|    0|    6|    96|\n",
      "|       Leicester|  29|    0|    9|    87|\n",
      "|       Tottenham|  27|    1|   10|    82|\n",
      "|          Wolves|  22|    4|   12|    70|\n",
      "|         Arsenal|  22|    3|   13|    69|\n",
      "|         Burnley|  14|   10|   14|    52|\n",
      "|     Southampton|  12|    9|   17|    45|\n",
      "|         Everton|  10|   14|   14|    44|\n",
      "|Sheffield United|   8|   11|   19|    35|\n",
      "|       Newcastle|   8|   10|   20|    34|\n",
      "|        Brighton|   6|   12|   20|    30|\n",
      "|        West Ham|   5|   13|   20|    28|\n",
      "|  Crystal Palace|   6|    8|   24|    26|\n",
      "|     Aston Villa|   4|   12|   22|    24|\n",
      "|           Leeds|   1|   12|   25|    15|\n",
      "|          Fulham|   1|   11|   26|    14|\n",
      "|       West Brom|   0|    6|   32|     6|\n",
      "+----------------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using LogisticRegressionClassifier\n",
      "+----------------+----+-----+-----+------+\n",
      "|        TeamName|Wins|Draws|Loses|Points|\n",
      "+----------------+----+-----+-----+------+\n",
      "|        Man City|  35|    1|    2|   106|\n",
      "|         Chelsea|  29|    3|    6|    90|\n",
      "|       Liverpool|  29|    1|    8|    88|\n",
      "|      Man United|  26|    4|    8|    82|\n",
      "|       Leicester|  25|    1|   12|    76|\n",
      "|       Tottenham|  21|    2|   15|    65|\n",
      "|        West Ham|  20|    1|   17|    61|\n",
      "|         Arsenal|  18|    5|   15|    59|\n",
      "|          Wolves|  16|   11|   11|    59|\n",
      "|     Aston Villa|  18|    3|   17|    57|\n",
      "|        Brighton|  15|    9|   14|    54|\n",
      "|         Everton|  15|    8|   15|    53|\n",
      "|     Southampton|  16|    3|   19|    51|\n",
      "|           Leeds|  14|    4|   20|    46|\n",
      "|       Newcastle|  14|    2|   22|    44|\n",
      "|         Burnley|   8|    8|   22|    32|\n",
      "|  Crystal Palace|   8|    2|   28|    26|\n",
      "|          Fulham|   5|    7|   26|    22|\n",
      "|Sheffield United|   3|    9|   26|    18|\n",
      "|       West Brom|   2|    2|   34|     8|\n",
      "+----------------+----+-----+-----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Results = []\n",
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.PL2020\").load()\n",
    "Accuracy_dt,Accuracy_rf,Accuracy_nb,Accuracy_lr,rmse_dt,rmse_rf,rmse_nb,rmse_lr =computeMLForLeagues(df,\"PL2021\")\n",
    "lst = []\n",
    "lst.append(\"Premier League\")\n",
    "lst.append(Accuracy_dt)\n",
    "lst.append(Accuracy_rf)\n",
    "lst.append(Accuracy_nb)\n",
    "lst.append(Accuracy_lr)\n",
    "lst.append(rmse_dt)\n",
    "lst.append(rmse_rf)\n",
    "lst.append(rmse_nb)\n",
    "lst.append(rmse_lr)\n",
    "Results.append(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# La Liga\n",
    "\n",
    "### 2019-20 Points Table\n",
    "\n",
    "|   TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|Real Madrid|  26|    9|    3|    87|\n",
    "|  Barcelona|  25|    7|    6|    82|\n",
    "| Ath Madrid|  18|   16|    4|    70|\n",
    "|    Sevilla|  19|   13|    6|    70|\n",
    "| Villarreal|  18|    6|   14|    60|\n",
    "|   Sociedad|  16|    8|   14|    56|\n",
    "|    Granada|  16|    8|   14|    56|\n",
    "|     Getafe|  14|   12|   12|    54|\n",
    "|   Valencia|  14|   11|   13|    53|\n",
    "|    Osasuna|  13|   13|   12|    52|\n",
    "| Ath Bilbao|  13|   12|   13|    51|\n",
    "|    Levante|  14|    7|   17|    49|\n",
    "|      Eibar|  11|    9|   18|    42|\n",
    "| Valladolid|   9|   15|   14|    42|\n",
    "|      Betis|  10|   11|   17|    41|\n",
    "|     Alaves|  10|    9|   19|    39|\n",
    "|      Celta|   7|   16|   15|    37|\n",
    "|    Leganes|   8|   12|   18|    36|\n",
    "|   Mallorca|   9|    6|   23|    33|\n",
    "|    Espanol|   5|   10|   23|    25|\n",
    "\n",
    "### 2020-21 Actual Points Table\n",
    "\n",
    "|   TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "| Ath Madrid|  26|    8|    4|    86|\n",
    "|Real Madrid|  25|    9|    4|    84|\n",
    "|  Barcelona|  24|    7|    7|    79|\n",
    "|    Sevilla|  24|    5|    9|    77|\n",
    "|   Sociedad|  17|   11|   10|    62|\n",
    "|      Betis|  17|   10|   11|    61|\n",
    "| Villarreal|  15|   13|   10|    58|\n",
    "|      Celta|  14|   11|   13|    53|\n",
    "| Ath Bilbao|  11|   13|   14|    46|\n",
    "|    Granada|  13|    7|   18|    46|\n",
    "|      Cadiz|  11|   11|   16|    44|\n",
    "|    Osasuna|  11|   11|   16|    44|\n",
    "|   Valencia|  10|   13|   15|    43|\n",
    "|    Levante|   9|   14|   15|    41|\n",
    "|     Getafe|   9|   11|   18|    38|\n",
    "|     Alaves|   9|   11|   18|    38|\n",
    "|      Elche|   8|   12|   18|    36|\n",
    "|     Huesca|   7|   13|   18|    34|\n",
    "| Valladolid|   5|   16|   17|    31|\n",
    "|      Eibar|   6|   12|   20|    30|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2020-21 Points table using DecisionTreeClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|  Barcelona|  25|    8|    5|    83|\n",
      "|   Sociedad|  21|   11|    6|    74|\n",
      "|Real Madrid|  20|   13|    5|    73|\n",
      "| Villarreal|  21|    9|    8|    72|\n",
      "| Ath Madrid|  16|   16|    6|    64|\n",
      "|    Sevilla|  16|   12|   10|    60|\n",
      "|     Getafe|  15|   12|   11|    57|\n",
      "|      Betis|  18|    2|   18|    56|\n",
      "|    Levante|  17|    4|   17|    55|\n",
      "| Ath Bilbao|  16|    5|   17|    53|\n",
      "|   Valencia|  15|    8|   15|    53|\n",
      "|    Osasuna|  13|   12|   13|    51|\n",
      "|    Granada|  13|    8|   17|    47|\n",
      "|      Eibar|  14|    5|   19|    47|\n",
      "|      Celta|  13|    7|   18|    46|\n",
      "|     Alaves|  11|    5|   22|    38|\n",
      "|     Huesca|   9|    8|   21|    35|\n",
      "| Valladolid|  10|    5|   23|    35|\n",
      "|      Cadiz|  10|    5|   23|    35|\n",
      "|      Elche|   7|    5|   26|    26|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using RandomForestClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|  Barcelona|  28|    7|    3|    91|\n",
      "|Real Madrid|  27|    9|    2|    90|\n",
      "| Ath Madrid|  27|    9|    2|    90|\n",
      "| Villarreal|  26|    4|    8|    82|\n",
      "|    Sevilla|  24|    6|    8|    78|\n",
      "|   Sociedad|  23|    7|    8|    76|\n",
      "| Ath Bilbao|  17|    7|   14|    58|\n",
      "|      Betis|  14|   11|   13|    53|\n",
      "|    Levante|  16|    4|   18|    52|\n",
      "|     Getafe|  14|   10|   14|    52|\n",
      "|    Granada|  15|    4|   19|    49|\n",
      "|      Eibar|  12|   11|   15|    47|\n",
      "|   Valencia|  15|    2|   21|    47|\n",
      "|    Osasuna|  12|    9|   17|    45|\n",
      "|      Celta|  13|    4|   21|    43|\n",
      "| Valladolid|   6|   11|   21|    29|\n",
      "|     Alaves|   6|    9|   23|    27|\n",
      "|     Huesca|   6|    9|   23|    27|\n",
      "|      Elche|   1|   11|   26|    14|\n",
      "|      Cadiz|   1|   10|   27|    13|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using NaiveBayesClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|Real Madrid|  38|    0|    0|   114|\n",
      "|  Barcelona|  36|    0|    2|   108|\n",
      "| Ath Madrid|  32|    0|    6|    96|\n",
      "|    Sevilla|  31|    1|    6|    94|\n",
      "| Villarreal|  30|    0|    8|    90|\n",
      "|   Sociedad|  25|    3|   10|    78|\n",
      "|    Granada|  20|    6|   12|    66|\n",
      "|     Getafe|  16|    8|   14|    56|\n",
      "|    Osasuna|  15|   10|   13|    55|\n",
      "|   Valencia|  16|    7|   15|    55|\n",
      "| Ath Bilbao|  13|   12|   13|    51|\n",
      "|    Levante|   8|   12|   18|    36|\n",
      "|      Eibar|   5|   15|   18|    30|\n",
      "|      Betis|   3|   15|   20|    24|\n",
      "|      Celta|   2|   16|   20|    22|\n",
      "| Valladolid|   1|   16|   21|    19|\n",
      "|     Alaves|   1|   14|   23|    17|\n",
      "|     Huesca|   0|   16|   22|    16|\n",
      "|      Cadiz|   0|   13|   25|    13|\n",
      "|      Elche|   0|   12|   26|    12|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using LogisticRegressionClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|  Barcelona|  33|    1|    4|   100|\n",
      "| Ath Madrid|  29|    7|    2|    94|\n",
      "|Real Madrid|  27|    9|    2|    90|\n",
      "|    Sevilla|  26|    6|    6|    84|\n",
      "| Villarreal|  23|    6|    9|    75|\n",
      "|   Sociedad|  20|   11|    7|    71|\n",
      "| Ath Bilbao|  19|   12|    7|    69|\n",
      "|      Betis|  18|    6|   14|    60|\n",
      "|    Osasuna|  15|   10|   13|    55|\n",
      "|   Valencia|  17|    1|   20|    52|\n",
      "|     Getafe|  12|   15|   11|    51|\n",
      "|    Levante|  11|    8|   19|    41|\n",
      "|    Granada|  11|    6|   21|    39|\n",
      "|     Huesca|  11|    6|   21|    39|\n",
      "|      Celta|  11|    4|   23|    37|\n",
      "|      Eibar|   8|   13|   17|    37|\n",
      "|     Alaves|   7|    4|   27|    25|\n",
      "| Valladolid|   3|   12|   23|    21|\n",
      "|      Elche|   4|    4|   30|    16|\n",
      "|      Cadiz|   2|    5|   31|    11|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.LaLiga2020\").load()\n",
    "Accuracy_dt,Accuracy_rf,Accuracy_nb,Accuracy_lr,rmse_dt,rmse_rf,rmse_nb,rmse_lr = computeMLForLeagues(df,\"LaLiga2021\")\n",
    "lst = []\n",
    "lst.append(\"La Liga\")\n",
    "lst.append(Accuracy_dt)\n",
    "lst.append(Accuracy_rf)\n",
    "lst.append(Accuracy_nb)\n",
    "lst.append(Accuracy_lr)\n",
    "lst.append(rmse_dt)\n",
    "lst.append(rmse_rf)\n",
    "lst.append(rmse_nb)\n",
    "lst.append(rmse_lr)\n",
    "Results.append(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Serie-A\n",
    "\n",
    "### 2019-20 Points Table\n",
    "\n",
    "|  TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|  Juventus|  26|    5|    7|    83|\n",
    "|     Inter|  24|   10|    4|    82|\n",
    "|     Lazio|  24|    6|    8|    78|\n",
    "|  Atalanta|  23|    9|    6|    78|\n",
    "|      Roma|  21|    7|   10|    70|\n",
    "|     Milan|  19|    9|   10|    66|\n",
    "|    Napoli|  18|    8|   12|    62|\n",
    "|  Sassuolo|  14|    9|   15|    51|\n",
    "|    Verona|  12|   13|   13|    49|\n",
    "|Fiorentina|  12|   13|   13|    49|\n",
    "|     Parma|  14|    7|   17|    49|\n",
    "|   Bologna|  12|   11|   15|    47|\n",
    "|  Cagliari|  11|   12|   15|    45|\n",
    "|   Udinese|  12|    9|   17|    45|\n",
    "| Sampdoria|  12|    6|   20|    42|\n",
    "|    Torino|  11|    7|   20|    40|\n",
    "|     Genoa|  10|    9|   19|    39|\n",
    "|     Lecce|   9|    8|   21|    35|\n",
    "|   Brescia|   6|    7|   25|    25|\n",
    "|      Spal|   5|    5|   28|    20|\n",
    "\n",
    "### 2020-21 Actual Points Table\n",
    "\n",
    "|  TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|     Inter|  28|    7|    3|    91|\n",
    "|     Milan|  24|    7|    7|    79|\n",
    "|  Atalanta|  23|    9|    6|    78|\n",
    "|  Juventus|  23|    9|    6|    78|\n",
    "|    Napoli|  24|    5|    9|    77|\n",
    "|     Lazio|  21|    5|   12|    68|\n",
    "|      Roma|  18|    9|   11|    63|\n",
    "|  Sassuolo|  17|   11|   10|    62|\n",
    "| Sampdoria|  15|    7|   16|    52|\n",
    "|    Verona|  10|   13|   15|    43|\n",
    "|     Genoa|  10|   12|   16|    42|\n",
    "|   Bologna|  10|   11|   17|    41|\n",
    "|Fiorentina|   9|   13|   16|    40|\n",
    "|   Udinese|  10|   10|   18|    40|\n",
    "|    Spezia|   9|   12|   17|    39|\n",
    "|  Cagliari|   9|   10|   19|    37|\n",
    "|    Torino|   7|   16|   15|    37|\n",
    "| Benevento|   7|   12|   19|    33|\n",
    "|   Crotone|   6|    5|   27|    23|\n",
    "|     Parma|   3|   11|   24|    20|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2020-21 Points table using DecisionTreeClassifier\n",
      "+----------+----+-----+-----+------+\n",
      "|  TeamName|Wins|Draws|Loses|Points|\n",
      "+----------+----+-----+-----+------+\n",
      "|  Atalanta|  34|    3|    1|   105|\n",
      "|     Inter|  32|    2|    4|    98|\n",
      "|    Napoli|  28|    3|    7|    87|\n",
      "|  Juventus|  26|    5|    7|    83|\n",
      "|     Milan|  24|    5|    9|    77|\n",
      "|      Roma|  24|    1|   13|    73|\n",
      "|     Lazio|  21|    3|   14|    66|\n",
      "|   Udinese|  17|    4|   17|    55|\n",
      "|  Sassuolo|  14|   10|   14|    52|\n",
      "|    Torino|  15|    4|   19|    49|\n",
      "|    Verona|  15|    3|   20|    48|\n",
      "|Fiorentina|  10|   10|   18|    40|\n",
      "|     Genoa|  10|    8|   20|    38|\n",
      "| Sampdoria|  10|    5|   23|    35|\n",
      "|   Bologna|   8|   10|   20|    34|\n",
      "| Benevento|   9|    7|   22|    34|\n",
      "|    Spezia|   8|    8|   22|    32|\n",
      "|  Cagliari|   7|    8|   23|    29|\n",
      "|   Crotone|   7|    8|   23|    29|\n",
      "|     Parma|   5|    5|   28|    20|\n",
      "+----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using RandomForestClassifier\n",
      "+----------+----+-----+-----+------+\n",
      "|  TeamName|Wins|Draws|Loses|Points|\n",
      "+----------+----+-----+-----+------+\n",
      "|  Atalanta|  32|    5|    1|   101|\n",
      "|     Inter|  31|    2|    5|    95|\n",
      "|  Juventus|  29|    7|    2|    94|\n",
      "|    Napoli|  30|    1|    7|    91|\n",
      "|     Milan|  27|    5|    6|    86|\n",
      "|      Roma|  27|    1|   10|    82|\n",
      "|     Lazio|  26|    2|   10|    80|\n",
      "|  Sassuolo|  21|    1|   16|    64|\n",
      "|   Udinese|  17|    1|   20|    52|\n",
      "| Sampdoria|  16|    1|   21|    49|\n",
      "|Fiorentina|  15|    3|   20|    48|\n",
      "|    Verona|  14|    2|   22|    44|\n",
      "|   Bologna|  13|    2|   23|    41|\n",
      "|  Cagliari|  12|    1|   25|    37|\n",
      "|     Genoa|  11|    1|   26|    34|\n",
      "|    Torino|  11|    0|   27|    33|\n",
      "|    Spezia|  10|    1|   27|    31|\n",
      "| Benevento|   8|    0|   30|    24|\n",
      "|   Crotone|   7|    1|   30|    22|\n",
      "|     Parma|   4|    1|   33|    13|\n",
      "+----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using NaiveBayesClassifier\n",
      "+----------+----+-----+-----+------+\n",
      "|  TeamName|Wins|Draws|Loses|Points|\n",
      "+----------+----+-----+-----+------+\n",
      "|  Juventus|  37|    0|    1|   111|\n",
      "|     Inter|  36|    0|    2|   108|\n",
      "|     Lazio|  32|    0|    6|    96|\n",
      "|    Napoli|  31|    1|    6|    94|\n",
      "|  Atalanta|  31|    0|    7|    93|\n",
      "|     Milan|  29|    0|    9|    87|\n",
      "|      Roma|  27|    0|   11|    81|\n",
      "|  Sassuolo|  21|    3|   14|    66|\n",
      "|    Verona|  19|    5|   14|    62|\n",
      "|Fiorentina|  15|    4|   19|    49|\n",
      "|   Bologna|  12|    9|   17|    45|\n",
      "|     Parma|  11|    7|   20|    40|\n",
      "|  Cagliari|  10|    6|   22|    36|\n",
      "|   Udinese|   8|   11|   19|    35|\n",
      "| Sampdoria|   8|    7|   23|    31|\n",
      "|     Genoa|   5|    5|   28|    20|\n",
      "|    Torino|   3|   11|   24|    20|\n",
      "| Benevento|   1|    7|   30|    10|\n",
      "|    Spezia|   1|    5|   32|     8|\n",
      "|   Crotone|   0|    5|   33|     5|\n",
      "+----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using LogisticRegressionClassifier\n",
      "+----------+----+-----+-----+------+\n",
      "|  TeamName|Wins|Draws|Loses|Points|\n",
      "+----------+----+-----+-----+------+\n",
      "|  Juventus|  32|    3|    3|    99|\n",
      "|     Inter|  32|    2|    4|    98|\n",
      "|  Atalanta|  31|    2|    5|    95|\n",
      "|    Napoli|  30|    4|    4|    94|\n",
      "|      Roma|  27|    0|   11|    81|\n",
      "|     Milan|  25|    6|    7|    81|\n",
      "|     Lazio|  23|    3|   12|    72|\n",
      "|   Udinese|  18|    4|   16|    58|\n",
      "|  Sassuolo|  18|    2|   18|    56|\n",
      "| Sampdoria|  17|    2|   19|    53|\n",
      "|    Spezia|  15|    0|   23|    45|\n",
      "|     Genoa|  12|    4|   22|    40|\n",
      "|Fiorentina|  12|    4|   22|    40|\n",
      "|    Torino|  12|    3|   23|    39|\n",
      "|    Verona|   9|   11|   18|    38|\n",
      "|   Bologna|  11|    2|   25|    35|\n",
      "|  Cagliari|   7|    7|   24|    28|\n",
      "| Benevento|   5|    5|   28|    20|\n",
      "|   Crotone|   6|    0|   32|    18|\n",
      "|     Parma|   3|    6|   29|    15|\n",
      "+----------+----+-----+-----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.SerieA2020\").load()\n",
    "Accuracy_dt,Accuracy_rf,Accuracy_nb,Accuracy_lr,rmse_dt,rmse_rf,rmse_nb,rmse_lr = computeMLForLeagues(df,\"SerieA2021\")\n",
    "lst = []\n",
    "lst.append(\"Serie-A\")\n",
    "lst.append(Accuracy_dt)\n",
    "lst.append(Accuracy_rf)\n",
    "lst.append(Accuracy_nb)\n",
    "lst.append(Accuracy_lr)\n",
    "lst.append(rmse_dt)\n",
    "lst.append(rmse_rf)\n",
    "lst.append(rmse_nb)\n",
    "lst.append(rmse_lr)\n",
    "Results.append(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bundesliga\n",
    "\n",
    "### 2019-20 Points Table\n",
    "|          TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|     Bayern Munich|  26|    4|    4|    82|\n",
    "|          Dortmund|  21|    6|    7|    69|\n",
    "|        RB Leipzig|  18|   12|    4|    66|\n",
    "|        M'gladbach|  20|    5|    9|    65|\n",
    "|        Leverkusen|  19|    6|    9|    63|\n",
    "|        Hoffenheim|  15|    7|   12|    52|\n",
    "|         Wolfsburg|  13|   10|   11|    49|\n",
    "|          Freiburg|  13|    9|   12|    48|\n",
    "|     Ein Frankfurt|  13|    6|   15|    45|\n",
    "|      Union Berlin|  12|    5|   17|    41|\n",
    "|            Hertha|  11|    8|   15|    41|\n",
    "|        Schalke 04|   9|   12|   13|    39|\n",
    "|             Mainz|  11|    4|   19|    37|\n",
    "|          Augsburg|   9|    9|   16|    36|\n",
    "|           FC Koln|  10|    6|   18|    36|\n",
    "|     Werder Bremen|   8|    7|   19|    31|\n",
    "|Fortuna Dusseldorf|   6|   12|   16|    30|\n",
    "|         Paderborn|   4|    8|   22|    20|\n",
    "\n",
    "### 2020-21 Actual Points Table\n",
    "\n",
    "|     TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|Bayern Munich|  24|    6|    4|    78|\n",
    "|   RB Leipzig|  19|    8|    7|    65|\n",
    "|     Dortmund|  20|    4|   10|    64|\n",
    "|    Wolfsburg|  17|   10|    7|    61|\n",
    "|Ein Frankfurt|  16|   12|    6|    60|\n",
    "|   Leverkusen|  14|   10|   10|    52|\n",
    "| Union Berlin|  12|   14|    8|    50|\n",
    "|   M'gladbach|  13|   10|   11|    49|\n",
    "|    Stuttgart|  12|    9|   13|    45|\n",
    "|     Freiburg|  12|    9|   13|    45|\n",
    "|   Hoffenheim|  11|   10|   13|    43|\n",
    "|        Mainz|  10|    9|   15|    39|\n",
    "|     Augsburg|  10|    6|   18|    36|\n",
    "|    Bielefeld|   9|    8|   17|    35|\n",
    "|       Hertha|   8|   11|   15|    35|\n",
    "|      FC Koln|   8|    9|   17|    33|\n",
    "|Werder Bremen|   7|   10|   17|    31|\n",
    "|   Schalke 04|   3|    7|   24|    16|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2020-21 Points table using DecisionTreeClassifier\n",
      "+-------------+----+-----+-----+------+\n",
      "|     TeamName|Wins|Draws|Loses|Points|\n",
      "+-------------+----+-----+-----+------+\n",
      "|   RB Leipzig|  29|    1|    4|    88|\n",
      "|Bayern Munich|  29|    0|    5|    87|\n",
      "|     Dortmund|  28|    1|    5|    85|\n",
      "|    Wolfsburg|  26|    1|    7|    79|\n",
      "|   M'gladbach|  25|    0|    9|    75|\n",
      "|Werder Bremen|  19|    9|    6|    66|\n",
      "|    Bielefeld|  19|    9|    6|    66|\n",
      "|Ein Frankfurt|  22|    0|   12|    66|\n",
      "|    Stuttgart|  18|    0|   16|    54|\n",
      "|   Hoffenheim|  17|    1|   16|    52|\n",
      "|     Freiburg|  17|    0|   17|    51|\n",
      "|   Leverkusen|  15|    2|   17|    47|\n",
      "|       Hertha|  12|    0|   22|    36|\n",
      "| Union Berlin|  11|    2|   21|    35|\n",
      "|     Augsburg|  10|    4|   20|    34|\n",
      "|        Mainz|  10|    3|   21|    33|\n",
      "|      FC Koln|   2|    3|   29|     9|\n",
      "|   Schalke 04|   1|    3|   30|     6|\n",
      "+-------------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using RandomForestClassifier\n",
      "+-------------+----+-----+-----+------+\n",
      "|     TeamName|Wins|Draws|Loses|Points|\n",
      "+-------------+----+-----+-----+------+\n",
      "|Bayern Munich|  31|    0|    3|    93|\n",
      "|    Wolfsburg|  30|    0|    4|    90|\n",
      "|     Dortmund|  26|    0|    8|    78|\n",
      "|   RB Leipzig|  26|    0|    8|    78|\n",
      "|   Leverkusen|  25|    0|    9|    75|\n",
      "| Union Berlin|  22|    1|   11|    67|\n",
      "|Ein Frankfurt|  22|    0|   12|    66|\n",
      "|   M'gladbach|  22|    0|   12|    66|\n",
      "|   Hoffenheim|  20|    0|   14|    60|\n",
      "|     Freiburg|  15|    3|   16|    48|\n",
      "|       Hertha|  15|    1|   18|    46|\n",
      "|Werder Bremen|   9|   12|   13|    39|\n",
      "|    Bielefeld|   9|   12|   13|    39|\n",
      "|    Stuttgart|   9|    0|   25|    27|\n",
      "|        Mainz|   8|    3|   23|    27|\n",
      "|      FC Koln|   7|    2|   25|    23|\n",
      "|     Augsburg|   4|    4|   26|    16|\n",
      "|   Schalke 04|   1|    2|   31|     5|\n",
      "+-------------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using NaiveBayesClassifier\n",
      "+-------------+----+-----+-----+------+\n",
      "|     TeamName|Wins|Draws|Loses|Points|\n",
      "+-------------+----+-----+-----+------+\n",
      "|Bayern Munich|  33|    0|    1|    99|\n",
      "|   Leverkusen|  30|    0|    4|    90|\n",
      "|     Dortmund|  29|    0|    5|    87|\n",
      "|   RB Leipzig|  29|    0|    5|    87|\n",
      "|   M'gladbach|  25|    0|    9|    75|\n",
      "|    Wolfsburg|  24|    0|   10|    72|\n",
      "|   Hoffenheim|  22|    0|   12|    66|\n",
      "|Ein Frankfurt|  17|    1|   16|    52|\n",
      "|     Freiburg|  16|    3|   15|    51|\n",
      "| Union Berlin|  16|    3|   15|    51|\n",
      "|       Hertha|  12|    5|   17|    41|\n",
      "|        Mainz|   5|   10|   19|    25|\n",
      "|Werder Bremen|   1|   19|   14|    22|\n",
      "|    Bielefeld|   1|   19|   14|    22|\n",
      "|      FC Koln|   2|    9|   23|    15|\n",
      "|     Augsburg|   1|   12|   21|    15|\n",
      "|    Stuttgart|   1|   11|   22|    14|\n",
      "|   Schalke 04|   1|    9|   24|    12|\n",
      "+-------------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using LogisticRegressionClassifier\n",
      "+-------------+----+-----+-----+------+\n",
      "|     TeamName|Wins|Draws|Loses|Points|\n",
      "+-------------+----+-----+-----+------+\n",
      "|Bayern Munich|  32|    0|    2|    96|\n",
      "|   RB Leipzig|  27|    2|    5|    83|\n",
      "|     Dortmund|  26|    0|    8|    78|\n",
      "|   Leverkusen|  24|    4|    6|    76|\n",
      "|    Wolfsburg|  24|    3|    7|    75|\n",
      "| Union Berlin|  20|    4|   10|    64|\n",
      "|Ein Frankfurt|  20|    1|   13|    61|\n",
      "|   Hoffenheim|  18|    0|   16|    54|\n",
      "|   M'gladbach|  17|    0|   17|    51|\n",
      "|       Hertha|  13|    5|   16|    44|\n",
      "|     Freiburg|  14|    1|   19|    43|\n",
      "|    Stuttgart|  11|    8|   15|    41|\n",
      "|Werder Bremen|   8|   14|   12|    38|\n",
      "|    Bielefeld|   8|   14|   12|    38|\n",
      "|        Mainz|  11|    5|   18|    38|\n",
      "|     Augsburg|   6|    6|   22|    24|\n",
      "|      FC Koln|   5|    3|   26|    18|\n",
      "|   Schalke 04|   1|    1|   32|     4|\n",
      "+-------------+----+-----+-----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.Bundesliga2020\").load()\n",
    "Accuracy_dt,Accuracy_rf,Accuracy_nb,Accuracy_lr,rmse_dt,rmse_rf,rmse_nb,rmse_lr = computeMLForLeagues(df,\"Bundesliga2021\")\n",
    "lst = []\n",
    "lst.append(\"Bundesliga\")\n",
    "lst.append(Accuracy_dt)\n",
    "lst.append(Accuracy_rf)\n",
    "lst.append(Accuracy_nb)\n",
    "lst.append(Accuracy_lr)\n",
    "lst.append(rmse_dt)\n",
    "lst.append(rmse_rf)\n",
    "lst.append(rmse_nb)\n",
    "lst.append(rmse_lr)\n",
    "Results.append(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ligue-1\n",
    "\n",
    "### 2019-20 Points Table\n",
    "\n",
    "|   TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|   Paris SG|  22|    2|   14|    68|\n",
    "|  Marseille|  16|    8|   14|    56|\n",
    "|     Rennes|  15|    5|   18|    50|\n",
    "|      Lille|  15|    4|   19|    49|\n",
    "|       Nice|  11|    8|   19|    41|\n",
    "|      Reims|  10|   11|   17|    41|\n",
    "|Montpellier|  11|    7|   20|    40|\n",
    "|       Lyon|  11|    7|   20|    40|\n",
    "|     Monaco|  11|    7|   20|    40|\n",
    "|     Angers|  11|    6|   21|    39|\n",
    "| Strasbourg|  11|    5|   22|    38|\n",
    "|     Nantes|  11|    4|   23|    37|\n",
    "|   Bordeaux|   9|   10|   19|    37|\n",
    "|      Brest|   8|   10|   20|    34|\n",
    "|       Metz|   8|   10|   20|    34|\n",
    "|      Dijon|   7|    9|   22|    30|\n",
    "| St Etienne|   8|    6|   24|    30|\n",
    "|      Nimes|   7|    6|   25|    27|\n",
    "|     Amiens|   4|   11|   23|    23|\n",
    "|   Toulouse|   3|    4|   31|    13|\n",
    "\n",
    "### 2020-21 Actual Points Table\n",
    "\n",
    "|   TeamName|Wins|Draws|Loses|Points|\n",
    "| --- | --- | --- | --- | --- |\n",
    "|      Lille|  24|   11|    3|    83|\n",
    "|   Paris SG|  26|    4|    8|    82|\n",
    "|     Monaco|  24|    6|    8|    78|\n",
    "|       Lyon|  22|   10|    6|    76|\n",
    "|  Marseille|  16|   12|   10|    60|\n",
    "|     Rennes|  16|   10|   12|    58|\n",
    "|       Lens|  15|   12|   11|    57|\n",
    "|Montpellier|  14|   12|   12|    54|\n",
    "|       Nice|  15|    7|   16|    52|\n",
    "|       Metz|  12|   11|   15|    47|\n",
    "| St Etienne|  12|   10|   16|    46|\n",
    "|   Bordeaux|  13|    6|   19|    45|\n",
    "|     Angers|  12|    8|   18|    44|\n",
    "|      Reims|   9|   15|   14|    42|\n",
    "| Strasbourg|  11|    9|   18|    42|\n",
    "|    Lorient|  11|    9|   18|    42|\n",
    "|      Brest|  11|    8|   19|    41|\n",
    "|     Nantes|   9|   13|   16|    40|\n",
    "|      Nimes|   9|    8|   21|    35|\n",
    "|      Dijon|   4|    9|   25|    21|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2020-21 Points table using DecisionTreeClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|       Lyon|  28|    1|    9|    85|\n",
      "|     Monaco|  27|    3|    8|    84|\n",
      "|      Lille|  22|    8|    8|    74|\n",
      "|   Paris SG|  23|    4|   11|    73|\n",
      "|     Rennes|  21|    4|   13|    67|\n",
      "|  Marseille|  20|    1|   17|    61|\n",
      "| St Etienne|  19|    3|   16|    60|\n",
      "|     Angers|  17|    8|   13|    59|\n",
      "|       Metz|  19|    2|   17|    59|\n",
      "| Strasbourg|  18|    3|   17|    57|\n",
      "|      Brest|  18|    3|   17|    57|\n",
      "|     Nantes|  16|    4|   18|    52|\n",
      "|   Bordeaux|  14|    8|   16|    50|\n",
      "|    Lorient|  15|    5|   18|    50|\n",
      "|      Nimes|  15|    5|   18|    50|\n",
      "|       Nice|  15|    4|   19|    49|\n",
      "|      Reims|  13|   10|   15|    49|\n",
      "|Montpellier|  15|    2|   21|    47|\n",
      "|      Dijon|  10|    3|   25|    33|\n",
      "|       Lens|   9|    6|   23|    33|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using RandomForestClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|       Lyon|  33|    0|    5|    99|\n",
      "|   Paris SG|  33|    0|    5|    99|\n",
      "|      Lille|  28|    5|    5|    89|\n",
      "|     Monaco|  27|    0|   11|    81|\n",
      "|     Rennes|  21|    7|   10|    70|\n",
      "|  Marseille|  22|    1|   15|    67|\n",
      "|      Nimes|  19|    9|   10|    66|\n",
      "|    Lorient|  19|    9|   10|    66|\n",
      "| St Etienne|  15|    8|   15|    53|\n",
      "|      Brest|  15|    6|   17|    51|\n",
      "|     Angers|  11|   16|   11|    49|\n",
      "|       Metz|  14|    6|   18|    48|\n",
      "|     Nantes|  13|    7|   18|    46|\n",
      "|Montpellier|  14|    3|   21|    45|\n",
      "|       Lens|  11|   10|   17|    43|\n",
      "| Strasbourg|  10|   12|   16|    42|\n",
      "|       Nice|  10|    9|   19|    39|\n",
      "|      Reims|   9|   10|   19|    37|\n",
      "|   Bordeaux|   7|   12|   19|    33|\n",
      "|      Dijon|   5|    4|   29|    19|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using NaiveBayesClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|      Lille|  36|    0|    2|   108|\n",
      "|   Paris SG|  35|    0|    3|   105|\n",
      "|     Rennes|  33|    0|    5|    99|\n",
      "|  Marseille|  32|    0|    6|    96|\n",
      "|       Nice|  27|    1|   10|    82|\n",
      "|       Lyon|  27|    0|   11|    81|\n",
      "|      Reims|  25|    0|   13|    75|\n",
      "|     Monaco|  22|    4|   12|    70|\n",
      "|Montpellier|  22|    2|   14|    68|\n",
      "|     Angers|  16|    7|   15|    55|\n",
      "| Strasbourg|  14|    8|   16|    50|\n",
      "|     Nantes|   8|    8|   22|    32|\n",
      "|   Bordeaux|   6|   13|   19|    31|\n",
      "|       Metz|   6|   12|   20|    30|\n",
      "|      Nimes|   1|   22|   15|    25|\n",
      "|    Lorient|   1|   22|   15|    25|\n",
      "| St Etienne|   3|   13|   22|    22|\n",
      "|      Brest|   3|   12|   23|    21|\n",
      "|      Dijon|   0|   13|   25|    13|\n",
      "|       Lens|   0|   11|   27|    11|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n",
      " 2020-21 Points table using LogisticRegressionClassifier\n",
      "+-----------+----+-----+-----+------+\n",
      "|   TeamName|Wins|Draws|Loses|Points|\n",
      "+-----------+----+-----+-----+------+\n",
      "|      Lille|  32|    3|    3|    99|\n",
      "|   Paris SG|  32|    0|    6|    96|\n",
      "|       Lyon|  31|    0|    7|    93|\n",
      "|     Monaco|  30|    2|    6|    92|\n",
      "|     Rennes|  24|    2|   12|    74|\n",
      "|    Lorient|  20|    9|    9|    69|\n",
      "|      Nimes|  20|    9|    9|    69|\n",
      "|  Marseille|  20|    2|   16|    62|\n",
      "|       Metz|  18|    5|   15|    59|\n",
      "|       Lens|  16|    7|   15|    55|\n",
      "|      Brest|  17|    3|   18|    54|\n",
      "| St Etienne|  17|    3|   18|    54|\n",
      "|       Nice|  16|    2|   20|    50|\n",
      "|Montpellier|  16|    1|   21|    49|\n",
      "|     Nantes|  14|    2|   22|    44|\n",
      "| Strasbourg|  13|    3|   22|    42|\n",
      "|   Bordeaux|  10|    9|   19|    39|\n",
      "|     Angers|   9|   10|   19|    37|\n",
      "|      Reims|   7|    7|   24|    28|\n",
      "|      Dijon|   1|    3|   34|     6|\n",
      "+-----------+----+-----+-----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = my_spark.read.format(\"mongo\").option(\"uri\",\"mongodb://127.0.0.1/LeagueStats.Ligue2020\").load()\n",
    "Accuracy_dt,Accuracy_rf,Accuracy_nb,Accuracy_lr,rmse_dt,rmse_rf,rmse_nb,rmse_lr = computeMLForLeagues(df,\"Ligue2021\")\n",
    "lst = []\n",
    "lst.append(\"Ligue-1\")\n",
    "lst.append(Accuracy_dt)\n",
    "lst.append(Accuracy_rf)\n",
    "lst.append(Accuracy_nb)\n",
    "lst.append(Accuracy_lr)\n",
    "lst.append(rmse_dt)\n",
    "lst.append(rmse_rf)\n",
    "lst.append(rmse_nb)\n",
    "lst.append(rmse_lr)\n",
    "Results.append(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tabulate in c:\\users\\aswath\\anaconda3\\envs\\py38\\lib\\site-packages (0.8.9)\n"
     ]
    }
   ],
   "source": [
    "!pip install tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results from  Premier League\n",
      "---------------------------\n",
      "| Classifier          |   AccuracyM |   RMSE |\n",
      "|---------------------+-------------+--------|\n",
      "| Decision Tree       |        43.7 |  11.38 |\n",
      "| Random Forest       |        53.4 |  11.69 |\n",
      "| Naive Bayes         |        53.2 |  23.42 |\n",
      "| Logistic Regression |        57.9 |  12    |\n",
      "\n",
      "\n",
      " The results generated by Logistic Regression Classifier has the better Accuracy than other Classifiers( 43.684210526315795 ) in  Premier League\n",
      "Results from  La Liga\n",
      "---------------------------\n",
      "| Classifier          |   AccuracyM |   RMSE |\n",
      "|---------------------+-------------+--------|\n",
      "| Decision Tree       |        50.5 |  11.33 |\n",
      "| Random Forest       |        54.7 |  13.28 |\n",
      "| Naive Bayes         |        53.2 |  21.48 |\n",
      "| Logistic Regression |        57.1 |  14.1  |\n",
      "\n",
      "\n",
      " The results generated by Logistic Regression Classifier has the better Accuracy than other Classifiers( 50.526315789473685 ) in  La Liga\n",
      "Results from  Serie-A\n",
      "---------------------------\n",
      "| Classifier          |   AccuracyM |   RMSE |\n",
      "|---------------------+-------------+--------|\n",
      "| Decision Tree       |        52.9 |  10.02 |\n",
      "| Random Forest       |        56.8 |  10.12 |\n",
      "| Naive Bayes         |        55.8 |  18.7  |\n",
      "| Logistic Regression |        58.7 |  10.4  |\n",
      "\n",
      "\n",
      " The results generated by Logistic Regression Classifier has the better Accuracy than other Classifiers( 52.89473684210526 ) in  Serie-A\n",
      "Results from  Bundesliga\n",
      "---------------------------\n",
      "| Classifier          |   AccuracyM |   RMSE |\n",
      "|---------------------+-------------+--------|\n",
      "| Decision Tree       |        39.2 |  17.38 |\n",
      "| Random Forest       |        45.5 |  15.21 |\n",
      "| Naive Bayes         |        41.6 |  19.07 |\n",
      "| Logistic Regression |        45.8 |  12    |\n",
      "\n",
      "\n",
      " The results generated by Logistic Regression Classifier has the better Accuracy than other Classifiers( 39.21052631578947 ) in  Bundesliga\n",
      "Results from  Ligue-1\n",
      "---------------------------\n",
      "| Classifier          |   AccuracyM |   RMSE |\n",
      "|---------------------+-------------+--------|\n",
      "| Decision Tree       |        49.7 |  11.6  |\n",
      "| Random Forest       |        56.6 |  13.08 |\n",
      "| Naive Bayes         |        52.6 |  23.11 |\n",
      "| Logistic Regression |        59.7 |  14.2  |\n",
      "\n",
      "\n",
      " The results generated by Logistic Regression Classifier has the better Accuracy than other Classifiers( 49.73684210526316 ) in  Ligue-1\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "for i in range(len(Results)):\n",
    "    print(\"Results from \",Results[i][0])\n",
    "    print(\"---------------------------\")\n",
    "    print(tabulate([[\"Decision Tree \",round(Results[i][1]*100,1),round(Results[i][5],2)],[\"Random Forest \",round(Results[i][2]*100,1),round(Results[i][6],2)],[\"Naive Bayes   \" ,round(Results[i][3]*100,1),round(Results[i][7],2)],[\"Logistic Regression \",round(Results[i][4]*100,1),round(Results[i][8],1)]],headers = [\"Classifier\",\"AccuracyM\",\"RMSE\"],tablefmt='orgtbl'))\n",
    "    print(\"\\n\")\n",
    "    if Results[i][1] == max(Results[i][1],max(Results[i][2],max(Results[i][3],Results[i][4]))):\n",
    "        print(\" The results generated by Decision Tree Classifier has the better Accuracy than other Classifiers(\",Results[i][1]*100,\") in \",Results[i][0])\n",
    "    elif Results[i][2] == max(Results[i][1],max(Results[i][2],max(Results[i][3],Results[i][4]))):\n",
    "        print(\" The results generated by Random Forest Classifier has the better Accuracy than other Classifiers(\",Results[i][2]*100,\") in \",Results[i][0])\n",
    "    elif Results[i][3] == max(Results[i][1],max(Results[i][2],max(Results[i][3],Results[i][4]))):\n",
    "        print(\" The results generated by Naive Bayes Classifier has the better Accuracy than other Classifiers(\",Results[i][3]*100,\") in \",Results[i][0])\n",
    "    else:\n",
    "        print(\" The results generated by Logistic Regression Classifier has the better Accuracy than other Classifiers(\",Results[i][1]*100,\") in \",Results[i][0])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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