Predicting_Football_Market_Value.ipynb

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      "source": [
        "**Eskinder Tsegaye Defar**\n",
        "\n",
        "**ID: 20215511**\n",
        "\n",
        "Statistical Machine Learning\n",
        "\n",
        "Project Title: **Predicting Total Market Value for Football dataset from Transfermarkt**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5871bf02",
      "metadata": {
        "id": "5871bf02"
      },
      "source": [
        "**Step-1) Data Loading and Preprocesssing**\n",
        "\n",
        "Importing Pandas library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "36882864",
      "metadata": {
        "id": "36882864"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sklearn.metrics as metrics\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "522e3d9c",
      "metadata": {
        "id": "522e3d9c"
      },
      "source": [
        "# Data Collection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be5003cf",
      "metadata": {
        "id": "be5003cf"
      },
      "outputs": [],
      "source": [
        "# load datasets\n",
        "players= pd.read_csv('C:/Users/Dell Inspiron/Downloads/players.csv')\n",
        "competitions= pd.read_csv('C:/Users/Dell Inspiron/Downloads/competitions.csv')\n",
        "games= pd.read_csv('C:/Users/Dell Inspiron/Downloads/games.csv')\n",
        "clubs= pd.read_csv('C:/Users/Dell Inspiron/Downloads/clubs.csv')\n",
        "appearances= pd.read_csv('C:/Users/Dell Inspiron/Downloads/appearances.csv')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26d29b4c",
      "metadata": {
        "id": "26d29b4c"
      },
      "source": [
        "# Data Cleaning:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af46619e",
      "metadata": {
        "scrolled": false,
        "id": "af46619e",
        "outputId": "49b2a3a0-1af8-422c-c66e-9cbb71d11e66"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>current_club_id</th>\n",
              "      <th>name</th>\n",
              "      <th>pretty_name</th>\n",
              "      <th>country_of_birth</th>\n",
              "      <th>country_of_citizenship</th>\n",
              "      <th>date_of_birth</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>foot</th>\n",
              "      <th>height_in_cm</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214258</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>andrey-naletov</td>\n",
              "      <td>Andrey Naletov</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1996-03-31</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/andrey-naletov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45247</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>damien-perquis</td>\n",
              "      <td>Damien Perquis</td>\n",
              "      <td>France</td>\n",
              "      <td>France</td>\n",
              "      <td>1986-03-08</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Right</td>\n",
              "      <td>186</td>\n",
              "      <td>360000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/damien-perquis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>ivan-lozenkov</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1984-04-14</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>NaN</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/ivan-lozenkov/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>217036</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>aleksandr-zakarlyuka</td>\n",
              "      <td>Aleksandr Zakarlyuka</td>\n",
              "      <td>Estonia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1995-06-24</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Central Midfield</td>\n",
              "      <td>Right</td>\n",
              "      <td>178</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/aleksandr-zaka...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>97205</td>\n",
              "      <td>2014</td>\n",
              "      <td>28095</td>\n",
              "      <td>nikita-bezlikhotnov</td>\n",
              "      <td>Nikita Bezlikhotnov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1990-08-19</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Right Winger</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>675000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/nikita-bezlikh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57208</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>felipe-saad</td>\n",
              "      <td>Felipe Saad</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1983-09-11</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>187</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1080000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/felipe-saad/pr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>140799</td>\n",
              "      <td>2014</td>\n",
              "      <td>403</td>\n",
              "      <td>jonas-heymans</td>\n",
              "      <td>Jonas Heymans</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>1993-02-06</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Left-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>NaN</td>\n",
              "      <td>450000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/jonas-heymans/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>55472</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>rafael-marques</td>\n",
              "      <td>Rafael Marques</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1983-09-21</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>187</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1620000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/rafael-marques...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37633</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>guillermo-rodriguez</td>\n",
              "      <td>Guillermo Rodriguez</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>1984-03-21</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>185</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2610000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/guillermo-rodr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>76902</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>fernandinho</td>\n",
              "      <td>Fernandinho</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1985-11-25</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>Left</td>\n",
              "      <td>173</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>1800000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/fernandinho/pr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id  last_season  current_club_id                  name  \\\n",
              "0     214258         2014             3729        andrey-naletov   \n",
              "1      45247         2014             1162        damien-perquis   \n",
              "2      61389         2014             3729         ivan-lozenkov   \n",
              "3     217036         2014             3729  aleksandr-zakarlyuka   \n",
              "4      97205         2014            28095   nikita-bezlikhotnov   \n",
              "5      57208         2014             1162           felipe-saad   \n",
              "6     140799         2014              403         jonas-heymans   \n",
              "7      55472         2014              276        rafael-marques   \n",
              "8      37633         2014              276   guillermo-rodriguez   \n",
              "9      76902         2014              276           fernandinho   \n",
              "\n",
              "            pretty_name country_of_birth country_of_citizenship date_of_birth  \\\n",
              "0        Andrey Naletov           Russia                 Russia    1996-03-31   \n",
              "1        Damien Perquis           France                 France    1986-03-08   \n",
              "2         Ivan Lozenkov            UdSSR                 Russia    1984-04-14   \n",
              "3  Aleksandr Zakarlyuka          Estonia                 Russia    1995-06-24   \n",
              "4   Nikita Bezlikhotnov            UdSSR                 Russia    1990-08-19   \n",
              "5           Felipe Saad           Brazil                 Brazil    1983-09-11   \n",
              "6         Jonas Heymans          Belgium                Belgium    1993-02-06   \n",
              "7        Rafael Marques           Brazil                 Brazil    1983-09-21   \n",
              "8   Guillermo Rodriguez          Uruguay                Uruguay    1984-03-21   \n",
              "9           Fernandinho           Brazil                 Brazil    1985-11-25   \n",
              "\n",
              "     position      sub_position   foot  height_in_cm  market_value_in_gbp  \\\n",
              "0    Defender       Centre-Back  Right           179                  NaN   \n",
              "1  Goalkeeper        Goalkeeper  Right           186             360000.0   \n",
              "2    Defender       Centre-Back   Left           182                  NaN   \n",
              "3    Midfield  Central Midfield  Right           178             203000.0   \n",
              "4      Attack      Right Winger  Right           179             225000.0   \n",
              "5    Defender       Centre-Back   Left           187                  NaN   \n",
              "6    Defender         Left-Back   Left           182                  NaN   \n",
              "7    Defender       Centre-Back  Right           187                  NaN   \n",
              "8    Defender       Centre-Back   Left           185              45000.0   \n",
              "9      Attack       Left Winger   Left           173             135000.0   \n",
              "\n",
              "   highest_market_value_in_gbp  \\\n",
              "0                      23000.0   \n",
              "1                     900000.0   \n",
              "2                     270000.0   \n",
              "3                     203000.0   \n",
              "4                     675000.0   \n",
              "5                    1080000.0   \n",
              "6                     450000.0   \n",
              "7                    1620000.0   \n",
              "8                    2610000.0   \n",
              "9                    1800000.0   \n",
              "\n",
              "                                                 url  \n",
              "0  https://www.transfermarkt.co.uk/andrey-naletov...  \n",
              "1  https://www.transfermarkt.co.uk/damien-perquis...  \n",
              "2  https://www.transfermarkt.co.uk/ivan-lozenkov/...  \n",
              "3  https://www.transfermarkt.co.uk/aleksandr-zaka...  \n",
              "4  https://www.transfermarkt.co.uk/nikita-bezlikh...  \n",
              "5  https://www.transfermarkt.co.uk/felipe-saad/pr...  \n",
              "6  https://www.transfermarkt.co.uk/jonas-heymans/...  \n",
              "7  https://www.transfermarkt.co.uk/rafael-marques...  \n",
              "8  https://www.transfermarkt.co.uk/guillermo-rodr...  \n",
              "9  https://www.transfermarkt.co.uk/fernandinho/pr...  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "players.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "057c015b",
      "metadata": {
        "id": "057c015b",
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      "outputs": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player_id</th>\n",
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              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214258</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>andrey-naletov</td>\n",
              "      <td>Andrey Naletov</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1996-03-31</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
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              "      <td>338000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/andrey-naletov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45247</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>damien-perquis</td>\n",
              "      <td>Damien Perquis</td>\n",
              "      <td>France</td>\n",
              "      <td>France</td>\n",
              "      <td>1986-03-08</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Right</td>\n",
              "      <td>186</td>\n",
              "      <td>360000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/damien-perquis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>ivan-lozenkov</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1984-04-14</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/ivan-lozenkov/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>217036</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>aleksandr-zakarlyuka</td>\n",
              "      <td>Aleksandr Zakarlyuka</td>\n",
              "      <td>Estonia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1995-06-24</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Central Midfield</td>\n",
              "      <td>Right</td>\n",
              "      <td>178</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/aleksandr-zaka...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>97205</td>\n",
              "      <td>2014</td>\n",
              "      <td>28095</td>\n",
              "      <td>nikita-bezlikhotnov</td>\n",
              "      <td>Nikita Bezlikhotnov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>1990-08-19</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Right Winger</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>675000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/nikita-bezlikh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57208</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>felipe-saad</td>\n",
              "      <td>Felipe Saad</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1983-09-11</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1080000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/felipe-saad/pr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>140799</td>\n",
              "      <td>2014</td>\n",
              "      <td>403</td>\n",
              "      <td>jonas-heymans</td>\n",
              "      <td>Jonas Heymans</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>1993-02-06</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Left-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>450000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/jonas-heymans/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>55472</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>rafael-marques</td>\n",
              "      <td>Rafael Marques</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1983-09-21</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1620000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/rafael-marques...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37633</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>guillermo-rodriguez</td>\n",
              "      <td>Guillermo Rodriguez</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>1984-03-21</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>185</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2610000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/guillermo-rodr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>76902</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>fernandinho</td>\n",
              "      <td>Fernandinho</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1985-11-25</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>Left</td>\n",
              "      <td>173</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>1800000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/fernandinho/pr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id  last_season  current_club_id                  name  \\\n",
              "0     214258         2014             3729        andrey-naletov   \n",
              "1      45247         2014             1162        damien-perquis   \n",
              "2      61389         2014             3729         ivan-lozenkov   \n",
              "3     217036         2014             3729  aleksandr-zakarlyuka   \n",
              "4      97205         2014            28095   nikita-bezlikhotnov   \n",
              "5      57208         2014             1162           felipe-saad   \n",
              "6     140799         2014              403         jonas-heymans   \n",
              "7      55472         2014              276        rafael-marques   \n",
              "8      37633         2014              276   guillermo-rodriguez   \n",
              "9      76902         2014              276           fernandinho   \n",
              "\n",
              "            pretty_name country_of_birth country_of_citizenship date_of_birth  \\\n",
              "0        Andrey Naletov           Russia                 Russia    1996-03-31   \n",
              "1        Damien Perquis           France                 France    1986-03-08   \n",
              "2         Ivan Lozenkov            UdSSR                 Russia    1984-04-14   \n",
              "3  Aleksandr Zakarlyuka          Estonia                 Russia    1995-06-24   \n",
              "4   Nikita Bezlikhotnov            UdSSR                 Russia    1990-08-19   \n",
              "5           Felipe Saad           Brazil                 Brazil    1983-09-11   \n",
              "6         Jonas Heymans          Belgium                Belgium    1993-02-06   \n",
              "7        Rafael Marques           Brazil                 Brazil    1983-09-21   \n",
              "8   Guillermo Rodriguez          Uruguay                Uruguay    1984-03-21   \n",
              "9           Fernandinho           Brazil                 Brazil    1985-11-25   \n",
              "\n",
              "     position      sub_position   foot  height_in_cm  market_value_in_gbp  \\\n",
              "0    Defender       Centre-Back  Right           179             338000.0   \n",
              "1  Goalkeeper        Goalkeeper  Right           186             360000.0   \n",
              "2    Defender       Centre-Back   Left           182             338000.0   \n",
              "3    Midfield  Central Midfield  Right           178             203000.0   \n",
              "4      Attack      Right Winger  Right           179             225000.0   \n",
              "5    Defender       Centre-Back   Left           187             338000.0   \n",
              "6    Defender         Left-Back   Left           182             338000.0   \n",
              "7    Defender       Centre-Back  Right           187             338000.0   \n",
              "8    Defender       Centre-Back   Left           185              45000.0   \n",
              "9      Attack       Left Winger   Left           173             135000.0   \n",
              "\n",
              "   highest_market_value_in_gbp  \\\n",
              "0                      23000.0   \n",
              "1                     900000.0   \n",
              "2                     270000.0   \n",
              "3                     203000.0   \n",
              "4                     675000.0   \n",
              "5                    1080000.0   \n",
              "6                     450000.0   \n",
              "7                    1620000.0   \n",
              "8                    2610000.0   \n",
              "9                    1800000.0   \n",
              "\n",
              "                                                 url  \n",
              "0  https://www.transfermarkt.co.uk/andrey-naletov...  \n",
              "1  https://www.transfermarkt.co.uk/damien-perquis...  \n",
              "2  https://www.transfermarkt.co.uk/ivan-lozenkov/...  \n",
              "3  https://www.transfermarkt.co.uk/aleksandr-zaka...  \n",
              "4  https://www.transfermarkt.co.uk/nikita-bezlikh...  \n",
              "5  https://www.transfermarkt.co.uk/felipe-saad/pr...  \n",
              "6  https://www.transfermarkt.co.uk/jonas-heymans/...  \n",
              "7  https://www.transfermarkt.co.uk/rafael-marques...  \n",
              "8  https://www.transfermarkt.co.uk/guillermo-rodr...  \n",
              "9  https://www.transfermarkt.co.uk/fernandinho/pr...  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Replace missing values in market value in group player with their median value.\n",
        "#Create copy of DataFrame\n",
        "data_new = players.copy()\n",
        "data_new = data_new.fillna(data_new[\"market_value_in_gbp\"].median())\n",
        "#Updated DataFrame\n",
        "data_new.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f92bc6f",
      "metadata": {
        "scrolled": true,
        "id": "7f92bc6f",
        "outputId": "ca61b3ec-9910-4983-e071-4ef54d85f5e3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\DELLIN~1\\AppData\\Local\\Temp/ipykernel_9668/3998685953.py:4: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
            "  data_new[\"date_of_birth\"] = data_new[\"date_of_birth\"].apply(lambda x : (pd.datetime.now().year - x.year))\n"
          ]
        },
        {
          "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_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>current_club_id</th>\n",
              "      <th>name</th>\n",
              "      <th>pretty_name</th>\n",
              "      <th>country_of_birth</th>\n",
              "      <th>country_of_citizenship</th>\n",
              "      <th>Age</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>foot</th>\n",
              "      <th>height_in_cm</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214258</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>andrey-naletov</td>\n",
              "      <td>Andrey Naletov</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>26</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/andrey-naletov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45247</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>damien-perquis</td>\n",
              "      <td>Damien Perquis</td>\n",
              "      <td>France</td>\n",
              "      <td>France</td>\n",
              "      <td>36</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Right</td>\n",
              "      <td>186</td>\n",
              "      <td>360000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/damien-perquis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>ivan-lozenkov</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/ivan-lozenkov/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>217036</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>aleksandr-zakarlyuka</td>\n",
              "      <td>Aleksandr Zakarlyuka</td>\n",
              "      <td>Estonia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>27</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Central Midfield</td>\n",
              "      <td>Right</td>\n",
              "      <td>178</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/aleksandr-zaka...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>97205</td>\n",
              "      <td>2014</td>\n",
              "      <td>28095</td>\n",
              "      <td>nikita-bezlikhotnov</td>\n",
              "      <td>Nikita Bezlikhotnov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>32</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Right Winger</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>675000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/nikita-bezlikh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57208</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>felipe-saad</td>\n",
              "      <td>Felipe Saad</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>39</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1080000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/felipe-saad/pr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>140799</td>\n",
              "      <td>2014</td>\n",
              "      <td>403</td>\n",
              "      <td>jonas-heymans</td>\n",
              "      <td>Jonas Heymans</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>29</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Left-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>450000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/jonas-heymans/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>55472</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>rafael-marques</td>\n",
              "      <td>Rafael Marques</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>39</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1620000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/rafael-marques...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37633</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>guillermo-rodriguez</td>\n",
              "      <td>Guillermo Rodriguez</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>185</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2610000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/guillermo-rodr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>76902</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>fernandinho</td>\n",
              "      <td>Fernandinho</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>37</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>Left</td>\n",
              "      <td>173</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>1800000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/fernandinho/pr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id  last_season  current_club_id                  name  \\\n",
              "0     214258         2014             3729        andrey-naletov   \n",
              "1      45247         2014             1162        damien-perquis   \n",
              "2      61389         2014             3729         ivan-lozenkov   \n",
              "3     217036         2014             3729  aleksandr-zakarlyuka   \n",
              "4      97205         2014            28095   nikita-bezlikhotnov   \n",
              "5      57208         2014             1162           felipe-saad   \n",
              "6     140799         2014              403         jonas-heymans   \n",
              "7      55472         2014              276        rafael-marques   \n",
              "8      37633         2014              276   guillermo-rodriguez   \n",
              "9      76902         2014              276           fernandinho   \n",
              "\n",
              "            pretty_name country_of_birth country_of_citizenship  Age  \\\n",
              "0        Andrey Naletov           Russia                 Russia   26   \n",
              "1        Damien Perquis           France                 France   36   \n",
              "2         Ivan Lozenkov            UdSSR                 Russia   38   \n",
              "3  Aleksandr Zakarlyuka          Estonia                 Russia   27   \n",
              "4   Nikita Bezlikhotnov            UdSSR                 Russia   32   \n",
              "5           Felipe Saad           Brazil                 Brazil   39   \n",
              "6         Jonas Heymans          Belgium                Belgium   29   \n",
              "7        Rafael Marques           Brazil                 Brazil   39   \n",
              "8   Guillermo Rodriguez          Uruguay                Uruguay   38   \n",
              "9           Fernandinho           Brazil                 Brazil   37   \n",
              "\n",
              "     position      sub_position   foot  height_in_cm  market_value_in_gbp  \\\n",
              "0    Defender       Centre-Back  Right           179             338000.0   \n",
              "1  Goalkeeper        Goalkeeper  Right           186             360000.0   \n",
              "2    Defender       Centre-Back   Left           182             338000.0   \n",
              "3    Midfield  Central Midfield  Right           178             203000.0   \n",
              "4      Attack      Right Winger  Right           179             225000.0   \n",
              "5    Defender       Centre-Back   Left           187             338000.0   \n",
              "6    Defender         Left-Back   Left           182             338000.0   \n",
              "7    Defender       Centre-Back  Right           187             338000.0   \n",
              "8    Defender       Centre-Back   Left           185              45000.0   \n",
              "9      Attack       Left Winger   Left           173             135000.0   \n",
              "\n",
              "   highest_market_value_in_gbp  \\\n",
              "0                      23000.0   \n",
              "1                     900000.0   \n",
              "2                     270000.0   \n",
              "3                     203000.0   \n",
              "4                     675000.0   \n",
              "5                    1080000.0   \n",
              "6                     450000.0   \n",
              "7                    1620000.0   \n",
              "8                    2610000.0   \n",
              "9                    1800000.0   \n",
              "\n",
              "                                                 url  \n",
              "0  https://www.transfermarkt.co.uk/andrey-naletov...  \n",
              "1  https://www.transfermarkt.co.uk/damien-perquis...  \n",
              "2  https://www.transfermarkt.co.uk/ivan-lozenkov/...  \n",
              "3  https://www.transfermarkt.co.uk/aleksandr-zaka...  \n",
              "4  https://www.transfermarkt.co.uk/nikita-bezlikh...  \n",
              "5  https://www.transfermarkt.co.uk/felipe-saad/pr...  \n",
              "6  https://www.transfermarkt.co.uk/jonas-heymans/...  \n",
              "7  https://www.transfermarkt.co.uk/rafael-marques...  \n",
              "8  https://www.transfermarkt.co.uk/guillermo-rodr...  \n",
              "9  https://www.transfermarkt.co.uk/fernandinho/pr...  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Calculate the age of players from their date of birth\n",
        "from datetime import date\n",
        "data_new[\"date_of_birth\"] = pd.to_datetime(data_new[\"date_of_birth\"])\n",
        "data_new[\"date_of_birth\"] = data_new[\"date_of_birth\"].apply(lambda x : (pd.datetime.now().year - x.year))\n",
        "data_new.rename(columns = {'date_of_birth':'Age'}, inplace = True)\n",
        "data_new.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "431583e9",
      "metadata": {
        "id": "431583e9",
        "outputId": "73f8b945-306d-464b-d42c-9b447e35e6b2"
      },
      "outputs": [
        {
          "data": {
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              "<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_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>current_club_id</th>\n",
              "      <th>name</th>\n",
              "      <th>pretty_name</th>\n",
              "      <th>country_of_birth</th>\n",
              "      <th>country_of_citizenship</th>\n",
              "      <th>Age</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>foot</th>\n",
              "      <th>height_in_cm</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214258</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>andrey-naletov</td>\n",
              "      <td>Andrey Naletov</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>26</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/andrey-naletov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45247</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>damien-perquis</td>\n",
              "      <td>Damien Perquis</td>\n",
              "      <td>France</td>\n",
              "      <td>France</td>\n",
              "      <td>36</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Right</td>\n",
              "      <td>186</td>\n",
              "      <td>360000.0</td>\n",
              "      <td>900000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/damien-perquis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>ivan-lozenkov</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/ivan-lozenkov/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>217036</td>\n",
              "      <td>2014</td>\n",
              "      <td>3729</td>\n",
              "      <td>aleksandr-zakarlyuka</td>\n",
              "      <td>Aleksandr Zakarlyuka</td>\n",
              "      <td>Estonia</td>\n",
              "      <td>Russia</td>\n",
              "      <td>27</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Central Midfield</td>\n",
              "      <td>Right</td>\n",
              "      <td>178</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/aleksandr-zaka...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>97205</td>\n",
              "      <td>2014</td>\n",
              "      <td>28095</td>\n",
              "      <td>nikita-bezlikhotnov</td>\n",
              "      <td>Nikita Bezlikhotnov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>Russia</td>\n",
              "      <td>32</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Right Winger</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>675000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/nikita-bezlikh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57208</td>\n",
              "      <td>2014</td>\n",
              "      <td>1162</td>\n",
              "      <td>felipe-saad</td>\n",
              "      <td>Felipe Saad</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>39</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1080000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/felipe-saad/pr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>140799</td>\n",
              "      <td>2014</td>\n",
              "      <td>403</td>\n",
              "      <td>jonas-heymans</td>\n",
              "      <td>Jonas Heymans</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>29</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Left-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>450000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/jonas-heymans/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>55472</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>rafael-marques</td>\n",
              "      <td>Rafael Marques</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>39</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1620000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/rafael-marques...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37633</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>guillermo-rodriguez</td>\n",
              "      <td>Guillermo Rodriguez</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>185</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2610000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/guillermo-rodr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>76902</td>\n",
              "      <td>2014</td>\n",
              "      <td>276</td>\n",
              "      <td>fernandinho</td>\n",
              "      <td>Fernandinho</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>37</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>Left</td>\n",
              "      <td>173</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>1800000.0</td>\n",
              "      <td>https://www.transfermarkt.co.uk/fernandinho/pr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id  last_season  current_club_id                  name  \\\n",
              "0     214258         2014             3729        andrey-naletov   \n",
              "1      45247         2014             1162        damien-perquis   \n",
              "2      61389         2014             3729         ivan-lozenkov   \n",
              "3     217036         2014             3729  aleksandr-zakarlyuka   \n",
              "4      97205         2014            28095   nikita-bezlikhotnov   \n",
              "5      57208         2014             1162           felipe-saad   \n",
              "6     140799         2014              403         jonas-heymans   \n",
              "7      55472         2014              276        rafael-marques   \n",
              "8      37633         2014              276   guillermo-rodriguez   \n",
              "9      76902         2014              276           fernandinho   \n",
              "\n",
              "            pretty_name country_of_birth country_of_citizenship  Age  \\\n",
              "0        Andrey Naletov           Russia                 Russia   26   \n",
              "1        Damien Perquis           France                 France   36   \n",
              "2         Ivan Lozenkov            UdSSR                 Russia   38   \n",
              "3  Aleksandr Zakarlyuka          Estonia                 Russia   27   \n",
              "4   Nikita Bezlikhotnov            UdSSR                 Russia   32   \n",
              "5           Felipe Saad           Brazil                 Brazil   39   \n",
              "6         Jonas Heymans          Belgium                Belgium   29   \n",
              "7        Rafael Marques           Brazil                 Brazil   39   \n",
              "8   Guillermo Rodriguez          Uruguay                Uruguay   38   \n",
              "9           Fernandinho           Brazil                 Brazil   37   \n",
              "\n",
              "     position      sub_position   foot  height_in_cm  market_value_in_gbp  \\\n",
              "0    Defender       Centre-Back  Right           179             338000.0   \n",
              "1  Goalkeeper        Goalkeeper  Right           186             360000.0   \n",
              "2    Defender       Centre-Back   Left           182             338000.0   \n",
              "3    Midfield  Central Midfield  Right           178             203000.0   \n",
              "4      Attack      Right Winger  Right           179             225000.0   \n",
              "5    Defender       Centre-Back   Left           187             338000.0   \n",
              "6    Defender         Left-Back   Left           182             338000.0   \n",
              "7    Defender       Centre-Back  Right           187             338000.0   \n",
              "8    Defender       Centre-Back   Left           185              45000.0   \n",
              "9      Attack       Left Winger   Left           173             135000.0   \n",
              "\n",
              "   highest_market_value_in_gbp  \\\n",
              "0                      23000.0   \n",
              "1                     900000.0   \n",
              "2                     270000.0   \n",
              "3                     203000.0   \n",
              "4                     675000.0   \n",
              "5                    1080000.0   \n",
              "6                     450000.0   \n",
              "7                    1620000.0   \n",
              "8                    2610000.0   \n",
              "9                    1800000.0   \n",
              "\n",
              "                                                 url  \n",
              "0  https://www.transfermarkt.co.uk/andrey-naletov...  \n",
              "1  https://www.transfermarkt.co.uk/damien-perquis...  \n",
              "2  https://www.transfermarkt.co.uk/ivan-lozenkov/...  \n",
              "3  https://www.transfermarkt.co.uk/aleksandr-zaka...  \n",
              "4  https://www.transfermarkt.co.uk/nikita-bezlikh...  \n",
              "5  https://www.transfermarkt.co.uk/felipe-saad/pr...  \n",
              "6  https://www.transfermarkt.co.uk/jonas-heymans/...  \n",
              "7  https://www.transfermarkt.co.uk/rafael-marques...  \n",
              "8  https://www.transfermarkt.co.uk/guillermo-rodr...  \n",
              "9  https://www.transfermarkt.co.uk/fernandinho/pr...  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Replace the missing position ('0') with Attack\n",
        "data_new['position'].replace('0', 'Attack', inplace=True)\n",
        "data_new.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68786df4",
      "metadata": {
        "id": "68786df4",
        "outputId": "6b707dbc-8d9e-48cb-b9d4-3bca898215f0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>Name</th>\n",
              "      <th>country_of_birth</th>\n",
              "      <th>Age</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>foot</th>\n",
              "      <th>height_in_cm</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214258</td>\n",
              "      <td>2014</td>\n",
              "      <td>Andrey Naletov</td>\n",
              "      <td>Russia</td>\n",
              "      <td>26</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>23000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45247</td>\n",
              "      <td>2014</td>\n",
              "      <td>Damien Perquis</td>\n",
              "      <td>France</td>\n",
              "      <td>36</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Right</td>\n",
              "      <td>186</td>\n",
              "      <td>360000.0</td>\n",
              "      <td>900000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>217036</td>\n",
              "      <td>2014</td>\n",
              "      <td>Aleksandr Zakarlyuka</td>\n",
              "      <td>Estonia</td>\n",
              "      <td>27</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Central Midfield</td>\n",
              "      <td>Right</td>\n",
              "      <td>178</td>\n",
              "      <td>203000.0</td>\n",
              "      <td>203000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>97205</td>\n",
              "      <td>2014</td>\n",
              "      <td>Nikita Bezlikhotnov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>32</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Right Winger</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>675000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57208</td>\n",
              "      <td>2014</td>\n",
              "      <td>Felipe Saad</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>39</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1080000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>140799</td>\n",
              "      <td>2014</td>\n",
              "      <td>Jonas Heymans</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>29</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Left-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>450000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>55472</td>\n",
              "      <td>2014</td>\n",
              "      <td>Rafael Marques</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>39</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>187</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>1620000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>37633</td>\n",
              "      <td>2014</td>\n",
              "      <td>Guillermo Rodriguez</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>185</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2610000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>76902</td>\n",
              "      <td>2014</td>\n",
              "      <td>Fernandinho</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>37</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>Left</td>\n",
              "      <td>173</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>1800000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id  last_season                  Name country_of_birth  Age  \\\n",
              "0     214258         2014        Andrey Naletov           Russia   26   \n",
              "1      45247         2014        Damien Perquis           France   36   \n",
              "2      61389         2014         Ivan Lozenkov            UdSSR   38   \n",
              "3     217036         2014  Aleksandr Zakarlyuka          Estonia   27   \n",
              "4      97205         2014   Nikita Bezlikhotnov            UdSSR   32   \n",
              "5      57208         2014           Felipe Saad           Brazil   39   \n",
              "6     140799         2014         Jonas Heymans          Belgium   29   \n",
              "7      55472         2014        Rafael Marques           Brazil   39   \n",
              "8      37633         2014   Guillermo Rodriguez          Uruguay   38   \n",
              "9      76902         2014           Fernandinho           Brazil   37   \n",
              "\n",
              "     position      sub_position   foot  height_in_cm  market_value_in_gbp  \\\n",
              "0    Defender       Centre-Back  Right           179             338000.0   \n",
              "1  Goalkeeper        Goalkeeper  Right           186             360000.0   \n",
              "2    Defender       Centre-Back   Left           182             338000.0   \n",
              "3    Midfield  Central Midfield  Right           178             203000.0   \n",
              "4      Attack      Right Winger  Right           179             225000.0   \n",
              "5    Defender       Centre-Back   Left           187             338000.0   \n",
              "6    Defender         Left-Back   Left           182             338000.0   \n",
              "7    Defender       Centre-Back  Right           187             338000.0   \n",
              "8    Defender       Centre-Back   Left           185              45000.0   \n",
              "9      Attack       Left Winger   Left           173             135000.0   \n",
              "\n",
              "   highest_market_value_in_gbp  \n",
              "0                      23000.0  \n",
              "1                     900000.0  \n",
              "2                     270000.0  \n",
              "3                     203000.0  \n",
              "4                     675000.0  \n",
              "5                    1080000.0  \n",
              "6                     450000.0  \n",
              "7                    1620000.0  \n",
              "8                    2610000.0  \n",
              "9                    1800000.0  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new.rename(columns = {'pretty_name':'Name'}, inplace = True)\n",
        "#Dropping columns that will usefull for prediction of market value\n",
        "players=data_new.drop([\"current_club_id\",\"name\",\"country_of_citizenship\",\"url\"],axis =1 )\n",
        "players.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61a3c136",
      "metadata": {
        "id": "61a3c136",
        "outputId": "f441804e-8ca5-4527-a384-e22a490b7fc0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player_id</th>\n",
              "      <th>game_id</th>\n",
              "      <th>competition_id</th>\n",
              "      <th>player_club_id</th>\n",
              "      <th>goals</th>\n",
              "      <th>assists</th>\n",
              "      <th>minutes_played</th>\n",
              "      <th>yellow_cards</th>\n",
              "      <th>red_cards</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>52453</td>\n",
              "      <td>2483937.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67064</td>\n",
              "      <td>2479929.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67064</td>\n",
              "      <td>2483937.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67064</td>\n",
              "      <td>2484582.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>67064</td>\n",
              "      <td>2485965.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>67064</td>\n",
              "      <td>2487345.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>67064</td>\n",
              "      <td>2498464.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>67064</td>\n",
              "      <td>2500260.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>67064</td>\n",
              "      <td>2506733.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>67064</td>\n",
              "      <td>2508366.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>28095</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id    game_id competition_id  player_club_id  goals  assists  \\\n",
              "0      52453  2483937.0            RU1           28095      0        0   \n",
              "1      67064  2479929.0            RU1           28095      0        0   \n",
              "2      67064  2483937.0            RU1           28095      0        0   \n",
              "3      67064  2484582.0            RU1           28095      0        0   \n",
              "4      67064  2485965.0            RU1           28095      0        0   \n",
              "5      67064  2487345.0            RU1           28095      0        0   \n",
              "6      67064  2498464.0            RU1           28095      0        0   \n",
              "7      67064  2500260.0            RU1           28095      0        1   \n",
              "8      67064  2506733.0            RU1           28095      1        0   \n",
              "9      67064  2508366.0            RU1           28095      0        0   \n",
              "\n",
              "   minutes_played  yellow_cards  red_cards  \n",
              "0              90             0          0  \n",
              "1              90             0          0  \n",
              "2              90             0          0  \n",
              "3              55             0          0  \n",
              "4              90             0          0  \n",
              "5              90             1          0  \n",
              "6              90             0          0  \n",
              "7              19             0          0  \n",
              "8              90             1          0  \n",
              "9               2             0          0  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "appearances=appearances.drop([\"appearance_id\"],axis =1 )\n",
        "appearances.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a1b3097d",
      "metadata": {
        "id": "a1b3097d"
      },
      "source": [
        "# Merge the Dataset of Players and Appearances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c36fb041",
      "metadata": {
        "scrolled": true,
        "id": "c36fb041",
        "outputId": "50e8993a-2103-488c-84b1-5e6432d56ba9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>Name</th>\n",
              "      <th>country_of_birth</th>\n",
              "      <th>Age</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>foot</th>\n",
              "      <th>height_in_cm</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>game_id</th>\n",
              "      <th>competition_id</th>\n",
              "      <th>player_club_id</th>\n",
              "      <th>goals</th>\n",
              "      <th>assists</th>\n",
              "      <th>minutes_played</th>\n",
              "      <th>yellow_cards</th>\n",
              "      <th>red_cards</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214258</td>\n",
              "      <td>2014</td>\n",
              "      <td>Andrey Naletov</td>\n",
              "      <td>Russia</td>\n",
              "      <td>26</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Right</td>\n",
              "      <td>179</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>2537239.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2485967.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2487344.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2489339.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2493787.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2498463.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2500264.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2500984.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2508364.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>61389</td>\n",
              "      <td>2014</td>\n",
              "      <td>Ivan Lozenkov</td>\n",
              "      <td>UdSSR</td>\n",
              "      <td>38</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>Left</td>\n",
              "      <td>182</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>2509352.0</td>\n",
              "      <td>RU1</td>\n",
              "      <td>3729</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   player_id  last_season            Name country_of_birth  Age  position  \\\n",
              "0     214258         2014  Andrey Naletov           Russia   26  Defender   \n",
              "1      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "2      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "3      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "4      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "5      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "6      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "7      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "8      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "9      61389         2014   Ivan Lozenkov            UdSSR   38  Defender   \n",
              "\n",
              "  sub_position   foot  height_in_cm  market_value_in_gbp  \\\n",
              "0  Centre-Back  Right           179             338000.0   \n",
              "1  Centre-Back   Left           182             338000.0   \n",
              "2  Centre-Back   Left           182             338000.0   \n",
              "3  Centre-Back   Left           182             338000.0   \n",
              "4  Centre-Back   Left           182             338000.0   \n",
              "5  Centre-Back   Left           182             338000.0   \n",
              "6  Centre-Back   Left           182             338000.0   \n",
              "7  Centre-Back   Left           182             338000.0   \n",
              "8  Centre-Back   Left           182             338000.0   \n",
              "9  Centre-Back   Left           182             338000.0   \n",
              "\n",
              "   highest_market_value_in_gbp    game_id competition_id  player_club_id  \\\n",
              "0                      23000.0  2537239.0            RU1            3729   \n",
              "1                     270000.0  2485967.0            RU1            3729   \n",
              "2                     270000.0  2487344.0            RU1            3729   \n",
              "3                     270000.0  2489339.0            RU1            3729   \n",
              "4                     270000.0  2493787.0            RU1            3729   \n",
              "5                     270000.0  2498463.0            RU1            3729   \n",
              "6                     270000.0  2500264.0            RU1            3729   \n",
              "7                     270000.0  2500984.0            RU1            3729   \n",
              "8                     270000.0  2508364.0            RU1            3729   \n",
              "9                     270000.0  2509352.0            RU1            3729   \n",
              "\n",
              "   goals  assists  minutes_played  yellow_cards  red_cards  \n",
              "0      0        0              90             1          0  \n",
              "1      0        0              90             0          0  \n",
              "2      0        0              90             0          0  \n",
              "3      0        0              90             1          0  \n",
              "4      0        0              90             0          0  \n",
              "5      0        0              45             0          0  \n",
              "6      0        0              90             1          0  \n",
              "7      0        0              45             1          0  \n",
              "8      0        0              90             0          0  \n",
              "9      0        0              90             1          0  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "player_list = pd.merge(players,appearances,on=\"player_id\")\n",
        "player_list.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c5de0aa",
      "metadata": {
        "scrolled": false,
        "id": "2c5de0aa",
        "outputId": "da8b191a-102d-4545-da17-43433daf94f0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\DELLIN~1\\AppData\\Local\\Temp/ipykernel_9668/537197158.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  player_list= player_list.groupby(['Name','Age','player_id', 'last_season','position','sub_position'])['goals','assists','market_value_in_gbp','highest_market_value_in_gbp','minutes_played','yellow_cards','red_cards'].sum().reset_index()\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>player_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>goals</th>\n",
              "      <th>assists</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>minutes_played</th>\n",
              "      <th>yellow_cards</th>\n",
              "      <th>red_cards</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alpha Bdul Razak</td>\n",
              "      <td>32</td>\n",
              "      <td>339560</td>\n",
              "      <td>2014</td>\n",
              "      <td>Attack</td>\n",
              "      <td>midfield</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>338000.0</td>\n",
              "      <td>9.000000e+04</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A J Soares</td>\n",
              "      <td>34</td>\n",
              "      <td>176850</td>\n",
              "      <td>2016</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2028000.0</td>\n",
              "      <td>1.620000e+06</td>\n",
              "      <td>421</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aaron Appindangoye</td>\n",
              "      <td>30</td>\n",
              "      <td>254249</td>\n",
              "      <td>2021</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Centre-Back</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40500000.0</td>\n",
              "      <td>9.720000e+07</td>\n",
              "      <td>5249</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aaron Bastiaans</td>\n",
              "      <td>20</td>\n",
              "      <td>578459</td>\n",
              "      <td>2020</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>1.800000e+05</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aaron Boupendza</td>\n",
              "      <td>26</td>\n",
              "      <td>427605</td>\n",
              "      <td>2020</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Centre-Forward</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>233100000.0</td>\n",
              "      <td>2.830500e+08</td>\n",
              "      <td>2586</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Aaron Chapman</td>\n",
              "      <td>32</td>\n",
              "      <td>270571</td>\n",
              "      <td>2020</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>Goalkeeper</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1080000.0</td>\n",
              "      <td>1.350000e+06</td>\n",
              "      <td>447</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Aaron Comrie</td>\n",
              "      <td>25</td>\n",
              "      <td>360102</td>\n",
              "      <td>2018</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Right-Back</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3780000.0</td>\n",
              "      <td>3.780000e+06</td>\n",
              "      <td>1114</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Aaron Connolly</td>\n",
              "      <td>22</td>\n",
              "      <td>434207</td>\n",
              "      <td>2020</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Centre-Forward</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>277200000.0</td>\n",
              "      <td>2.772000e+08</td>\n",
              "      <td>2214</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Aaron Cresswell</td>\n",
              "      <td>33</td>\n",
              "      <td>92571</td>\n",
              "      <td>2021</td>\n",
              "      <td>Defender</td>\n",
              "      <td>Left-Back</td>\n",
              "      <td>9</td>\n",
              "      <td>33</td>\n",
              "      <td>957600000.0</td>\n",
              "      <td>2.872800e+09</td>\n",
              "      <td>23134</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Aaron Dhondt</td>\n",
              "      <td>27</td>\n",
              "      <td>351390</td>\n",
              "      <td>2016</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Second Striker</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>676000.0</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Name  Age  player_id  last_season    position  \\\n",
              "0    Alpha Bdul Razak   32     339560         2014      Attack   \n",
              "1          A J Soares   34     176850         2016    Defender   \n",
              "2  Aaron Appindangoye   30     254249         2021    Defender   \n",
              "3     Aaron Bastiaans   20     578459         2020      Attack   \n",
              "4     Aaron Boupendza   26     427605         2020      Attack   \n",
              "5       Aaron Chapman   32     270571         2020  Goalkeeper   \n",
              "6        Aaron Comrie   25     360102         2018    Defender   \n",
              "7      Aaron Connolly   22     434207         2020      Attack   \n",
              "8     Aaron Cresswell   33      92571         2021    Defender   \n",
              "9        Aaron Dhondt   27     351390         2016      Attack   \n",
              "\n",
              "     sub_position  goals  assists  market_value_in_gbp  \\\n",
              "0        midfield      0        0             338000.0   \n",
              "1     Centre-Back      1        0            2028000.0   \n",
              "2     Centre-Back      0        0           40500000.0   \n",
              "3     Left Winger      1        0             180000.0   \n",
              "4  Centre-Forward     22        1          233100000.0   \n",
              "5      Goalkeeper      0        0            1080000.0   \n",
              "6      Right-Back      0        0            3780000.0   \n",
              "7  Centre-Forward      6        3          277200000.0   \n",
              "8       Left-Back      9       33          957600000.0   \n",
              "9  Second Striker      0        0             676000.0   \n",
              "\n",
              "   highest_market_value_in_gbp  minutes_played  yellow_cards  red_cards  \n",
              "0                 9.000000e+04              11             0          0  \n",
              "1                 1.620000e+06             421             2          0  \n",
              "2                 9.720000e+07            5249             9          0  \n",
              "3                 1.800000e+05              16             0          0  \n",
              "4                 2.830500e+08            2586             7          0  \n",
              "5                 1.350000e+06             447             0          0  \n",
              "6                 3.780000e+06            1114             2          0  \n",
              "7                 2.772000e+08            2214             1          0  \n",
              "8                 2.872800e+09           23134            32          0  \n",
              "9                 4.500000e+05              11             0          0  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "player_list= player_list.groupby(['Name','Age','player_id', 'last_season','position','sub_position'])['goals','assists','market_value_in_gbp','highest_market_value_in_gbp','minutes_played','yellow_cards','red_cards'].sum().reset_index()\n",
        "player_list.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59ec8ed7",
      "metadata": {
        "id": "59ec8ed7",
        "outputId": "a2c7f128-01b7-43d5-f61f-5f685e0be1df"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "loc=player_list.position.value_counts()\n",
        "loc.plot(kind='pie' ,autopct='%.1f',fontsize=10,subplots=True)\n",
        "plt.title(\"Player's Positions Distribution\")\n",
        "plt.ylabel(\"\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "16869430",
      "metadata": {
        "scrolled": true,
        "id": "16869430",
        "outputId": "5b6b897d-2b14-4bc4-82cb-b77100e172c3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "loc=player_list.sub_position.value_counts()\n",
        "loc.plot(kind='pie' ,autopct='%.1f',fontsize=10,subplots=True)\n",
        "plt.title(\"Player's Sub_Positions Distribution\")\n",
        "plt.ylabel(\"\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7d071be",
      "metadata": {
        "scrolled": false,
        "id": "a7d071be",
        "outputId": "719f0396-3949-4db6-cd28-7e8cb146e9a8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10),dpi=200)\n",
        "graph = sns.catplot(y=\"position\", x=\"market_value_in_gbp\", kind=\"bar\",  data=player_list)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bb3a6a1",
      "metadata": {
        "id": "7bb3a6a1",
        "outputId": "90f2228b-8b6a-48f9-9add-e5ed653dd459"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10),dpi=200)\n",
        "graph = sns.catplot(y=\"sub_position\", x=\"market_value_in_gbp\", kind=\"bar\",  data=player_list)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8aebc550",
      "metadata": {
        "id": "8aebc550",
        "outputId": "bef31663-2057-493d-ebb5-7fd250efd73a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(1, figsize=(6,6))\n",
        "sns.set(style=\"whitegrid\")\n",
        "plt.hist( x= 'market_value_in_gbp', bins=100,data=player_list)\n",
        "plt.title('Value distribution of all players')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6c8c8c4",
      "metadata": {
        "id": "b6c8c8c4",
        "outputId": "e5454e45-7c0e-4983-bf97-76078094469e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = player_list.market_value_in_gbp\n",
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(x).set_title('Frequency Distribution Plot of Player Actual Values')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d03b63a6",
      "metadata": {
        "id": "d03b63a6"
      },
      "source": [
        "# Top 11 Valuable Player"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c09ab00",
      "metadata": {
        "id": "9c09ab00",
        "outputId": "ae8ef6f2-8c95-4c43-f122-9b862909b142"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>goals</th>\n",
              "      <th>position</th>\n",
              "      <th>sub_position</th>\n",
              "      <th>minutes_played</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9555</th>\n",
              "      <td>Kylian Mbappe</td>\n",
              "      <td>24</td>\n",
              "      <td>156</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Centre-Forward</td>\n",
              "      <td>16820</td>\n",
              "      <td>3.326400e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12026</th>\n",
              "      <td>Mohamed Salah</td>\n",
              "      <td>30</td>\n",
              "      <td>194</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Right Winger</td>\n",
              "      <td>28084</td>\n",
              "      <td>3.141000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14583</th>\n",
              "      <td>Romelu Lukaku</td>\n",
              "      <td>29</td>\n",
              "      <td>175</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Centre-Forward</td>\n",
              "      <td>26819</td>\n",
              "      <td>3.051000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6726</th>\n",
              "      <td>Harry Kane</td>\n",
              "      <td>29</td>\n",
              "      <td>220</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Centre-Forward</td>\n",
              "      <td>27305</td>\n",
              "      <td>2.952000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2603</th>\n",
              "      <td>Bruno Fernandes</td>\n",
              "      <td>28</td>\n",
              "      <td>117</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Attacking Midfield</td>\n",
              "      <td>25719</td>\n",
              "      <td>2.713500e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13955</th>\n",
              "      <td>Raheem Sterling</td>\n",
              "      <td>28</td>\n",
              "      <td>130</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>26778</td>\n",
              "      <td>2.692800e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9197</th>\n",
              "      <td>Kevin De Bruyne</td>\n",
              "      <td>31</td>\n",
              "      <td>88</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Attacking Midfield</td>\n",
              "      <td>25894</td>\n",
              "      <td>2.664900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6859</th>\n",
              "      <td>Heung Min Son</td>\n",
              "      <td>30</td>\n",
              "      <td>127</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>22768</td>\n",
              "      <td>2.390400e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2305</th>\n",
              "      <td>Bernardo Silva</td>\n",
              "      <td>28</td>\n",
              "      <td>67</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Attacking Midfield</td>\n",
              "      <td>24598</td>\n",
              "      <td>2.389500e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14849</th>\n",
              "      <td>Sadio Mane</td>\n",
              "      <td>30</td>\n",
              "      <td>134</td>\n",
              "      <td>Attack</td>\n",
              "      <td>Left Winger</td>\n",
              "      <td>25670</td>\n",
              "      <td>2.340000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8701</th>\n",
              "      <td>Joshua Kimmich</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "      <td>Midfield</td>\n",
              "      <td>Defensive Midfield</td>\n",
              "      <td>22972</td>\n",
              "      <td>2.226150e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Name  Age  goals  position        sub_position  \\\n",
              "9555     Kylian Mbappe   24    156    Attack      Centre-Forward   \n",
              "12026    Mohamed Salah   30    194    Attack        Right Winger   \n",
              "14583    Romelu Lukaku   29    175    Attack      Centre-Forward   \n",
              "6726        Harry Kane   29    220    Attack      Centre-Forward   \n",
              "2603   Bruno Fernandes   28    117  Midfield  Attacking Midfield   \n",
              "13955  Raheem Sterling   28    130    Attack         Left Winger   \n",
              "9197   Kevin De Bruyne   31     88  Midfield  Attacking Midfield   \n",
              "6859     Heung Min Son   30    127    Attack         Left Winger   \n",
              "2305    Bernardo Silva   28     67  Midfield  Attacking Midfield   \n",
              "14849       Sadio Mane   30    134    Attack         Left Winger   \n",
              "8701    Joshua Kimmich   27     33  Midfield  Defensive Midfield   \n",
              "\n",
              "       minutes_played  market_value_in_gbp  \n",
              "9555            16820         3.326400e+10  \n",
              "12026           28084         3.141000e+10  \n",
              "14583           26819         3.051000e+10  \n",
              "6726            27305         2.952000e+10  \n",
              "2603            25719         2.713500e+10  \n",
              "13955           26778         2.692800e+10  \n",
              "9197            25894         2.664900e+10  \n",
              "6859            22768         2.390400e+10  \n",
              "2305            24598         2.389500e+10  \n",
              "14849           25670         2.340000e+10  \n",
              "8701            22972         2.226150e+10  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "player_list.nlargest(11,columns=\"market_value_in_gbp\")[[\"Name\",\"Age\",\"goals\",\"position\",\"sub_position\",\"minutes_played\",\"market_value_in_gbp\"]]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b75d22d",
      "metadata": {
        "id": "9b75d22d"
      },
      "source": [
        "# View the correlations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2cab9c12",
      "metadata": {
        "scrolled": false,
        "id": "2cab9c12",
        "outputId": "cf9d1a92-e198-4314-d215-300ca561bcb5"
      },
      "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>Age</th>\n",
              "      <th>player_id</th>\n",
              "      <th>last_season</th>\n",
              "      <th>goals</th>\n",
              "      <th>assists</th>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <th>minutes_played</th>\n",
              "      <th>yellow_cards</th>\n",
              "      <th>red_cards</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.853182</td>\n",
              "      <td>-0.473766</td>\n",
              "      <td>0.096652</td>\n",
              "      <td>0.100886</td>\n",
              "      <td>-0.057893</td>\n",
              "      <td>0.033531</td>\n",
              "      <td>0.238127</td>\n",
              "      <td>0.212342</td>\n",
              "      <td>0.123792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>player_id</th>\n",
              "      <td>-0.853182</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.392313</td>\n",
              "      <td>-0.154182</td>\n",
              "      <td>-0.181492</td>\n",
              "      <td>-0.011175</td>\n",
              "      <td>-0.097975</td>\n",
              "      <td>-0.322806</td>\n",
              "      <td>-0.273667</td>\n",
              "      <td>-0.143472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_season</th>\n",
              "      <td>-0.473766</td>\n",
              "      <td>0.392313</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.235201</td>\n",
              "      <td>0.282345</td>\n",
              "      <td>0.190727</td>\n",
              "      <td>0.201860</td>\n",
              "      <td>0.400444</td>\n",
              "      <td>0.333526</td>\n",
              "      <td>0.170455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>goals</th>\n",
              "      <td>0.096652</td>\n",
              "      <td>-0.154182</td>\n",
              "      <td>0.235201</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.759202</td>\n",
              "      <td>0.504839</td>\n",
              "      <td>0.628319</td>\n",
              "      <td>0.557552</td>\n",
              "      <td>0.417730</td>\n",
              "      <td>0.198777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assists</th>\n",
              "      <td>0.100886</td>\n",
              "      <td>-0.181492</td>\n",
              "      <td>0.282345</td>\n",
              "      <td>0.759202</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.524681</td>\n",
              "      <td>0.645871</td>\n",
              "      <td>0.676140</td>\n",
              "      <td>0.544371</td>\n",
              "      <td>0.230597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>market_value_in_gbp</th>\n",
              "      <td>-0.057893</td>\n",
              "      <td>-0.011175</td>\n",
              "      <td>0.190727</td>\n",
              "      <td>0.504839</td>\n",
              "      <td>0.524681</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.859451</td>\n",
              "      <td>0.470054</td>\n",
              "      <td>0.333701</td>\n",
              "      <td>0.145507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <td>0.033531</td>\n",
              "      <td>-0.097975</td>\n",
              "      <td>0.201860</td>\n",
              "      <td>0.628319</td>\n",
              "      <td>0.645871</td>\n",
              "      <td>0.859451</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.582716</td>\n",
              "      <td>0.424916</td>\n",
              "      <td>0.193820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minutes_played</th>\n",
              "      <td>0.238127</td>\n",
              "      <td>-0.322806</td>\n",
              "      <td>0.400444</td>\n",
              "      <td>0.557552</td>\n",
              "      <td>0.676140</td>\n",
              "      <td>0.470054</td>\n",
              "      <td>0.582716</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.820009</td>\n",
              "      <td>0.416888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yellow_cards</th>\n",
              "      <td>0.212342</td>\n",
              "      <td>-0.273667</td>\n",
              "      <td>0.333526</td>\n",
              "      <td>0.417730</td>\n",
              "      <td>0.544371</td>\n",
              "      <td>0.333701</td>\n",
              "      <td>0.424916</td>\n",
              "      <td>0.820009</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.438417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>red_cards</th>\n",
              "      <td>0.123792</td>\n",
              "      <td>-0.143472</td>\n",
              "      <td>0.170455</td>\n",
              "      <td>0.198777</td>\n",
              "      <td>0.230597</td>\n",
              "      <td>0.145507</td>\n",
              "      <td>0.193820</td>\n",
              "      <td>0.416888</td>\n",
              "      <td>0.438417</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  Age  player_id  last_season     goals  \\\n",
              "Age                          1.000000  -0.853182    -0.473766  0.096652   \n",
              "player_id                   -0.853182   1.000000     0.392313 -0.154182   \n",
              "last_season                 -0.473766   0.392313     1.000000  0.235201   \n",
              "goals                        0.096652  -0.154182     0.235201  1.000000   \n",
              "assists                      0.100886  -0.181492     0.282345  0.759202   \n",
              "market_value_in_gbp         -0.057893  -0.011175     0.190727  0.504839   \n",
              "highest_market_value_in_gbp  0.033531  -0.097975     0.201860  0.628319   \n",
              "minutes_played               0.238127  -0.322806     0.400444  0.557552   \n",
              "yellow_cards                 0.212342  -0.273667     0.333526  0.417730   \n",
              "red_cards                    0.123792  -0.143472     0.170455  0.198777   \n",
              "\n",
              "                              assists  market_value_in_gbp  \\\n",
              "Age                          0.100886            -0.057893   \n",
              "player_id                   -0.181492            -0.011175   \n",
              "last_season                  0.282345             0.190727   \n",
              "goals                        0.759202             0.504839   \n",
              "assists                      1.000000             0.524681   \n",
              "market_value_in_gbp          0.524681             1.000000   \n",
              "highest_market_value_in_gbp  0.645871             0.859451   \n",
              "minutes_played               0.676140             0.470054   \n",
              "yellow_cards                 0.544371             0.333701   \n",
              "red_cards                    0.230597             0.145507   \n",
              "\n",
              "                             highest_market_value_in_gbp  minutes_played  \\\n",
              "Age                                             0.033531        0.238127   \n",
              "player_id                                      -0.097975       -0.322806   \n",
              "last_season                                     0.201860        0.400444   \n",
              "goals                                           0.628319        0.557552   \n",
              "assists                                         0.645871        0.676140   \n",
              "market_value_in_gbp                             0.859451        0.470054   \n",
              "highest_market_value_in_gbp                     1.000000        0.582716   \n",
              "minutes_played                                  0.582716        1.000000   \n",
              "yellow_cards                                    0.424916        0.820009   \n",
              "red_cards                                       0.193820        0.416888   \n",
              "\n",
              "                             yellow_cards  red_cards  \n",
              "Age                              0.212342   0.123792  \n",
              "player_id                       -0.273667  -0.143472  \n",
              "last_season                      0.333526   0.170455  \n",
              "goals                            0.417730   0.198777  \n",
              "assists                          0.544371   0.230597  \n",
              "market_value_in_gbp              0.333701   0.145507  \n",
              "highest_market_value_in_gbp      0.424916   0.193820  \n",
              "minutes_played                   0.820009   0.416888  \n",
              "yellow_cards                     1.000000   0.438417  \n",
              "red_cards                        0.438417   1.000000  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "corr_mat = player_list.corr()\n",
        "corr_mat"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cf4c0671",
      "metadata": {
        "id": "cf4c0671"
      },
      "source": [
        "# Heatmap of correlation values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "16e28542",
      "metadata": {
        "scrolled": true,
        "id": "16e28542",
        "outputId": "16e62dfb-3bb7-4844-9095-97fd7728c604"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(1, figsize=(18, 10))\n",
        "sns.set(style=\"whitegrid\")\n",
        "sns.heatmap(corr_mat, cmap=\"seismic\", annot=True, vmin=-1, vmax=1)\n",
        "plt.yticks(rotation=0);\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fb8e94a8",
      "metadata": {
        "id": "fb8e94a8"
      },
      "source": [
        "# Model based on most selected columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17c74c22",
      "metadata": {
        "id": "17c74c22",
        "outputId": "f239c0c5-d15d-48f2-b839-125cf28c65fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                 OLS Regression Results                                 \n",
            "========================================================================================\n",
            "Dep. Variable:     market_value_in_gbp   R-squared (uncentered):                   0.751\n",
            "Model:                             OLS   Adj. R-squared (uncentered):              0.751\n",
            "Method:                  Least Squares   F-statistic:                              8962.\n",
            "Date:                 Tue, 12 Apr 2022   Prob (F-statistic):                        0.00\n",
            "Time:                         19:09:09   Log-Likelihood:                     -3.8816e+05\n",
            "No. Observations:                17823   AIC:                                  7.763e+05\n",
            "Df Residuals:                    17817   BIC:                                  7.764e+05\n",
            "Df Model:                            6                                                  \n",
            "Covariance Type:             nonrobust                                                  \n",
            "===============================================================================================\n",
            "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------\n",
            "Age                         -3.106e+05   2.36e+05     -1.317      0.188   -7.73e+05    1.52e+05\n",
            "assists                     -5.648e+06   8.82e+05     -6.404      0.000   -7.38e+06   -3.92e+06\n",
            "yellow_cards                -1.058e+06   7.72e+05     -1.371      0.171   -2.57e+06    4.55e+05\n",
            "red_cards                   -2.074e+07    1.1e+07     -1.880      0.060   -4.24e+07    8.89e+05\n",
            "minutes_played               -817.4468   2161.099     -0.378      0.705   -5053.410    3418.517\n",
            "highest_market_value_in_gbp     0.5220      0.003    170.981      0.000       0.516       0.528\n",
            "==============================================================================\n",
            "Omnibus:                     7206.089   Durbin-Watson:                   2.021\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         10482441.709\n",
            "Skew:                          -0.028   Prob(JB):                         0.00\n",
            "Kurtosis:                     121.808   Cond. No.                     5.11e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
            "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[3] The condition number is large, 5.11e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ],
      "source": [
        "X1= player_list.loc[:,['Age','assists','yellow_cards','red_cards', 'minutes_played','highest_market_value_in_gbp']]\n",
        "y1= player_list.loc[:,\"market_value_in_gbp\"]\n",
        "player_model1 = sm.OLS(y1, X1, data=player_list)\n",
        "results1 = player_model1.fit()\n",
        "print(results1.summary());"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a11aca63",
      "metadata": {
        "id": "a11aca63"
      },
      "source": [
        "# Splitting data into train, test and validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2e65887",
      "metadata": {
        "id": "d2e65887",
        "outputId": "985ed931-50bc-4843-bc98-0ff48de51e3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8044338889746534\n",
            "3.776429153222936e+17\n",
            "[-2.05879355e+07 -6.59810733e+06  9.15752086e+04 -2.92444807e+07\n",
            "  3.91984985e+03  5.04263779e-01]\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X1,y1,test_size=0.2, random_state=10)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X1,y1, test_size=.25, random_state=10)\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train,y_train)\n",
        "pred = lr.predict(X_val)\n",
        "mse = np.mean((pred-y_val)**2)\n",
        "\n",
        "print(lr.score(X_test,y_test))\n",
        "print(mse)\n",
        "print(lr.coef_)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "712e974a",
      "metadata": {
        "id": "712e974a"
      },
      "source": [
        "# What coefficients our regression model has chosen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "18ce12ef",
      "metadata": {
        "id": "18ce12ef",
        "outputId": "3d4b229e-d0c7-4acf-f26a-95d11f40b71f"
      },
      "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>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>minutes_played</th>\n",
              "      <td>2.965015e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highest_market_value_in_gbp</th>\n",
              "      <td>5.181157e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yellow_cards</th>\n",
              "      <td>-1.057956e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assists</th>\n",
              "      <td>-6.300191e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>red_cards</th>\n",
              "      <td>-1.874718e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-2.056025e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Coefficient\n",
              "minutes_played               2.965015e+03\n",
              "highest_market_value_in_gbp  5.181157e-01\n",
              "yellow_cards                -1.057956e+06\n",
              "assists                     -6.300191e+06\n",
              "red_cards                   -1.874718e+07\n",
              "Age                         -2.056025e+07"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "regressor = LinearRegression()\n",
        "regressor.fit(X1,y1)\n",
        "coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=['Coefficient'])\n",
        "coeff_df.sort_values(\"Coefficient\",ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "31a94410",
      "metadata": {
        "id": "31a94410"
      },
      "source": [
        "# Prediction on all data and calculate difference between tha actual value and predicted value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d55471d1",
      "metadata": {
        "id": "d55471d1",
        "outputId": "eab4e8be-51e9-43d7-e415-823abf9824d7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Philippe Coutinho</th>\n",
              "      <td>4.779000e+09</td>\n",
              "      <td>2.031491e+10</td>\n",
              "      <td>-1.553591e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Eden Hazard</th>\n",
              "      <td>4.730400e+09</td>\n",
              "      <td>2.005736e+10</td>\n",
              "      <td>-1.532696e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luis Suarez</th>\n",
              "      <td>3.222000e+09</td>\n",
              "      <td>1.422471e+10</td>\n",
              "      <td>-1.100271e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sergio Busquets</th>\n",
              "      <td>3.110400e+09</td>\n",
              "      <td>1.406434e+10</td>\n",
              "      <td>-1.095394e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lionel Messi</th>\n",
              "      <td>2.041200e+10</td>\n",
              "      <td>3.058249e+10</td>\n",
              "      <td>-1.017049e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Christian Eriksen</th>\n",
              "      <td>4.293000e+09</td>\n",
              "      <td>1.440387e+10</td>\n",
              "      <td>-1.011087e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ivan Rakitic</th>\n",
              "      <td>2.494800e+09</td>\n",
              "      <td>1.254075e+10</td>\n",
              "      <td>-1.004595e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Isco</th>\n",
              "      <td>2.405700e+09</td>\n",
              "      <td>1.215461e+10</td>\n",
              "      <td>-9.748907e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antoine Griezmann</th>\n",
              "      <td>1.728000e+10</td>\n",
              "      <td>2.638377e+10</td>\n",
              "      <td>-9.103766e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sergio Aguero</th>\n",
              "      <td>8.585200e+07</td>\n",
              "      <td>9.146584e+09</td>\n",
              "      <td>-9.060732e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Actual     Predicted    Difference\n",
              "Name                                                       \n",
              "Philippe Coutinho  4.779000e+09  2.031491e+10 -1.553591e+10\n",
              "Eden Hazard        4.730400e+09  2.005736e+10 -1.532696e+10\n",
              "Luis Suarez        3.222000e+09  1.422471e+10 -1.100271e+10\n",
              "Sergio Busquets    3.110400e+09  1.406434e+10 -1.095394e+10\n",
              "Lionel Messi       2.041200e+10  3.058249e+10 -1.017049e+10\n",
              "Christian Eriksen  4.293000e+09  1.440387e+10 -1.011087e+10\n",
              "Ivan Rakitic       2.494800e+09  1.254075e+10 -1.004595e+10\n",
              "Isco               2.405700e+09  1.215461e+10 -9.748907e+09\n",
              "Antoine Griezmann  1.728000e+10  2.638377e+10 -9.103766e+09\n",
              "Sergio Aguero      8.585200e+07  9.146584e+09 -9.060732e+09"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = regressor.predict(X1)\n",
        "df = pd.DataFrame({'Name':player_list.Name,'Actual': y1, 'Predicted': y_pred})\n",
        "df[\"Difference\"] = df[\"Actual\"]-df[\"Predicted\"]\n",
        "df_under = round(df.sort_values('Difference').set_index('Name').head(10),2)\n",
        "df_over = round(df.sort_values('Difference').set_index('Name').tail(10),2)\n",
        "df_under"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cd3d2089",
      "metadata": {
        "id": "cd3d2089",
        "outputId": "4bd2e401-2521-4a57-ea61-832eeb9bf3bb"
      },
      "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Leon Goretzka</th>\n",
              "      <td>1.543500e+10</td>\n",
              "      <td>7.807968e+09</td>\n",
              "      <td>7.627032e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bernardo Silva</th>\n",
              "      <td>2.389500e+10</td>\n",
              "      <td>1.620783e+10</td>\n",
              "      <td>7.687171e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mikel Oyarzabal</th>\n",
              "      <td>1.713600e+10</td>\n",
              "      <td>8.703990e+09</td>\n",
              "      <td>8.432010e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marquinhos</th>\n",
              "      <td>1.869750e+10</td>\n",
              "      <td>9.693955e+09</td>\n",
              "      <td>9.003545e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marcus Rashford</th>\n",
              "      <td>2.080800e+10</td>\n",
              "      <td>1.054331e+10</td>\n",
              "      <td>1.026469e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heung Min Son</th>\n",
              "      <td>2.390400e+10</td>\n",
              "      <td>1.351354e+10</td>\n",
              "      <td>1.039046e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Joshua Kimmich</th>\n",
              "      <td>2.226150e+10</td>\n",
              "      <td>1.178925e+10</td>\n",
              "      <td>1.047225e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kylian Mbappe</th>\n",
              "      <td>3.326400e+10</td>\n",
              "      <td>2.119201e+10</td>\n",
              "      <td>1.207199e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno Fernandes</th>\n",
              "      <td>2.713500e+10</td>\n",
              "      <td>1.348768e+10</td>\n",
              "      <td>1.364732e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romelu Lukaku</th>\n",
              "      <td>3.051000e+10</td>\n",
              "      <td>1.556692e+10</td>\n",
              "      <td>1.494308e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Actual     Predicted    Difference\n",
              "Name                                                     \n",
              "Leon Goretzka    1.543500e+10  7.807968e+09  7.627032e+09\n",
              "Bernardo Silva   2.389500e+10  1.620783e+10  7.687171e+09\n",
              "Mikel Oyarzabal  1.713600e+10  8.703990e+09  8.432010e+09\n",
              "Marquinhos       1.869750e+10  9.693955e+09  9.003545e+09\n",
              "Marcus Rashford  2.080800e+10  1.054331e+10  1.026469e+10\n",
              "Heung Min Son    2.390400e+10  1.351354e+10  1.039046e+10\n",
              "Joshua Kimmich   2.226150e+10  1.178925e+10  1.047225e+10\n",
              "Kylian Mbappe    3.326400e+10  2.119201e+10  1.207199e+10\n",
              "Bruno Fernandes  2.713500e+10  1.348768e+10  1.364732e+10\n",
              "Romelu Lukaku    3.051000e+10  1.556692e+10  1.494308e+10"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_over"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "267305fe",
      "metadata": {
        "id": "267305fe"
      },
      "source": [
        "# Plot the comparision of Actual and Predicted Values for Over Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "690937a3",
      "metadata": {
        "id": "690937a3",
        "outputId": "b16fff52-03a9-42ae-b858-7b2aa8b3ea27"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_under[[\"Actual\",\"Predicted\"]].plot(kind='bar',figsize=(10,5))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.title(\"Actual vs Predicted Values for Over Predicted\")\n",
        "plt.xlabel(\"Player Name\")\n",
        "plt.ylabel('Value')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0ea757d9",
      "metadata": {
        "id": "0ea757d9"
      },
      "source": [
        "# Plot the comparision of Actual and Predicted Values for under Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "621a6207",
      "metadata": {
        "id": "621a6207",
        "outputId": "8ea78af8-28f0-477f-ed57-b1fd4a059a1f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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s7Iy0tDS0bt1aMSWFp6cnunfvDiMjI9SqVeuTecuVK4eKFSvizp07aNasGYD3BcnWrVvh4OAAmUwGKysrFC9eHFFRUUrHDhgwAGPGjMHx48dRsWJFxZc9fX19LFq0CFOnTsXy5cuRkZGBoUOHol69eqhatSouXLgAR0dHGBoaonTp0p8d7D5gwABMnz4dbdu2RUZGBmrVqoXx48d/9nfRpk0bnDx5Ei1btkTJkiVhZWWl1KuUk6zXP6fXr0OHDoiJiYG7uztkMhlKly6NadOmfTbH1/rU88yaNQtjx47F5s2bUb58eVSsWDHb8Z06dcKTJ0/Qrl07CIIAKysreHh4ICEhAQYGBnBzc8OWLVswb948+Pn5ITU1FYIgwM/PD2XLlkWnTp3w6NEjtG7dGiYmJp89jUyU32TC5/6StYCdnR3Wrl2Lw4cP4/Hjx/D29gYAJCYmIjMzE0WKFFHcd8yYMbCyskK7du2Qnp6OBg0a4OLFi9DV1cWzZ8/QtWvXHL/dExEREamDVp+m/FiDBg1w8OBBvHr1CoIgwNfXF2vWrMn1/gUKFIClpaWiyzo4OBhNmzbNr7hERERE0irGLCwsMGjQIHTv3h1OTk6Qy+Xo16/fJ4/x8fFBYGAgHB0dcenSJQwbNix/whIRERFBIqcpiYiIiLSVpHrGiIiIiLSNVl5NKZfLkZiYiAIFCvDqRyIiItJogiAgPT0dRkZGOU7ErZXFWGJiYo5z0RARERFpql9++SXHdVG1shjLWkLkl19+gb6+fr4855xzczDMeli+PJcY2D7tJeW2AWyftmP7tJeU2wbkb/vS0tJw584dRf3yMa0sxrJOTerr68PAwCBfnjNZSM635xID26e9pNw2gO3Tdmyf9pJy2wBx2pfb0CoO4CciIiISEYsxIiIiIhFp5WnKT0lPT0d0dDRSUlJU+rj2Re0RERGh0sfUJJ9qX8GCBVG2bNlcz3UTERHRt5NcMRYdHY3ChQujQoUKKp324um7p/ix8I8qezxNk1v7BEHAq1evEB0dDXNzcxGSERERSZvkTlOmpKTA1NSU84+piEwmg6mpqcp7GomIiOg9yRVjQO5XK9C34etJRESkPpIsxoiIiIi0heSLsbT0TJU8zsfjqb72ce/cuYMqVapg//79n7zf48ePMW7cuK/Ol6VKlSrffCwRERHlP8kN4P+YfgFdOI/cofLHDZnp8lX337ZtGxwcHBAQEAB7e/tc7/f06VM8fvw4r/GIiIhIS0i+Z0wTpKenIyQkBMOGDUN4eDgePXoEADhz5gzatGkDZ2dn/Pnnn0hISMCUKVMQFhaGiRMn4vz58/Dw8FA8zpgxY7B9+3YAwOzZs+Hu7g57e3t4eHjg5cuXorSNiEgKvvUsypD6w1WchL5Hku8Z0wTHjx/Hjz/+CHNzc7Ro0QIBAQEYOnQo/v77b6xYsQJVq1bFzJkzERQUBG9vbyxYsAA+Pj44f/58jo8XFRWFBw8eYPPmzdDR0cGoUaOwc+dO9OrVK59bRkQkDd96FuVrz5IQ5YTFWD7Ytm0bWrduDQBwdHTE33//DXt7e5QsWRJVq1YFAIwcORIAci3APvTTTz9h9OjR2LJlCx4+fIirV6+ifPny6msAERERqQ2LMTV79eoVTp48ifDwcKxduxaCIODt27c4ceKE0pQR7969Q2JiotKxMpkMgiAobqenpwMAwsLCMHLkSPTo0QP29vbQ0dFRuh8RERFpD44ZU7MdO3bA2toaJ06cwJEjR3D06FH0798fJ06cwKtXr3Dv3j0AwPLly7Fp0ybo6uoiIyMDAFCsWDE8fvwYqampiIuLQ2hoKADg4sWLsLKyQufOnVGhQgUcO3YMmZmquWqUiIiI8pfke8bS0jPVck4/LT0T+gV0P3u/oKAgDB+uPMCzS5cuWL58OZYtW4ZRo0YhPT0d5cuXh5+fH9LS0vDu3Tt4enpixowZaNasGZycnFCmTBnUq1cPwPtTnYMGDYKzszMAoEaNGoiOjlZ5G4mIiEj9JF+MfUnB9CU+XrvxSx83JCQk27bixYvj2rVrAKC4OjKLkZERdu3apbg9adKkHB93y5YtOW6/ffv2F+UiIiIizcDTlEREREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCLJF2PyjDSVPM6H01qo8nGJiIjo+yb5ecZ09PTxYGp7lT9uRa9tX3S/6OhoODg4oFKlSpDJZEhPT4eZmRn+/fdflCpV6qufd/v27bhw4QKmTZuGvn37YsqUKShZsmSO9503bx5sbGxgaWn5xY9fpUoVzlVGRESUjyTfM6YJzMzMsGPHDgQHB2P37t2oUqUK/Pz88vy4y5Yty7UQA94vm8RlkoiIiDSb5HvGNFGDBg0wa9Ys2NnZoVatWoiIiMDGjRtx8uRJrFmzBnK5HNWrV4ePjw8MDAwQHByMxYsXw9jYGGXKlEGhQoUAAHZ2dli7di1KlCiBiRMnIjQ0FAUKFMBff/2FtLQ0hIWFwdvbGwsWLEDBggXh6+uLuLg4FCxYEOPHj0e1atUQHR0NT09PxCfEw7LOl/egERERkWqwZyyfpaenY//+/ahduzYAoGnTpti/fz9ev36NwMBAbN68GTt27ICpqSlWrFiBmJgY+Pv7Y8OGDQgICEBiYmK2x1y3bh2SkpKwd+9erFq1CgsXLoSjoyNq1KiBKVOmoEqVKhg9ejQ8PT0RFBSEyZMnK9bLnDx5Mtq1a4flG5ejbt26+flSEBEREdgzli9evHgBF5f3i5WnpaWhVq1aGDlyJE6fPo1ff/0VAHD+/HlERUXB3d0dwPuirVq1arhy5Qrq1KmDH374AQDg7OyMc+fOKT3+xYsX4e7uDh0dHZQoUQK7d+9W2p+YmIiwsDCMHTtWsS0pKQlv3rzBhQsXMHPmTLwV3qJNmzbw9vZW2+tARERE2bEYywdZY8ZyYmBgAADIzMxEq1atFMVQYmIiMjMzcfbsWQiCoLi/nl72X5menh5kMpnidlRUFEqXLq24LZfLoa+vr5Th+fPnMDExAQDF48tkMujosLOUiIgoP/GTV0M0aNAABw8exKtXryAIAnx9fbFmzRrUq1cPV69eRUxMDORyOfbs2ZPt2Pr162PPnj0QBAGvXr1C165dkZaWBl1dXWRmZqJw4cKoUKGCohg7ffo0unTpAgCwsbHBzp07AQAHDhxAampq/jWaiIiIpN8zJs9I++JpKL72cXX09FX2eBYWFhg0aBC6d+8OuVyOqlWrol+/fjAwMIC3tzd69OgBQ0ND/Pzzz9mO/eOPPzBlyhS0adMGADB+/HgYGxujSZMm8PHxwfTp0zFjxgz4+vpi+fLlKFCgAGbPng2ZTIYJEybA09MT6zetR51adWBkZKSyNhEREdHnSb4YU1XB9PTdU6WJX7/0ccuWLYsjR47kuO/j7R06dECHDh2y3c/BwQEODg6fPH7SpEnZ9vfu3Ru9e/dW3F63bl22+5QsWRJr165VtO+ff/7JvTFEREQaJi09E/oFdL/6uCH1h6shzbdRazE2d+5c7N+/HzKZDG5ubujZs6fS/oiICHh5eSExMRGWlpaYOHFijmOiiIiIiHKiX0AXziNzHpf9KSEzXdSQ5tuobczYhQsXcO7cOezcuRPbtm3DunXr8ODBA6X7eHp6YsKECdi/fz8EQUBgYKC64hARERFpJLUVY1ZWVli7di309PTw6tUrZGZmKiYrBYAnT54gJSVFMd9Wu3btsG/fPpU894dXH1Le8fUkIiJSH7WeEyxQoADmzZuHlStXwsHBQWnpnhcvXqBEiRKK2yVKlEBMTMxXPf6cc3OQLCQrbbM2toZutC6MihopTfeQV+9S3+Epnqrs8TRNbu0TBAGJ8YmISoxCwLEAEZKpxrHIY/A95it2DLWQctsAtk/baUv7fJv7fvuxWtC+b8HfneoYygzRwrhFrvvVPkBryJAh6Nu3L/r374/AwEB07NgRwPu5rz4slgRB+OriaZj1MMU8XVnS09MRHR2Nt0/e5j38B+JS4iAvKFfpY2qST7WvYMGC+O3X3+BQIPtFBNrC95hvnv5gNZmU2wawfdpO6u0D8lYMaDL+7lQnNTUVYWFhue5XWzF2//59pKWloWrVqjA0NETLli1x+/Ztxf5SpUohNjZWcfvly5cwMzPL8/MWKFAA5ubmeX6cj/ke84VvHV+VP66mkHr7iIiINJXaxoxFR0fD29sbaWlpSEtLw+HDh1GvXj3F/jJlysDAwAChoaEAgB07dqBp06bqikNERESkkdRWjDVr1gzNmzdH27Zt0b59e9SpUwdOTk7o27cvbty4AQDw9/fHv//+CwcHByQlJaFbt27qikNERESkkdQ6Zmzw4MEYPHiw0rZly5YpfrawsMDWrVvVGYGIiIhIo3FtSiIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIglLS8/8puOG1B+u4iSUGz2xAxAREZH66BfQhfPIHV99XMhMFzWkoZywZ4yIiIhIRCzGiIiIiETEYoyIiIhIRCzGiIiIiETEYoyIiIhIRCzGiIiIiETEYoyIiIhIRCzGiIiIiETEYoyIiIhIRCzGiIiIiETEYoyIiIhIRGpdm3LBggXYu3cvAKBZs2YYNWpUtv3btm1DkSJFAADu7u7o0qWLOiMRERERaRS1FWNnzpzBqVOnEBQUBJlMhj59+uDgwYP4/fffFfcJCwvDrFmzUKdOHXXFICIiItJoaivGSpQogTFjxkBfXx8AUKlSJTx9+lTpPmFhYViyZAmePHmC+vXrY/To0TAwMFBXJCIiIiKNo7YxY5UrV0bt2rUBAJGRkdi7dy+aNWum2J+YmIiqVavC09MTQUFBePv2LRYtWqSuOEREREQaSa1jxgDg7t27+PPPPzFq1ChUqFBBsd3IyAjLli1T3O7VqxfGjRuH4cOHf/Fjzzk3B8lCsirj5upY5DH4HvPNl+cSA9unvaTcNoDt03ba0j7f5r7ffqyGt0/KbQO0o32GMkO0MG6R6361FmOhoaEYMmQIxo0bBycnJ6V9T58+xZkzZ+Dm5gYAEAQBenpfF2eY9bB8O63pe8w3T79wTcf2aS8ptw1g+7Sd1NsH5K0Y0HRSbhuQf+1LTU1FWFhYrvvVdpry2bNnGDhwIPz9/bMVYgBQsGBBzJgxA48fP4YgCNiwYYPS4H4iIiKi74HaesZWrFiB1NRUTJs2TbGtU6dOOHLkCIYMGYKaNWti0qRJGDBgANLT01G3bl307NlTXXGIiIiINJLaijFvb294e3tn2965c2fFz/b29rC3t1dXBCIiIiKNxxn4iYiIiETEYoyIiIhIRCzGiIiIiETEYkxi0tIzv+m4IfW/fH43IsqOf3tE9K3UPukr5S/9ArpwHrnjq48LmemihjRE3w/+7RHRt2LPGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwREdFncSF0IvXhQuFERPRZXAidSH3YM0ZEREQkIhZjRERERCJiMUZEREQkIhZjRERERCJiMUZEREQkIhZjRERERCJiMUZERPSN5Blp+XocSRPnGSMiIvpGOnr6eDC1/VcfV9FrmxrSkLZizxgRERGRiFiMEVG+4HI6REQ542lKIsoXXE6HiChn7BkjIiIiEpFai7EFCxbAyckJTk5O8PPzy7Y/IiIC7dq1g729Pby8vJCRkaHOOEREREQaR23F2JkzZ3Dq1CkEBQUhODgY4eHhOHjwoNJ9PD09MWHCBOzfvx+CICAwMFBdcYiIiIg0ktqKsRIlSmDMmDHQ19dHgQIFUKlSJTx9+lSx/8mTJ0hJSUHt2rUBAO3atcO+ffvUFYeIiIhII6ltAH/lypUVP0dGRmLv3r3YtGmTYtuLFy9QokQJxe0SJUogJiZGXXGIiIiINJLar6a8e/cu/vzzT4waNQoVKlRQbJfL5ZDJZIrbgiAo3f4Sc87NQbKQrKqon3Qs8hh8j/nmy3PlhW9z328/Vgva96205ff3LbSlbVJ/b7J9nzhW4u375ufMp9eFv7tPHJtP7TOUGaKFcYtc96u1GAsNDcWQIUMwbtw4ODk5Ke0rVaoUYmNjFbdfvnwJMzOzr3r8YdbDYGBgoJKsn+N7zFeUP9b8JOX2Sfn3J+W2ZWH7tJvU2/ettOF10YaMeZFf7UtNTUVYWFiu+9U2ZuzZs2cYOHAg/P39sxViAFCmTBkYGBggNDQUALBjxw40bdpUXXGIiIiINJLaesZWrFiB1NRUTJs2TbGtU6dOOHLkCIYMGYKaNWvC398f3t7eSEhIQPXq1dGtWzd1xSEiIiLSSGorxry9veHt7Z1te+fOnRU/W1hYYOvWreqKQERERKTxOAM/ERERkYhYjBERERGJ6IuKsefPn+P48ePIzMxUmriViIiIiPLms8XYsWPH0KlTJ0ycOBGvXr2Ck5MTDh06lB/ZiIiIiCTvs8XYwoULERgYiCJFisDMzAwbN27EvHnz8iMbERERkeR9thjLzMxUmoy1atWqXz1TPhF9Xlp65jcdN6T+cBUnISKi/PTZqS0MDQ3x9OlTRQF26dKlfJv1nuh7ol9AF84jd3z1cSEzXdSQhoiI8stni7GRI0eiV69eiI2NRceOHREZGYn58+fnRzYiIiIiyftsMVa3bl0EBgbiypUrkMvl+PXXX1G8ePH8yEZEREQkeZ8txsLDwwEAP/zwA4D3a04+e/YM1atXV28yIiIiou/AZ4uxwYMHK35OT09HbGwsatSowWWMiIiIiFTgs8XYkSNHlG6fP38eISEhagtERERE9D356uWQGjRooDh1SURERER588VjxgBAEASEhYUhJSVFraGIiIiIvhdfNWZMJpOhePHi8PX1VWcmIiIiou/GV48ZIyIiIiLVybUYmzJlyicP9Pb2VnkYIiIiou9NrsWYiYlJPsYg+jJp6ZnQL6D71cdx/UYiItJUuRZjgwYNyvWgpKQktYQh+hyu30hERFLz2TFjhw4dwrx585CUlARBECCXyxEXF4crV67kRz4iIiIiSftsMebn54dhw4Zh06ZN6Nu3Lw4dOgQjI6P8yEZEREQkeZ+d9NXQ0BCOjo6oXbs2DAwM4Ovri2PHjuVDNCIiIiLp+2wxZmBggLS0NJQvXx4RERHQ0dGBTCbLj2xEREREkpdrMTZw4ECcOXMGdnZ26NevH5o2bYrVq1dj8ODBKFasWH5mJCIiIpKsXMeM1atXD5MnTwYAtG7dGkZGRli0aBEuXryI1q1b51tAyh/yjDTo6Onn23FERET0Xq7FWK9evdCrVy9cvHgRAQEB+P3332Fvb48uXbrA1NQ0PzNSPtDR08eDqe2/+riKXtvUkIaIiOj78dmrKevXr4/69esjLi4OO3bswOjRo2FsbIy1a9fmRz4iIiIiSfvsAP4s+vr6KFSoEIyMjPDmzRt1ZiIiIiL6bny2Zyw0NBRbt27F4cOHYWNjg8GDB8PKyio/shERERFJXq7F2LJly7Bt2zYkJyejQ4cO2LVrF8zMzPIzGxEREZFaaNKFa7kWYydPnsSwYcPw+++/Q1f36xdmBoCEhAR06tQJ//33H8qWLau0b8GCBdi2bRuKFCkCAHB3d0eXLl2+6Xm+BheaJiIiIk26cC3XYiyvA/SvXbsGb29vREZG5rg/LCwMs2bNQp06dfL0PF+LC00TERGRJvniAfxfKzAwED4+Prme2gwLC8OSJUvg7OyMSZMmITU1VV1RiIiIiDSW2oqxqVOnwtLSMsd9iYmJqFq1Kjw9PREUFIS3b99i0aJF6opCREREpLE+ezWlOhgZGWHZsmWK27169cK4ceMwfPjXjcuac24OkoXkrzrGt7nvV91f6dhj335sfslL+775OfPxdZHy70/KbQPYvk8ey/aJTsr/d/J3p4bn/MrXxVBmiBbGLXLdL0ox9vTpU5w5cwZubm4AAEEQoKf39VGGWQ+DgYGBquPlSoxfuDbQltdFW3J+Cym3DWD7tJ3U2/ettOF10YaMYvja1yU1NRVhYWG57lfbacpPKViwIGbMmIHHjx9DEARs2LABv//+uxhRiIiIiESVr8VY3759cePGDRQvXhyTJk3CgAED4ODgAEEQ0LNnz/yMQkRERKQR1H6a8siRI4qfPxwnZm9vD3t7e3U/PREREZFGE+U0JRERERG9x2KMiIiISEQsxoiIiIhExGKMiIiISEQsxoiIiIhExGKMiIiISEQsxoiIRCTPSMvX44i+FN+b+UeU5ZCIiOg9HT19PJja/quPq+i1TQ1piP6P7838w54xIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiJSG3lGWr4eR6SN9MQOQERE0qWjp48HU9t/9XEVvbapIQ2RZmLPGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRkUbjpKFEJHVqnfQ1ISEBnTp1wn///YeyZcsq7YuIiICXlxcSExNhaWmJiRMnQk+Pc9ASkTJOGkpEUqe2nrFr166hc+fOiIyMzHG/p6cnJkyYgP3790MQBAQGBqorChEREZHGUlsxFhgYCB8fH5iZmWXb9+TJE6SkpKB27doAgHbt2mHfvn3qikJERESksdR2XnDq1Km57nvx4gVKlCihuF2iRAnExMR89XPMOTcHyULyVx3j29z3q59Hceyxbz82v+Slfd/8nPn4ukj59yfltgF8b6rlOdk+1T2XhNsn5bYB2tE+Q5khWhi3yHW/KIO05HI5ZDKZ4rYgCEq3v9Qw62EwMDBQZbRPEuMXrg205XXRlpzfQsptywupvy5sn3aTcvuk3Dbg69uXmpqKsLCwXPeLcjVlqVKlEBsbq7j98uXLHE9nEhEREUmdKMVYmTJlYGBggNDQUADAjh070LRpUzGiEGk9Tv1ARKTd8vU0Zd++fTFkyBDUrFkT/v7+8Pb2RkJCAqpXr45u3brlZxQiyeDUD0RE2k3txdiRI0cUPy9btkzxs4WFBbZu3arupyciIiLSaJyBn4iIiEhELMaIiIiIRMRijIiIiEhELMaIiIiIRMRijIiIiEhELMbou8C5uIiISFOJshwSUX7jXFxERKSp2DNGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY1+IUyMQERGROnBqiy/EqRGIiIhIHdgzRkRERCQiFmNEREREImIxRkRERCQiFmNEREREImIxRkRERCQiFmNEREREImIxRkRERCQiFmNEREREImIxRkRERCQiFmNEREREImIxRkRERCQiFmNEREREImIxRkRERCQiFmNEREREImIxRkRERCQiFmNEREREIlJrMRYSEgJHR0e0bNkSGzZsyLZ/wYIFsLW1hYuLC1xcXHK8DxEREZGU6anrgWNiYjB79mxs374d+vr66NSpExo0aICff/5ZcZ+wsDDMmjULderUUVcMIiIiIo2mtp6xM2fOwNraGiYmJihUqBDs7e2xb98+pfuEhYVhyZIlcHZ2xqRJk5CamqquOEREREQaSW09Yy9evECJEiUUt83MzHD9+nXF7cTERFStWhWenp746aefMGbMGCxatAjDhw//4ueYc24OkoXkr8rl29z3q+6vCr7H8u852T41PGc+tU/KbQPYPrU8J9unuueScPuk3DZAO9pnKDNEC+MWue5XWzEml8shk8kUtwVBULptZGSEZcuWKW736tUL48aN+6pibJj1MBgYGKgmsBqJ8UbJT2yf9pJy2wC2T9uxfdpLym0Dvr59qampCAsLy3W/2k5TlipVCrGxsYrbsbGxMDMzU9x++vQptm7dqrgtCAL09NRWGxIRERFpJLUVYzY2Njh79ixev36N5ORkHDhwAE2bNlXsL1iwIGbMmIHHjx9DEARs2LABv//+u7riEBEREWkktRVjJUuWxPDhw9GtWze0bdsWrVu3Rq1atdC3b1/cuHEDxYsXx6RJkzBgwAA4ODhAEAT07NlTXXGIiIiINJJazws6OzvD2dlZaduH48Ts7e1hb2+vzghEREREGo0z8BMRERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYjUWoyFhITA0dERLVu2xIYNG7Ltj4iIQLt27WBvbw8vLy9kZGSoMw4RERGRxlFbMRYTE4PZs2dj48aNCA4ORkBAAO7du6d0H09PT0yYMAH79++HIAgIDAxUVxwiIiIijaSnrgc+c+YMrK2tYWJiAgCwt7fHvn37MGjQIADAkydPkJKSgtq1awMA2rVrh3nz5uGPP/747GMLggAASEtL+6ZsJka6X31MamoqMg2LftNx+Y3ty05b2ifltgFsX07YvtyPy29Sbp+U2wZofvuy6pWs+uVjMiG3PXm0ZMkSJCUlYfjw4QCALVu24Pr165g8eTIA4MqVK/Dz88OmTZsAAFFRUejXrx/279//2cd+9+4d7ty5o47YRERERGrxyy+/oHDhwtm2q61nTC6XQyaTKW4LgqB0+3P7P8XIyAi//PILChQo8MXHEBEREYlBEASkp6fDyMgox/1qK8ZKlSqFS5cuKW7HxsbCzMxMaX9sbKzi9suXL5X2f4qOjk6OlSURERGRJipYsGCu+9Q2gN/GxgZnz57F69evkZycjAMHDqBp06aK/WXKlIGBgQFCQ0MBADt27FDaT0RERPQ9UNuYMeD91BZLlixBeno63Nzc0LdvX/Tt2xdDhgxBzZo1cevWLXh7eyMhIQHVq1fHv//+C319fXXFISIiItI4ai3GiIiIiOjTOAM/ERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYjUNukrEdHXePr06Sf3//jjj/mURP0SEhLw7t07pXXqpNQ+qQsNDcWdO3fQvn17XLt2DfXr1xc7kkqkpaXhwYMHsLCwQEhICG7evIm+ffuiePHiYkeTPE5tkYvXr19j586dSExMhCAIkMvliI6Ohp+fn9jRVOLq1auK9UOz2vf06VMcOXJE7Gh5smDBgk/uz1qoXkoSEhLw7NkzVK5cWewoeWJnZweZTIbU1FS8evUK5cqVg46ODh49eoRy5cp90bq12uC///7D0qVLYWJiotgmk8lw+PBh8UKpUGBgIGbPno24uDgA/1/qLiIiQtxgKrJmzRocOnQIL168wObNm/HHH3/Azc0NvXv3Fjtang0dOhRly5ZFy5Yt4enpCRcXF1y/fh1LliwRO5pKeHh45LiE4tq1a0VIo4w9Y7kYNmwYSpcujatXr6JFixY4duwYatasKXYslRk3bhx69+6NoKAgeHh44MCBA6hWrZrYsegLbdmyBaGhoRg1ahTatm0LIyMjuLi4oH///mJH+2ZZXwSGDx+OLl26wNLSEgBw/fp1LF++XMxoKrV161YcOnRIsr0N//33H9auXav1Xw5yExQUhMDAQLi7u6NYsWLYunUrOnToIIliLDo6GnPnzsWMGTPg5uaGfv36oX379mLHUpnBgwcrfs7IyMDhw4dRpEgRERP9H4uxXLx48QJr167F9OnT0bJlS/Tp0wfdu3cXO5bK6Ovro3379njy5AmKFCkCPz8/ODs7ix0rz3Lr+RIEAdHR0fmcRn02bdqE//77D7t27cJvv/0GLy8vuLu7a3UxluX+/fuKQgwAatWqhYcPH4qYSLVKly6NokWLih1DbUxNTSVbiAHv10b+cKUYAwMD6OrqiphIdTIzM/H69WscOnQI8+fPR2xsLFJTU8WOpTJWVlZKt21sbNChQwcMHTpUpET/x2IsF1n/WZqbm+PWrVv49ddfRU6kWgYGBoiLi4O5uTmuXbuGhg0bIjMzU+xYKhMQEIDp06cjOTlZsa1s2bI4ePCgiKlUy8zMDMePH0e3bt2gp6cnmf80S5Uqhblz58LR0RGCIGDHjh2oUKGC2LFUpkKFCvjjjz/QoEEDpQ91bT+FHhwcDOD92LcBAwbgt99+g57e/z9i2rZtK04wFbOyslL833Lo0CEEBATA2tpa7Fgq0bt3b7i7u8POzg6//PIL7O3tNaJQUZUPx6UKgoB79+4pTqeLjWPGcjF79mw8fPgQo0ePRq9evdCgQQPcunULgYGBYkdTib179yIwMBDz589Hhw4doKOjAwsLC8ycOVPsaCphZ2eHNWvWYM6cORg+fDiOHz+Oy5cvS6Z9o0aNQnx8PCIjIxESEoJRo0bB0NAQ//77r9jR8iw+Ph7z5s3DhQsXAACNGjXCoEGDYGxsLHIy1chtXKO2F2Njx4795H4pvDcBQC6XIzAwEGfOnIEgCGjQoAE6deqkVHhqu/j4eBQtWhQZGRmSalfWuNSscYzFixfHoEGD0KxZM7GjsRj7lEePHqF8+fIICwvDpUuX4OjoCDMzM7FjqcTr169RrFgxyGQyJCUlITIyEhYWFtDRkcZsJx06dMCWLVuwdOlS/Pzzz7Czs0Pr1q2xa9cusaOpREZGBq5cuYJffvkFRYsWxZEjR9C0aVNJ/Mc5duxYyXxw5+b169e4du0aMjMzUbt2bfzwww9iR1Kpmzdvolq1anj37h3CwsLQsGFDsSOpVEREBM6ePQs9PT00atQIlSpVEjuSSty6dQvDhg1DSkoKAgIC0LVrV8yZMwfVq1cXO5pKyOXybJ9xMTExKFmypEiJ/k8an7xqsHHjRpQvXx4AUKNGDVhbW2PgwIEip1IdV1dXDBgwAHv27IGuri6qVasmmUIMAAwNDXHu3DlUqVIFR48eRWxsLFJSUsSOpTLp6ek4evQoevbsCRcXF5w7dw5paWlix1KJO3fuIDExUewYanPy5Em4uLhg+/btCAoKQps2bXD06FGxY6nMzJkz4e/vDwBITk7GokWLMH/+fJFTqc6KFSswdOhQxMbGIjo6GgMGDMC2bdvEjqUSkydPxsKFC2FiYoKSJUvC19cXPj4+YsdSGU9PT6XpZNavXw9XV1cRE/2f9n+NVpNdu3YhMzMT7u7umDt3LkJCQjBy5EixY6nM0aNHce7cOezatQv+/v5o0KAB2rRpI5lvsOPHj8eWLVswZswYbN26FQ4ODkpX0mi7SZMmwdDQEP/88w+A99MJ+Pj4YMaMGSInyzsdHR3Y2trC3NwcBgYGiu2acPm5KsyePRsbN25EuXLlAACPHz/GoEGDYGtrK3Iy1Th69Ch27NgB4P24xlWrVsHV1VUyf3+BgYHYvn274rT5wIED0blzZ0lcdZicnKzUy9eoUSNMnz5dxESqVbx4cQwfPhz9+vXDxIkTUahQIWzcuFHsWABYjOVq5cqVGDRoEJYuXYrmzZtj165dkroCSkdHBzY2NrCxscH58+cxffp0DBo0CKGhoWJHU4nKlStj3LhxSEhIwIwZM1CwYEGxI6lUeHg4du7cqbg9YcIEODo6iphIdTw9PcWOoFYZGRmKQgwAypUrB7lcLmIi1crIyEBKSgqMjIwAvO/FlRITExOl4QCGhoaKtmo7ExMT3Lp1SzEX186dOyX1uefl5YUFCxbAzc0NkydP1qgCmsXYR7KuCAKAli1bIiIiAoUKFVKcRpDKFUE3b95ESEgIDh48CHNzc/Ts2RO///672LFU5vbt2xgzZozi6pmKFSti+vTpilPP2k4QBLx9+1YxR87bt28lc3m9lZUVjh8/jnPnziEjIwMNGjRAixYtxI6lMj/++CNWr14NNzc3AO/nHStTpozIqVSnU6dOaNeuHezs7AAAJ06cQJcuXUROpToVK1ZEx44d4eTkBD09PRw8eBDGxsaKCzO0+UIMX19fjB49Gnfv3oWlpSV++uknSfS2f3xxSbFixRAYGIhLly4B0IyLSziA/yPfyxVB7dq1g4uLC5ycnCQ3eBh4/4EwYMAAxVUyBw8exJo1a7B+/XqRk6nGtm3bsGTJEtjZ2UEQBBw9ehT9+vVTfMBrs2XLluHAgQNwdnaGIAgICQnBb7/9hgEDBogdTSVevXqFyZMn49y5cxAEAdbW1vDy8pLMxUEAcOPGDVy8eBF6enqwtLSU1ITS38MqH0lJSZDL5ZK5gjkoKOiT+zVh3BiLsVyEhYWhRo0aStv27t2LVq1aiZRINb6X9f9cXV2z/QG2bdtWqedT2925cwcXL16EXC6HlZUVqlSpInYklXB2dsaWLVsUp5aTk5PRrl077N27V+Rk9ClHjx6Fra1trn9jUjmrALwvqENDQ6GrqwtLS0utP5WX2zJBWaQyXhMA4uLikJycDEEQkJmZiejoaI0YK83TlLno0KEDunTpgrFjxypO/yxdulTri7GuXbsq/ug+rsOlsD5eVrFpYWGBpUuXws3NDbq6uggJCVGa1V3bpaen49mzZ4qxKhEREYiIiJDEB54gCEpj/AwMDCQxZUdycjLmzZuHVq1aoVatWvj3338RGBiIatWqYdasWRpxeX1e3LhxA7a2tjh//nyO+6Xw3gTej6OaPn066tWrh8zMTPj6+mLKlCkaMVfVt8q6uCIwMBAFCxZE27Ztoaenh127dklmMmkAmD9/PlavXo2MjAwUK1YMMTExqFGjBrZs2SJ2NPaM5cbZ2RnW1taIiIjAvHnzULx4ccn1rEjRh5P6fUwKxWaWv/76C7GxsahUqZLSN1opnEafMmUKYmJiFKcOgoODYWZmBm9vb5GT5c348eOhq6uLwYMHIywsDGPHjsXGjRtx8+ZN7N69GwsXLhQ7In0BBwcHrFmzRlE8P3nyBP3790dISIjIyfKuffv22abpaNeuHbZv3y5SItWys7PDzp07MXXqVAwYMAAPHjzAxo0bsXTpUrGjsWcsN3p6evDy8sLWrVvRqVMnzJo1SxLfzufPn4/BgwfnOjZO2z/MsxablroHDx5g3759YsdQqaioKPz000/w8vLCxo0bERwcrBhT1bFjR7Hj5dnVq1cVH9iHDx9Gq1atUKFCBVSoUOGz45C0yb59+7B06VLEx8crbZfKFyEjIyOUKFFCcbtMmTIoUKCAiIlUJzU1FQ8fPoS5uTmA9xdCZWRkiJxKdczMzGBsbIzKlSvj1q1baNmypcasyqL91YWaZPWsuLm5oWLFihg6dCiSkpJETpV3WTMpf7xgqtRERkZi/fr1SEpKgiAIkMvliI6OxoYNG8SOphLly5fH06dPJTPGD3g/8DkkJAQDBw7EokWLJHUFHgClSZXPnz+vNIWHlKZ/mD59Ovz8/CT13vxQzZo10bdvX7Rv3x66urrYu3cvzMzMFGdNtPl07JgxY+Dh4YGSJUtCEAS8evVKY4oVVTA2NkZwcDCqV6+O9evXw8zMTGMmA2cxlosPr9yqW7cu1q9fj8WLF4uYSDWyLjfPOgUkl8tx8+ZNlC9fXjFNghSMGDECzZs3R2hoKFxdXXHw4EFUrlxZ7Fh5ljXQ9vXr13B2doaFhYXSlBbaPNC2QIEC6Ny5M27fvo1u3bpl26/NbQPez+F0/fp1JCUl4cWLF7CxsQHwvjArVaqUyOlUp3z58qhXr56kVvT4UGpqKszMzHDy5EkA7+cZMzQ0VIyV0+ZirHHjxjhy5Aju3LkDmUyGKlWqSOKMUJapU6di9+7daNu2LY4ePYoJEyZg+PDhYscCwGIsV7t27YK9vb3idunSpREVFSViItWIiorC8OHDMWTIENjY2KBLly549eoV5HI5Zs6ciXr16okdUSXS09MxZMgQZGRkoFq1anB3d9eoCf6+lVRmMc/J2rVrERERAS8vL0lMD/CxcePGYfjw4Xj16hV8fHxQqFAhLFq0COvWrcOSJUvEjqcyvXr1Qrdu3VC/fn2lLwpS+Z3mNJQjJSVFEhNLP3nyBOvXr0d8fLzSuFttH76SJS0tDb169QLwvhdQEASNme6IxdhHBg0ahIiICLx48QK//fabYntmZiZKly4tYjLVmDJlCnr37o1mzZph69atSEpKwoEDB/D48WOMHTsWmzdvFjuiShgaGiItLQ0VKlRAeHi4ZK6k/PD08t27d7P9p6nNjI2NUb9+fWzevBnFixcXO47KValSBXv27FHa5uTkBA8PDxQuXFikVKq3ePFimJubS2YS4o8dOXIEc+bMURoCkZycjHPnzokdLc+GDRsGS0tLWFpafnKqC23Vp08fLF26FD/99BNu376N8ePHo1ChQvDw8BA7Gouxj02bNg1xcXGYOnWq0tVbenp6MDU1FTGZasTExMDJyQkAcObMGdjb20NPTw/m5uZISEgQOZ3qtGnTBv3794e/vz86duyIkydPav3UAR+aNGkSjhw5orSsjkwm0/pTecD7gd6zZs1CXFwcgPfjN2UyGSIiIsQNpgY//fST2BFULj09XTI9KTn5999/MXnyZKxatQr9+/fHoUOHkJycLHYslcjIyMDo0aPFjqE2//77LwYMGAAbGxscOHAAI0aM0JjTyizGPmJsbAxjY2MsXrwYoaGhuHPnDtq3b49r165J4sM8qxdFEAScP39eMUhaEARJXKCQpWvXrmjbti2MjY2xbt063LhxA40aNRI7lsqcOnUK+/btk8SpkY8tXrwYa9eulcQYv+9Ro0aNsH79ejRp0kTpKkOpDOgvXLgwrK2tcfnyZbx79w6enp6SWRe2Xr16OHLkCBo3bgx9fX2x46hc3bp1MWvWLPTp0wczZ85EgwYNxI6kwGIsF2vWrMGhQ4fw4sULODg4YMKECXBzc0Pv3r3FjpYnVapUwdKlS5GWlgZ9fX3UrVsXaWlpWLlyJWrXri12PJVxdnbG5MmTUbt2bZQqVQqlSpXKcVZ+bVWuXDnJnJ78mKmpKQsxLbZr1y4AwMqVKxXbpDTHX8GCBfHw4UNUqlQJFy5cgLW1tWSuht23b1+2MVRS6JW2sLDINv9k9+7dAWhO+zjpay7atm2LwMBAuLu7Izg4GImJiejQoUO2MR/a5t27d5g5cyZevnyJAQMGoHr16vD19cX9+/cxe/ZsyaxT2bRpUxQuXBjdu3eHu7s7AGkthzRixAhcvXoVderUUfoGq82nh7J+N0ePHkVaWhp+++03pSu5NOV0Ql69ffsWISEhiIuLU/pwkMoAd6m7cOECNmzYgBkzZqBz58549OgR3NzcJH16j9SPPWO50NHRUfqQMzAwkMSA1MKFC8PX11dp28e3pcDU1BSrV6/G0KFDERYWhgkTJkjqUvsmTZqgSZMmYsdQqaypAQoVKoRChQohNDRUab9UirGhQ4eicOHCqFy5siQHST948ACBgYHZJn3V5i8KH7KyslJcSLNt2zbEx8dr/dqUWV6/fo2dO3ciMTFRaX5GPz8/saOpRG6TK2vCFyEWY7mwsrLC9OnTkZycjEOHDiEgIADW1tZix6IvJAgCihYtihUrVuCff/6Bh4eHJNZYi42NRYkSJTRqrIOq5PRhnZCQgGfPnknqtOXLly+xatUqsWOozaBBg+Do6CiZhes/9vz5c0yZMgUXLlxAgQIF0LBhQ4wbN04SVwAPGzYMpUuXxtWrV9GiRQscO3YMNWvWFDuWWqSnp+PkyZP49ddfxY4CgKcpcyWXyxEYGIgzZ85ALpfD2toanTp1ktQEeFI2evRoTJ8+XXE7MDAQkyZNQlhYmIip8u7PP//EkiVLclyDUyrjcrZs2YLQ0FCMGjUKbdu2hZGREVxcXNC/f3+xo6nEqFGj0KtXL1hYWIgdRS06deokmSlyctKrVy/89ttvcHFxAQBs3boVZ8+elcRccQ4ODti3bx+mT58OBwcHlC9fHt27d8fOnTvFjqYWWfOOacJcY6wsctG3b1+sWLECnTp1EjuKWiUkJEAul0tq9n0A2U5Juru7K8aOabOs//ClvAbnpk2b8N9//2HXrl347bff4OXlBXd3d8kUY3fv3oWrqytMTU1hYGCgmLpDCoU08H51j9mzZ8Pa2lrpy2v9+vVFTKU6r1+/Vlqqq0ePHpK5MCjrdKu5uTlu3bqlMb1G6pKYmIinT5+KHQMAi7FcJScn49mzZ5KY6DUnjx49wogRI/Do0SMIgoAyZcpg9uzZigVitd2dO3eQmJgIIyMjsaOohVQXes9iZmaG48ePo1u3btDT05PEKeYsUloUPCdXrlzB5cuXcfnyZcU2qcyBBwC1atXC7t27FfM1Hj16FDVq1BA5lWpYW1tjyJAhGD16NHr16oXw8HBJTZ+TdUYBeD+UJT4+XmNmSOBpyly0atUKkZGRkv322rNnT3Ts2BEODg4AgD179mDTpk1Yt26dyMlUo0OHDoiKioK5uTkMDAwU26XygfDhN/GMjAwcPnwYFStWxKhRo0RMpRqjRo1CfHw8IiMjERISglGjRqFgwYKYNm2a2NHy5OjRo7C1tc31il6pXKDg7OyMkJAQsWOo3MfTIxgaGkImkyEpKQlFixZVXICi7R49eoTy5csjPDwcFy9ehKOjI8zMzMSOpRJPnjxR/CyTyVCkSBEYGxuLmOj/2DOWi+XLl4sdQa3evHmjKMQAwNHRURILoWfx9PQUO4JaZS30nsXNzQ2dO3cWKY1q/fPPP7hy5QoqV64MfX19tGnTBs2aNRM7Vp7duHEDtra2uX5oS6UYq1y5Mm7duiW5MXG3bt0SO4LaZWRk4MGDB4peTRMTE5w5c0Yy780yZcpk29avXz8sXbpUhDTKWIzl4scff8SmTZtw7tw5ZGRkwNraGl27dhU7lsro6+sjPDwc1atXBwCEhYXB0NBQ5FSqY2VlhZs3byrWj8vMzER0dLTS2o5Scv/+fbx48ULsGCrx9u1bhIeH48KFC4rL6/ft26f1l9cPGTIEgHROJefmwYMHcHV1RYkSJVCgQAHJnFX43ByFUihYRo4ciadPn6JSpUpK065IoW25uXTpktgRALAYy5Wfnx+ioqLQvn17CIKA7du34/Hjx/Dy8hI7mkqMGzcOgwcPhomJieLc+axZs8SOpTLe3t64cOEC4uPjUbFiRdy6dQt169aFm5ub2NFU4uNTJsWLF8eIESNETqUaUr28vlu3bp/cL5VT6AsXLhQ7glqMGTMGpqamaNiwodIyT1mkULDcvn0be/fuleT8d5qOxVguTp8+jeDgYMVVec2bN4ezs7PIqVSndu3a2L9/PyIjIyGXy2Fubi6ptcjOnDmD/fv3Y/LkyejWrRuSk5O1fszRh6R8yuTFixdYu3Ytpk+fjpYtW6JPnz6KpUu0WXx8PGJjY+Hg4IDmzZtLamA08P8xcRcvXsxxf06niLRJUFAQ9uzZg9OnT8PCwgKOjo6wsbGR1GTSlSpVQmxsrGTGiGkTFmO5yMzMREZGhqJAyczMlMQM/LldhZdFKqdQzMzMUKBAAVSqVAm3b9+Gk5MT3r17J3YslfncFXmaMKP0t5Lq5fU7duzAw4cPsWfPHsyfPx/ly5dHq1at0LRpU0l8EZL6mLiqVauiatWqGDlyJG7cuIE9e/Zg1qxZqFGjBpycnCQxEXNKSgocHBzwyy+/KL0ntb3X9sOrKD8kCAJSUlJESJQdi7FcODs7o1u3borLl3fv3o3WrVuLnCrvssZMHT16FImJiWjTpg309PSwZ88eFC5cWOR0qlOyZEksWbIEDRs2xIwZMwC8n+BPKp49e4br16+jdevW0NPTw4EDB2BsbIw6deqIHS3PpHx5vbm5OQYOHIiBAwfi7t272Lt3L5YsWYJKlSppfc9t1pjanL7Qacq4HFWpWbMmatasiUuXLsHf3x8hISG4cuWK2LHyrHfv3pKc2FwbZgng1BafcOLECZw9exaCIMDa2hrNmzcXO5LKdOjQAQEBAYoudrlcDnd3d2zdulXkZKqRkJCA48ePw8nJCevWrcOZM2fQvXt3ySxp1alTJ6xcuRKFChUC8L7Q9PDwQEBAgMjJVCPr8vqwsDBcunRJUpfXA+972s+cOYN9+/bh/PnzqFevntKKEdrI2dkZa9euRbFixRTbBEHAggULsGrVKqV5x7SVIAi4ePEi9u3bhxMnTqBq1apwcHCAra2t4m9Rm7m6ukpmAlttI70SWEXS09PRtGlTFClSBOnp6ZIaFwAA7969Q1xcnGI9tZcvXyIpKUnkVKozc+ZMxfQPHh4e8PDwEDmRar1580ap2z0tLU3rf3+CIODUqVMoWrQoatWqBQCoUaMGChQogLFjx2LFihUiJ8yb9PR0nD59Gvv27cOFCxdgaWkJBwcH+Pj4SOI0pYuLC3r06IG1a9eiaNGieP78OUaMGIHU1FRs2bJF7Hh55uPjg5MnT6JatWpo1aoVPD09JXUFOgD88MMPuHTpEmrVqiWJ96Q2Yc/YR2JiYhQL3fbs2RO2trYoW7Ysnjx5gjFjxqBly5ZiR1SJ4OBg+Pv7o27duhAEAVevXsX48eMl076goCAEBwfj9evXcHFxgYuLC0qUKCF2LJVZtmwZtm/fruitPXr0KPr376/V43J8fHxw4sQJpKSkYPz48bCzs8P06dOxbds2tG3bFpMmTRI7Yp5YWlqicOHCaNmyZY7jxKSwXNDq1auxc+dOeHh4YPr06ejUqRMGDRokiVNfFhYWMDExUfSAfTwGSdun7gDeDxGIi4sDAMXV2jKZDBEREeIG+w6wGPvI0KFDYWVlpVh7rG3btggODsatW7cwdepUrTj3/CVu3bqF4sWL48qVK5DJZKhXrx5MTU3FjqVyz549w65du7B582b8/PPP6NChA1q0aCF2rDx7/fo1oqOjcfHiRRQsWBDW1taoVKmS2LHyxM7ODiEhIXj9+jXGjh2LhIQEmJqaYuzYsfj555/Fjpdnn+qdldJyQWvXrsW0adPw33//oWnTpmLHUZkPZ2/PibZfLfo90OTVL7T/64qK3bp1C3Pnzs223cLCAs+fPxchkXoMHz4ce/fuhb29vdhR1Obx48fYuXMndu/ejZ9++gm///479u7diwMHDmj9BKJdunTB3r17FafzpKBw4cIwMjKCkZER7t+/j/79+0tiSossUvkil5usBZdbtGiBmJgYzJ8/HxUqVFD0iv34449ixsuz76HYSktLw8qVK/Hw4UOMHz8eq1evRr9+/SRzyvLDK33T09MRGhoKS0tLFmOa6OOxYR+OdZDS+ICff/4ZCxYswK+//qp0pZoUTpUAQOfOnfHy5Uu4uLhg+fLlig+Ctm3bSuLbuoWFBYKDg1GrVi2l3582f+B9eNrH1NRUUoXY96Br165KExEDQI8ePQBAEjPwfw8mTZqE4sWLIzw8HLq6uoiKisK4cePg7+8vdjSV+PhK37i4OAwfPlykNMpYjH3khx9+wPXr1xU9DlkzLV+/fh0//PCDmNFUKi4uDufPn1f6piClUyVDhgxBw4YNs23X09PDmTNnREikWteuXcO1a9eUtmn7B96HxVhOM5yTZjty5IjYESiPwsPDERQUhBMnTsDQ0BB+fn6Smuz8Y4UKFfrs6ef8wmLsI3/99ZdiHiBLS0vIZDKEhoZi0aJFmD17ttjxVEbKp0wiIyOxa9cuTJs2DTKZDDVq1EDv3r1hbm4udjSVkeIHX0REBKpWrQrg/ZWVH/7MQcSkSe7evYv4+HilXkApnFWQyWRIS0tTfDH6+Kptbefh4aFojyAIiI6O1pgzJRzAn4NLly5h8eLFuHr1KgCgVq1aGDRoEOrVqyduMBW6evUqlixZolhIWy6X4+nTp1r/IR8REYFevXqhffv2sLS0RHp6Oq5cuYKgoCCsWrUKFhYWYkdUicjISKxfv17p9xcdHY0NGzaIHY0+4+NVMGQyGQoWLIhKlSqhQ4cOkhmfI1UTJ07E0aNHUa5cOcU2qZxVCA4OxpYtWxAVFYVWrVrh0KFDGDhwoGTW9L1w4YLiZ5lMhmLFimnMxUEsxr5Tjo6O6N27N4KCguDh4YEDBw7A1NQU48aNEztanvTp0we9evWCjY2N0vZTp05h1apVWj9XVZZ27dqhefPmOHr0KFxdXXHw4EFUqlQJvr6+Ykejz/D29kZ8fLxi0PCePXuQkZGBEiVKIDExUTJLkklVy5YtsXPnTsmsCvGxe/fu4fz588jMzISVlZUkvsDmtl5qFk3o1eRpyu+Uvr4+2rdvjydPnqBIkSKSGRsQGxubrRADgMaNG+Off/4RIZF6pKenY8iQIcjIyEC1atXg7u6O9u3bix2LvkBERAS2bdumuG1nZ4cOHTpg7ty5aNOmjYjJVEPqvbblypWD1Pow5syZg2HDhgF4fxFQ1tROUjFv3rxc92lKryaLse+UgYEB4uLiYG5ujmvXrqFhw4bIzMwUO1aefWoWeim0L4uhoSHS0tJQoUIFhIeHw9LSUuxI9IWSkpIQGxurmIT41atXSE1NBSCN9+iIESPQvHlzhIaGKnptK1euLHYslSlatCicnJxQp04dpVPK2tyjefz4cUUx1qVLF8ktiaQNY6RZjH2nevTogeHDh2P+/Pno0KEDQkJCUKNGDbFj5Vnt2rWxevVqxSX1WZYuXSqpMX8uLi7o378//P390bFjR5w8eRKlSpUSO5ZKxMXF4ebNm7CxscGSJUsQHh6Ov//+G+XLlxc7mkoMHjwY7dq1Q506dSCXyxEWFgYvLy/Mnz8/x15dbSP1XtsmTZqgSZMmYsdQqQ97+qTW6/ehJ0+ewNvbG0+ePMGGDRswcuRI/PPPPyhbtqzY0ViM5ebJkydYv359titmtPnbz4datWoFBwcHyGQybNu2DZGRkYqr17TZmDFj0K1bNxw5cgS1atVCZmYmrly5gpSUFKxZs0bseHmWNYO0kZERbG1tceLECbi6uqJQoUKSmQ5i5MiRiqJk37596N69O7y8vLTi2+2XcHR0hLW1NUJDQ6Gjo6OY26l+/fowMTERO16eSb3X1tXVFXFxcUhOToYgCMjMzER0dLTYsfLkwysmpXT15McmTJiA3r17w9/fHz/88ANat26N0aNHa8QpdBZjuRg2bBgsLS0V01tIjVSLTVNTU2zfvh179uzBjRs3IJPJ0LlzZ7Rq1UoSV6mNGTMGpqamaNiwoWSKr4/Fx8ejd+/emDx5MlxdXdG2bVuNGNOhKm/fvsXevXsRFxcHQRAUU3YMGjRI5GSq0aZNm2y9tiVLlhQ7lsrMnz8fq1evRkZGBooVK4aYmBjUqFFDqxdDj4uLQ3BwMARBUPz8IU2YoV4V3rx5g8aNG8Pf3x8ymQzu7u4aUYgBLMZylZGRgdGjR4sdQ22kXGwaGBjA1dUVrq6uYkdRuaCgIOzZswenT5+GhYUFHB0dYWNjk23lCG2Wderu0KFDWL9+PSIiIiQxlirL0KFDUbhwYVSuXFlyf3vA+5n427ZtC2NjY6xbtw43btxA48aNxY6lMkFBQTh+/DimTp2KAQMG4MGDB9i4caPYsfLE2tpaMQH4hz9nkUoxVrBgQTx//lzxd3fp0iWN+ZLOqS1yMWXKFNjY2KBx48Ya88tSJVdXV8kN0vze3LhxA3v27MH58+dRo0YNODk5oUGDBmLHyrOzZ89i8eLFsLOzQ48ePeDu7o7hw4fnuKKCNnJ2dkZISIjYMdRmwYIFOW6XSs9fp06dsHnzZqxcuRJly5ZFy5YtJf87lYobN27A29sbjx49Qvny5REfH485c+agdu3aYkdjz1hu9u3bh/Xr1yttk9Is4PXq1cORI0ckW2x+D2rWrImaNWvi0qVL8Pf3R0hICK5cuSJ2rDxr2LChUuEVGBgoYhrVq1q1Km7duiWJ+Zs+Jz09HSdPnsSvv/4qdhSVMTY2RnBwMKpXr47169fDzMwMKSkpYseiL1CzZk1s3boVkZGRyMzMRMWKFTXm8489Y9+pxo0b4+XLl0pLQ0ih2Hz69Okn92vzQtpZBEHAxYsXsW/fPpw4cQJVq1aFg4MDbG1tUahQIbHj5ZmFhUW203dmZmY4fvy4SIlUy9XVFbdu3YKpqSkMDAwUf3vavK7op6SlpaFXr17Zvtxqq5iYGOzevRu9evXCtGnTcObMGfz5559wcnISOxp9xoMHDxAYGIj4+Hil7ZowVprFWC6Sk5OxYMECnD17FpmZmbC2tsbQoUMl8WEHQLLfzO3s7CCTyXK8PFsKH3g+Pj44efIkqlWrhlatWsHOzg6GhoZix1Kb9PR0HDp0CFevXs22jJC2ym1h4jJlyuRzkvzx5s0btG/fXuuXWvueJCQkQC6Xo0iRImJHUSlHR0c4Ojpm+1vThPHFLMZyMXbsWBgaGsLd3R3A+1Ml7969w4wZM0ROphqtWrXC3r17xY5BX8nCwgImJiaKLwUf9yBpe7GZGxcXF+zYsUPsGCrx8ZVqWaQySDrrCxHwvhc36+rYv/76S+RkeZM1zjar5zarR1MqZxUA4NGjRxgxYgQePXoEQRBQpkwZzJ49G+bm5mJHU4ms8X6aiMVYLtq0aYOdO3cqbXN0dMSePXtESqRagwcPRpUqVfDrr78qrbGmCWt0qUJ8fDxmzJiBR48eYd68eZg+fTrGjh2r9d/0cutVySKF3pUPixVBEHD37l2cP39eaQkhbfZhD196ejpCQ0NhaWkpmS96H75HZTIZihQpAmNjYxET0Zfq2bMnOnbsCAcHBwDv103dtGmTZOb4CwgIwNOnT2FtbQ09vf8PmdeEzz0O4M+FIAh4+/at4sP77du30NXVFTmV6sTFxeH8+fNKlzBryhpdqjB+/Hg0atQI169fR6FChWBmZoa///4bS5cuFTtankih2Pqcjy+rL1asGObMmSNOGDX4eHxKXFwchg8fLlIa1TMzM8OZM2fw5s0bpe3a3vOX21WiWaRwteibN28UhRjwvgNi8eLFIiZSrStXruDy5cu4fPmyYpumfO6xGMtFjx494ObmBjs7OwDAkSNH0K9fP5FTqY5UvunkJjo6Gh07dsSmTZugr6+P4cOHS2IR5u/Bv//+i5s3b6JatWp49+4dwsLCUK5cObFjqU2hQoU+2+OpTYYOHYrY2FhUqlRJ6TS6thdj3wN9fX2Eh4ejevXqAICwsDBJjUkNDw/HgQMHxI6RIxZjuWjfvj1q1qyJixcvQi6XY/78+ahSpYrYsVRGk9foUgVdXV28e/dO8WEQGRkpqYlRpWzmzJkIDw/HypUrkZycjEWLFuHSpUsYPHiw2NFUwsPDQ2lMVXR0NJo1ayZyKtV58OAB9u3bJ3YMlcvq+Vq+fDlcXFwUC71Lybhx4zB48GCYmJgoxvvNmjVL7FgqU7lyZY29eI3F2Cfcvn0bL168wJ9//okDBw5IqhjT5DW6VGHw4MHw8PDAs2fP8Ndff+Hq1av4559/xI5FX+Do0aOKwfpmZmZYtWoVXF1dJVOMfdgOmUyGYsWK4eeffxYxkWqVL18eT58+lcQ0MjlJSUmBh4cHypcvD1dXV7Ro0UIyS5PVrl0b+/fvR2RkJORyOczNzTVmHi5VePDgAVxdXVGiRAkUKFBAo6aV4QD+XPj7++P58+cIDw/Hli1bMGDAAFSvXh1jxowRO5pKtGvXDtu3b0fbtm0VA6aldMUaALx+/RrXr19HZmYmfv31VxgZGUmqy12qHBwcsG3bNhgZGQF4P82Mu7u7pGY4Dw0NxZ07d9C+fXtcu3ZNIwYQ51VWj9/r16/x7NkzWFhYQFdXV/GBpwnjclTp0qVL2LVrFy5cuABra2t06NABVatWFTtWnkh1zeIsmjytDHvGcnHq1CkEBQXB1dUVxsbGWLVqFdq0aSOZYkyT1+hSBX9/f/z9999o3rw5AODYsWOYNGkS5zrSAp06dUK7du0U4zVPnDiBLl26iJxKddasWYNDhw7hxYsXcHBwwIQJE+Dm5obevXuLHS1PpNJz+SWSkpIQHR2Nx48fQ0dHB0WLFsXUqVNRp04djBw5Uux430zKaxYD7yf93rRpE86dO4eMjAxYW1uja9euYscCwGIsV1nji7LekGlpaZIaczR27Fj8+eefePToEVxcXBAfH4+5c+eKHUtlHj16hGnTpqFPnz6YPHky7t27h2nTpokdi75AmzZtUK9ePVy8eBF6enqYMWMGqlWrJnYslQkKCkJgYCDc3d1RrFgxbN26FR06dND6YszKygrA+/8rHzx4AAsLC4SEhODmzZvo27evyOlU5++//8a5c+fQtGlTDBgwAJaWlgDet7tx48ZaXYxlZGRg9OjRYsdQGz8/P0RFRaF9+/YQBAHbt2/H48eP4eXlJXY0FmO5cXBwwLBhwxAfH4/Vq1dj586dklruQpPX6FKFOXPmwNvbG7/99hv69+8Pf39/yYzrkLouXbpg7969qFmzpthR1EJHR0fpb83AwEBS0+Z4enqibNmySEtLw/z58+Hi4oKxY8diyZIlYkdTCWtra0yaNElpNZa0tDTo6+tj9+7dIibLO6mvWXz69GkEBwcrOlaaN28OZ2dnkVO9x2IsF/369cPJkyfx448/4tmzZxg8eDBsbW3FjqUS9+/fR+HChWFmZoajR4/i8uXLqFGjBvr06aM0Aaw2+nAuoNKlS8PY2Bg3b95UfBBIYS4gqbOwsEBwcDBq1aql9H6UyoBwKysrTJ8+HcnJyTh06BACAgJgbW0tdiyViY6Oxty5czFjxgy4ubmhX79+aN++vdix8mz69OkYPXo03NzclLbfv38fI0aMwI4dO7T+Cst9+/ZlW0NUKqsLAEBmZiYyMjIUhWZmZqbGfBFiMfYJTZo0QZMmTRS3fX194evrK14gFVi7di1WrlwJXV1dWFlZ4eHDh3B0dMSFCxcwfvx4ycwCDrz/T6Rz585ix6CvdO3aNVy7dk1pm6Zc8aQKo0aNQmBgIKpUqYLg4GA0a9YMnTp1EjuWymRmZuL169c4dOgQ5s+fj9jYWKSmpoodK88uX76M2bNnK03QGxAQAD8/P/Ts2VPEZKpz6tQpsSOolbOzM7p166Y4y7V7926NOePFqym/Qt26dZVm7tVGTk5O2LJlC5KTk9GiRQucOnUKRkZGyMzMRNu2bSV1xRoR5b+QkBDMnTsXdnZ2GDduHOzt7TF06FA4OjqKHS1PEhIS0LdvX9jY2KB79+7w8vLC3bt34efnh1q1aokdTyVyW2VASmcUTpw4gbNnz0IQBFhbWysu8hIbe8a+ghTqVj09PRQqVAiFChVCuXLlFNMH6OrqKq3Vpa0+Xsw3i5QW85W6yMhIrF+/HklJSRAEAXK5HNHR0Vo/B97H78ksUntvOjs7w97eHpGRkYiIiMDu3bsl8X+LsbExli9fjn79+mH9+vVwdHTEjBkztH5oR27S09Nx8uRJ/Prrr2JHUYkHDx7AyMgITZs2RdOmTQEAr169woQJEzBp0iSR07EY+ypSuNT3wytCNeVcuSoFBQUBAG7duiVyEvpWI0aMQPPmzREaGgpXV1ccPHgQlStXFjtWnn34nvxwfj+puXHjBoYOHQoTExPI5XK8fPkSCxculMSHupGREZYvX44///wTJUuWlFwh9nEP2MCBA9GrVy+R0qjO/PnzsXLlSgDAwoULYWNjgxUrVmDhwoWoU6eOyOneYzH2kQ+XKvmQIAiSGPcQGRmJbt26ZftZEARERUWJGU0lPvcBx/XxNF96ejqGDBmCjIwMVKtWDe7u7pIYAP4hKXyxy83UqVMxe/ZsRfF19epVTJ48GVu3bhU5Wd58+NmQnJyMOXPm4Pjx44qrtKU2qS0AJCYm4unTp2LHyLPg4GDs378fL168wLx587By5UrExMRg7ty5SuPCxcRi7CNSn7hQKpeX52bMmDEwNTVFw4YNc5zKgsWY5jM0NERaWhoqVKiA8PBwxTxOUiKFIQ+5SUpKUuoFq127tiS+yEr9swEA7OzslNZNjY+PR58+fUROlXdGRkYwMzODmZkZrl+/jrZt22LJkiUadXaIxdhHsiYulCqpty8oKAh79uzB6dOnYWFhAUdHR9jY2Ehqwl6pa9OmjWJuuI4dO+LkyZMoWbKk2LFUSso9Y0WLFsWhQ4fQokULAMChQ4dgYmIibigVkPr/ncD7+RlNTU0BvH+PFilSBMbGxiKnyrsP//8vVqyYRq6kw6spSbJu3LiBPXv24Pz586hRowacnJzQoEEDsWPRF0hISICxsTGeP3+OGzduoFGjRkqTbGqjD3sdYmJiFAWmJi1WrAoPHz7EqFGj8OjRIwBAuXLlMGPGDJibm4ucjD6nVatW2Lt3r9gxVC7rwq6Pf9YkLMZI8i5dugR/f3/cvn0bV65cETsO5ULq4/1yW6Q4iyYsVqxKSUlJkMvlkuhZ+V4MHz4czZo1k9yEyzVq1FB8+dHUL0IsxkhyBEHAxYsXsW/fPpw4cQJVq1aFg4MDbG1ttb53RcosLCw+Od7v33//FSEVfamxY8dm26anp4dy5cqhc+fOKFy4sAip6GvY2dll26YpxUpeaMMXIRZjJCk+Pj44efIkqlWrhlatWsHOzg6GhoZix6IvEBERwfF+WiynUz+CIOD27du4d+8eVqxYIUIqIu3AYowkxcLCAiYmJooesI8HSmv7N7zvBcf7SYuTk5PWL6ItdR+uWbx06VJJrVmsDViMkaRoQ3c0fTmO99NusbGxOHToEIKCghAYGCh2HMrFp9YsNjQ0lNSaxZqKU1uQpLDY0m45jffz8PCAra2t2NHoG0RFReH69evw8/MTOwp9QkBAAPbs2ZNtzeIuXbpo/YUz2oLFGBFphI/H+3l6enK8n5aztLSU5KS9UiP1NYu1AV9lItIIAQEBMDExwc2bN3Hz5k3MmjVLaT/H+xGph9TXLNYGHDNGRBqB4/2IxFGnTh3UrFkTwPuLZ7J+FgQB4eHhuHz5spjxvgssxoiIiL5jFy5c+OT+72EpKLGxGCMiIiISEWdTJCIiIhIRizEiIiIiEbEYIyLRRUdHo2rVqnBxcVH8a9OmDbZu3QoA2L59O/788898z2VnZwdPT0+lbTdu3MhxDT8iom/FqS2ISCMULFgQO3bsUNyOiYlB69atUaNGDRFTAfv27UPjxo3h4uIiag4iki4WY0SkkUqWLImffvoJkZGRStuvXr2KGTNmIC0tDbGxsbCxscE///yDxYsX4969e5g5cyaA90spTZkyBcHBwbh8+TL8/f2RnJwMHR0dDBo0CLa2tti+fTu2bt2K5ORkGBsbY926ddlyDB8+HFOmTEHdunVRrlw5pX1JSUnw9fVFVFQU4uLiYGRkBH9/f1SsWBEeHh6oXr06rl69itevX8Pd3R0vX77EhQsXkJycjDlz5qBKlSp49+4dpk6dijt37iA9PR0NGzbEqFGjONkm0XeEpymJSCNduXIFjx49wq+//qq0fe3atRgyZAi2bNmC3bt348iRIwgLC4O7uzuOHTuGuLg4AEBgYCA6deqE+Ph4jB07Fn5+fggKCsKiRYvg6+uLp0+fAgDu3buHdevW5ViIAUD9+vXxxx9/4O+//0ZGRobSvhMnTqBIkSIICAjA/v37UaNGDWzYsEGx/8mTJ9i8eTNmzJiBGTNmwMrKCtu3b0eTJk2wfv16AMA///yD6tWrY/v27QgODsabN2+watUqVb2MRKQF+NWLiDRCSkqK4lRgZmYmihUrhhkzZqB06dJK95s2bRpOnDiB//77Dw8ePEBqaiqSkpJgamqK5s2bY8eOHWjbti1OnToFHx8fXLp0CbGxsRg4cKDiMWQyGW7fvg0AqFKlCoyNjT+ZbfDgwTh79izmz5+PFi1aKLY7ODigXLlyWLduHaKionDhwgXUqVNHsf/3338HAEWPWpMmTQAA5cuXV8ztdOzYMdy4cUMxPi4lJeXrXzwi0mosxohII3w8Ziw3Xbt2RZUqVdCkSRO0atUK165dQ9Z0iV26dIGvry/09PTQsmVLGBkZITMzE5UqVcKWLVsUjxETE4PixYsjJCQEhQoV+uxz6unpYebMmWjXrh1MTEwU2zdu3IjAwEB06dIFzs7OMDExQXR0tGK/vr6+0uMUKFAg22PL5XLMnTsXlSpVAgC8ffsWMpnss5mISDp4mpKItMbbt29x48YN/P3332jZsiWeP3+OR48eQS6XAwDq1q0LHR0drFixAp06dQIA1K5dG1FRUbh48SIAICIiAvb29oiJifmq5y5Xrhy8vLyU1sw8deoUXF1d0aFDB5ibm+PIkSPIzMz8qsdt3LgxVq9eDUEQkJaWhgEDBihOYRLR94E9Y0SkNYoUKYJ+/frB1dUVhQoVQsmSJVG3bl1ERUWhYcOGAIB27dphz549sLCwAAAUL14c8+bNg5+fH1JTUyEIAvz8/FC2bNnPLgPzsazTn1lr9fXq1QsTJkxQnGKsXbs27ty581WP6eXlhalTp8LZ2Rnp6emwsbFBnz59vuoxiEi7cTkkIpKMjIwMDBo0CG3atIGjo6PYcYiIvghPUxKRJNy7dw8NGzZEsWLF4ODgIHYcIqIvxp4xIiIiIhGxZ4yIiIhIRCzGiIiIiETEYoyIiIhIRCzGiIiIiETEYoyIiIhIRCzGiIiIiET0P1DxMPHR06wyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_over[[\"Actual\",\"Predicted\"]].plot(kind='bar',figsize=(10,5))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.title(\"Actual vs Predicted Values for under Predicted\")\n",
        "plt.xlabel(\"Player Name\")\n",
        "plt.ylabel('Value')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "215eeade",
      "metadata": {
        "id": "215eeade"
      },
      "source": [
        "# Calculating MAE, MSE, RMSE and R^2 values of our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a868aeaa",
      "metadata": {
        "id": "a868aeaa",
        "outputId": "241ed582-798a-47ea-f26f-8ee3e75261d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 218344882.78296986\n",
            "Mean Squared Error: 4.71150004644331e+17\n",
            "Root Mean Squared Error: 686403674.7019432\n",
            "Linear Regression R^2: 0.747228846456097\n"
          ]
        }
      ],
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y1, y_pred))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y1, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y1, y_pred)))\n",
        "print('Linear Regression R^2:',regressor.score(X1,y1))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}