Feature_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Tennis Ace**\n",
    "\n",
    "#### Here i've created tested and build several regression models that predicts the outcome for a tennis player based on their playing habits. By analyzing and modeling the [Association of Tennis Professionals (ATP)](https://en.wikipedia.org/wiki/Association_of_Tennis_Professionals) data, this model will determine what it takes to be one of the best tennis players in the world.\n",
    "\n"
   ]
  },
  {
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### tennis_stats.csv is data from the men’s professional tennis league, which is called the [ATP (Association of Tennis Professionals)](https://www.atptour.com/). Data from the top 1500 ranked players in the ATP over the span of 2009 to 2017 are provided in file. The statistics recorded for each player in each year include service game (offensive) statistics, return game (defensive) statistics and outcomes."
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Year</th>\n",
       "      <th>FirstServe</th>\n",
       "      <th>FirstServePointsWon</th>\n",
       "      <th>FirstServeReturnPointsWon</th>\n",
       "      <th>SecondServePointsWon</th>\n",
       "      <th>SecondServeReturnPointsWon</th>\n",
       "      <th>Aces</th>\n",
       "      <th>BreakPointsConverted</th>\n",
       "      <th>BreakPointsFaced</th>\n",
       "      <th>...</th>\n",
       "      <th>ReturnGamesWon</th>\n",
       "      <th>ReturnPointsWon</th>\n",
       "      <th>ServiceGamesPlayed</th>\n",
       "      <th>ServiceGamesWon</th>\n",
       "      <th>TotalPointsWon</th>\n",
       "      <th>TotalServicePointsWon</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Losses</th>\n",
       "      <th>Winnings</th>\n",
       "      <th>Ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro Sousa</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.38</td>\n",
       "      <td>8</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>39820</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Roman Safiullin</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.07</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>9</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17334</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro Sousa</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2</td>\n",
       "      <td>0.38</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>17</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.59</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>109827</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rogerio Dutra Silva</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.33</td>\n",
       "      <td>2</td>\n",
       "      <td>0.33</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.34</td>\n",
       "      <td>15</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9761</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Daniel Gimeno-Traver</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32879</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player  Year  FirstServe  FirstServePointsWon  \\\n",
       "0           Pedro Sousa  2016        0.88                 0.50   \n",
       "1       Roman Safiullin  2017        0.84                 0.62   \n",
       "2           Pedro Sousa  2017        0.83                 0.60   \n",
       "3   Rogerio Dutra Silva  2010        0.83                 0.64   \n",
       "4  Daniel Gimeno-Traver  2017        0.81                 0.54   \n",
       "\n",
       "   FirstServeReturnPointsWon  SecondServePointsWon  \\\n",
       "0                       0.38                  0.50   \n",
       "1                       0.26                  0.33   \n",
       "2                       0.28                  0.53   \n",
       "3                       0.34                  0.59   \n",
       "4                       0.00                  0.33   \n",
       "\n",
       "   SecondServeReturnPointsWon  Aces  BreakPointsConverted  BreakPointsFaced  \\\n",
       "0                        0.39     0                  0.14                 7   \n",
       "1                        0.07     7                  0.00                 7   \n",
       "2                        0.44     2                  0.38                10   \n",
       "3                        0.33     2                  0.33                 5   \n",
       "4                        0.33     1                  0.00                 2   \n",
       "\n",
       "   ...  ReturnGamesWon  ReturnPointsWon  ServiceGamesPlayed  ServiceGamesWon  \\\n",
       "0  ...            0.11             0.38                   8             0.50   \n",
       "1  ...            0.00             0.20                   9             0.67   \n",
       "2  ...            0.16             0.34                  17             0.65   \n",
       "3  ...            0.14             0.34                  15             0.80   \n",
       "4  ...            0.00             0.20                   2             0.50   \n",
       "\n",
       "   TotalPointsWon  TotalServicePointsWon  Wins  Losses  Winnings  Ranking  \n",
       "0            0.43                   0.50     1       2     39820      119  \n",
       "1            0.41                   0.57     0       1     17334      381  \n",
       "2            0.45                   0.59     4       1    109827      119  \n",
       "3            0.49                   0.63     0       0      9761      125  \n",
       "4            0.35                   0.50     0       1     32879      272  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading and investigating the data:\n",
    "df = pd.read_csv('tennis_stats.csv')\n",
    "df.head()\n"
   ]
  },
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# performing exploratory analysis:\n",
    "plt.figure()\n",
    "plt.scatter(df.BreakPointsOpportunities, df.Winnings, alpha = 0.4, color = 'green')\n",
    "plt.show()\n",
    "\n",
    "# There is a strong relationship between the BreakPointsOpportunities feature and the Winnings outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Feature Linear Regression\n",
    "\n",
    "With `FirstServeReturnPointsWon` Feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This single feature Linear model R^2 value is 0.06258032988574369\n"
     ]
    }
   ],
   "source": [
    "## performing single feature linear regressions  :\n",
    "\n",
    "features = df[['FirstServeReturnPointsWon']]\n",
    "outcome = df[['Winnings']]\n",
    "\n",
    "\n",
    "## Train - test Split\n",
    "feature_train, feature_test, outcome_train, outcome_test = train_test_split(features, outcome, train_size = 0.8, test_size = 0.2, random_state = 6)\n",
    "\n",
    "\n",
    "## Model fitting\n",
    "lm = LinearRegression()\n",
    "lm.fit(feature_train, outcome_train)\n",
    "\n",
    "\n",
    "## Model R^2\n",
    "print('This single feature Linear model R^2 value is {}'.format(lm.score(feature_test, outcome_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "## Comparison plotting\n",
    "predict = lm.predict(feature_test)\n",
    "plt.scatter(outcome_test, predict, alpha = 0.4,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obviously it does not fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another Single Feature Linear Regression\n",
    "\n",
    "With `BreakPointsOpportunities` Feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This single feature Linear model R^2 value is 0.8081205523550063\n"
     ]
    }
   ],
   "source": [
    "## performing another single feature linear regressions  :\n",
    "\n",
    "features1 = df[['BreakPointsOpportunities']]\n",
    "outcome1 = df[['Winnings']]\n",
    "\n",
    "\n",
    "## Train - test Split\n",
    "feature1_train, feature1_test, outcome1_train, outcome1_test = train_test_split(features1, outcome1, train_size = 0.8, test_size = 0.2, random_state = 6)\n",
    "\n",
    "\n",
    "## Model fitting\n",
    "lm1 = LinearRegression()\n",
    "lm1.fit(feature1_train, outcome1_train)\n",
    "\n",
    "\n",
    "## Model R^2\n",
    "print('This single feature Linear model R^2 value is {}'.format(lm1.score(feature1_test, outcome1_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Comparison plotting\n",
    "plt.figure()\n",
    "predict1 = lm1.predict(feature1_test)\n",
    "plt.scatter(outcome1_test, predict1, alpha = 0.4, color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing Double feature linear regressions  :\n",
    "\n",
    "With `BreakPointsOpportunities` and `FirstServeReturnPointsWon` Feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This single feature Linear model R^2 value is 0.8099289371304075\n"
     ]
    }
   ],
   "source": [
    "features1 = df[['BreakPointsOpportunities', 'FirstServeReturnPointsWon']]\n",
    "outcome1 = df[['Winnings']]\n",
    "\n",
    "\n",
    "## Train - test Split\n",
    "feature1_train, feature1_test, outcome1_train, outcome1_test = train_test_split(features1, outcome1, train_size = 0.8, test_size = 0.2, random_state = 6)\n",
    "\n",
    "\n",
    "## Model fitting\n",
    "lm1 = LinearRegression()\n",
    "lm1.fit(feature1_train, outcome1_train)\n",
    "\n",
    "\n",
    "## Model R^2\n",
    "print('This single feature Linear model R^2 value is {}'.format(lm1.score(feature1_test, outcome1_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WRRH5GeDrcbufVtWLXc7VmDDyWkIyJWiq4ducgK7rWJlrGhnlgtJLxdcy1nYqazXWcUNCANLkcPToUT1x4sSoL8MYAstPLIde0anHn/GQUPdsJQeSsVLty0L+Py02P9LVYCpp10Ojl30HyTgnug0TEXlGVY82e88ys42xJb9bdNMOFkDPK37NU3lLhZkfHh8h0ctC1u9FsBetoHj37lOPP+fRNaVyc6WeNDgKxtnkUxZMUBhjS9Ger5eVylsrndvTS3KX2YupZxCNgXrpsJZ/Hqu/u0r2aoabcbibHJJI3xsWGcPFBIUx1nRztziMzmsdX0sPDuBBOI97telXF6vUdtWQWwWfevSikq6nQVj8zirVv2SCYhwxQWHsOMoUndOLqadX5/GWWlWPUVfZ5Szks+R+oypopqSvpCM1QRndY4LCKB2DNguVKTqnF1NPL/u206p6+X8nexNq361BAlIp/J+ny9+Vz2iO9aMwSkW7pK1+ZP6WKfu5lwS7XvYdZE2p6u1V/KoPEWgKpKBecYfdWOa2GCYojJLRagHrJPO3E0FSpuznXjJ2e9l3kDWlqotVKm+pgIDWQn5LspDgKm5sc1t2OmZ6MkpFS7PQc7T0LbQzpxRNWlTCXS5XGGr281Zmta6dz13u24vZqhNmfniG1adXUa/4K570VIo4YfquRulkjAOmURilopVZqN1d8Ha0EUkEajBz18zQGgGVqRbSwIvgLVapvK2Cv+BhFdyswx1wpC+kIy8UaGwfExRGqWi1gLXzLbQSJGXo81CGa8gZRpE6PaNUjlSo/kCVys0Vkv3J2BZr3OmY6ckoFe3CM1vF+Lcyp5Qh0qkM11Bk0BnLZZvvoClLEucgMEFhlI6tFrB2QqRlsthzDNQm3wmD9gu0YhSL2CjnO2zKlMQ5CExQjIhJvvsYJK3ugrerjWSXM9QrS59fGspnMKpKpqNaxHZS5dYyJXEOAhMUI2DS7z46ZRDCslNthArBT+AE9gRtY+UrK6zOr0LKQATHqHpNjGoRK0NvjWEx6WY2ExQjYNLvPjphFMKyKESWn1jGJxslsX3myS5nuGVHcksysOsZRSXTUS5iO6Vy66Sb2SzqaQQMMtlpXBh1BFDjZ+AveKQSuuWNOiKp35QpE31SKVMS5yAwjWIETPrdRyeMWlVv/Ax0XcGBVDfXJtru9TQm9SFAbTCmrE7ZSb6CUTHpZjYTFCPAfrijF5bXfQYulJtwhwtK9javp2hO00TJTgUhkywOzpTVCZO+iJWFSTazmaAYAfbDHb2wvO4zeFOCv+JxFYeqdnU9RXOavqJIVRAEvajIzVLfZiRaxQQvYsbgMUExInb6D7cMwrLxM7guCquLPgy5OU3XFeKuuh770u8wP5QxOZigMEZG2YRlP/ow5OY0mRI0DQJCpqLfY4f5oYzJwQSF0TGDShIse/Jhp9dXNKfJfsGf9ihKcjjZiILZQX6oUVL279S4YYLC6IiVZ1dYe3oN9Rps8F7Jns56ds6WPflwO9dXNKfp5VAQL496crvdxPqhyrYol/07NY6YoDDaUjtdY+23QyC+m3GQhbwDd8D17Jwte/Lhdq+vbOa0VvRjgS/jolz279Q4YoLCaEvtuRpkhB+cEMpfpIK/4slcb87ZUedTtKPs19ct/Vrgy7goT+pnljMKDc4EhdGW7HKGzEbnbP6NSUBXtWfnbLt8ilGbNUad7zEo+rXAl3FRntTPDEanwVkJD6Mtyd4EmRfIQNNYomBdESc9lyhoVfqgDB3hJrU0Q7/KyJSxPMikfmYwutI3JiiMtlRvryKJ4A44pCKwCghM3zXdl2qvW3Va6/ZHUTtdY/mJZZY+v8TyE8s9CZZhdIIbBf1a4Mu4KE/qZwajqxNnpiejLZuS45KM5C39NQFt5QDuxqzRN9t7E5PX3OJcfXz16dVSRPh0S78y48uQOLnVdY36GgbBqMxqJiiMjvwAo/jhdfOj6IftfSthk74tJX0hLVWET7f0c4Gf1EW5jIyq9I0Jih1OGcMbc7r5UfTDubqVsFk/vk5yKClVhE8v2AI/foxKgzNBscMpY3gjbGg5uqr4Kx6qUF2otv1RbCeKaqsy4OnZFL/qoRbKb7gDDpkT/FVPcqRBmxmgfXjUEV9GORmFgDdBscMZRHhjrwtcUctxh92GJtHBcWRBSH87DXkfFdBMIYPKWyqsPLtSNx1tVQY8fVuKX/KgsUZTCtmZDHfA4ebDtQzDPlxmTc/YeVjU0w6n3+GN/Qhp7SXaKX0hxR1waKLoksIqyMHQG3vt6bXQwW4mlv6uCq7qwut4jvXj67j94WehmaKJgoJ/3TP1nqmhRfiMugOgYRTpWFCISCIiz4rIr8e/94vIkyLyUnzeV9j2YRE5KSIvisg9hfF3icg34nufFBGJ49Mi8sU4flxEbinsc388x0sicn8/Jm1s0Gl4Y6chp/1Y4LoNAczPnexLcFMOt8ch84KsSL0+lV4NFV11XSEhJA4WyoD7qx63z+EWYihwjTCf3cLsHbNNwy6BvoXj9vo/MIxBsB3T008BLwB74t8PAU+p6sdF5KH49z8WkbcD9wHvAN4M/KaIfL+qZsCngAeB3wOeAN4PfBl4ALikqt8nIvcBnwD+sojsBz4GHAUUeEZEHlfVSz3N2qjTiXOs0QySXkhZf2wdt9tReVNlk0lou6asZmaqbkMAG/tB5G1Nc0EgM4KubJT+blYGPDcvJfMJzIdhXQ1CIf9/tfrf9MtENMnZxcb40ZFGISJHgD8L/Hxh+F7gkfj6EeDHC+NfUNU1VX0ZOAm8W0QWgD2q+lVVVeCzDfvkx3oMuDtqG/cAT6rqxSgcniQIF6OPVBerzB2bY/eHdzN3bO66Ba6oJfhrHn8h2PD9qr/OtLQdU9ZWZipZkK5MPMVzy1TIJCfbEARuj6uXHpH9gtYUX/PhdTzHds1LgzIRlTGRzdi5dGp6+lngHwG+MHajqp4BiM+H4/gi8Gphu1NxbDG+bhzftI+qpsAbwIEWx9qEiDwoIidE5MT58+c7nJLRKUUziF4MJhuZimaZhoVxOwvcVousntGuMmuL55b9gq95tKZ1QSBOmP6RadysQzKhcqRC5aYKkkn9HFuZl7Y696BMRJOcXWyMH21NTyLy54BzqvqMiPxoB8eUJmPaYrzbfTYGVD8NfBrg6NGj173fDRaauEHRDFJv8Vm4Uy8ujNuJ825lpppbvF6zacd1/SBuqoRvS9rQD+KO9sfp9NyDNBFZnoNRFjrxUbwX+PMicozwc9gjIv8NeE1EFlT1TDQrnYvbnwJuKux/BPheHD/SZLy4zykRqQA3ABfj+I827PO/Op5dl1ho4mY2dW6bimYhUZIDcTFsWBg7XeB6WWS3EuTDXlxHlSlrGMOkrelJVR9W1SOqegvBSf1bqvoTwONAHoV0P/Cl+Ppx4L4YyXQrcBvwtWieWhKRO6P/4Scb9smP9cF4DgW+ArxPRPbFqKr3xbGBYqGJmymaQZgKY3kSWi+2827t8GWoKptjJiJjJ9BLwt3HgUdF5AHgFeBDAKr6vIg8CnwLSIGPxogngI8AvwDMEqKdvhzHPwN8TkROEjSJ++KxLorIzwBfj9v9tKpe7OGaO6KMNfZHTfFO/bq7+R5qBHVTjqBs2eRmIjImHQk37pPD0aNH9cSJEz0dY/mJ5XC3OrMhLPIQybljc71e4o6mH76fpc8vIXuCtpejqugVZfeHd/f7kg1jRyAiz6jq0WbvWQmPJpjdeTB04vvpRJCMS46BBUQYk4KV8GiC2Z0HQzvfT6e+h3HIMSiTH8UwesU0ii0wu3P/aef76dT3UNZmOUXK5kcxjF4wQWEMjUaTkb/myV7LwAe/UHo2DdVii2wRRFB2QW4BEcYkYYLC6Ipu7O9F349PPdnpzWW+/ZJHvUIt1mqaCsX4KvvH72s6Ln4Uw+gE81EY26Zb+3vR9+PPetyUI1lMSOZj17g58Oc8uqahl8Sakp3NkIVmCfrlZhz8KIbRKeN3q2aMnG7s740aSJ645896dCrUY5JU0IqGek/r4dkdcOgZbVt2o2yMgx/FMDrFBIWxbbopJd5YplwvKToTmwilip7RUMBvXqjcvPG1VNWxteuX3Y9iGJ1ipidj22y3K15jWKxe1ZCfsg6SCZJEoZPFUuAdHtcwjOFggsLYNtu1vzeW4tb1oElQJfgiagpTIDeElqVm1zeMcmGmJ2PbbNf+3hgBlFegdXOubmbKS6RUb6+aXd8wSoYJCqMrtmN/byyJIvOhU5476FDVTSVStnNcK5FhGMPBBIUxcKqLVdK3pawfX8df9bh5R/WdVdyq61pzGGTPEBNAhrEZExTGwKmdrpG+kJIcSkiOBEe4nlWqd1WZW9y6Gm+rBTt3kPvUo+c0+D0SYfV3Vqn+pe4XdWtaZRjXY4LC6Ihe7rK7zbtotWBnlzM0UfwZDwlQBc2U9JWU2unt1VMqzs1f8cguwc24jq/VMCYdExRGWzq9y95KmHRT96itcKlA9gcZZIRv8TQ4cTC9vUX9urmd9bAKTEEyn3R0rYYx6Vh4rNGWTlrDtirrsd28C7g+pBaoL9i107WQi1EDhPB8BfwVDwq1M52X8m6cm5t1CIJeLDT0slwOY4djgsJoy1aLdu1MjeUnlln6/BLLv7aMem0qTLqpe9RKuNSeq+FucEFL8QStAoIJyoMuacd9Hxrn5g44FEVXLJfDMHJMUBhtabZo+0seXdK6BqHLSvZ6RvZ6FvwEJ2ukr6WkZ9PNxQDPebLzGX7FU3uutuWCXhQu2dWM2ndr1F6ukV3NSM+mMA3JjUn4BlcJtaMUEHAH3SZtZztzc7scycEEmRNrWmUYEfNRGG1p1hrWXwx5ELkPQWajsDiT4XY5pBoK+/ma3+Rczi5nuBuCL6FVRFGe1Lf6u6tkr2a4GYe7ySGJkC1lwYewLwlCIqPuq5C9gi4pa2fX6tfeapFvNjdxwuwHZk04GEbENAqjLc1aw7rdDrd34+sj+yX4CjxoomgWbPxuv6trDsu/tkx2NiM7l+Gv+aa+jsbzJrsSqrdWqdxaqZcjd/sd/nUfsrnnXOhbMSe4wy4UG1xTZK6z8ufW9tYw2mMahdERjRnTy08sbyrLkcwnZC4Ltx61UKZDDgtulyM9m4bQ02UfSojHarEsBFNPq4iiZhFTbp9D18Oink1lsBZ9C0vRAS0E81HUdtpFQVmVV8NojQkKoyuammxi/4hk30aEkK6GZDh3g8PNOkgJhQDREFlUaR1RtFWnuOpClbljIVkvD8tdO7uGzAnJwQS3y9WvzUJbDaM3TFA0wUo4tKdZYcDpH5kmfSFFV7UuPHRNgx9hOtz1Z2cyJBVwhMiiWONpy/M0EUiN+xQ1Ar/iN3IvwEJbDaMPmKBowEo4NGcr4dn4P6kdrrH6O6tk34n9sN+c4CouREld1WB28goaek+08wdsp1JtJ0LFMIztY4KigW7KTZSddhpSJ+9vS3imUDlSqS/W6fkUfUM3/q810FSZes9UR//TTn0I1n7UMAaDCYoGuik3UWZWnl1h7bfXIIshrJmSPZ3VF/lOhEBRePprHn/B41c8y7+2zNwH5jYLlWaCthb8F5t6YR8cTC9sc0xvYCZUo1+YoGhgK+fpONq5a6drrD0d8gk0UfSKwiVgJuQnVD9U7UiDyoWnv+aDj8HFRX9ZNwmV2uka6y+to6q4aYc74HC7HJopQue9sG2B6x0zoRr9xPIoGuim3ERZqT1XC/4ABywTIo48sALpy6HKaquaSjl59rK/4BEn4fbCBw0lz4OoL0xJ7IGdQnYm5kskglQatLQthG+rmlFG53RSn8swOsUERQOTlICVXc6QGUGvachc1vjwwDos/+bydSUssqsZtZdqpK+kXP65yyz98hKyEFqX+hWPuuCQJotJdnmhvnxhOiSQhfBXBLLXQqY2s3QkfG2B6w+d3AAYRqeY6akJZbZzt2zm0/Ae1RBZlF2Mi0OhICoO/GmP/DFBXwhv+NSH0t3r1MNX0z9I0SWlekcVf8Wjyxo0icNCMp+gq0qyN6mbp5KZBBZALwZhgMDsPbPh+jpwMk+aj2hUTJIJ1Rg9JijGiFZ2Z+C69/wVj0jIWaC4ziZsVFo9o21UQBIAABOzSURBVMzcNRNCWr+bBfOUsPHNSCG7klE5U2HuA3P1czDNhmZwZxWeCzkMWZoFIbGuSCWU1cgFQifC1xa4/mChwkY/MUFRYho1hOxatsnx7DNPdiHj2mPXwvj85s5syd4kOJJ3C3o5+iryhwd2hTv1KtUgICAICQh/59stwfpL61RvD2a5rTSDa49fC+eJx9FEkauyrY5ztsD1BwsVNvpJWx+FiNwkIv9TRF4QkedF5Kfi+H4ReVJEXorP+wr7PCwiJ0XkRRG5pzD+LhH5RnzvkyIicXxaRL4Yx4+LyC2Ffe6P53hJRO7v5+SbUTtd6LHwxPLInKjNnLrpH6b41APUI5Byv4MuK9nZWI77ZPAx+NRDCrv+wi5klwQtIgoCmRKS/Um9v4NMS3gvfyibhIckUl/A547NsfvDu5k7tjk0Vpd18zEcMMW2/AuT5CMaNdXF6paflWFsh040ihT4h6r6+yKyG3hGRJ4E/hrwlKp+XEQeAh4C/rGIvB24D3gH8GbgN0Xk+1U1Az4FPAj8HvAE8H7gy8ADwCVV/T4RuQ/4BPCXRWQ/8DHgKGHpekZEHlfVS/36BxTpV0hhT/2l477rL62HaKFD0e4/A24m9HNI5pN6BJIS8hK0pnA19I12u0NNpex0RuWmCtXFKtN/cpq1p9fqzYXcHoc4qd/Byx6BOWCJDac3BI0iATkUIpe2SjysPVcLZq49EOU/pKHrXOa2518os4/IMHYibTUKVT2jqr8fXy8BLwCLwL3AI3GzR4Afj6/vBb6gqmuq+jJwEni3iCwAe1T1q6qqwGcb9smP9Rhwd9Q27gGeVNWLUTg8SRAuA6EfETe9hHeuPLvCtceusf7tdXRF8esef8aTXQ0LrRySeuiuX/MhsihGH+VRRvhYcC9f6ePT7B2z7PrQLqZ/cJpkb0Jlf4XK2ypBoJ3PghN7mvCNKDq9K+AWXegf3cKpnF3OkFm5zheSO7sNwxhftuWjiCahO4DjwI2qegaCMBGRw3GzRYLGkHMqjtXi68bxfJ9X47FSEXkDOFAcb7JP3+lHxE23JUCKyXF5NjNroNOxyuo8uIpDbo59nSUKs4UQfeTPenQ6hsHGMt/usNswH7H5Tr2oPbk3ObI/zMI5p9noQ+3AvdlRORi/Ji2cyrk/RC9EIZUA69S1lmbztaQ6wxgPOs6jEJF54FeAv6eqV1pt2mRMW4x3u0/x2h4UkRMicuL8+fMtLq01rfo0d0q38et5cpxMBQFQ79y2DP4NT3YxQ9eUmffOMHdsjl0f3IXb73AVh6qGJLcslMrYOOjW114UaMl8ErSBBEjB3eBIbk5gFliio8TD6u1VJAllxqUisAoITN81fX3hQEuqM4yxoiNBISJVgpD4RVX91Tj8WjQnEZ/PxfFTwE2F3Y8A34vjR5qMb9pHRCrADcDFFsfahKp+WlWPqurRQ4cOdTKlpvQjK7tbYZMnx5GBr/mQy5BQ9xf4C57K2yqbQk2LTl92s5FMVwmRQtnZDFloJmubCDQPsluQ2VBqIzmYkCxGLaEDp3J+PZUDFdwNjqkfnGLXB3cxe8fsddtaUp1hjBedRD0J8BngBVX9N4W3HgfyKKT7gS8Vxu+LkUy3ArcBX4tmqiURuTMe8ycb9smP9UHgt6If4yvA+0RkX4yqel8cGwj9iLjpVtgkexPcHhdKbqzEQQGqUPkjFSo3VUIRPTYis/IopJm7ZqgeqpIsJEGjSINmkdyY1Pdpdr6iQJMpqZusclzFMXXb1JZRM40RYrB1RFQRyxo2jPGiEx/Fe4G/CnxDRP5PHPsnwMeBR0XkAeAV4EMAqvq8iDwKfItgIf9ojHgC+AjwCwSjxpfjA4Ig+pyInCRoEvfFY10UkZ8Bvh63+2lVvdjlXDui14ibbuPXq7dXyZ7OQnOfU1nQDBwkC6FbW3Y1I30ppfaZGrqkuIOhZ3VutkkvpzgJLUJlSpD90rLNaGO+gsyHon/uYDBltctf6CVCzJLqDGO8kHDjPjkcPXpUT5w4MerL6IpiaCwOkhuDkPDXPOmpFDflQiRRLI1RFyKXMrJT0XQ1RfBtZKGjXOVApd4ydKvz5QJNFgQ9o5sczEBTp3PeM7vYTU5Xgxa21fmK5y1meOdCyfIlDGN0iMgzqnq02XuWmV0icm2merpar8SqqmSvbYTH+rMepkAywV/wQZBc8eGTlDBOEvIp/EVP9ceuNxc1Lvxzi4WF/Y7N226lNXQaIbZldJNlDRvG2GCComTkC6tf8XAl+gw8JItJKMI3Fau3JqDrQRvUVcXtCv0f/AUfxqdCgt52O9UVF3Z/xSO7NpcFgbDAd2I+anc+EwyGMR6YoCgRK8+uNM2eTuaTEP5KTK47o2gWhIGuamgkNB98Em5XWNRzM1CRdjke1y3sZ30Ic50iJNxBXWuYuWumbU2mSWwraxg7EetHURJqp2uhZSlBE8hNS+qDPyKPpMrRZYXVYGKavms6mKnaRFq1izZqDFt1sw5BQsJfTtQaOokQs+gmw5gMTKMoCbXnasEJPUO9zLekQnYhQxDcbkd2IQsCohqilPIucpXbKi2ruua0Mxc1+h3cAUf6vRRWaBoJ1c58ZNFNhjEZmEZREprVSvLew1VCKY3D8aPKS38LYQFW6qU/2uUwtMvxaMytcLscycEEmZOu8komqa2sYexkTKMoCU1rJa1QD5MVCU5tQpXxjR7UU+BXfT2ruVX9pHbRRs16QYgTZj8w25VPwaKbDGMyMEFREjYl3F3K4A1CuuKujW1kKva/LuqBGciMkJ5Ng1bSJgGulbloEAu7RTcZxvhjgqIkVBerpG9LWf3fq6EnRAXYFe7os1czsmoWek7kZicFMlCvuH0Ovaq4G9x1EUarv7NKbb7zKq22sBuG0Yj5KEpC7XSN9d9fD+Go+aeSgtaCXV+XQ2mOvKqsv+bRREO1VhfHGyKMfOpDpzur0moYRg+YRtEn2vVXKL5PFfyyr/eXdvsc2etZ8EkowT8BwbGdF1uvEarECqE204yQ7E3q56o9VyO9mKJLWq/3pGtqeQyGYfSMCYo+0C4Dufi+Jhq6ya0TSiMC2SvZ5i4bGUGrEIKfIu91nQuQNORRzPyFjQik9FxK9q0sOLmrMe/imuIWG5RGy2MwDGObmKDogaZF/MRdn/EcE9myqxn+tN8IgV1lY/HPyf0P62y0bcridkVNg82agZ7RUFb8quKXfRAwCv6UZ/1S6L+NEq7zTdfnMVjHOcMwtsIERSRfKNOzaaiVVIXqQnXLBXNTBdTYXCg7k8FCyD8o3rmnZ9MQybTccJBY5XVTn+qiZlF8XWhilDu0174Vkh6qt1fJLme4fQ4/5cN5YplyMuAqqGiIoMrAX/HUTm8ImV5KhhuGMfmYoGBjoVSvZFdiL4h1WLuwxvr/W2f6rmlm75jdsmCeTAuaKiKx7IYqej7UY1r65SWyc7Ef9Vb4LcZz7QKChpCbn/LtM1h7bo21b63hDjmStSSU20jZcIgXo6RSSG5OcBW3SRuxmkyGYbTCBAUbC6U/50Nto/XNPTryGkzpC2nTgnl5oT7vfQhtvQg4kAUhfSVtLSTakRC0goJw2PSeAOvgXwvmJl3RDW0id4zH1zIVCgyq6iY/Raclww3D2JmYoGBjodT1EDGEEB6e0AhoFdaPr5McSup33W7W4Zc82XcKi2luGqoQQlUvE8xARc1gO8RcCWDDX5EfJyGExNYnAW6Pw9d8aGwEofTGmoZjSKHNaUO9JavJ1B7z4Rg7GcujIC6Ul32ozrpGcCSvEe7Q3wjCw1/wZOcysqth5VYXt80dzXl5jTxa6SroUlzV+9FE0AXN5TqBkRNDaOc+MEdyY7IhRCpsCIr90rTektVkak1umrR8FGOnYhoFwUSUfbOJHyHa9QFIQu4Db0DmYnhrTvF17qAeBBlBANTY0F7iQ2alXv6be2D1d1fJvpeFXhVHQslwUnC73XVlOawmU2vMh2PsdExQEEJLqRA0gq1uEjOuj1oaJj50tJMbBX1Nw3XmORYVkN1S1wCqi1WqH9reAmalO7bGfDjGTscEBfEHn7KhPZQVBZc49KAiVcFfCh7uZCFh5r2dl/82tof5cIydjgkKCOacdfrjSxgEsZERDvSaMveB5v0mjMHQrPx6Y9tXw5hkzJkN5RUQEExLu4MPovr9VdweZ0JiyHTS9tUwJhnTKGDD1l8G01OVjRpPGv4WJ6GGk5k7Rob5cIydjAkKwNf8cIREu3yKvM1pXjEWQtXYquL2OjN3lATLqTB2GmZ6Avz5rWpo9JG8x/VhrusbUX8/z8GIUUxMAdUQ0lo5UDFzRwmwnApjJ2IaBYQ+EMNgN1R2V8jWM3RKQ7htJfSW0JqGkiBR63D7QkMiEw7lwnIqjJ2IaRTD5CJoovVcDdktyGxccPIb0lgl1l/wVN5WscWnZGSXs+s1QsupMCYcExQwnP9CXO/1oiKzEooPaqjDpCtarxEl00Ly1oTKkUpIBDRKRbI3CaVbiliQgTHhmKAAkpsG9CN31DOnw4lidvV+QQnlwN2CqzuwZVZwC45kPrG71JJidbGMnYgJCmD27lm4ocudi5UdcsEwRb0jnczLRjRTdaPUd3Igwc05JBPcPkdyU0L1j1SDkAC7Sy0pllNh7ETMmU348c//xXmu/spVuMpGiGor9hBMEDWCsIjRSbJbqOyvIAvC2tNraKqhN/ZaOGZewVUSYfYDs5t6auuqWubvGGA5FcZOwwRFJBcW9U5338s226JdfMyBm3dUF6qkZ1P8ksftd7h9bmOBj3H1lcOVerw9VerVaBsruFr1VsMwyowJigLFBZt1QgvTCrg5F5zOXnEHHJX9FeaOzQFNkq8aBECni73dpRqGUVbGQlCIyPuBnyNY/n9eVT8+qHMVF+yVZ1dYe3oNv+qRGannNmxq+mMLvGEYE07pBYWIJMB/AP40cAr4uog8rqrfGvS5Z++Y3WQ+snINhmHsREovKIB3AydV9bsAIvIF4F5g4IICTGMwDMMYh/DYReDVwt+n4lgdEXlQRE6IyInz588P9eIMwzAmnXEQFNJkbFPwqqp+WlWPqurRQ4cODemyDMMwdgbjIChOATcV/j4CfG9E12IYhrHjGAdB8XXgNhG5VUSmgPuAx0d8TYZhGDuG0juzVTUVkb8NfIUQHvtfVPX5EV+WYRjGjkFUJ6tCqYicB/6wy90PAq/38XLKiM1xMrA5TgZlmuNbVLWpk3fiBEUviMgJVT066usYJDbHycDmOBmMyxzHwUdhGIZhjBATFIZhGEZLTFBs5tOjvoAhYHOcDGyOk8FYzNF8FIZhGEZLTKMwDMMwWmKCwjAMw2iJCYqIiLxfRF4UkZMi8tCor6cREblJRP6niLwgIs+LyE/F8f0i8qSIvBSf9xX2eTjO50URuacw/i4R+UZ875MiInF8WkS+GMePi8gthX3uj+d4SUTuH/BcExF5VkR+fRLnKCJ7ReQxEfl2/Dx/aALn+Pfj9/SbIvJLIjIz7nMUkf8iIudE5JuFsZHOSULFiuNx/IsSqlf0H1Xd8Q9Cxvd3gLcCU8BzwNtHfV0N17gAvDO+3g38P+DtwL8EHorjDwGfiK/fHucxDdwa55fE974G/BCh4OKXgT8Tx/8W8J/i6/uAL8bX+4Hvxud98fW+Ac71HwCfB349/j1RcwQeAf5mfD0F7J2kORKqO78MzMa/HwX+2rjPEfgR4J3ANwtjI51T/N/eF1//J+AjA/nODuKg4/aIH9pXCn8/DDw86utqc81fIjRzehFYiGMLwIvN5kAogfJDcZtvF8b/CvCfi9vE1xVCxqgUt4nv/WfgrwxoXkeAp4AfY0NQTMwcgT2ERVQaxidpjnlrgP3x/L8OvG8S5gjcwmZBMbI5xfdeBypxfNM61s+HmZ4CbXtelImokt4BHAduVNUzAPH5cNxsqzktxteN45v2UdUUeAM40OJYg+BngX8E+MLYJM3xrcB54L9G89rPi8guJmiOqnoa+NfAK8AZ4A1V/Q0maI4FRjmnA8DluG3jsfqKCYpA254XZUFE5oFfAf6eql5ptWmTMW0x3u0+fUNE/hxwTlWf6XSXJmOlniPhTvGdwKdU9Q7gGsFksRVjN8dop7+XYHJ5M7BLRH6i1S5bXFdp59gBw5jT0OZqgiIwFj0vRKRKEBK/qKq/GodfE5GF+P4CcC6ObzWnU/F14/imfUSkAtwAXGxxrH7zXuDPi8gfAF8AfkxE/huTNcdTwClVPR7/fowgOCZpjn8KeFlVz6tqDfhV4IeZrDnmjHJOrwN747aNx+ovg7BnjduDcJf3XcIdUO7Mfseor6vhGgX4LPCzDeP/is3OtH8ZX7+Dzc6077LhTPs6cCcbzrRjcfyjbHamPRpf7yfY1ffFx8vA/gHP90fZ8FFM1ByB/w38QHz9z+L8JmaOwHuA54G5eG2PAH9nEubI9T6Kkc4J+GU2O7P/1kC+s4M46Dg+gGOESKLvAP901NfT5Pr+BEGt/L/A/4mPYwQ75VPAS/F5f2Gffxrn8yIxsiKOHwW+Gd/792xk6M/EL95JQmTGWwv7/I04fhL460OY74+yISgmao7AHwdOxM/yv8cf/6TN8Z8D347X9znCgjnWcwR+ieBzqRHu8h8Y9ZwIPq+vxfFfBqYH8XlaCQ/DMAyjJeajMAzDMFpigsIwDMNoiQkKwzAMoyUmKAzDMIyWmKAwDMMwWmKCwjAMw2iJCQrDMAyjJf8fGj5gU3bWG0wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Comparison plotting\n",
    "plt.figure()\n",
    "predict1 = lm1.predict(feature1_test)\n",
    "plt.scatter(outcome1_test, predict1, alpha = 0.6, color = 'violet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing Multiple feature linear regressions  :\n",
    "\n",
    "With 18 Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This single feature Linear model R^2 value is 0.8290563866666136\n"
     ]
    }
   ],
   "source": [
    "features1 = df[['FirstServe','FirstServePointsWon','FirstServeReturnPointsWon',\n",
    "'SecondServePointsWon','SecondServeReturnPointsWon','Aces',\n",
    "'BreakPointsConverted','BreakPointsFaced','BreakPointsOpportunities',\n",
    "'BreakPointsSaved','DoubleFaults','ReturnGamesPlayed','ReturnGamesWon',\n",
    "'ReturnPointsWon','ServiceGamesPlayed','ServiceGamesWon','TotalPointsWon',\n",
    "'TotalServicePointsWon']]\n",
    "outcome1 = df[['Winnings']]\n",
    "\n",
    "\n",
    "## Train - test Split\n",
    "feature1_train, feature1_test, outcome1_train, outcome1_test = train_test_split(features1, outcome1, train_size = 0.8, test_size = 0.2, random_state = 6)\n",
    "\n",
    "\n",
    "## Model fitting\n",
    "lm1 = LinearRegression()\n",
    "lm1.fit(feature1_train, outcome1_train)\n",
    "\n",
    "\n",
    "## Model R^2\n",
    "print('This single feature Linear model R^2 value is {}'.format(lm1.score(feature1_test, outcome1_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Comparison plotting\n",
    "plt.figure()\n",
    "predict1 = lm1.predict(feature1_test)\n",
    "plt.scatter(outcome1_test, predict1, alpha = 0.4, color = 'navy')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}