project545.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-3NgKOQIOmQ"
      },
      "source": [
        "# **CIS545 Project**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NCBfJFpE8KC"
      },
      "source": [
        "# **0. Set up libraries and import data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmEcEwknEwIG",
        "outputId": "c105bf90-0a9c-4e02-ffa3-a5a87e0cc0ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pandasql\n",
            "  Downloading pandasql-0.7.3.tar.gz (26 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from pandasql) (1.21.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from pandasql) (1.3.5)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.8/dist-packages (from pandasql) (1.4.44)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->pandasql) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->pandasql) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->pandasql) (1.15.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.8/dist-packages (from sqlalchemy->pandasql) (2.0.1)\n",
            "Building wheels for collected packages: pandasql\n",
            "  Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pandasql: filename=pandasql-0.7.3-py3-none-any.whl size=26787 sha256=c09c00a5f01cc99f1042bc2e60347326c476dd52fb1c49d22898a746188aae23\n",
            "  Stored in directory: /root/.cache/pip/wheels/ed/8f/46/a383923333728744f01ba24adbd8e364f2cb9470a8b8e5b9ff\n",
            "Successfully built pandasql\n",
            "Installing collected packages: pandasql\n",
            "Successfully installed pandasql-0.7.3\n"
          ]
        }
      ],
      "source": [
        "!pip install pandasql"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "M1WlYbfL0ep1"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import glob\n",
        "import pandas as pd\n",
        "import pandasql as ps #SQL on Pandas Dataframe\n",
        "import numpy as np\n",
        "import sqlite3\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import plotly as py\n",
        "import plotly.graph_objs as go\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAsybGOj0WT4"
      },
      "source": [
        "**Step 1**:\n",
        "\n",
        "Read csv files.\n",
        "\n",
        "**Note**: import all the Kaggle files of the European Soccer Database to \"/content/\".\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VG1JiXVF_Oq",
        "outputId": "a39449d9-4bd9-4097-ceff-29943c59ced1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "SYk_aw58At67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc62581c-c90d-4745-c8a3-0d5cb8de024e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.9.24)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eb8rNHRZFXO6"
      },
      "outputs": [],
      "source": [
        "! mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "p2Bq10y4FjPG"
      },
      "outputs": [],
      "source": [
        "!cp /content/drive/MyDrive/kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVsHRyTtFj1O",
        "outputId": "8e5fdd42-560f-48a9-ee78-a98c82cb7d81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading soccer.zip to /content\n",
            " 76% 25.0M/32.7M [00:00<00:00, 135MB/s] \n",
            "100% 32.7M/32.7M [00:00<00:00, 144MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d hugomathien/soccer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vpOO1bEGFwfC",
        "outputId": "5ea2fc72-c491-42e7-a327-fa6ae01d7ffe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/soccer.zip\n",
            "  inflating: database.sqlite         \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/soccer.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "3R_HIqtLcNDF"
      },
      "outputs": [],
      "source": [
        "con = sqlite3.connect(\"database.sqlite\")\n",
        "# country_df = pd.read_sql_query(\"SELECT * from country\", con)\n",
        "# league_df = pd.read_sql_query(\"SELECT * from league\", con)\n",
        "# match_df = pd.read_sql_query(\"SELECT * from match\", con)\n",
        "player_df = pd.read_sql_query(\"SELECT * from player\", con)\n",
        "player_attributes_df = pd.read_sql_query(\"SELECT * from player_attributes\", con)\n",
        "team_df = pd.read_sql_query(\"SELECT * from team\", con)\n",
        "team_attributes_df = pd.read_sql_query(\"SELECT * from team_attributes\", con)\n",
        "\n",
        "con.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vImVZYwOwWr0"
      },
      "source": [
        "To Do List: \n",
        "* Investigate data import  \n",
        "* Link data between files\n",
        "  *  Get team linked to match to player to get player team by year\n",
        "* remove columns from tables that we do not want or need\n",
        "* convert column data type to int/float\n",
        "*   Determine list of players who appear in the first and last years of the data, as these players will be our primary cohort for testing?\n",
        "* https://www.kaggle.com/code/manasaudupa/european-soccer-data-analysis-using-sql/notebook\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWPrsAfhAOP5"
      },
      "source": [
        "# **1. Clean & Stage the Data**\n",
        "\n",
        "To work with the data, we would require to clean the data i.e drop null values, remove columns that are not needed, drop duplicates, etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IH_mfpVUCccK"
      },
      "source": [
        "## **1.1 Drop null values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "mZkTDddTzXau"
      },
      "outputs": [],
      "source": [
        "player_cleaned_df = player_df.dropna()\n",
        "player_attributes_cleaned_df = player_attributes_df.dropna()\n",
        "team_cleaned_df = team_df.dropna()\n",
        "team_attributes_cleaned_df = team_attributes_df.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKfvqdSHkjtx"
      },
      "source": [
        "## **1.2 Drop Duplicates**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "ZrDUeWAtk1H2",
        "outputId": "0632ec77-10a4-4892-bebc-97d0330e842c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id  team_fifa_api_id  team_api_id                 date  buildUpPlaySpeed  \\\n",
              "1    2               434         9930  2014-09-19 00:00:00                52   \n",
              "2    3               434         9930  2015-09-10 00:00:00                47   \n",
              "7    8                77         8485  2014-09-19 00:00:00                58   \n",
              "8    9                77         8485  2015-09-10 00:00:00                59   \n",
              "13  14               614         8576  2014-09-19 00:00:00                59   \n",
              "\n",
              "   buildUpPlaySpeedClass  buildUpPlayDribbling buildUpPlayDribblingClass  \\\n",
              "1               Balanced                  48.0                    Normal   \n",
              "2               Balanced                  41.0                    Normal   \n",
              "7               Balanced                  64.0                    Normal   \n",
              "8               Balanced                  64.0                    Normal   \n",
              "13              Balanced                  57.0                    Normal   \n",
              "\n",
              "    buildUpPlayPassing buildUpPlayPassingClass  ... chanceCreationShooting  \\\n",
              "1                   56                   Mixed  ...                     64   \n",
              "2                   54                   Mixed  ...                     64   \n",
              "7                   62                   Mixed  ...                     57   \n",
              "8                   53                   Mixed  ...                     63   \n",
              "13                  52                   Mixed  ...                     52   \n",
              "\n",
              "    chanceCreationShootingClass chanceCreationPositioningClass  \\\n",
              "1                        Normal                      Organised   \n",
              "2                        Normal                      Organised   \n",
              "7                        Normal                      Organised   \n",
              "8                        Normal                      Free Form   \n",
              "13                       Normal                      Organised   \n",
              "\n",
              "    defencePressure defencePressureClass  defenceAggression  \\\n",
              "1                47               Medium                 44   \n",
              "2                47               Medium                 44   \n",
              "7                41               Medium                 42   \n",
              "8                49               Medium                 45   \n",
              "13               38               Medium                 47   \n",
              "\n",
              "   defenceAggressionClass defenceTeamWidth  defenceTeamWidthClass  \\\n",
              "1                   Press               54                 Normal   \n",
              "2                   Press               54                 Normal   \n",
              "7                   Press               60                 Normal   \n",
              "8                   Press               63                 Normal   \n",
              "13                  Press               53                 Normal   \n",
              "\n",
              "   defenceDefenderLineClass  \n",
              "1                     Cover  \n",
              "2                     Cover  \n",
              "7                     Cover  \n",
              "8                     Cover  \n",
              "13                    Cover  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th></th>\n",
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              "      <th>team_fifa_api_id</th>\n",
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              "      <th>buildUpPlayDribblingClass</th>\n",
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              "      <th>buildUpPlayPassingClass</th>\n",
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              "      <th>defencePressure</th>\n",
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              "      <td>2014-09-19 00:00:00</td>\n",
              "      <td>52</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>48.0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>56</td>\n",
              "      <td>Mixed</td>\n",
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              "      <td>64</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Organised</td>\n",
              "      <td>47</td>\n",
              "      <td>Medium</td>\n",
              "      <td>44</td>\n",
              "      <td>Press</td>\n",
              "      <td>54</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Cover</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2015-09-10 00:00:00</td>\n",
              "      <td>47</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>41.0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>54</td>\n",
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              "      <td>64</td>\n",
              "      <td>Normal</td>\n",
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              "      <td>47</td>\n",
              "      <td>Medium</td>\n",
              "      <td>44</td>\n",
              "      <td>Press</td>\n",
              "      <td>54</td>\n",
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              "      <td>Cover</td>\n",
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              "      <td>42</td>\n",
              "      <td>Press</td>\n",
              "      <td>60</td>\n",
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              "      <td>52</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          document.querySelector('#df-c9feb7df-30be-45fb-8945-4c22d7c000b6 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c9feb7df-30be-45fb-8945-4c22d7c000b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "player_cleaned_df = player_cleaned_df.drop_duplicates()\n",
        "player_attributes_cleaned_df = player_attributes_cleaned_df.drop_duplicates()\n",
        "team_cleaned_df = team_cleaned_df.drop_duplicates()\n",
        "team_attributes_cleaned_df = team_attributes_cleaned_df.drop_duplicates()\n",
        "team_attributes_cleaned_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qBxTFPeo_dC"
      },
      "source": [
        "Let's check the datatypes for our player table\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lrd87IBqpAkc",
        "outputId": "01eb034c-dcb9-48ab-82f2-bd010d947a85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                       int64\n",
              "player_fifa_api_id       int64\n",
              "player_api_id            int64\n",
              "date                    object\n",
              "overall_rating         float64\n",
              "potential              float64\n",
              "preferred_foot          object\n",
              "attacking_work_rate     object\n",
              "defensive_work_rate     object\n",
              "crossing               float64\n",
              "finishing              float64\n",
              "heading_accuracy       float64\n",
              "short_passing          float64\n",
              "volleys                float64\n",
              "dribbling              float64\n",
              "curve                  float64\n",
              "free_kick_accuracy     float64\n",
              "long_passing           float64\n",
              "ball_control           float64\n",
              "acceleration           float64\n",
              "sprint_speed           float64\n",
              "agility                float64\n",
              "reactions              float64\n",
              "balance                float64\n",
              "shot_power             float64\n",
              "jumping                float64\n",
              "stamina                float64\n",
              "strength               float64\n",
              "long_shots             float64\n",
              "aggression             float64\n",
              "interceptions          float64\n",
              "positioning            float64\n",
              "vision                 float64\n",
              "penalties              float64\n",
              "marking                float64\n",
              "standing_tackle        float64\n",
              "sliding_tackle         float64\n",
              "gk_diving              float64\n",
              "gk_handling            float64\n",
              "gk_kicking             float64\n",
              "gk_positioning         float64\n",
              "gk_reflexes            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "player_attributes_cleaned_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BK5l2nvDLPw",
        "outputId": "1bfab36d-3a6c-4695-8161-1e040991f038"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                      int64\n",
              "player_api_id           int64\n",
              "player_name            object\n",
              "player_fifa_api_id      int64\n",
              "birthday               object\n",
              "height                float64\n",
              "weight                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "player_cleaned_df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3gY_z62pJUB"
      },
      "source": [
        "## **1.3 Join the tables**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seK3l2_WpOOD"
      },
      "source": [
        "1.   Join player attributes and player table data using player_api_id\n",
        "2.   Remove unwanted columns and rename the columns after joining for cleaner table data\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "_Cjug3vdpKGl",
        "outputId": "ff9b1d21-76fa-433d-8b77-4dcce68aac5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  player_fifa_api_id                 date  overall_rating  potential  \\\n",
              "0   1              218353  2016-02-18 00:00:00            67.0       71.0   \n",
              "1   2              218353  2015-11-19 00:00:00            67.0       71.0   \n",
              "2   3              218353  2015-09-21 00:00:00            62.0       66.0   \n",
              "3   4              218353  2015-03-20 00:00:00            61.0       65.0   \n",
              "4   5              218353  2007-02-22 00:00:00            61.0       65.0   \n",
              "\n",
              "  attacking_work_rate defensive_work_rate  crossing  finishing  \\\n",
              "0              medium              medium      49.0       44.0   \n",
              "1              medium              medium      49.0       44.0   \n",
              "2              medium              medium      49.0       44.0   \n",
              "3              medium              medium      48.0       43.0   \n",
              "4              medium              medium      48.0       43.0   \n",
              "\n",
              "   heading_accuracy  ...  gk_diving  gk_handling  gk_kicking  gk_positioning  \\\n",
              "0              71.0  ...        6.0         11.0        10.0             8.0   \n",
              "1              71.0  ...        6.0         11.0        10.0             8.0   \n",
              "2              71.0  ...        6.0         11.0        10.0             8.0   \n",
              "3              70.0  ...        5.0         10.0         9.0             7.0   \n",
              "4              70.0  ...        5.0         10.0         9.0             7.0   \n",
              "\n",
              "   gk_reflexes  player_api_id         player_name             birthday  \\\n",
              "0          8.0         505942  Aaron Appindangoye  1992-02-29 00:00:00   \n",
              "1          8.0         505942  Aaron Appindangoye  1992-02-29 00:00:00   \n",
              "2          8.0         505942  Aaron Appindangoye  1992-02-29 00:00:00   \n",
              "3          7.0         505942  Aaron Appindangoye  1992-02-29 00:00:00   \n",
              "4          7.0         505942  Aaron Appindangoye  1992-02-29 00:00:00   \n",
              "\n",
              "   height  weight  \n",
              "0  182.88     187  \n",
              "1  182.88     187  \n",
              "2  182.88     187  \n",
              "3  182.88     187  \n",
              "4  182.88     187  \n",
              "\n",
              "[5 rows x 45 columns]"
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              "\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>player_fifa_api_id</th>\n",
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              "      <th>...</th>\n",
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              "      <td>218353</td>\n",
              "      <td>2015-09-21 00:00:00</td>\n",
              "      <td>62.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>49.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>505942</td>\n",
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              "      <td>1992-02-29 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>187</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>218353</td>\n",
              "      <td>2015-03-20 00:00:00</td>\n",
              "      <td>61.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>48.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>70.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>505942</td>\n",
              "      <td>Aaron Appindangoye</td>\n",
              "      <td>1992-02-29 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>218353</td>\n",
              "      <td>2007-02-22 00:00:00</td>\n",
              "      <td>61.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
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              "      <td>43.0</td>\n",
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              "  </tbody>\n",
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              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-8d89f854-46d3-45aa-9b6a-953afb7ad8ea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d89f854-46d3-45aa-9b6a-953afb7ad8ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "players_and_skills_df = player_attributes_cleaned_df.merge(player_cleaned_df, on=\"player_fifa_api_id\")\n",
        "players_and_skills_df = players_and_skills_df.drop(columns=['player_api_id_x', 'id_y', 'preferred_foot'])\n",
        "players_and_skills_df = players_and_skills_df.rename(columns={\"player_api_id_y\": \"player_api_id\", \"id_x\" : \"id\"})\n",
        "players_and_skills_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zA1UCwfOpU_K"
      },
      "source": [
        "## **1.4 Calculate the age of the players**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "PlWsIjYppVwx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "7e5542fc-c68c-4fb1-c105-2d86f2b68f89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  player_fifa_api_id                 date  overall_rating  potential  \\\n",
              "0   1              218353  2016-02-18 00:00:00            67.0       71.0   \n",
              "1   2              218353  2015-11-19 00:00:00            67.0       71.0   \n",
              "2   3              218353  2015-09-21 00:00:00            62.0       66.0   \n",
              "3   4              218353  2015-03-20 00:00:00            61.0       65.0   \n",
              "4   5              218353  2007-02-22 00:00:00            61.0       65.0   \n",
              "\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "players_and_skills_df['age'] = players_and_skills_df.apply(lambda x: pd.to_datetime(x['date']).year - pd.to_datetime(x['birthday']).year, axis=1)\n",
        "players_and_skills_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmxP2Bgjpclp"
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      "source": [
        "We can also observe that our table has some categorical values that we will need to convert to integer/float to run our ML model. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
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        "outputId": "8124f0a5-7c80-423c-88be-2a4d859bc66a"
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              "   id  player_fifa_api_id                 date  overall_rating  potential  \\\n",
              "0   1              218353  2016-02-18 00:00:00            67.0       71.0   \n",
              "1   2              218353  2015-11-19 00:00:00            67.0       71.0   \n",
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              "4   5              218353  2007-02-22 00:00:00            61.0       65.0   \n",
              "\n",
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              "      <td>187</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>218353</td>\n",
              "      <td>2007-02-22 00:00:00</td>\n",
              "      <td>61.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>48.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>505942</td>\n",
              "      <td>Aaron Appindangoye</td>\n",
              "      <td>1992-02-29 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>187</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b75d4ac8-1737-409c-b439-0ce24a1ed1af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b75d4ac8-1737-409c-b439-0ce24a1ed1af button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b75d4ac8-1737-409c-b439-0ce24a1ed1af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "def assign(x):\n",
        "  if x == \"low\":\n",
        "    x = 3\n",
        "    return x \n",
        "  elif x == \"medium\":\n",
        "    x = 6\n",
        "    return x\n",
        "  elif x == \"high\":\n",
        "    x = 9\n",
        "    return x\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "players_and_skills_df['defensive_work_rate'] = players_and_skills_df['defensive_work_rate'].apply(lambda x: assign(x))\n",
        "players_and_skills_df['attacking_work_rate'] = players_and_skills_df['attacking_work_rate'].apply(lambda x: assign(x))\n",
        "players_and_skills_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nYIFjSG7ZYX"
      },
      "source": [
        "## **1.5 Visualize data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRsxHdbsKtsQ"
      },
      "source": [
        "Let's visualize what our distribution of players looks like by year:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "RQw6pn4DFd0k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "df9ad47e-d786-4c26-eab3-a30604bdd7bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "player_year_query = \"\"\"\n",
        "WITH player_years AS (\n",
        "    SELECT player_fifa_api_id,\n",
        "           (strftime('%Y-%m',date)) AS year_month\n",
        "    FROM players_and_skills_df\n",
        "    \n",
        "    GROUP BY player_fifa_api_id, (strftime('%Y-%m',date))\n",
        ")\n",
        "\n",
        "SELECT year_month, COUNT(player_fifa_api_id) AS player_count\n",
        "FROM player_years\n",
        "GROUP BY year_month\n",
        "\"\"\"\n",
        "sql_player_year_df = ps.sqldf(player_year_query, locals())\n",
        "\n",
        "def addlabels(x,y):\n",
        "    for i in range(len(x)):\n",
        "        plt.text(i, y[i], y[i], ha = 'center')\n",
        "\n",
        "plt.subplots(figsize=(18,5))\n",
        "plt.bar(sql_player_year_df['year_month'], sql_player_year_df['player_count'])\n",
        "plt.title(\"Distribution of Player Count by Year-Month\",fontsize=18)\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('# of Distinct Players', fontsize=14)\n",
        "plt.xticks(rotation=90)\n",
        "addlabels(sql_player_year_df['year_month'], sql_player_year_df['player_count'])\n",
        "plt.show()\n",
        "\n",
        "#https://www.geeksforgeeks.org/adding-value-labels-on-a-matplotlib-bar-chart/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ks614hvc7F9L"
      },
      "source": [
        "We will limit our data to just full soccer seasons, which begin in August. Therefore we can remove the data in 2007-02. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "zVRS1IO_Ne1e"
      },
      "outputs": [],
      "source": [
        "#Remove the players that get added in 2007-02\n",
        "players_and_skills_df = players_and_skills_df[players_and_skills_df['date'] >= '2007-08-01']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvYr3OfEpayd"
      },
      "source": [
        "# **2. Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVXJm0a9sJ1H"
      },
      "source": [
        "## **2.1 Find correlation between Player skills**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "jbdo7m2XpgPF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "0624fc33-0b02-4588-a14a-fe9ccdb7b4b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-3ff953532c03>:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  mask = np.zeros_like(player_skills_heatmap.corr(), dtype=np.bool)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f41009850>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "player_skills_heatmap = players_and_skills_df.drop(columns=['id', 'player_fifa_api_id', 'player_api_id'])\n",
        "\n",
        "mask = np.zeros_like(player_skills_heatmap.corr(), dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "plt.figure(figsize=(14, 10))\n",
        "sns.heatmap(player_skills_heatmap.corr(), mask=mask, cmap=\"RdBu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_w-Lu87mxut"
      },
      "source": [
        "## **2.2 Understanding key aspects of player data**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRWk2fgu-TLM"
      },
      "source": [
        "We want to understand how many years players we have in the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "S3Z8s1RM80-F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "f10b1df3-2f2e-400b-e317-f883e0cfd4a5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "player_avg_year_query = \"\"\"\n",
        "WITH player_years AS (\n",
        "    SELECT player_fifa_api_id,\n",
        "           (strftime('%Y',date)) AS year\n",
        "    FROM players_and_skills_df\n",
        "    GROUP BY player_fifa_api_id, (strftime('%Y',date))\n",
        "),\n",
        "player_avg_years AS (\n",
        "  SELECT player_fifa_api_id,\n",
        "          COUNT(DISTINCT year) AS data_years\n",
        "  FROM player_years\n",
        "  GROUP BY player_fifa_api_id \n",
        ")\n",
        "\n",
        "SELECT data_years, COUNT(player_fifa_api_id) AS player_count\n",
        "FROM player_avg_years\n",
        "GROUP BY data_years\n",
        "\"\"\"\n",
        "sql_player_avg_year_df = ps.sqldf(player_avg_year_query, locals())\n",
        "\n",
        "def addlabels(x,y):\n",
        "    for i in range(len(x)):\n",
        "        plt.text(i, y[i], y[i], ha = 'center')\n",
        "\n",
        "plt.subplots(figsize=(18,8))\n",
        "plt.bar(sql_player_avg_year_df['data_years'], sql_player_avg_year_df['player_count'])\n",
        "plt.title(\"Distribution of Player Count by Years in Data\",fontsize=18)\n",
        "plt.xlabel('# of Years', fontsize=14)\n",
        "plt.ylabel('# of Players', fontsize=14)\n",
        "plt.xticks(np.arange(1,len(sql_player_avg_year_df['data_years'])+1, step=1))\n",
        "addlabels(sql_player_avg_year_df['data_years'], sql_player_avg_year_df['player_count'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8vQEH04-cE7"
      },
      "source": [
        "It appears that we have over 2000 players with a full 10 years in the data. With this cohort of players with the most data, we can see when they \"peaked\" by finding the last date they had their highest 'overall_rating' score. \n",
        "\n",
        "For a given player, we want to answer the following question: \n",
        "*   Over the next period/season, will the player's overall rating be in RISE or DECLINE phase?\n",
        "\n",
        "We'll develop machine learning models to answer this question. Our models will be a \"screening\" method for scouters \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Dr34OxJpjqTr"
      },
      "outputs": [],
      "source": [
        "#First limit to players with 10 years of results\n",
        "player_yrs_10_query = \"\"\"\n",
        "WITH player_years AS (\n",
        "    SELECT player_fifa_api_id,\n",
        "           (strftime('%Y',date)) AS year\n",
        "    FROM players_and_skills_df\n",
        "    GROUP BY player_fifa_api_id, (strftime('%Y',date))\n",
        "),\n",
        "player_avg_years AS (\n",
        "  SELECT player_fifa_api_id,\n",
        "          COUNT(DISTINCT year) AS data_years\n",
        "  FROM player_years\n",
        "  GROUP BY player_fifa_api_id \n",
        ")\n",
        "\n",
        "SELECT player_fifa_api_id\n",
        "FROM player_avg_years\n",
        "WHERE data_years >= 10\n",
        "GROUP BY player_fifa_api_id\n",
        "\"\"\"\n",
        "sql_player_yrs_10_df = ps.sqldf(player_yrs_10_query, locals())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "M2GiYzH7jtk-"
      },
      "outputs": [],
      "source": [
        "#Join table to master table and limit data to just these players\n",
        "player_ratings_limited_query = \"\"\"\n",
        "WITH player_rating_age AS (\n",
        "    SELECT player_fifa_api_id, overall_rating, age\n",
        "    FROM players_and_skills_df\n",
        "    GROUP BY player_fifa_api_id, overall_rating, age\n",
        ")\n",
        "\n",
        "SELECT X.player_fifa_api_id, X.overall_rating, X.age\n",
        "FROM player_rating_age X\n",
        "    JOIN sql_player_yrs_10_df Y on X.player_fifa_api_id = Y.player_fifa_api_id\n",
        "GROUP BY X.player_fifa_api_id, X.overall_rating, X.age\n",
        "\"\"\"\n",
        "sql_player_ratings_limited_df = ps.sqldf(player_ratings_limited_query, locals())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ePqVpJrn_Azi"
      },
      "outputs": [],
      "source": [
        "\n",
        "#Determine the max overall rating for these players. If multiple, then select latest date\n",
        "player_ratings_peak_query = \"\"\"\n",
        "WITH player_rating_max AS (\n",
        "    SELECT player_fifa_api_id, max(overall_rating) AS max_rating\n",
        "    FROM sql_player_ratings_limited_df X\n",
        "    GROUP BY player_fifa_api_id\n",
        "),\n",
        "player_rating_age_max AS (\n",
        "    SELECT X.player_fifa_api_id, MAX(X.age) as max_age\n",
        "    FROM sql_player_ratings_limited_df X\n",
        "      JOIN player_rating_max Y on X.player_fifa_api_id = Y.player_fifa_api_id AND X.overall_rating = Y.max_rating\n",
        "    GROUP BY X.player_fifa_api_id\n",
        ")\n",
        "\n",
        "SELECT X.player_fifa_api_id, \n",
        "       X.overall_rating, \n",
        "       X.age,\n",
        "       CASE\n",
        "          WHEN X.age < Y.max_age THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating < Z.max_rating THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating = Z.max_rating THEN 'PEAK'\n",
        "          ELSE 'DECLINE'\n",
        "      END AS peak_flg\n",
        "FROM sql_player_ratings_limited_df X\n",
        "    LEFT JOIN player_rating_age_max Y \n",
        "      on X.player_fifa_api_id = Y.player_fifa_api_id\n",
        "    LEFT JOIN player_rating_max Z \n",
        "      on X.player_fifa_api_id = Z.player_fifa_api_id \n",
        "GROUP BY X.player_fifa_api_id, \n",
        "       X.overall_rating, \n",
        "       X.age,\n",
        "       CASE\n",
        "          WHEN X.age < Y.max_age THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating < Z.max_rating THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating = Z.max_rating THEN 'PEAK'\n",
        "          ELSE 'DECLINE'\n",
        "      END\n",
        "\"\"\"\n",
        "sql_player_ratings_peak_df = ps.sqldf(player_ratings_peak_query, locals())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RKDpz4ol42P"
      },
      "source": [
        "Doing a check to see what the data looks like to ensure that we are properly capturing the rise, peak, and decline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "qEgM6Tk_INbI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "0e951b0e-fe78-43ca-8b50-fc3f61c7d85f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     player_fifa_api_id  overall_rating  age peak_flg\n",
              "100                 684            77.0   35     RISE\n",
              "97                  684            75.0   36     RISE\n",
              "101                 684            77.0   37     RISE\n",
              "104                 684            79.0   37     RISE\n",
              "106                 684            80.0   38     RISE\n",
              "107                 684            80.0   39     RISE\n",
              "109                 684            81.0   39     PEAK\n",
              "108                 684            80.0   40  DECLINE\n",
              "103                 684            78.0   41  DECLINE\n",
              "105                 684            79.0   41  DECLINE\n",
              "98                  684            76.0   42  DECLINE\n",
              "102                 684            77.0   42  DECLINE\n",
              "95                  684            73.0   43  DECLINE\n",
              "99                  684            76.0   43  DECLINE\n",
              "96                  684            73.0   44  DECLINE"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e747db6f-3ab5-4b13-b9e5-dc56ffb4c2ed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>player_fifa_api_id</th>\n",
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              "      <td>80.0</td>\n",
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              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>684</td>\n",
              "      <td>78.0</td>\n",
              "      <td>41</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>684</td>\n",
              "      <td>79.0</td>\n",
              "      <td>41</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>684</td>\n",
              "      <td>76.0</td>\n",
              "      <td>42</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>684</td>\n",
              "      <td>77.0</td>\n",
              "      <td>42</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>684</td>\n",
              "      <td>73.0</td>\n",
              "      <td>43</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>684</td>\n",
              "      <td>76.0</td>\n",
              "      <td>43</td>\n",
              "      <td>DECLINE</td>\n",
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              "      <td>44</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "sql_player_ratings_peak_df[(sql_player_ratings_peak_df['player_fifa_api_id'] == 684)].sort_values(by = 'age').head(100)\n",
        "#sql_player_ratings_peak_df[(sql_player_ratings_peak_df['age'] == 18) & (sql_player_ratings_peak_df['peak_flg'] == 'PEAK')] #684 11983 158718 189484"
      ]
    },
    {
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      "execution_count": 27,
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          "height": 512
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plot the distribution of the average age they reach peak.\n",
        "player_peak_query = \"\"\"\n",
        "SELECT COUNT(player_fifa_api_id) AS player_count, \n",
        "       overall_rating, \n",
        "       age\n",
        "FROM sql_player_ratings_peak_df  \n",
        "WHERE peak_flg = 'PEAK'\n",
        "GROUP BY overall_rating, \n",
        "       age\n",
        "\"\"\"\n",
        "players_peak_df = ps.sqldf(player_peak_query, locals())\n",
        "\n",
        "def addlabels(x,y):\n",
        "    for i in range(len(x)):\n",
        "        plt.text(i, y[i], y[i], ha = 'center')\n",
        "\n",
        "plt.subplots(figsize=(18,8))\n",
        "plt.bar(players_peak_df['age'], players_peak_df['player_count'])\n",
        "plt.title(\"Distribution of Player Rating Peak by Age\",fontsize=18)\n",
        "plt.xlabel('Age', fontsize=14)\n",
        "plt.ylabel('# of Players', fontsize=14)\n",
        "plt.xticks(np.arange(min(players_peak_df['age']),max(players_peak_df['age'])+1, step=1))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "lb1_wnwUsjdX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "8b56e492-fe72-4371-b89a-573f420fd8ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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M+DlZ8eE73flTSosiYm/gp2Td1ksn6/uIbLWDioobFT3/4sJ5/gocCTQn+xX4SLIhIX3LtR8ZEXuQTe53FtkQi7vIVjZ4kexX8rLtj4mIp8guYH9LVtD4hOzX4N9WJWPBoMK2hOwidiowkmxCxu+M/08pfR4R2wJnkBVS9iVbOnIq2YoLN5RpuzAidiu0PbTw2uaTdd3/x3JkXCEppccKBZL/I+tF045s7oKJZHMnvLaUw/cgWxL00mU8zb1kr2sgcEdK6ZqImEh2of1LoAnZd+cJvj0B69Jy/yYi3iX7Dl5EVggaDRyaUnq2TNNbyS6qjyy8ti/IlkY9IKV0b6HNq2R/JzuSrcJSRLbM7gVkczxUt9PJJt8s/a5MIZuH4mWywsV3pJTeLXy3f0glxciU0isRsQXZd30f4KTC87xPVqB4oor5rilkO5GsB8VMsh4bv0gpPVXFc0iSapkorDglSZKqQUTsT/ar9KCU0p1555FqQmE+mX5Ah5TSV8tqL0lSWc5RIUnSShCZpuX2NSL7RfprKhmiIK1qChNt7g7cZpFCkrQiHPohSdLK0QSYHBG3k80l0IZsvoHNyVY9+CTPcFJ1i4ityZbz/SXZcJeaGJoiSVoFWaiQJGnlWEQ22ee+wDpkKyW8DfwspXR1nsGkGnIy2bLBk8gmDX0/3ziSpLqqxuaoiIgNySYVK7Ue8Aey2ZmPJ1vbHeCslNLDFRy/B9ns40XADSmli6o3sSRJkiRJqmm5TKZZWDLsQ2BrsjXFv0wp/XUZ7SeQLTc3lWym6UEppTdrIK4kSZIkSaoheQ392BmYmFKaHBFVab8V8G5KaRJARNxJ1rV2qYWKtm3bpm7dun3PqJIkSZIkaWUaM2bMpymldhU9lleh4hBgWJn7P4+InwAlwBkppdnl2nfk22uXTyXrjbFU3bp1o6Sk5PtmlSRJkiRJK1FETK7ssRpfnjQiGgP7AP8s7LoG6A70Bj7me84QHREnRERJRJTMmDFj2QdIkiRJkqRao8YLFcCewNiU0jSAlNK0lNLilNIS4HqyYR7lfQh0LnO/U2Hfd6SUrkspFaeUitu1q7AXiSRJkiRJqqXyKFQMosywj4hYp8xjA4DXKzjmZaBHRKxb6JFxCPBgtaaUJEmSJEk1rkYLFRHRnGzljvvK7L4kIsZFxGvATsBphbYdIuJhgJTS18DPgceAt4C7U0pv1GR2SZIkSZJU/Wq0UJFSmptSapNS+rzMviNSSj1TSpunlPZJKX1c2P9RSmmvMu0eTiltkFLqnlI6vyZzrwxvv/02vXv3/mZr2bIll19++TePDx48mIjg008/rfD43/zmN2y22WZsttlm3HXXXTUVW5IkSZKkGpXXqh/1zoYbbsgrr7wCwOLFi+nYsSMDBgwAYMqUKQwfPpwuXbpUeOxDDz3E2LFjeeWVV1iwYAE77rgje+65Jy1btqyx/JIkSZIk1YQ85qio95544gm6d+9O165dATjttNO45JJLiIgK27/55ptsv/32NGzYkObNm7P55pvz6KOP1mRkSZIkSZJqhIWKHNx5550MGjQIgAceeICOHTvSq1evStv36tWLRx99lHnz5vHpp5/y1FNPMWXKlJqKK0mSJElSjXHoRw1buHAhDz74IBdeeCHz5s3jggsuYPjw4Us9ZrfdduPll1+mf//+tGvXjn79+lFUVFRDiSVJkiRJqjn2qKhhjzzyCH369GGttdZi4sSJvPfee/Tq1Ytu3boxdepU+vTpwyeffPKd4373u9/xyiuvMGLECFJKbLDBBjmklyRJkiSpetmjooYNGzbsm2EfPXv2ZPr06d881q1bN0pKSmjbtu23jlm8eDGfffYZbdq04bXXXuO1115jt912q9HckiRJkiTVBAsVNWju3LmMGDGCIUOGLLNtSUkJ1157LTfccAOLFi1iu+22A6Bly5bcdtttNGzoRydJkiRJWvVESinvDNWmuLg4lZSU5B1DkiRJkiSVERFjUkrFFT3mHBWSJEmSJKnWsFAhSZIkSZJqDQsVkiRJkiSp1rBQUYt8vXgJU2bNyzuGJEmSJEm5sVBRi1z86Hh+/Pfn+O8Hs/OOIkmSJElSLixU1CJHbNONNVZrxKHXj2bkhBl5x5EkSZIkqcZZqKhFurRpxj0n9Wfdts05dujLPPDKh3lHkiRJkiSpRlmoqGXard6EO0/chuJua3LKna/wj+ffyzuSJEmSJEk1xkJFLdSyaSOGHr0Ve2y6Nuf++03+8th4Ukp5x5IkSZIkqdpZqKilmjYq4qrD+jBoqy5c9dREfnvfOL5evCTvWJIkSZIkVauGeQdQ5YoaBBcM2Ix2LRpz5ZPvMmvuQq4ctAVNGxXlHU2SJEmSpGphj4paLiI4fbcNOXefTRnx1jSOvOklvpi/KO9YkiRJkiRVCwsVdcSR/btxxSFbMPaD2Rw85EWmz5mfdyRJkiRJklY6CxV1yD69OnDTUVsyeeZcDrhmFJNnzs07kiRJkiRJK5WFijpmux7tGHb8NsyZv4j9rxnF6x9+nnckSZIkSZJWGgsVdVCvzq245+T+NGnYgEOue5FRE2fmHUmSJEmSpJXCQkUd1b1dC+49uT8dWjXlyJte4tHXP847kiRJkiRJ35uFijps7TWacveJ/ejZaQ1+evtY7hj9Qd6RJEmSJEn6XixU1HGtmjXmtmO3ZscN23PWv8bxtyfeIaWUdyxJkiRJklaIhYpVwGqNixhyRF8G9unI4BETOOfBN1iyxGKFJEmSJKnuaZh3AK0cjYoa8NcDetG2RROue2YSs+YtYvCBvWjc0FqUJEmSJKnusFCxCmnQIDhrr41p07wxFz4yns/mLeTaw/vSvIkfsyRJkiSpbvDn9lXQiTt05y8HbM4LE2dy6PUvMmvuwrwjSZIkSZJUJRYqVlEHFndmyOF9Gf/JHA649gWmzp6XdyRJkiRJkpbJQsUqbJdN1uK247bm0zkLOOCaUUyYNifvSJIkSZIkLZWFilXclt1ac/dJ/UgkDrx2FGMmz8o7kiRJkiRJlbJQUQ9stHZL7jmpP62bN+awG0bz5PhpeUeSJEmSJKlCFirqic6tm/HPk/rRo/3qHH/LGO4dMzXvSJIkSZIkfYeFinqkbYsmDDthG/qt14Yz/vkq1z8zKe9IkiRJkiR9i4WKeqZFk4bceFQxe2++Duc//BYXPvwWKaW8Y0mSJEmSBEDDvAOo5jVpWMSVh2xBm+aNGfLMJGbOXchFA3vSsMi6lSRJkiQpXxYq6qmiBsG5+2xKm+ZNuOzxCXw2byF/G9SH1RoX5R1NkiRJklSP+RN6PRYRnLJLD87bbzOeGD+dI24czefzFuUdS5IkSZJUj1moEIdv05WrDu3Da1M/56Aho5j2xfy8I0mSJEmS6ikLFQJgr57rMPToLZk6ex4Dr36BSTO+zDuSJEmSJKkeslChb/Rfvy13ntCP+YsWc8C1o3ht6md5R5IkSZIk1TMWKvQtPTutwT0n96dZ4yIGXfciz73zad6RJEmSJEn1iIUKfce6bZtz38n96dy6GUcPfYn/vPZR3pEkSZIkSfWEhQpVqH3Lptx1Yj+26Lwmvxj2X24d9X7ekSRJkiRJ9YCFClVqjdUaccuxW7HzRmvx+wfe4LIRE0gp5R1LkiRJkrQKs1ChpWraqIhrD+/DQcWduOKJdzj7/tdZvMRihSRJkiSpejTMO4Bqv4ZFDbh4/81p06IJ1zw9kdnzFnLZwb1p0rAo72iSJEmSpFWMhQpVSUTwmz02ok3zxpz30Ft8Nu9lhhzRl9WbNso7miRJkiRpFVJjQz8iYsOIeKXM9kVEnFrm8TMiIkVE20qOX1zm2AdrKre+7bjt1uOyg3vx0nuzGHT9i8yYsyDvSJIkSZKkVUiN9ahIKb0N9AaIiCLgQ+Bfhfudgd2AD5Zyiq9SSr2rO6eWbcAWnWjVrDEn3zaGA699gVuP3ZrOrZvlHUuSJEmStArIazLNnYGJKaXJhfuXAWcCztJYR+y0YXtuP24bZs9bxMBrXuCtj7/IO5IkSZIkaRWQV6HiEGAYQETsC3yYUnp1Gcc0jYiSiHgxIvar9oRapr5d1+Sek/rRsEFw0JBRvPTerLwjSZIkSZLquBovVEREY2Af4J8R0Qw4C/hDFQ7tmlIqBg4FLo+I7pWc/4RCQaNkxowZKy23KtZjrdW55+T+tF+9CUfcOJoRb07LO5IkSZIkqQ7Lo0fFnsDYlNI0oDuwLvBqRLwPdALGRsTa5Q9KKX1Y+HcS8DSwRUUnTyldl1IqTikVt2vXrnpegb6lY6vV+OdJ/dl4nZaceGsJd788Je9IkiRJkqQ6Ko9CxSAKwz5SSuNSSu1TSt1SSt2AqUCflNInZQ+IiDUjoknhdltgW+DNmo2tpWndvDF3HL81P+jRjjPvfY1rnp5ISk45IkmSJElaPjVaqIiI5sCuwH1VaFscETcU7m4MlETEq8BTwEUpJQsVtUyzxg254SfF7Nu7Axc/Op7zHnqLJUssVkiSJEmSqq7GlicFSCnNBdos5fFuZW6XAMcVbr8A9KzufPr+GjdswGUH9aZ188bc+Nx7zJq7kEsO2JxGRXnN2ypJkiRJqktqtFCh+qFBg+APP9qEti2a8JfH3mb2vIVcfVgfmjX26yZJkiRJWjp/5la1iAh+ttP6XDSwJ89MmMFhN4xm9tyFeceSJEmSJNVyFipUrQ7ZqgvXHN6XNz76ggOHjOKjz77KO5IkSZIkqRazUKFqt/uma3PLMVsx7fP5HHDNC7w7fU7ekSRJkiRJtZSFCtWIbdZrw50nbsPCxYkDrh3Ffz+YnXckSZIkSVItZKFCNWbTDmtw38n9WWO1Rhx6/WhGTpiRdyRJkiRJUi1joUI1qkubZtxzUn/WbducY4e+zAOvfJh3JEmSJElSLWKhQjWu3epNuPPEbSjutian3PkK/3j+vbwjSZIkSZJqCQsVykXLpo0YevRW7LHp2pz77zf5y2PjSSnlHUuSJEmSlDMLFcpN00ZFXHVYHwZt1YWrnprIb+8bx9eLl+QdS5IkSZKUo4Z5B1D9VtQguGDAZrRr0Zgrn3yXWXMXcuWgLWjaqCjvaJIkSZKkHNijQrmLCE7fbUPO+fEmjHhrGkfe9BJfzF+UdyxJkiRJUg4sVKjWOGrbdbnikC0Y+8FsDh7yItO/mJ93JEmSJElSDbNQoVpln14duPHILZk8cy4HXDuKyTPn5h1JkiRJklSDLFSo1tl+g3bccfw2zJm/iP2veYHXP/w870iSJEmSpBpioUK1Uu/Orbjn5P40aVjEIde9yKiJM/OOJEmSJEmqARYqVGt1b9eCe0/uT4dWTTnyppd49PWP844kSZIkSapmFipUq629RlPuPrEfPTutwU9vH8sdoz/IO5IkSZIkqRpZqFCt16pZY247dmt23LA9Z/1rHH974h1SSnnHkiRJkiRVAwsVqhNWa1zEkCP6MrBPRwaPmMA5D77BkiUWKyRJkiRpVdMw7wBSVTUqasBfD+hF2xZNuO6ZScyat4jBB/aicUPrbZIkSZK0qrBQoTqlQYPgrL02pk3zxlz4yHg+m7eQaw/vS/MmfpUlSZIkaVXgT9Gqk07coTt/OWBzXpg4k0Ovf5GZXy7IO5IkSZIkaSWwUKE668Dizgw5vC/jP5nDgUNGMXX2vLwjSZIkSZK+JwsVqtN22WQtbjtuaz6ds4ADrhnFhGlz8o4kSZIkSfoeLFSoztuyW2vuPqkficSB145izORZeUeSJEmSJK0gCxVaJWy0dkvuOak/rZs35rAbRvPk+Gl5R5IkSZIkrQALFVpldG7djH+e1I8e7Vfn+FvGcO+YqXlHkiRJkiQtJwsVWqW0bdGEYSdswzbrteaMf77Kdc9MzDuSJEmSJGk5WKjQKqdFk4bcdNSW7L35Olzw8HgufPgtUkp5x5IkSZIkVUHDvANI1aFJwyKuPGQL2jRvzJBnJjFz7kIuGtiThkXW5iRJkiSpNrNQoVVWUYPg3H02pU3zJlz2+ARmz13I3w/tw2qNi/KOJkmSJEmqhD8va5UWEZyySw/O228znnx7OkfcOJrP5y3KO5YkSZIkqRIWKlQvHL5NV646tA+vTf2cg4aM4pPP5+cdSetPQ8QAACAASURBVJIkSZJUAQsVqjf26rkOQ4/ekqmz57H/NS8wacaXeUeSJEmSJJVjoUL1Sv/123LnCf2Yv2gxB1w7itemfpZ3JEmSJElSGRYqVO/07LQG95zcn2aNixh03Ys8986neUeSJEmSJBVYqFC9tG7b5tx7cn86t27G0UNf4j+vfZR3JEmSJEkSFipUj63Vsil3ndiPLTqvyS+G/ZdbRr2fdyRJkiRJqvcsVKheW2O1Rtxy7FbsvNFa/OGBN7h0xARSSnnHkiRJkqR6y0KF6r2mjYq49vA+HFTciSufeIez73+dxUssVkiSJElSHhrmHUCqDRoWNeDi/TenTYsmXPP0RGbPW8hlB/emScOivKNJkiRJUr1ioUIqiAh+s8dGtGnemPMeeovP5r3MkCP6snrTRnlHkyRJkqR6w6EfUjnHbbcelx3ci5fem8Wg619kxpwFeUeSJEmSpHrDQoVUgQFbdOL6I4t5d/qXHHjtC0yZNS/vSJIkSZJUL1iokCqx04btuf24bZg9bxEDr3mBtz7+Iu9IkiRJkrTKs1AhLUXfrmtyz0n9aNggOGjIKEZPmpl3JEmSJElapVmokJahx1qrc8/J/Wm/ehMO/st9dN9oM3r37k3v3r1p2bIll19++TdtBw8eTETw6aefVnium2++mR49etCjRw9uvvnmmnoJkiRJklRnuOqHVAUdW63GP0/qzzFDG/Faq4s5a+Dm7N+nAx07dmTAgAEATJkyheHDh9OlS5cKzzFr1izOPfdcSkpKiAj69u3LPvvsw5prrlmTL0WSJEmSajV7VEhV1Lp5Y24/bmt+0KMdZ977Gqdeegvdu3ena9euAJx22mlccsklRESFxz/22GPsuuuutG7dmjXXXJNdd92VRx99tCZfgiRJkiTVehYqpOXQvElDbvhJMfv06sA/br2DNXvuyJIliQceeICOHTvSq1evSo/98MMP6dy58zf3O3XqxIcfflgTsSVJkiSpzqixQkVEbBgRr5TZvoiIU8s8fkZEpIhoW8nxR0bEO4XtyJrKLZXXuGEDLhmwCUveL+HVRhvzy9te5PzzL+BPf/pT3tEkSZIkqc6rsUJFSuntlFLvlFJvoC8wD/gXQER0BnYDPqjo2IhoDfwR2BrYCvhjRDiwX7l57LFH+cE2W/J/A7fhvpFjGTf+HTbv1Ytu3boxdepU+vTpwyeffPKtYzp27MiUKVO+uT916lQ6duxY09ElSZIkqVbLa+jHzsDElNLkwv3LgDOBVEn73YERKaVZKaXZwAhgj+qPKVVs2LBhDBo0iJ/ttD6Xnvhj1vnZrfQ583b++8YEOnXqxNixY1l77bW/dczuu+/O8OHDmT17NrNnz2b48OHsvvvuOb0CSZIkSaqd8ipUHAIMA4iIfYEPU0qvLqV9R2BKmftTC/ukGjd37lxGjBjBwIEDAThkqy5cc3hf3vjoCw4cMorFS/5XbyspKeG4444DoHXr1vz+979nyy23ZMstt+QPf/gDrVu3zuU1SJIkSVJtFSlV1omhmp4wojHwEbApMAd4CtgtpfR5RLwPFKeUPi13zK+Apiml8wr3fw98lVL6awXnPwE4AaBLly59J0+eXL6JVC1enDST428uoUXThtx67Fas3371vCNJkiRJUq0UEWNSSsUVPZZHj4o9gbEppWlAd2Bd4NVCkaITMDYi1i53zIdA5zL3OxX2fUdK6bqUUnFKqbhdu3YrPbxUmW3Wa8OdJ27DosWJ/a8Zxb1jplLThUBJkiRJquvyKFQMojDsI6U0LqXUPqXULaXUjWxIR5+U0ifljnkM2C0i1ixMorlbYZ9Uq2zaYQ3uO7k/3ds154x/vsqh149m0owv844lSZIkSXVGjRYqIqI5sCtwXxXaFkfEDQAppVnAn4GXC9ufCvukWqdLm2bcc1J/zh+wGW989Dl7XP4slz8+gQVfL847miRJkiTVejU+R0VNKi4uTiUlJXnHUD02Y84CznvoTR545SPWa9uc8wZsRv/ubfOOJUmSJEm5qm1zVEj1RrvVm3DFIVtwyzFb8fWSxKHXj+aMu19l1tyFeUeTJEmSpFrJQoVUA7bfoB3DT9uen++0Pg+++iE/HPw0d5dMcbJNSZIkSSrHQoVUQ5o2KuJXu2/Iw7/cjh7tW3DmPa9x8HUv8u70OXlHkyRJkqRaw0KFVMN6rLU6d53Qj4v378nbn8xhzyue5dLhbzN/kZNtSpIkSZKFCikHDRoEB2/ZhSfO2IEfb96BK598lz0uf4bn3vk072iSJEmSlCsLFVKO2rZowqUH9+b247YmIjj8xtGceud/+fTLBXlHkyRJkqRcWKiQaoFt12/LI6dsxy937sFD4z5m58EjufOlD1iyxMk2JUmSJNUvFiqkWqJpoyJO33UDHjllezZae3X+775xHDRkFBOmOdmmJEmSpPrDQoVUy6zfvgV3nrANfzlgcybO+JK9rniWSx4dz1cLnWxTkiRJ0qrPQoVUC0UEBxZ35okzdmS/LTpy9dMT2f3yZxg5YUbe0SRJkiSpWlmokGqx1s0b89cDezHs+G1oWBQcedNL/GLYf5k+Z37e0SRJkiSpWliokOqAft3b8Mgp23HaLhvw2BufsPPgkdz24mQn25QkSZK0yrFQIdURTRoWccouPXj0lO3o2XENzr7/dfa/9gXGf/JF3tEkSZIkaaWxUCHVMeu1a8Htx23NpQf1YvLMeex95XNc+MhbzFv4dd7RJEmSJOl7s1Ah1UERwcA+nXjyjB04sG8nhoycxK6XPsNT46fnHU2SJEmSvhcLFVId1qpZYy7af3PuPrEfqzUu4uihL/Oz28cy7Qsn25QkSZJUN1mokFYBW63bmod/uR2/3n1DHn9rGrsMHskto95nsZNtSpIkSapjLFRIq4jGDRvws53WZ/hp29O7Syv+8MAbDLz6ed746PO8o0mSJElSlVmokFYxXds055ZjtuKKQ3rz4Wdfsc/fn+e8/7zJ3AVOtilJkiSp9rNQIa2CIoJ9e3fkidN35OAtO3PDc++x66UjefzNaXlHkyRJkqSlslAhrcLWaNaICwb05N6T+7F600Ycd0sJJ95awseff5V3NEmSJEmqkIUKqR7o27U1//nlD/jNHhsxcsIMdhk8kpuee8/JNiVJkiTVOhYqpHqiUVEDTt6xOyNO24Hibq3503/eZL+rnmfcVCfblCRJklR7WKiQ6pnOrZsx9Ogt+fuhW/DJF/PZ96rnOPffb/Clk21KkiRJqgUsVEj1UETwo8078MQZO3DY1l0Z+sL77DJ4JI++/gkpORxEkiRJUn4sVEj1WMumjfjzfptx38n9adWsESfdNobjbxnDh5852aYkSZKkfFiokMQWXdbk37/4AWfttRHPv/spu146khuencTXi5fkHU2SJElSPWOhQhKQTbZ5wvbdGXH69myzXhvOe+gt9vn787wy5bO8o0mSJEmqRyxUSPqWTms248Yji7nmsD7MnLuAAVc/zx8feJ0v5i/KO5okSZKkesBChaTviAj27LkOj5++A0f268YtL05ml8EjeXjcx062KUmSJKlaWaiQVKnVmzbinH025f6fbku71Zvw09vHcszQl5kya17e0SRJkiStoixUSFqmXp1b8cDPtuXsvTdm9Huz2PWykQwZOZFFTrYpSZIkaSWzUCGpShoWNeC47dbj8dN3YPse7bjwkfH8+G/PMWby7LyjSZIkSVqFWKiQtFw6tFqN635SzJAj+vL5V4s44NoX+N2/xvH5V062KUmSJOn7s1AhaYXsvunajDh9B47Zdl2GvfQBOw8eyb9f/cjJNiVJkiR9LxYqJK2wFk0a8vsfbcKDP/8BHVo15RfD/suR/3iZD2Y62aYkSZKkFWOhQtL3tlnHNfjXT7flnB9vwtjJs9n1spFc9dS7LPzayTYlSZIkLR8LFZJWiqIGwVHbrsvjp+/ADzdqz18ee5sf/e1ZSt6flXc0SZIkSXVIlQoVEdGlkq1zRLSr7pCS6o6112jKNYf35cYji5m7YDEHXDuK3973Gp/NW5h3NEmSJEl1QMMqtnsfqHSGvIj4AvgHcGZK6euVkEtSHbfzxmvRr3sbLn/8HW587j2GvzGN3/9oE/bt3YGIyDueJEmSpFqqqkM/BgFTgbOBXQvb2cAHwDHAOcARwO9XfkRJdVWzxg05a6+N+ffPf0Dn1s049a5XOOLGl3j/07l5R5MkSZJUS0VVlhKMiKeBK1NK95XbPxA4JaW0Q0QMAs5NKW1QLUlXQHFxcSopKck7hiRg8ZLEHaMnc8mjb7Ng8RJ+sdP6nLDDejRpWJR3NEmSJEk1LCLGpJSKK3qsqj0qtgbGVbD/dWDLwu1RQKfljyepPihqEBzRrxtPnLEDu26yFoNHTGCvK55l9KSZeUeTJEmSVItUtVAxGTihgv3Hkw3/AGgHOL2/pKVq37IpVx3ah38cvSULvl7Cwde9yK//+Sqz5zrZpiRJkqSqT6Z5BnBvROwFvFzYVwx0B/Yv3N8SuHvlxpO0qtppw/aMOG0HrnjiHW54dhJPjJ/O7/bamIF9OjrZpiRJklSPVWmOCoCI6Az8FNiwsGs8cG1K6YPKj8qXc1RIdcP4T77grPvGMfaDz+i3XhvOG7AZ3du1yDuWJEmSpGqytDkqqlyoqIssVEh1x5IliWEvf8DFj4xn/qIlnLxjd07esTtNGznZpiRJkrSqWVqhoqpDP4iIZkBvoD3l5rYovxqIJC2vBg2Cw7buym6brM15D73JFU+8w79f/YjzBmxG/+5t844nSZIkqYZUdXnSXYBhQJsKHk4ppVr5k6c9KqS665kJMzj7/tf5YNY8BvbpyO/22pg2LZrkHUuSJEnSSrAylie9AngI6JRSalBuq5VFCkl12/YbtGP4advz853W59+vfsTOl47k7pensCoPV5MkSZJU9UJFN+DPKaWPqjGLJH1L00ZF/Gr3DXn4l9vRo30Lzrz3NQ6+7kXenT4n72iSJEmSqklVCxXP87/VPlZIRGwYEa+U2b6IiFMj4s8R8Vph3/CI6FDJ8YvLHPvg98kiqW7psdbq3HVCPy7evydvfzKHPa94lsHD32b+osV5R5MkSZK0klV1joqBwHnApcA4YFHZx1NKY5frSSOKgA+BrYHZKaUvCvt/CWySUjqpgmO+TCkt13qFzlEhrXo+/XIBFzz0Fvf990O6tWnGefv15Ac9nGxTkiRJqktWxqof9xT+va6CxxKwvPNU7AxMTClNLre/eeF8klShti2acOnBvdm/byfOvv91Dr9xNPv17sDZP9qEtk62KUmSJNV5VS1UrLuSn/cQslVEAIiI84GfAJ8DO1VyTNOIKAG+Bi5KKd2/kjNJqkO2Xb8tj5yyHVc/PZFrnn6XJ8dP57d7bczBxZ1p0CDyjidJkiRpBVVp6MdKfcKIxsBHwKYppWnlHvst0DSl9McKjuuYUvowItYDngR2TilNrKDdCcAJAF26dOk7eXL5ThuSVjXvTv+S3/1rHKPfm0Vx1zU5f0BPNlx79bxjSZIkSarE0oZ+VFqoKMxL8e+U0qLC7UqllO5bjjD7Aj9LKe1WwWNdgIdTSpst4xxDgf+klO5ZWjvnqJDqj5QS94yZygUPv8Wc+V9zwvbr8Ysf9mC1xq6gLEmSJNU2KzpHxT3A2sB0/jdHRUWWd46KQXx72EePlNI7hbv7AuPLHxARawLzUkoLIqItsC1wyXI8p6RVXERwYHFndt54LS54+C2ufnoi/37tI/6872bsuGH7vONJkiRJqqJKlydNKTVIKU0vc7uyrcpFiohoDuwKlO2BcVFEvB4RrwG7AacU2hZHxA2FNhsDJRHxKvAU2RwVby7XK5VUL7Ru3pi/HtiLYcdvQ6OiBhz1j5f5+R1jmf7F/LyjSZIkSaqCqi5Puj3wQkrp63L7i4BtU0rPVFO+78WhH1L9tuDrxVz79CSuevpdmjRswG/22IhDt+rCO+9M4OCDD/6m3aRJk/jTn/7EzJkzeeCBB2jQoAHt27dn6NChdOjQocJzf/HFF2yyySbst99+/P3vf6+plyRJkiStElZojopyJ1gMrFPaw6LM/jbA9OXpVVGTLFRIApg040vOvv91Xpg4ky26tOKCAT3ZeJ2WACxevJiOHTsyevRo1lxzTVq2zPZfeeWVvPnmm1x77bUVnvOUU05hxowZtG7d2kKFJEmStJyWVqiodOhH+XOQzUVRXhtg7ooGk6SasF67Ftx+3NZcelAvJs+cx4/+9hwXPvwW8xZ+zRNPPEH37t3p2rXrN0UKgLlz5xJR8TKnY8aMYdq0aey223fmBJYkSZL0PS1tMk0i4sHCzQTcFhELyjxcBGwGvFBN2SRppYkIBvbpxA83as9Fj4xnyDOT+M9rH9Py5X8waNCgb9r97ne/45ZbbmGNNdbgqaee+s55lixZwhlnnMFtt93G448/XpMvQZIkSaoXltWjYmZhC2B2mfszganAtcDh1RlQklamVs0ac9H+m3P3if1o0mAJwx95mJIGGzCtMNnm+eefz5QpUzjssMMqHNJx9dVXs9dee9GpU6eaji5JkiTVC0vtUZFSOhogIt4H/ppScpiHpFXCVuu25qfrz+HjTXoy6qPF7Dx4JL/efUMO36YrRQ2Cww47jL322otzzz33W8eNGjWKZ599lquvvpovv/yShQsX0qJFCy666KKcXokkSZK0allqoaJUSuncZbeSpLrlnrvv4syfHcsP99mes+9/nbOGDue+sZtwwcCePPnAA2y00UbfOeb222//5vbQoUMpKSmxSCFJkiStRFWdTJOIODoihkfE+IiYVHarzoCSVB3mzp3LiBEjGDhwIF3bNOeWY7aiy8T7Gf7nw+nTuzdX3XovF14yGICSkhKOO+64nBNLkiRJ9UNVlyf9NfBbYAhwGnA1sD6wPdmQkPOqM+SKcnlSScvr83mLuPix8dwx+gM6rNGUiw/YnO16tMs7liRJkrRKWRnLkx4PnJBS+i2wCPh7SmkfYDDQdeXElKT8rdGsERcM6Mm9J/djtcZFHHHjS/zxgdf5auHivKNJkiRJ9UJVCxWdgJcKt78CWhZuDwP2X9mhJClvfbu25qFfbscx267LzaMms/eVz/LKlM/yjiVJkiSt8qpaqPgEaFu4PRnoV7i9PrDssSOSVAc1bVTEH368CXcctzXzFy1m/2te4NIRE1i0eEne0SRJkqRVVlULFU8C+xRu3whcGhFPAXcB91VHMEmqLfqv35ZHTt2efXt34Mon3mHg1S/w7vQ5eceSJEmSVklVnUyzAdAgpfR14f7BwLbABGBISmlRtaZcQU6mKWlle2Tcx5z1r3HMW7iY3+yxEUf170aDBpF3LEmSJKlOWdpkmlUqVCzj5J1TSlO+10mqiYUKSdVh+pz5/PbecTwxfjr91mvDXw/qRcdWq+UdS5IkSaozVsaqHxWddO2IuIqsV4Uk1RvtV2/KDUcWc9HAnrw29TP2uOwZ7h0zle9b+JUkSZK0jEJFRLSKiNsjYkZEfBQRv4zMH4FJwNbAMTWSVJJqkYjgkK268Mgp27PROqtzxj9f5eTbxjLzywV5R5MkSZLqtGX1qLgA2B64GZgFXAY8COwA7JlSKk4pDaveiJJUe3Vp04w7T+jHb/fciCfHT2f3y5/libem5R1LkiRJqrOWVajYGzg6pfQrslU/ApiYUvphSmlktaeTpDqgqEFw4g7deeDn29K2RWOOvbmE/7v3Nb5c8HXe0SRJkqQ6Z1mFig7AmwAppUnAfOD66g4lSXXRxuu05IGfb8vJO3bn7pIp7HnFM7z03qy8Y0mSJEl1yrIKFQ2AskuPLgbmVV8cSarbmjQs4jd7bMTdJ/YjCA6+bhQXPvIWC75enHc0SZIkqU5ouIzHA7gtIkpnh2sKXB8R3ypWpJT2qY5wklRXFXdrzSOnbMd5D73FkJGTGPn2DC49qDebdGiZdzRJkiSpVltWj4qbgY+AmYXtNmBKmfulmySpnOZNGnLhwJ7cdFQxn365kH2veo6rn36XxUtcxlSSJEmqTKS06v4Hc3FxcSopKck7hiQxa+5Czr5/HA+P+4Tirmsy+KBedG3TPO9YkiRJUi4iYkxKqbiix5bVo0KStBK0bt6Yqw7tw2UH9+LtaXPY84pnuWP0B6zKxWJJkiRpRViokKQaEhEM2KITj526PVt0acVZ/xrHsTeXMH3O/LyjSZIkSbWGhQpJqmEdWq3GrcdszR9/vAnPv/spu1/2DA+P+zjvWJIkSVKtYKFCknLQoEFw9Lbr8tAvt6Nz62b89PaxnHbXK3z+1aJlHyxJkiStwixUSFKO1m/fgntP7s+pu/TgwVc/Yo/Ln+H5dz/NO5YkSZKUmyoXKiKiZ0T8PSIeiYh1Cvv2i4gtqi+eJK36GhU14NRdNuC+k/uzWuMiDrthNOc8+AbzFy3OO5okSZJU46pUqIiI3YCXgY7AD4HVCg91B/5YPdEkqX7p1bkVD/1iO47q342hL7zP3lc+y6tTPss7liRJklSjqtqj4s/A6SmlAcDCMvufBrZa2aEkqb5arXER5+yzKbcduzXzFi5m4DUvcPnjE1i0eEne0SRJkqQaUdVCxWbAwxXsnwW0XnlxJEkAP+jRlkdP3Z59enXg8sff4YBrXuDd6V/mHUuSJEmqdlUtVMwiG/ZRXh9g6sqLI0kqtcZqjbjs4N5cfVgfJs+ax95XPsvQ599jyZKUdzRJkiSp2lS1UHEH8JeI6AQkoGFE7AD8FbilusJJkmCvnusw/NTt6d+9Def8+01+ctNLfPTZV3nHkiRJkqpFVQsVZwPvAZOBFsCbwJPAc8D51RNNklSqfcum3HTUllwwoCdjP5jN7pc/w/3//ZCU7F0hSZKkVUssz3/kRkR3YAuyAsd/U0rvVFewlaG4uDiVlJTkHUOSVqrJM+dyxt2vUjJ5Nnv1XJvz9+vJms0b5x1LkiRJqrKIGJNSKq7wsVX51zgLFZJWVYuXJK57ZhKXjnibVs0ac8n+m7PTRu3zjiVJkiRVydIKFQ2reIKbKnkoAfOBd4G7UkofrVhESdLyKGoQnLxjd3bYoB2n3/0KRw99mUFbdeHsvTemeZMq/U+7JEmSVCtVdY6KdsBAYD9g/cK2X2HfhsCZwNsR0bs6QkqSKrZJh5Y88PNtOXGH9bjz5Q/Y84pnKXl/Vt6xJEmSpBVW1ULF88AjQKeU0vYppe2BTsDDwHCgK/AQMLhaUkqSKtWkYRG/3XNj7jqhH4nE/7N332FSlXf/x9/f3QVWepGiIKhgN4qICirEEuwx9lhiDVGMBc1jYnry5Im/2BKNJWLDEhVijagIojECIihiwwIIiBQFG70s7N6/P2bABRdcYHdny/t1XXPtzJlzZj6zJ+ZiPnvu+z7l9le4ZtgHrFhVnOtokiRJ0kYr1xwVEfEJcEhK6f11tu8KvJBS2ioi9gKeTym1qpyoG885KiTVNYtXrOLPT7/H4NdmsnO7Jtx4ald2btc017EkSZKktWxojoryXlHRGNiqjO3tss8BLKScc15IkipH4wYFXH3iHtx1Vnc+X7yCY29+mdtfmkpxSe2dOFmSJEm1S3mLiieAuyPi5IjYNns7GbgbeDy7z77A5MoIKUnaON/btS3DL+vNITu34S/PfsBpd4xl5pdLcx1LkiRJ+lblLSr6AcOBB4Cp2dsDwDDgp9l93gd+UtEBJUmbplXjBtz2o2789eQ9ef+ThRxx40j+9drH1OZlqSVJklTzlWuOijU7RzQCOmcfTk0pLamUVBXEOSokKWP2/GVc8fBbvDLtC763Sxv+csIetG7SINexJEmSVEdVxBwVAKSUlqSU3s7eqnVJIUn6WvvmW/Bg3/343TG7MnLK5xx+40iGTfwk17EkSZKkbyj35JcRcTBwGtARqF/6uZTSIRWcS5JUwfLygh8fuB29d9iSnz38Fv0emMAJ3drzx2N3o2lhvVzHkyRJkoByXlEREecAzwJNgIOAz4AWQDfgvUrKJkmqBDu0bcLjP92fSw/pwpNvzuGIG0Yy5sPPcx1LkiRJAso/9OMK4OKU0mnASuBXKaW9yEyoubiywkmSKke9/Dx+dthOPNqvJ4X18jn9rnH86an3WL6yONfRJEmSVMeVt6jYHng+e38F0Dh7/xbgnArOJEmqInt1bMEzl/bi7J6dGPjydI65eTTvzFqQ61iSJEmqw8pbVHxBZtgHwGxg9+z9VsAWFR1KklR1tqifz//+YHfuP29fFi9fxfH/eJmbXpjCquKSXEeTJElSHVTeomIUcFj2/sPATRFxDzAIGFGeF4iInSLizVK3hRFxWUT8X0S8nd32XERsvZ7jz46IKdnb2eXMLUkqp947tmb4Zb05eo+t+NuIyZw44BWmfeboPkmSJFWtSCl9+04RLYHClNKciMgDfg4cAEwG/pxSmr9RbxqRT+bKjP2Ar1JKC7PbLwV2TSn1K+P9xwPdgQS8DuydUvpqQ+/TvXv3NH78+I2JJkkCnn57Dr95YiIrVhXz66N24cwenYiIXMeSJElSLRERr6eUupf13LdeURERBcCpqx+nlEpSSteklI5NKV2xsSVF1qHA1JTSjNUlRVYjMkXEug4HRqSUvsyWEyOAIzbhfSVJ5XDMHlvz3OW92W+7Vvz+yXc5a+CrfLpgea5jSZIkqQ741qIipbQKuA6oV4HveyqZYSMARMRVETETOAP4fRn7twdmlno8K7tNklRJ2jYt5N5z9+HPx+3O+I++4rAbXuLJN2fnOpYkSZJqufLOUTEW2Lsi3jAi6gPHAo+s3pZS+k1KaRvgQeDizXz98yNifESM/+yzzzYvrCTVcRHBj3p04tn+vejSpjH9B7/JxQ9NYP7SolxHkyRJUi1V3qLiTuD67OSXvSKiW+nbRr7nkcCElNLcMp57EDixjO2zgW1KPe6Q3fYNKaU7UkrdU0rdW7duy37DoQAAIABJREFUvZHRJEll2XbLRjx8QU9+fvhODH/3Uw67YST/nTQv17EkSZJUC5V3Ms0NrVGXUkr55X7DiMHA8JTSPdnHO6SUpmTvXwJ8N6V00jrHtCQzgebqUmQCmck0v9zQezmZpiRVvHfnLODyf73J5LmLOWO/jvzm6F1oWL8g17EkSZJUg2xoMs3y/styuwoK0gjoA1xQavPVEbETUALMAPpl9+0O9Esp9U0pfRkR/we8lj3mT99WUkiSKsduWzdjyMUH8rcRk7lz1DRe/vBz/npKV/bu1CLX0SRJklQLlOuKiprKKyokqXKNnfYF//PwW3yyYBkXHtSZ/ofuSP2C8o4qlCRJUl21WcuTlnqRIyPi6Yh4LyK2yW7rGxGHVlRQSVLN0mP7Vgy7rBcn7d2BW1+cyvH/eJnJcxflOpYkSZJqsHIVFRFxBvAwMIXMMJDVS5XmA7+onGiSpJqgSWE9rj1pT+44c28+XbCcY24ezZ0jp1FSUnuv2JMkSVLlKe8VFb8AfpJSuhxYVWr7WKBrhaeSJNU4h+3WjuGX9+agHVtz1dD3Oe3Oscz8cmmuY0mSJKmGKW9RsQPwShnbFwNNKy6OJKkm27JxA24/c2+uO2kP3p2zkCP/PoqHx8+kNs+HJEmSpIpV3qJiDrBjGdt7A1MrLo4kqaaLCE7uvg3DLuvF7u2b8otH3+b8f77O54tX5DqaJEmSaoDyFhV3ADdFxAHZx9tExNnAtcBtlZJMklSjdWjRkIf69uC3R+/CS5M/4/AbRvLcu5/mOpYkSZKquXIVFSmla4HHgRFAI+BFYAAwIKV0a+XFkyTVZHl5Qd9e2/P0JQfSrlkh5//zda545C0WLV+Z62iSJEmqpmJjxg1HRENgVzIFx3sppcWVFawidO/ePY0fPz7XMSRJQNGqEm56YQr/+O+HbNVsC64/eU96dm6V61iSJEnKgYh4PaXUvaznyrs86WUR0SaltDSlND6l9Gp1LykkSdVL/YI8rjh8Jx69cH/q5Qen3zWWPz/9HstXFuc6miRJkqqR8s5R8TNgVkQMi4gzsldWSJK00bp1bMHQ/r04Y7+O3DV6Ot+/eTQTZy/IdSxJkiRVE+UtKjoBhwMzgZuBuRHxQEQcERHlfQ1JkgBoWL+APx/3He47b18WLFvJcbe+zC3/mcKq4pJcR5MkSVKOlXcyzZRSejGl9BOgHXA2UEhmgs1ZlZhPklSLfXfH1jx3eW+O2L0d1z83mZNvf4Xpny/JdSxJkiTl0EZfDZFSKgJeyd5mkCkuJEnaJM0b1ueW07tx02l7MXXeYo76+yj+OXYGGzPZsyRJkmqPchcVEdEkIs6NiOeBj4G+wENA58oKJ0mqO47dc2ueu/y7dN+2Bb/790TOvuc15i5cnutYkiRJqmLlXfXjUWAu8BfgXWD/lNIuKaX/SylNr8yAkqS6o12zQu4/b1/+7we78er0LzjshpE89dacXMeSJElSFSrvFRUrgBOB9iml/iml11Y/ERHfq5RkkqQ6KSI4s+e2DL20F9tt2YhLBr3BpYPeYP7SolxHkyRJUhUo72SaZ6SUnk0pFQNERPuI+G1ETAOGV2pCSVKdtH3rxjzarydXHLYjQ9/5hMNvHMnIyZ/lOpYkSZIq2cbMUZEfESdExFDgI+B4YADQpZKySZLquIL8PC4+ZAf+fdEBNC2sx1kDX+X3T05kadGqXEeTJElSJfnWoiIidoqI64A5wPXAhOxTZ6aUrnWOCklSZdu9fTOeuuRA+h64Hf8cO4OjbxrNGx9/letYkiRJqgQbLCoiYhQwFmgBnJJS2j6l9NsqSSZJUimF9fL57TG78lDfHhStKuHE28bw1+cmsbK4JNfRJEmSVIG+7YqKnsD9wA0ppZeqII8kSRvUs3Mrnr2sFyd068DN//mQ4//xMlPmLsp1LEmSJFWQbysq9gEKgNER8UZEXB4R7aoglyRJ69W0sB7Xn7wnA360N3PmL+fom0dz16hplJSkXEeTJEnSZtpgUZFSeiOldBGwFfA34FhgZva4oyOiReVHlCSpbEfs3o7hl/Wm9w5b8udn3ueMu8Yxe/6yXMeSJEnSZoiUNu6vTxHRBegLnAW0Av6TUjqyErJttu7du6fx48fnOoYkqRJMmjSJH/7wh2seT/5wKk0POJ3iRV9Qf86btGrakM6dO3PPPffQvHnzDR47bdo0/vSnP3HZZZdVWX5JkqS6LCJeTyl1L/O5jS0qSr1oPnAMcF5K6Qebka/SWFRIUt1QXFxM+/bteWL4f7ly4DBmNNieI76zNQ0mDGaL+vlcc80133rsuHHj6NSpUxWmliRJqrs2VFQUbOqLppSKgSezN0mScuaFF16gc+fO9NxzZ168YSfuHj2N64dPpnhuI7ZdOLFcx1pSSJIkVQ/fNpmmJEnV3uDBgznttNMAyM8Lzu/dmacuOZBFbz/PW7Edv3niHZavLP7WYyVJkpR7FhWSpBqtqKiIIUOGcPLJJ6+1/dG7b6Jnl9b8rN95PDjuY4679WU+nLe4XMdKkiQpdywqJEk12rPPPku3bt1o27btmm333nsvTz/9NIMeeohfH70r95y7D/MWreD7N4/m0ddnbfBYSZIk5ZZFhSSpRhs0aNBaQzeGDRvGtddey5AhQ2jYsCEAB+/Uhmf792LPbZpxxSNv8bOH32TJilXfOFaSJEm5t8mrftQErvohSbXbkiVL6NixI9OmTaNZs2YAdOnShRUrVtCqVSsAevTowYABA5gzZw4//nFfDv+fG7nphSls0ySPt647gxkfTV9zrCRJkqpGpSxPWhNYVEiSyvLK1C+47F9v8NXSlfzu6F34UY9ORESuY0mSJNUZGyoqHPohSapzenZuxdBLe7F/51b87sl3+emDE1iwbGWuY0mSJAmLCklSHdWqcQMGnr0Pvz5qZ0a8N5ejbxrFGx9/letYkiRJdZ5FhSSpzsrLC87v3ZlH+vUE4OQBr3DHyKmUlNTeYZGSJEnVnUWFJKnO26tjC565tBd9dm3L/xv6Aefd9xpfLF6R61iSJEl1kkWFJElAsy3q8Y8zuvF/P9iNMVO/4KibRjF22he5jiVJklTnWFRIkpQVEZzZc1ue+On+NKpfwOl3juXG5ydT7FAQSZKkKmNRIUnSOnbbuhlPXXIgx3Vtz43PT+FHd41j7sLluY4lSZJUJ1hUSJJUhkYNCvjbD7ty/cl78ubM+Rz191H8d9K8XMeSJEmq9SwqJEnagJP27sBTlxxI6yYNOOee1/jL0PdZWVyS61iSJEm1lkWFJEnfokubxvz7ogM4Y7+O3D5yGqfc/gozv1ya61iSJEm1kkWFJEnlUFgvn6uO/w63nt6ND+cu5uibRjFs4ie5jiVJklTrWFRIkrQRjt5jK565tBfbbdmIfg9M4PdPTmT5yuJcx5IkSao1LCokSdpIHVs15JF++/OTXttx/yszOOEfY5j22eJcx5IkSaoVLCokSdoE9Qvy+M3RuzLwnO58smAZx9w8mifemJXrWJIkSTWeRYUkSZvhkJ3bMrR/L3bfuhmX/+strnjkLZYWrcp1LEmSpBrLokKSpM20VbMteOgn+3HpIV14bMIsvn/zaN7/ZGGuY0mSJNVIFhWSJFWAgvw8fnbYTjz44/1YuHwVx936Mg+Om0FKKdfRJEmSahSLCkmSKtD+Xbbk2f692He7lvzmiYlcPOgNFi5fmetYkiRJNYZFhSRJFWzLxg2479x9ufKInRk28VOOuWk0b82cn+tYkiRJNUKVFRURsVNEvFnqtjAiLouI6yLig4h4OyKeiIjm6zn+o4h4J3vs+KrKLUnSpsjLCy48qDMPX9CD4pLESQPGcNeoaQ4FkSRJ+hZVVlSklCallLqmlLoCewNLgSeAEcDuKaU9gMnArzbwMgdnX6N75SeWJGnz7d2pJc9ceiAH79SGPz/zPn3vG89XS4pyHUuSJKnaytXQj0OBqSmlGSml51JKq9dxGwt0yFEmSZIqRfOG9bn9zL354/d3ZdSUzznqplG8Ov3LXMeSJEmqlnJVVJwKDCpj+3nAs+s5JgHPRcTrEXF+pSWTJKkSRATnHLAdj/90fxoU5HHqHa9w8wtTKC5xKIgkSVJpVV5URER94FjgkXW2/wZYBTy4nkMPTCl1A44ELoqI3ut5/fMjYnxEjP/ss88qMLkkSZtv9/bNePrSXnx/z63564jJnHn3OOYtXJ7rWJIkSdVGLq6oOBKYkFKau3pDRJwDHAOckdYzy1hKaXb25zwyc1vsu5797kgpdU8pdW/dunVFZ5ckabM1blDAjT/syrUn7sGEj7/iqJtGMXKy5bokSRLkpqg4jVLDPiLiCOAXwLEppaVlHRARjSKiyer7wGHAxCrIKklSpYgITtlnG566+EBaNWrAWQNf5ZphH7CyuCTX0SRJknKqSouKbMnQB3i81OZbgCbAiOzSowOy+24dEUOz+7QFRkfEW8CrwDMppWFVGF2SpEqxQ9sm/PuiAzht32247b9TOfWOscyevyzXsSRJknImavN67t27d0/jx4/PdQxJksplyFtz+PXj75CfF1x30h4ctlu7XEeSJEmqFBHxekqpe1nP5WrVD0mStI5j99yapy85kI4tG3L+P1/nj0PeZcWq4lzHkiRJqlIWFZIkVSPbbtmIRy/syXkHbMe9Yz7ixNvGMP3zJbmOJUmSVGUsKiRJqmYaFOTz++/vyp1ndWfml8s45qZRPPnm7FzHkiRJqhIWFZIkVVN9dm3L0P692GWrpvQf/CZXPvo2y4ocCiJJkmo3iwpJkqqx9s23YPD5Pbjo4M48/PpMjr1lNJPnLsp1LEmSpEpjUSFJUjVXkJ/Hzw/fmX+etx9fLV3JsbeMZvCrH1ObV+6SJEl1l0WFJEk1xIE7bMnQ/gfSvVNLfvn4O1w6+E0WLV+Z61iSJEkVyqJCkqQapE2TQu4/b19+fvhODH3nE465eTTvzFqQ61iSJEkVxqJCkqQaJi8vuOjgLgw+vwdFq0o44baXuefl6Q4FkSRJtYJFhSRJNdQ+27Zk6KW9+O6Orfnfp97j/H++zvylRbmOJUmStFksKiRJqsFaNKrPnWd153fH7Mp/J83jqL+PYvxHX+Y6liRJ0iazqJAkqYaLCH584HY8duH+FOTn8cM7xnLrix9SUuJQEEmSVPNYVEiSVEvs0aE5T196IEfu3o7rhk/i7Hte5bNFK3IdS5IkaaNYVEiSVIs0LazHzaftxV9O+A6vTv+SI/8+itFTPs91LEmSpHKzqJAkqZaJCE7btyNDLj6QFg3rcebAcVw/fBKriktyHU2SJOlbWVRIklRL7dSuCU9efAAn792BW178kNPuHMsnC5blOpYkSdIGWVRIklSLNaxfwLUn7cnfT+3Ke3MWcuTfR/H8e3NzHUuSJGm9LCokSaoDftC1PU9f2ov2zbeg7/3j+dNT71G0yqEgkiSp+rGokCSpjthuy0Y8/tP9OWf/bRn48nROGjCGGV8syXUsSZKktVhUSJJUhzQoyOePx+7GgB/tzUefL+Hom0bz1Ftzch1LkiRpDYsKSZLqoCN2b8fQ/r3YoW1jLhn0Br96/B2WryzOdSxJkiSLCkmS6qoOLRry8AU96ffdzgx69WN+cMvLTJm7KNexJElSHWdRIUlSHVYvP49fHrkz9523L58vXsGxt7zMw+NnklLKdTRJklRHWVRIkiS+u2Nrnu3fi706NucXj77N5f96k8UrVuU6liRJqoMsKiRJEgBtmhbyzx/vx8/67MiQt+bw/ZtHM3H2glzHkiRJdYxFhSRJWiM/L7j00B0Y9JMeLC1axQn/GMN9Yz5yKIgkSaoyFhWSJOkb9tu+Fc/2782BO2zJH4a8S78HXmfB0pW5jiVJkuoAiwpJklSmlo3qc9dZ3fnt0bvwwvvzOOqmUbw+46tcx5IkSbWcRYUkSVqvvLygb6/tefTC/cnLg1Nuf4UBL02lpMShIJIkqXJYVEiSpG/VdZvmPH1JL47YrR1XP/sB59z7Gp8vXpHrWJIkqRayqJAkSeXSbIt63HL6Xlx1/O6MnfYFR/19FGOmfp7rWJIkqZaxqJAkSeUWEZyxXyeevOgAmhQWcMZd4/jbiMmsKi7JdTRJklRLWFRIkqSNtstWTRly8YGcsFcHbnphCqffNY5PFyzPdSxJklQLWFRIkqRN0qhBAX89ZU/+dsqeTJy9gCP/PpL/fDA317EkSVINZ1EhSZI2ywndOvDUJQfStmkh5907nqueeY+iVQ4FkSRJm8aiQpIkbbbOrRvz74sO4Mwenbhz1HROvv0VZn65NNexJKlOmjRpEl27dl1za9q0KTfeeCM///nP2Xnnndljjz04/vjjmT9/fpnHn3feebRp04bdd9+9ipNLGZFS7V0HvXv37mn8+PG5jiFJUp3y7Duf8IvH3gbgmhP34KjvbJXjRJJUdxUXF9O+fXvGjRvHpEmTOOSQQygoKODKK68E4JprrvnGMSNHjqRx48acddZZTJw4saojq46IiNdTSt3Les4rKiRJUoU68jtbMfTSXmzfujE/fXACv/33OyxfWZzrWJJUJ73wwgt07tyZTp06cdhhh1FQUABAjx49mDVrVpnH9O7dm5YtW1ZlTGktFhWSJKnCbdOyIY9c0JPze2/PA2M/5rhbX+bDeYtzHUuS6pzBgwdz2mmnfWP7wIEDOfLII3OQSPp2FhWSJKlS1C/I49dH7cI95+7DvEUrOPaW0Tz6etl/vZMkVbyioiKGDBnCySefvNb2q666ioKCAs4444wcJZM2zKJCkiRVqoN3asPQS3vxnfbNuOKRt/jZw2+yZMWqXMeSpFrv2WefpVu3brRt23bNtnvvvZenn36aBx98kIjIYTpp/SwqJElSpWvXrJCHftKD/ofuwBNvzOb7t4zmvTkLcx1Lkmq1QYMGrTXsY9iwYVx77bUMGTKEhg0b5jCZtGEWFZIkqUrk5wWX99mRB/vux+LlqzjuHy/zz7EzqM0rkElSrixZsoQRI0ZwwgknrNl28cUXs2jRIvr06UPXrl3p168fAHPmzOGoo45as99pp51Gz549mTRpEh06dODuu++u8vyq21yeVJIkVbnPF6/gfx5+i5cmf8aRu7fj6hP3oNkW9XIdS5IkVRGXJ5UkSdXKlo0bcM85+/CrI3dmxHtzOfqmUbw5c36uY0mSpGrAokKSJOVEXl5wwXc783C/nqQEJ902hjtHTqOkpPZe7SlJ1UFKiQVLV7LYiY1VTRXkOoAkSarbunVswdBLe3HlY29z1dD3GTP1c64/eU9aNW6Q62iSVOMsKypm7sLlfLpwOXMXLmfewhVrHs9buIK5izLbl68sobBeHucdsB0XfLezw+9UrThHhSRJyplJkybxwx/+8OvHU6bSeP/TKVj+FfXnvEGzRlvQuXNn7rnnHpo3b/6N44cNG0b//v0pLi6mb9++/PKXv6zK+Cpl3XM5bdo0/vSnPzF79myeeuop6tev77msATyP1VfRqhI+W7wiWz4s59MFy5m7aMWaMmJ1MbFo+Tevkiisl0e7poW0XXNrQNumhbwzewFPvjmH5g3rcdFBXTizZycK6+Xn4NOpLtrQHBUWFZIkqVooLi6mffv2/HPICH5+5zAWtNyJ/n12ZvZzd5EXwTXXXPON/XfccUdGjBhBhw4d2GeffRg0aBC77rprjj6BVlt9LseNG8ekSZM45JBDKCgo4MorrwTwXNYQnseqUVKS+GJJEXOzRcPc7BUQpR/PW7SczxcXfePYgrygbdNC2jRtQNsmhbRr9vX9tk0LadesAW2aFtKkQQERUeb7T5y9gGuHT2Lk5M/YulkhPztsJ47fqz35eWXvL1WUDRUVDv2QJEnVwgsvvEDnzp3ps+93GL3nLvzu3xO58fkpbLOoJW2+ePMb+7/66qt06dKF7bffHoBTTz2VJ5980i9F1cDqc9mpUyc6deq0ZnuPHj149NFHv7G/57J68jxunpQSC5etYu6i7NUPC5czL3sFxOqrIeYtXM5ni1awap25eSKgVaMGtGvWgK2aFbLnNs1p27TBmqsi2mSviGjZsD55m1ko7N6+Gfefty9jPvycq4d9wBWPvMWdI6dx5ZE7cfBObdZbcEiVyaJCkiRVC4MHD+a0004DoHGDAv52yp7s37kV557+J7bc4yD+8OREWjZqQMvG9WnVqD5vvP4BTbdsx+eLV9CiYX06dOjAuHHjcvwpBGufy9IGDhy41rCC1WbPns0222yz5rHnsnrwPK7f0qJVZV75sO4wjBWrSr5xbLMt6q0ZerFDmy3X3G+TvSKibdMGbNm4AfXyq3bdg/27bMmTFx3A0Hc+5brhH3DevePZd9uWXHnkzuzdqUWVZpGqrKiIiJ2Af5XatD3we6A98H2gCJgKnJtS+sb6ZBFxBPB3IB+4K6V0daWHliRJVaKoqIghQ4bwl7/8Zc22iGDy8PvZv0sbmh56LE+8MZuFpcZeL/lgMsunf0r3Pz9PBBRPfoeST2czd8AYWjaqX+rWgJaN6tGyUQNaldruOOzKUda5BLjqqqsoKCjgjDPOyFEybYy6eh5XzwPx6YLMPBBzF2bngViwPDsJ5Yr1zgOxRb38zNCLJg3omr0Com0Z80JU5//viQiO3mMrDtutLYNfm8nfn5/CibeN4fDd2vLzw3emS5vGuY6oOqLKioqU0iSgK0BE5AOzgSeAnYBfpZRWRcQ1wK+AK0sfm93/VqAPMAt4LSKGpJTeq6r8kiSp8jz77LN069aNtm3brtl277338vTTT/PCCy/QsGFDAFYWl/DVkiK+XFrEyFF5DLjhFfofuxtfLiniqVkjWNohM676o8+X8vqM+Xy1tIji9Sx32rB+Pi0bZa7OaJEtL1bfb7Wm4Pi62GhauP4x3vrat53Lsn6H7du3Z+bMmWsez5o1i/bt21dJXpWttp3H4pLEF0tWrLUCxtyFK9aUEZ9m73+x5JvzQNTLD9o0yQy32KFNYw7ssuVac0K0bfrt80DUNPXy8zizRydO2Ks9A0dP5/aR0xjx3kuc0n0bLvvejrRrVpjriKrlcjX041BgakppBjCj1PaxwEll7L8v8GFKaRpARAwGfgBYVEiSVAsMGjRorUvMhw0bxrXXXstLL720pqSAzD+e2zQtpE3TQroc34err7yI3lsl2rfflnuveJGHHnqI3Xbbbc3+JSWJhctX8uWSIr5cUsQXS4r4Kvvzy1K3LxYXMWXuYr5YsoLlK795qXbmvYMWDeuvVV6sW2y0aFSPVtmCo0XDehRU8aXb1UF5z2Vp++yzD1OmTGH69Om0b9+ewYMH89BDD1VVZJWhppzH1fNAfFpqCMa8RSvWzAmxeh6IeYtWfKO0jIAtGzegbdMGbN2skK7bNM/OAdFgzTwQ7ZoW0qIC5oGoqRo1KOCSQ3fg9P06csuLH/LA2Bk88cZszjtwO/q5pKkqUU5W/YiIgcCElNIt62x/CvhXSumBdbafBByRUuqbfXwmsF9K6eINvY+rfkiSVP0tWbKEjh07Mm3aNJo1awZAly5dWLFiBa1atQIyk/cNGDCAOXPm0LdvX4YOHQrA0KFDueyyyyguLua8887jN7/5zWbnWVZUzBdLVqxVbKy+/+XizNUcXxccK9YajrKuZlvUW2u4yVolR+P6tGhYP1NsNK5Py4b12aJ+9b0kvDyq27nUpqku53FD80CUvv9t80CUHnZR+nEu5oGo6WZ+uZS/jZjMv9+cTdPCelx0cGfO6rlttR7OouqrWi1PGhH1gTnAbimluaW2/wboDpyQ1gm1MUVFRJwPnA/QsWPHvWfMmLHuLpIkSRVmZXEJXy1d+wqN1VdpfLW0VMGRHbLy1ZKib8zwv9oW9fLXKTGyxUa2yFj9XMtGDWjZsD5Nt6g9l5qr7ihaVcK8RWUPvVgzD8SC5SxaseF5IDLLb359v23TQtplr4Twi3PlenfOAq4dNomXskuaXt5nR07o1sElTbVRqltR8QPgopTSYaW2nQNcAByaUlpaxjE9gT+mlA7PPv4VQErpL+vuW5pXVEiSpOpm9aXqXyxZkSkyFheVefVG6eeWrSwu87UK8iIzv0bDrwuNVo2yJUfjMq7kaFi/Tg5HUdUoPQ/Ep6Umn5y3zpwQG5oHovSVD21KLcdZG+eBqA3GTP2ca579gLdmLWDHto35xeE7c+guLmmq8qluRcVgYHhK6Z7s4yOAvwHfTSl9tp5jCoDJZOa2mA28BpyeUnp3Q+9lUSFJkmqDZUXFmSEni4vWDEtZ6+qNdUqOBctWrve1mm1R7xvlxeqCo+Vac25khqXU9OEodVVJSWJlSQnFJYmVxYniksSq4hJWliSKi0s/9/U+q1bfL0kUl5RktyVWlZSwKvsaq49buGzl18MwsqtifLZ4w/NAtG1SSNtmhZmf6wzLqMvzQNR0KSWenfgp1w2fxPTPl7DPti345ZE7s3enlrmOpmqu2hQVEdEI+BjYPqW0ILvtQ6AB8EV2t7EppX4RsTWZZUiPyu53FHAjmeVJB6aUrvq297OokCRJddHK4hLmL12ZLS4yxUZZk4iWLjnWNxylsF7emglC13crPQ9H08J6NeILZ0pff4Ffmf0ivtYX8uISVpWU+qJesvaX9q9/Zm/FpR+XfPO41a9X6nUy77/2cStLvX/xOu//jf1XFw9l7L+e01mhmm1Rb81Qi9WFQ7vsZLerH7du3MCreOqIlcUlPDx+Jjc+P4XPFq2gz65t+cXhO7FD2ya5jqZqqtoUFVXNokKSJOnbpZRYuHxVtrxYwZdLVvLlkhXrmUA0MyxlaVHZw1Hy82LN/BprrYKSLTTqF+RlvmCX/gt98dd/xV9V/PUX8tJ//V+1zhf4lSWlvvwXr33lQJn7r1UUVM0X+XUV5AUF+UG9vDzy84OCvLyvt+XnkZ8Xax6X/VxeqeeDgvw86uXHOs99vW2TobheAAAWkElEQVSt11zrubWPW3f/1fsUfGPb148bNyhwHgiVaWnRKgaOns6Al6axtGgVJ++9DZf12YGtmm2R62iqZiwqJEmSVKGWryxep8hYsabEWF1orJ5A9MslRcxfuv7hKKsV5H39ZXjNl/G8r78oF+R//UU9Py+PeuvbPz+ol7f2/qu/yOdni4LSX/ZLf5Ev+MZx2WPzS/8s+7VL7/N1GRFrPpfj9lWXfLmkiFtf/JB/vjKDCDjngG356Xe70KyhS5oqw6JCkiRJObWquISvlq6kuCR9s3jwi7xUa838cik3jJjME2/OpkmDAi46uAtn7++SprKoyHUMSZIkSarT3v9kIdcO+4AXJ33GVs0Kufx7O3JCt/bOYVKHbaio8H8VkiRJkqRKtctWTbnn3H0Z9JMetGlayC8ee5sj/z6KEe/NpTb/8VybxqJCkiRJklQlenZuxb9/uj8DftSN4pLET+4fz8kDXmH8R1/mOpqqEYsKSZIkSVKViQiO2H0rnru8N//v+O/w8ZdLOWnAK/S9bzyT5y7KdTxVA85RIUmSJEnKmWVFxQx8eToD/juVJUWrOLFbBy7vsyNbN3dJ09rMyTQlSZIkSdXaV9klTe9/ZQYEnLv/tlx4UGeaN6yf62iqBBYVkiRJkqQaYdZXS7lhxBQef2MWTRoUcOFBXTj3AJc0rW0sKiRJkiRJNcoHny7k2mGT+M8H82jXtJDL++zAid06uKRpLeHypJIkSZKkGmXndk0ZeM4+/Ov8HmzVvJArH3uHI/4+iuHvfuqSprWcRYUkSZIkqdrab/tWPH7h/gz40d6UpMQF/3ydE28bw6vTXdK0trKokCRJkiRVa5klTdvx3GW9ufqE7zB7/jJOuf0Vfnzva0z61CVNaxvnqJAkSZIk1SjLioq5Z8x0bvvvVBav+HpJ0/YuaVpjOJmmJEmSJKnW+WpJEbe9NJV7x3wEwDn7b8tPXdK0RrCokCRJkiTVWrPnL+OGEZN5bMIsGjco4MKDOnPu/tuxRX2XNK2uLCokSZIkSbXepE8Xcd3wD3j+/Xm0bdqAy763Iyfv7ZKm1ZHLk0qSJEmSar2d2jXhrrP34eELetK++Rb86vF3OOzGkQyb6JKmNYlFhSRJkiSpVtl3u5Y8duH+3H7m3gTQ74HXOeG2MYyb9kWuo6kcLCokSZIkSbVORHD4bu0YfllvrjnxO3wyfzk/vGMs5937Gh98ujDX8bQBzlEhSZIkSar1lhUVc++Yj7jtvx+yaMUqTtirA5f32YEOLRrmOlqd5GSakiRJkiQB85cWcdt/p3LPmI8gwVk9O3HRwV1o0cglTauSRYUkSZIkSaXMmb+MG5+fzKOvz6JR/QL6HdSZcw/Ylob1C3IdrU6wqJAkSZIkqQyT5y7i2mGTeP79ubRpklnS9JTuLmla2VyeVJIkSZKkMuzYtgl3nd2dR/r1ZJuWDfn1E6uXNP3EJU1zxKJCkiRJklTn7bNtSx7t15M7z+pOfgT9HpjA8f8Yw1iXNK1yFhWSJEmSJJFZ0rTPrm15tn8vrj1xD+YuXM6pd4zl3Hte5f1PXNK0qjhHhSRJkiRJZVi+spj7xnzErS9mljQ9vmt7Lu+zI9u0dEnTzeVkmpIkSZIkbaIFS1fyj5c+5N6XPyIlODO7pGlLlzTdZBYVkiRJkiRtpk8WLOPGEVN45PWZNKpfwAXf3Z7zDtzOJU03gUWFJEmSJEkVZMrcRVw7fBIj3ptL6yYNuOx7O3BK922o55Km5ebypJIkSZIkVZAd2jbhzrO689iFPdm2VUN+88REDrthJEPfcUnTimBRIUmSJEnSJti7U0sevqAnd53VnYK84KcPTuC4f4xhzNTPcx2tRrOokCRJkiRpE0UE39u1LcMu6811J+3BvIXLOf3OcZw98FXem+OSppvCOSokSZIkSaogy1cWc/8rH3Hri1NZuHwlx3Vtz89c0vQbnKNCkiRJkqQqUFgvn/N7d2bkLw6m33c7M/SdTzj0ry/xv0+9yxeLV5T7dSZNmkTXrl3X3Jo2bcqNN97II488wm677UZeXh4b+sP8/PnzOemkk9h5553ZZZddeOWVVyri41UJr6iQJEmSJKmSfLpgOTc+P5mHx8+kYf0CLui9PT/utXFLmhYXF9O+fXvGjRvH0qVLycvL44ILLuD666+ne/cyL0rg7LPPplevXvTt25eioiKWLl1K8+bNK+pjbbYNXVHhYq+SJEmSJFWSds0KufrEPejbazuuGz6Jv46YzH2vzKD/93bg1H3Kt6TpCy+8QOfOnenUqVO53nPBggWMHDmSe++9F4D69etTv379zfkYVcqhH5IkSZIkVbIubZpw+5ndeezC/dl+y0b87t8T6fO3l3jm7W9f0nTw4MGcdtpp5X6v6dOn07p1a84991z22msv+vbty5IlSzb3I1QZiwpJkiRJkqrI3p1a8K8LejDwnO40KMjnoocmcNytLzPmw7KXNC0qKmLIkCGcfPLJ5X6PVatWMWHCBC688ELeeOMNGjVqxNVXX11RH6HSWVRIkiRJklSFIoJDdm7L0P69uP7kPfls0QpOv2scZw18lXfnLFhr32effZZu3brRtm3bcr9+hw4d6NChA/vttx8AJ510EhMmTKjQz1CZLCokSZIkScqB/LzgpL078J8rDuK3R+/C27Pmc/RNo+k/+A0+/mIpAIMGDdqoYR8A7dq1Y5tttmHSpElAZo6LXXfdtcLzVxZX/ZAkSZIkqRpYsGwlt780lYEvT6e4JHHynq0Z0O8Ipk+fRrNmzQB44oknuOSSS/jss89o3rw5Xbt2Zfjw4cyZM4e+ffsydOhQAN588801K35sv/323HPPPbRo0SKXH28tG1r1w6JCkiRJkqRqZO7C5dz4/BQeHj+TwoI8zu/dmb69tqNRg9qzcKdFhSRJkiRJNcyH8xZz/fBJDHv3U7Zs3ID+h3bh1H07lmtJ0+puQ0VFzf90kiRJkiTVQl3aNGbAmXvz+E/3Z/vWjfjdk+/S528v8dRbcygpqb0XHVhUSJIkSZJUjXXr2IJ/nd+De87Zh8J6+Vwy6A1+cOvLvLyeJU1rOosKSZIkSZKquYjg4J3b8MylvfjbKXvy5ZIizrhrHGfePY5PFyzPdbwKVXtm4pAkSZIkqZbLzwtO6NaBo76zFQ+MncHjE2bTvGG9XMeqUBYVkiRJkiTVMIX18unba3t+fOB2RESu41Qoh35IkiRJklRD1baSAqrwioqI2An4V6lN2wO/B2YDfwR2AfZNKZW5nmhEfAQsAoqBVetbxkSSJEmSJNVcVVZUpJQmAV0BIiKfTEHxBNAQOAG4vRwvc3BKqXZOaypJkiRJknI2R8WhwNSU0ozVG2rj5SqSJEmSJGnj5GqOilOBQRt5TAKei4jXI+L8SsgkSZIkSZJyrMqvqIiI+sCxwK828tADU0qzI6INMCIiPkgpjSzj9c8Hzgfo2LHjZueVJEmSJElVJxdXVBwJTEgpzd2Yg1JKs7M/55GZ22Lf9ex3R0qpe0qpe+vWrTc7rCRJkiRJqjq5KCpOYyOHfUREo4hosvo+cBgwsRKySZIkSZKkHKrSoiJbMvQBHi+17fiImAX0BJ6JiOHZ7VtHxNDsbm2B0RHxFvAq8ExKaVhVZpckSZIkSZWvSueoSCktAVqts+0JMkM51t13DnBU9v40YM+qyChJkiRJknInV6t+SJIkSZIkfYNFhSRJkiRJqjYsKiRJkiRJUrVhUSFJkiRJkqoNiwpJkiRJklRtWFRIkiRJkqRqw6JCkiRJkiRVGxYVkiRJkiSp2oiUUq4zVJqI+AyYkescG2lL4PNch9Bm8zzWHp7L2sHzWDt4HmsHz2Pt4HmsHTyPtUNNPY+dUkqty3qiVhcVNVFEjE8pdc91Dm0ez2Pt4bmsHTyPtYPnsXbwPNYOnsfawfNYO9TG8+jQD0mSJEmSVG1YVEiSJEmSpGrDoqL6uSPXAVQhPI+1h+eydvA81g6ex9rB81g7eB5rB89j7VDrzqNzVEiSJEmSpGrDKyokSZIkSVK1YVFRTUTEwIiYFxETc51Fmy4itomIFyPivYh4NyL65zqTNl5EFEbEqxHxVvY8/m+uM2nTRUR+RLwREU/nOos2TUR8FBHvRMSbETE+13m0aSKieUQ8GhEfRMT7EdEz15m08SJip+x/i6tvCyPislzn0saLiMuz/86ZGBGDIqIw15m08SKif/Ycvlub/lt06Ec1ERG9gcXA/Sml3XOdR5smIrYCtkopTYiIJsDrwHEppfdyHE0bISICaJRSWhwR9YDRQP+U0tgcR9MmiIifAd2BpimlY3KdRxsvIj4CuqeUauIa8cqKiPuAUSmluyKiPtAwpTQ/17m06SIiH5gN7JdSmpHrPCq/iGhP5t83u6aUlkXEw8DQlNK9uU2mjRERuwODgX2BImAY0C+l9GFOg1UAr6ioJlJKI4Evc51Dmyel9ElKaUL2/iLgfaB9blNpY6WMxdmH9bI3W90aKCI6AEcDd+U6i1SXRUQzoDdwN0BKqciSolY4FJhqSVFjFQBbREQB0BCYk+M82ni7AONSSktTSquAl4ATcpypQlhUSJUkIrYF9gLG5TaJNkV2uMCbwDxgRErJ81gz3Qj8AijJdRBtlgQ8FxGvR8T5uQ6jTbId8BlwT3Yo1l0R0SjXobTZTgUG5TqENl5KaTZwPfAx8AmwIKX0XG5TaRNMBHpFRKuIaAgcBWyT40wVwqJCqgQR0Rh4DLgspbQw13m08VJKxSmlrkAHYN/spXWqQSLiGGBeSun1XGfRZjswpdQNOBK4KDtcUjVLAdANuC2ltBewBPhlbiNpc2SH7xwLPJLrLNp4EdEC+AGZEnFroFFE/Ci3qbSxUkrvA9cAz5EZ9vEmUJzTUBXEokKqYNk5DR4DHkwpPZ7rPNo82UuTXwSOyHUWbbQDgGOz8xsMBg6JiAdyG0mbIvuXP1JK84AnyIzFVc0yC5hV6uq0R8kUF6q5jgQmpJTm5jqINsn3gOkppc9SSiuBx4H9c5xJmyCldHdKae+UUm/gK2ByrjNVBIsKqQJlJ2G8G3g/pfS3XOfRpomI1hHRPHt/C6AP8EFuU2ljpZR+lVLqkFLalszlyf9JKfnXohomIhplJycmO1TgMDKXuqoGSSl9CsyMiJ2ymw4FnGi6ZjsNh33UZB8DPSKiYfbfr4eSmVtNNUxEtMn+7EhmfoqHcpuoYhTkOoAyImIQcBCwZUTMAv6QUro7t6m0CQ4AzgTeyc5vAPDrlNLQHGbSxtsKuC87m3ke8HBKyaUtpdxoCzyR+Xc0BcBDKaVhuY2kTXQJ8GB2yMA04Nwc59EmypaGfYALcp1FmyalNC4iHgUmAKuAN4A7cptKm+ixiGgFrAQuqi0TFbs8qSRJkiRJqjYc+iFJkiRJkqoNiwpJkiRJklRtWFRIkiRJkqRqw6JCkiRJkiRVGxYVkiRJkiSp2rCokCRJ1UJEHBQRKSK2zHWWyhYRf4yIibnOIUlSdWRRIUlSDRYRD0TEmxFRf53th0bEyojYP1fZ1hURx0XEKxExPyIWR8QHEXFXqV3GAFsBX+QoIgARcW+2MEnZ3+G0iLg+IhrlMpckSXWFRYUkSTXbxUAr4A+rN0REU2AgcF1KaUxFv+G6pUg5jzkUeAR4CugB7AX8HIjV+6SUilJKn6aUUkVl3QzPkylNtgd+C/wUuD6niSRJqiMsKiRJqsFSSvOBc4FfRMS+2c03AF8Bf4yI9hExOCK+yt6eiYgdVh8fEZ0j4smI+DQilkTEhIg4pvR7RMRH2aEKAyNiPvBgdvvvI2JGRKzIHn//BqJ+HxiXUvp/KaUPUkpTUkpPpZR+XOp91hr6ERHnZK+8ODQiJmbzvRgR262T76iIGBcRyyLii4h4KiIKs8/Vj4hrImJWRCyNiNci4vBy/GpXZEuTmSmlh7Kf+bjsa0ZE/CIipmbf852I+NE6ma6OiEnZ5z+KiGtXZypLRHTMXmFyX0QUlCOfJEm1lkWFJEk1XErpeeA24L6IOAk4AzgTKABeBJYD3wV6Ap8Az0dEw+zhjYFngT7AnsBjwOMRsfM6b/Mz4AOgO/DriDgRuILMlQY7AMcAr24g5qfAzhGx50Z+vAbAr4DzsvmbAwNWPxkRRwBDgBHA3sDBwEt8/W+ce7Kf/XRgd+A+4KlNyLEMqJe9/2fgx8BFwK7AX4DbI+LoUvsvyWbehczv6FTgN2W9cETsArwMDAXOSSmt2shskiTVKlE9rq6UJEmbIyK2AN4gUxr8MqV0XUScR+ZL/o6rh1NERD4wD7gwpfTwel5rLPB0SunP2ccfAe+klL5fap+fARcAu6eUVpYjXyPgYeAoYBYwjszwigdSSouz+xxEplhpnVL6PCLOIVM07JxSmpTd5wwyw1oKU0opIl4GZqaUTi3jPTsDU4BtU0ofl9r+b2BOSumn68l6L7BlSumY7ON9yZQIL5ApHz4HDkspjSp1zI1kfs9Hrec1+wFXpJS6ZB//ETiJTOHxDHBDSumqDf0OJUmqK7yiQpKkWiCltIzMHAorgL9mN+8NbAcsyg6hWAwsAFoAnSFTIGSHJbyXHRqymMxVEx3XeYvx6zx+BCgEpkfE3RFxckQ02EC+JSmlo4EuwP8C88lcifBuRLTdwEdbsbqkyJoD1M9+BsjMdfHCeo7tRmYOjPdWf/7s5zt69effgCOy+y8HXgFGApeQuYKiEBi2zmteWPo1I+KkiBidHRKzmMxwnHV/p+3JlDXXWFJIkvQ1x0BKklR7rAJKUkol2cd5wJtkhh2s68vsz+uBI8gM45gCLAXuJ1MGlLak9IOU0syI2Ak4FPgemXLkDxGxX0ppCeuRUpoKTAXuioirgMlkvuT/cQOfaa2XKPXZvk1edv99gHWv+lj2LceOBM7PHjdn9VUjpebH+D7w8TrHrN6nBzCYTCFzOZlS5li+ORnn58BHwKkRcVdK6atyfCZJkmo9iwpJkmqvCcBpwOfZSTfLciBwf0rpMYDshI+dyRQIG5RSWk5m2MIzEXE1mXkoDgCeK2e+j8gUI43LuX9Z3iBTlty5nucCaJdSenEjX3dpSunDMra/R+aqlU4ppf+s59gDgNkppf9bvSEiOpWx3woyBcZTwIiI+N4GzpMkSXWGRYUkSbXXg2SulHgyIn5P5gqAbYAfAANSSlPIFBLHR8STZK4I+AOZoQ0blJ0/ooDMXBOLgR9mj5+ynv3/CDQkM9fDDDKTYl5KpqQYsqkfELiKzOSYHwIPkSkmDgNuTylNjogHgXsj4n/IFDctgYOAaSmlxzf2zVJKiyLieuD6iAgyV140JrPkaklK6Q4yv9P22fk0XgEOJ1MYlfV6yyLi+8DTZMqKPpYVkqS6zjkqJEmqpVJKS4HewDQyc0p8QGbVixZkli+FzGoe84BRZFb/GJu9/23mk5kIchQwETgROCGlNH09+79EZr6M+4D3geHAtsCxKaWRG/nR1kgpDQWOB44kcwXFS2RW/lg9/OVcMhNyXkvm8z9N5ncyY1PfE/gdmaEqVwDvkllx5ERgejbTU8B1wI3A22RWVPn9Bj7DMjKrpvz/9u6gBgAQBoIgEvAvEwV9IIHPkswo6HtzSc+6sWI/3AYA3/P1AwAAAMiwqAAAAAAyhAoAAAAgQ6gAAAAAMoQKAAAAIEOoAAAAADKECgAAACBDqAAAAAAyhAoAAAAgQ6gAAAAAMgaYEg69F3cP+AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#From peak, plot the average trajectory of score decline \n",
        "#For consistency, take the highest number in each year, so we can compare declines accurately\n",
        "player_decline_query = \"\"\"\n",
        "WITH decline_years AS (\n",
        "  SELECT player_fifa_api_id,\n",
        "        overall_rating, \n",
        "        age\n",
        "  FROM sql_player_ratings_peak_df\n",
        "  WHERE peak_flg = 'DECLINE'\n",
        "  GROUP BY player_fifa_api_id,\n",
        "        overall_rating, \n",
        "        age\n",
        ")\n",
        "\n",
        "SELECT X.player_fifa_api_id,\n",
        "      MAX(X.overall_rating) AS rating_year,\n",
        "      X.age\n",
        "  FROM players_and_skills_df X\n",
        "      JOIN decline_years Y ON X.player_fifa_api_id = Y.player_fifa_api_id AND X.overall_rating = Y.overall_rating AND X.age = Y.age\n",
        "  GROUP BY X.player_fifa_api_id,\n",
        "      X.age\n",
        "\"\"\"\n",
        "player_decline_df = ps.sqldf(player_decline_query, locals())\n",
        "\n",
        "#Find the average score by years after peak \n",
        "player_decline_df['rank'] = player_decline_df.groupby(by = 'player_fifa_api_id')['age'].rank(method = 'first')\n",
        "player_avg_decline_df = player_decline_df.groupby(by = 'rank')['rating_year'].mean().reset_index(name = 'avg_rating')\n",
        "\n",
        "#Plot the resulting line\n",
        "def addlabels(x,y):\n",
        "    for i in range(len(x)):\n",
        "        plt.text(i+1, y[i], y[i], ha = 'center')\n",
        "\n",
        "plt.subplots(figsize=(18,8))\n",
        "plt.plot(player_avg_decline_df['rank'], player_avg_decline_df['avg_rating'])\n",
        "plt.title(\"Average Decline Across Players\",fontsize=18)\n",
        "plt.xlabel('Years Since Peak', fontsize=14)\n",
        "plt.ylabel('Average Rating', fontsize=14)\n",
        "plt.xticks(np.arange(min(player_avg_decline_df['rank']),max(player_avg_decline_df['rank'])+1, step=1))\n",
        "addlabels(player_avg_decline_df['rank'], round(player_avg_decline_df['avg_rating'],1))\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2dOq1JVPmSY"
      },
      "source": [
        "## **2.3 Finalizing our dataset for classification analyses**\n",
        "\n",
        "After performing the above analyses, we determined that we will need at least three years of data to have a baseline understanding of a players rise, peak, and decline. We will limit our dataset to players with three years and then proceed to add a column with the three classifications of a players trajectory\n",
        "\n",
        "We understand there a limitations to this approach, since we are no viewing a players full rise and decline. However, we believe that defining this trajectory will be sufficiently useful for classification and testing our predictions. \n",
        "\n",
        "For the below additional columns, we will limit to 'Rise' and 'Decline' in order make the column entries binary. We understand a player's peak to be their final rating at 'Rise'.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "HjrcjomOPl5k"
      },
      "outputs": [],
      "source": [
        "#First limit to players with 3+ years of results\n",
        "player_yrs_3_query = \"\"\"\n",
        "WITH player_years AS (\n",
        "    SELECT player_fifa_api_id,\n",
        "           (strftime('%Y',date)) AS year\n",
        "    FROM players_and_skills_df\n",
        "    GROUP BY player_fifa_api_id, (strftime('%Y',date))\n",
        "),\n",
        "player_avg_years AS (\n",
        "  SELECT player_fifa_api_id,\n",
        "          COUNT(DISTINCT year) AS data_years\n",
        "  FROM player_years\n",
        "  GROUP BY player_fifa_api_id \n",
        ")\n",
        "\n",
        "SELECT player_fifa_api_id\n",
        "FROM player_avg_years\n",
        "WHERE data_years >= 3\n",
        "GROUP BY player_fifa_api_id\n",
        "\"\"\"\n",
        "player_yrs_3_df = ps.sqldf(player_yrs_3_query, locals())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "B1VxpS2NQxs4"
      },
      "outputs": [],
      "source": [
        "#Join table to master table and limit data to just these players\n",
        "player_ratings_3_query = \"\"\"\n",
        "WITH player_rating_age AS (\n",
        "    SELECT player_fifa_api_id, overall_rating, age\n",
        "    FROM players_and_skills_df\n",
        "    GROUP BY player_fifa_api_id, overall_rating, age\n",
        ")\n",
        "\n",
        "SELECT X.player_fifa_api_id, X.overall_rating, X.age\n",
        "FROM player_rating_age X\n",
        "    JOIN player_yrs_3_df Y on X.player_fifa_api_id = Y.player_fifa_api_id\n",
        "GROUP BY X.player_fifa_api_id, X.overall_rating, X.age\n",
        "\"\"\"\n",
        "sql_player_ratings_3_df = ps.sqldf(player_ratings_3_query, locals())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "q1-vMEQfRBZI"
      },
      "outputs": [],
      "source": [
        "#Determine the max overall rating for these players. If multiple, then select latest date\n",
        "player_ratings_peak_3_query = \"\"\"\n",
        "WITH player_rating_max AS (\n",
        "    SELECT player_fifa_api_id, max(overall_rating) AS max_rating\n",
        "    FROM sql_player_ratings_3_df X\n",
        "    GROUP BY player_fifa_api_id\n",
        "),\n",
        "player_rating_age_max AS (\n",
        "    SELECT X.player_fifa_api_id, MAX(X.age) as max_age\n",
        "    FROM sql_player_ratings_3_df X\n",
        "      JOIN player_rating_max Y on X.player_fifa_api_id = Y.player_fifa_api_id AND X.overall_rating = Y.max_rating\n",
        "    GROUP BY X.player_fifa_api_id\n",
        ")\n",
        "\n",
        "SELECT X.player_fifa_api_id, \n",
        "       X.overall_rating, \n",
        "       X.age,\n",
        "       CASE\n",
        "          WHEN X.age < Y.max_age THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating < Z.max_rating THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating = Z.max_rating THEN 'RISE'\n",
        "          ELSE 'DECLINE'\n",
        "      END AS peak_flg\n",
        "FROM sql_player_ratings_3_df X\n",
        "    LEFT JOIN player_rating_age_max Y \n",
        "      on X.player_fifa_api_id = Y.player_fifa_api_id\n",
        "    LEFT JOIN player_rating_max Z \n",
        "      on X.player_fifa_api_id = Z.player_fifa_api_id \n",
        "GROUP BY X.player_fifa_api_id, \n",
        "       X.overall_rating, \n",
        "       X.age,\n",
        "       CASE\n",
        "          WHEN X.age < Y.max_age THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating < Z.max_rating THEN 'RISE'\n",
        "          WHEN X.age = Y.max_age AND X.overall_rating = Z.max_rating THEN 'RISE'\n",
        "          ELSE 'DECLINE'\n",
        "      END\n",
        "\"\"\"\n",
        "sql_player_ratings_peak_3_df = ps.sqldf(player_ratings_peak_3_query, locals())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Z3vyRNPaSMjk"
      },
      "outputs": [],
      "source": [
        "#Merge datasets to make a master dataset for usage below \n",
        "TEMP_players_and_skills_df = players_and_skills_df.merge(sql_player_ratings_peak_3_df, on= [\"player_fifa_api_id\", \"overall_rating\", \"age\"])\n",
        "TEMP_players_and_skills_df['rank'] = TEMP_players_and_skills_df.groupby(by = 'player_fifa_api_id')['date'].rank(method = 'first')\n",
        "\n",
        "#Merge again with next row to get the future peak flag\n",
        "MASTER_players_and_skills_query = \"\"\"\n",
        "SELECT X.*,\n",
        "      Y.peak_flg AS peak_flg_2\n",
        "\n",
        "FROM TEMP_players_and_skills_df X\n",
        "  LEFT JOIN TEMP_players_and_skills_df Y ON X.player_fifa_api_id = Y.player_fifa_api_id AND X.rank = Y.rank - 1\n",
        "\"\"\"\n",
        "MASTER_players_and_skills_df = ps.sqldf(MASTER_players_and_skills_query, locals())\n",
        "\n",
        "MASTER_players_and_skills_df = MASTER_players_and_skills_df.drop(columns = ['rank'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MASTER_players_and_skills_df[(MASTER_players_and_skills_df['player_fifa_api_id'] == 11983)].sort_values(by = ['player_fifa_api_id','date']).tail(10)"
      ],
      "metadata": {
        "id": "-5u71nfO0zEV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "outputId": "1c477409-5275-40ca-9c54-1b0488dd5c49"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  player_fifa_api_id                 date  overall_rating  \\\n",
              "20284  22674               11983  2012-08-31 00:00:00            80.0   \n",
              "20283  22673               11983  2013-02-15 00:00:00            80.0   \n",
              "20282  22672               11983  2013-04-26 00:00:00            80.0   \n",
              "20281  22671               11983  2013-09-20 00:00:00            79.0   \n",
              "20280  22670               11983  2013-10-11 00:00:00            79.0   \n",
              "20279  22669               11983  2014-01-10 00:00:00            79.0   \n",
              "20278  22668               11983  2014-01-24 00:00:00            79.0   \n",
              "20277  22667               11983  2014-04-25 00:00:00            78.0   \n",
              "20276  22666               11983  2014-09-18 00:00:00            78.0   \n",
              "20275  22665               11983  2015-03-06 00:00:00            77.0   \n",
              "\n",
              "       potential  attacking_work_rate  defensive_work_rate  crossing  \\\n",
              "20284       80.0                    6                    6      17.0   \n",
              "20283       80.0                    6                    6      17.0   \n",
              "20282       80.0                    6                    6      17.0   \n",
              "20281       79.0                    6                    6      25.0   \n",
              "20280       79.0                    6                    6      25.0   \n",
              "20279       79.0                    6                    6      25.0   \n",
              "20278       79.0                    6                    6      25.0   \n",
              "20277       78.0                    6                    6      25.0   \n",
              "20276       78.0                    6                    6      25.0   \n",
              "20275       77.0                    6                    6      25.0   \n",
              "\n",
              "       finishing  heading_accuracy  ...  gk_positioning  gk_reflexes  \\\n",
              "20284       18.0              18.0  ...            83.0         80.0   \n",
              "20283       18.0              18.0  ...            83.0         80.0   \n",
              "20282       18.0              18.0  ...            83.0         80.0   \n",
              "20281       25.0              25.0  ...            82.0         79.0   \n",
              "20280       25.0              25.0  ...            82.0         79.0   \n",
              "20279       25.0              25.0  ...            82.0         79.0   \n",
              "20278       25.0              25.0  ...            82.0         78.0   \n",
              "20277       25.0              25.0  ...            80.0         77.0   \n",
              "20276       25.0              25.0  ...            82.0         76.0   \n",
              "20275       25.0              25.0  ...            82.0         74.0   \n",
              "\n",
              "       player_api_id   player_name             birthday  height  weight  age  \\\n",
              "20284          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   41   \n",
              "20283          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   42   \n",
              "20282          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   42   \n",
              "20281          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   42   \n",
              "20280          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   42   \n",
              "20279          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   43   \n",
              "20278          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   43   \n",
              "20277          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   43   \n",
              "20276          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   43   \n",
              "20275          30380  Brad Friedel  1971-05-18 00:00:00  187.96     203   44   \n",
              "\n",
              "       peak_flg  peak_flg_2  \n",
              "20284   DECLINE     DECLINE  \n",
              "20283   DECLINE     DECLINE  \n",
              "20282   DECLINE     DECLINE  \n",
              "20281   DECLINE     DECLINE  \n",
              "20280   DECLINE     DECLINE  \n",
              "20279   DECLINE     DECLINE  \n",
              "20278   DECLINE     DECLINE  \n",
              "20277   DECLINE     DECLINE  \n",
              "20276   DECLINE     DECLINE  \n",
              "20275   DECLINE        None  \n",
              "\n",
              "[10 rows x 48 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f234ab20-d61a-4e44-b126-d703ce7b1286\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>id</th>\n",
              "      <th>player_fifa_api_id</th>\n",
              "      <th>date</th>\n",
              "      <th>overall_rating</th>\n",
              "      <th>potential</th>\n",
              "      <th>attacking_work_rate</th>\n",
              "      <th>defensive_work_rate</th>\n",
              "      <th>crossing</th>\n",
              "      <th>finishing</th>\n",
              "      <th>heading_accuracy</th>\n",
              "      <th>...</th>\n",
              "      <th>gk_positioning</th>\n",
              "      <th>gk_reflexes</th>\n",
              "      <th>player_api_id</th>\n",
              "      <th>player_name</th>\n",
              "      <th>birthday</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>peak_flg</th>\n",
              "      <th>peak_flg_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20284</th>\n",
              "      <td>22674</td>\n",
              "      <td>11983</td>\n",
              "      <td>2012-08-31 00:00:00</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>...</td>\n",
              "      <td>83.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>41</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20283</th>\n",
              "      <td>22673</td>\n",
              "      <td>11983</td>\n",
              "      <td>2013-02-15 00:00:00</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>...</td>\n",
              "      <td>83.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>42</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20282</th>\n",
              "      <td>22672</td>\n",
              "      <td>11983</td>\n",
              "      <td>2013-04-26 00:00:00</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>...</td>\n",
              "      <td>83.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>42</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20281</th>\n",
              "      <td>22671</td>\n",
              "      <td>11983</td>\n",
              "      <td>2013-09-20 00:00:00</td>\n",
              "      <td>79.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>42</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20280</th>\n",
              "      <td>22670</td>\n",
              "      <td>11983</td>\n",
              "      <td>2013-10-11 00:00:00</td>\n",
              "      <td>79.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>42</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20279</th>\n",
              "      <td>22669</td>\n",
              "      <td>11983</td>\n",
              "      <td>2014-01-10 00:00:00</td>\n",
              "      <td>79.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>43</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20278</th>\n",
              "      <td>22668</td>\n",
              "      <td>11983</td>\n",
              "      <td>2014-01-24 00:00:00</td>\n",
              "      <td>79.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>43</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20277</th>\n",
              "      <td>22667</td>\n",
              "      <td>11983</td>\n",
              "      <td>2014-04-25 00:00:00</td>\n",
              "      <td>78.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>80.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>43</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20276</th>\n",
              "      <td>22666</td>\n",
              "      <td>11983</td>\n",
              "      <td>2014-09-18 00:00:00</td>\n",
              "      <td>78.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>43</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>DECLINE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20275</th>\n",
              "      <td>22665</td>\n",
              "      <td>11983</td>\n",
              "      <td>2015-03-06 00:00:00</td>\n",
              "      <td>77.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>30380</td>\n",
              "      <td>Brad Friedel</td>\n",
              "      <td>1971-05-18 00:00:00</td>\n",
              "      <td>187.96</td>\n",
              "      <td>203</td>\n",
              "      <td>44</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 48 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f234ab20-d61a-4e44-b126-d703ce7b1286')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MASTER_players_and_skills_df.describe()"
      ],
      "metadata": {
        "id": "k03Zt9uU00dm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "cafbb5b2-5c45-4476-a29e-f4ef975d0ca4"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id  player_fifa_api_id  overall_rating      potential  \\\n",
              "count  165091.00000       165091.000000   165091.000000  165091.000000   \n",
              "mean    91925.42548       165175.708209       69.196673      73.703418   \n",
              "std     53020.56658        52541.102142        6.700451       6.454431   \n",
              "min         6.00000            2.000000       33.000000      39.000000   \n",
              "25%     46071.50000       155862.000000       65.000000      69.000000   \n",
              "50%     91824.00000       183256.000000       69.000000      74.000000   \n",
              "75%    137714.50000       198951.000000       74.000000      78.000000   \n",
              "max    183977.00000       230592.000000       94.000000      97.000000   \n",
              "\n",
              "       attacking_work_rate  defensive_work_rate       crossing      finishing  \\\n",
              "count        165091.000000        165091.000000  165091.000000  165091.000000   \n",
              "mean              6.468196             6.029311      55.772035      50.365756   \n",
              "std               1.764604             1.749202      17.241680      19.066173   \n",
              "min               0.000000             0.000000       1.000000       1.000000   \n",
              "25%               6.000000             6.000000      46.000000      35.000000   \n",
              "50%               6.000000             6.000000      60.000000      54.000000   \n",
              "75%               6.000000             6.000000      68.000000      66.000000   \n",
              "max               9.000000             9.000000      95.000000      97.000000   \n",
              "\n",
              "       heading_accuracy  short_passing  ...  sliding_tackle      gk_diving  \\\n",
              "count     165091.000000  165091.000000  ...   165091.000000  165091.000000   \n",
              "mean          57.685392      63.070773  ...       48.317195      14.789359   \n",
              "std           16.519574      14.053400  ...       21.679517      16.986953   \n",
              "min            1.000000       3.000000  ...        2.000000       1.000000   \n",
              "25%           50.000000      58.000000  ...       25.000000       7.000000   \n",
              "50%           60.000000      66.000000  ...       54.000000      10.000000   \n",
              "75%           69.000000      72.000000  ...       67.000000      13.000000   \n",
              "max           95.000000      97.000000  ...       95.000000      94.000000   \n",
              "\n",
              "         gk_handling     gk_kicking  gk_positioning    gk_reflexes  \\\n",
              "count  165091.000000  165091.000000   165091.000000  165091.000000   \n",
              "mean       15.947756      19.762513       16.022903      16.333561   \n",
              "std        15.991221      20.737289       16.236961      17.350154   \n",
              "min         1.000000       1.000000        1.000000       1.000000   \n",
              "25%         8.000000       8.000000        8.000000       8.000000   \n",
              "50%        11.000000      11.000000       11.000000      11.000000   \n",
              "75%        15.000000      15.000000       15.000000      15.000000   \n",
              "max        92.000000      97.000000       96.000000      96.000000   \n",
              "\n",
              "       player_api_id         height         weight            age  \n",
              "count  165091.000000  165091.000000  165091.000000  165091.000000  \n",
              "mean   127922.836260     181.884515     168.911933      26.131007  \n",
              "std    124048.816181       6.406929      15.106036       4.417982  \n",
              "min      2625.000000     157.480000     117.000000      15.000000  \n",
              "25%     34479.000000     177.800000     159.000000      23.000000  \n",
              "50%     74744.000000     182.880000     168.000000      26.000000  \n",
              "75%    185618.000000     185.420000     179.000000      29.000000  \n",
              "max    722766.000000     208.280000     243.000000      44.000000  \n",
              "\n",
              "[8 rows x 43 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>player_fifa_api_id</th>\n",
              "      <th>overall_rating</th>\n",
              "      <th>potential</th>\n",
              "      <th>attacking_work_rate</th>\n",
              "      <th>defensive_work_rate</th>\n",
              "      <th>crossing</th>\n",
              "      <th>finishing</th>\n",
              "      <th>heading_accuracy</th>\n",
              "      <th>short_passing</th>\n",
              "      <th>...</th>\n",
              "      <th>sliding_tackle</th>\n",
              "      <th>gk_diving</th>\n",
              "      <th>gk_handling</th>\n",
              "      <th>gk_kicking</th>\n",
              "      <th>gk_positioning</th>\n",
              "      <th>gk_reflexes</th>\n",
              "      <th>player_api_id</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>165091.00000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "      <td>165091.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>91925.42548</td>\n",
              "      <td>165175.708209</td>\n",
              "      <td>69.196673</td>\n",
              "      <td>73.703418</td>\n",
              "      <td>6.468196</td>\n",
              "      <td>6.029311</td>\n",
              "      <td>55.772035</td>\n",
              "      <td>50.365756</td>\n",
              "      <td>57.685392</td>\n",
              "      <td>63.070773</td>\n",
              "      <td>...</td>\n",
              "      <td>48.317195</td>\n",
              "      <td>14.789359</td>\n",
              "      <td>15.947756</td>\n",
              "      <td>19.762513</td>\n",
              "      <td>16.022903</td>\n",
              "      <td>16.333561</td>\n",
              "      <td>127922.836260</td>\n",
              "      <td>181.884515</td>\n",
              "      <td>168.911933</td>\n",
              "      <td>26.131007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>53020.56658</td>\n",
              "      <td>52541.102142</td>\n",
              "      <td>6.700451</td>\n",
              "      <td>6.454431</td>\n",
              "      <td>1.764604</td>\n",
              "      <td>1.749202</td>\n",
              "      <td>17.241680</td>\n",
              "      <td>19.066173</td>\n",
              "      <td>16.519574</td>\n",
              "      <td>14.053400</td>\n",
              "      <td>...</td>\n",
              "      <td>21.679517</td>\n",
              "      <td>16.986953</td>\n",
              "      <td>15.991221</td>\n",
              "      <td>20.737289</td>\n",
              "      <td>16.236961</td>\n",
              "      <td>17.350154</td>\n",
              "      <td>124048.816181</td>\n",
              "      <td>6.406929</td>\n",
              "      <td>15.106036</td>\n",
              "      <td>4.417982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2625.000000</td>\n",
              "      <td>157.480000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46071.50000</td>\n",
              "      <td>155862.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>34479.000000</td>\n",
              "      <td>177.800000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>91824.00000</td>\n",
              "      <td>183256.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>74744.000000</td>\n",
              "      <td>182.880000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>137714.50000</td>\n",
              "      <td>198951.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>185618.000000</td>\n",
              "      <td>185.420000</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>183977.00000</td>\n",
              "      <td>230592.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>722766.000000</td>\n",
              "      <td>208.280000</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>44.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9eae137-9f9e-45ec-9aa6-45271acada0d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPwnnFMxOqpI"
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      "source": [
        "## **2.4 Selecting a subset of players to further analyze**\n",
        "We'll select a group of players and then visualize the evolution of their overall rating over their last 10 periods of data (last 10 data entries for each player).\n",
        "\n",
        "After we build our classification models, we'll use our best model to make the following prediction for each player: **Will a player's overall rating be in DECLINE phase over the next period?** For some of them who are in their **RISE** phase, the question is even more interesting."
      ]
    },
    {
      "cell_type": "code",
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            "text/plain": [
              "         player_name  overall_rating                 date\n",
              "0     Andres Iniesta            88.0  2015-10-16 00:00:00\n",
              "1     Andres Iniesta            88.0  2015-09-21 00:00:00\n",
              "2     Andres Iniesta            89.0  2014-11-07 00:00:00\n",
              "3     Andres Iniesta            89.0  2014-10-17 00:00:00\n",
              "4     Andres Iniesta            89.0  2014-09-18 00:00:00\n",
              "..               ...             ...                  ...\n",
              "223  Virgil van Dijk            71.0  2013-03-28 00:00:00\n",
              "224  Virgil van Dijk            71.0  2013-03-22 00:00:00\n",
              "225  Virgil van Dijk            71.0  2012-08-31 00:00:00\n",
              "226  Virgil van Dijk            68.0  2012-02-22 00:00:00\n",
              "227  Virgil van Dijk            64.0  2011-08-30 00:00:00\n",
              "\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "#Query to get historical data\n",
        "historical_data_query = \"\"\"\n",
        "SELECT player_name, overall_rating, date\n",
        "FROM MASTER_players_and_skills_df\n",
        "WHERE player_name IN ('Lionel Messi', 'Santi Cazorla', 'Sergio Garcia','Gerard Pique', 'Cristiano Ronaldo', 'Edinson Cavani', 'Eden Hazard', 'Virgil van Dijk', 'Andres Iniesta')\n",
        "\"\"\"\n",
        "historical_rating_df = ps.sqldf(historical_data_query, locals())\n",
        "historical_rating_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "t5fDJJMZD3UT",
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          "height": 1000
        },
        "outputId": "840f7760-f14b-43d1-bdba-640e579c4859"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py:1667: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[key] = value\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          player_name  overall_rating                 date  period\n",
              "0      Andres Iniesta            88.0  2015-10-16 00:00:00    10.0\n",
              "1      Andres Iniesta            88.0  2015-09-21 00:00:00     9.0\n",
              "2      Andres Iniesta            89.0  2014-11-07 00:00:00     8.0\n",
              "3      Andres Iniesta            89.0  2014-10-17 00:00:00     7.0\n",
              "4      Andres Iniesta            89.0  2014-09-18 00:00:00     6.0\n",
              "5      Andres Iniesta            89.0  2014-05-16 00:00:00     5.0\n",
              "6      Andres Iniesta            89.0  2013-12-20 00:00:00     4.0\n",
              "7      Andres Iniesta            89.0  2013-09-20 00:00:00     3.0\n",
              "8      Andres Iniesta            90.0  2013-06-07 00:00:00     2.0\n",
              "9      Andres Iniesta            90.0  2013-05-24 00:00:00     1.0\n",
              "24  Cristiano Ronaldo            93.0  2015-10-16 00:00:00    10.0\n",
              "25  Cristiano Ronaldo            93.0  2015-09-25 00:00:00     9.0\n",
              "26  Cristiano Ronaldo            93.0  2015-09-21 00:00:00     8.0\n",
              "27  Cristiano Ronaldo            92.0  2015-05-15 00:00:00     7.0\n",
              "28  Cristiano Ronaldo            92.0  2015-04-17 00:00:00     6.0\n",
              "29  Cristiano Ronaldo            92.0  2014-10-31 00:00:00     5.0\n",
              "30  Cristiano Ronaldo            92.0  2014-09-18 00:00:00     4.0\n",
              "31  Cristiano Ronaldo            92.0  2014-02-07 00:00:00     3.0\n",
              "32  Cristiano Ronaldo            92.0  2014-01-17 00:00:00     2.0\n",
              "33  Cristiano Ronaldo            92.0  2013-09-20 00:00:00     1.0\n",
              "48        Eden Hazard            88.0  2016-01-28 00:00:00    10.0\n",
              "49        Eden Hazard            89.0  2015-09-25 00:00:00     9.0\n",
              "50        Eden Hazard            89.0  2015-09-21 00:00:00     8.0\n",
              "51        Eden Hazard            88.0  2015-04-17 00:00:00     7.0\n",
              "52        Eden Hazard            88.0  2015-02-13 00:00:00     6.0\n",
              "53        Eden Hazard            88.0  2015-01-23 00:00:00     5.0\n",
              "54        Eden Hazard            88.0  2014-10-10 00:00:00     4.0\n",
              "55        Eden Hazard            88.0  2014-09-18 00:00:00     3.0\n",
              "56        Eden Hazard            88.0  2014-05-16 00:00:00     2.0\n",
              "57        Eden Hazard            88.0  2014-04-25 00:00:00     1.0"
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2015-10-16 00:00:00</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2015-09-25 00:00:00</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2015-09-21 00:00:00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2015-05-15 00:00:00</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2015-04-17 00:00:00</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2014-10-31 00:00:00</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2014-09-18 00:00:00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2014-02-07 00:00:00</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2014-01-17 00:00:00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>92.0</td>\n",
              "      <td>2013-09-20 00:00:00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2016-01-28 00:00:00</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>89.0</td>\n",
              "      <td>2015-09-25 00:00:00</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>89.0</td>\n",
              "      <td>2015-09-21 00:00:00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2015-04-17 00:00:00</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2015-02-13 00:00:00</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2015-01-23 00:00:00</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2014-10-10 00:00:00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2014-09-18 00:00:00</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2014-05-16 00:00:00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Eden Hazard</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2014-04-25 00:00:00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9edb9e75-0462-4b1a-ba50-fbd1a2bf09f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9edb9e75-0462-4b1a-ba50-fbd1a2bf09f1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9edb9e75-0462-4b1a-ba50-fbd1a2bf09f1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "#Assign row numbers (periods of data) to each player and keep only the last 10 periods\n",
        "historical_rating_df.loc[:, ('period_backwards')] = historical_rating_df.groupby('player_name')['date'].rank(method='first', ascending=False)\n",
        "historical_rating_df = historical_rating_df[historical_rating_df['period_backwards']<=10]\n",
        "historical_rating_df.loc[:, ('period')] = historical_rating_df['period_backwards'].apply(lambda x: 11 - x)\n",
        "historical_rating_df.drop(columns=['period_backwards'], inplace=True)\n",
        "\n",
        "historical_rating_df.head(30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "bVivSvH1ITwz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "cd53f72b-1f8e-4e0f-a454-8ca39a31ce22"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "players_plot = sns.lineplot(data=historical_rating_df, x=\"period\", y=\"overall_rating\", hue=\"player_name\")\n",
        "plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
        "plt.yticks([x for x in range(70,100) if x%2 == 0]);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0GF-ZJupkMu"
      },
      "source": [
        "# **3. Classification Models**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9MiIqkoFjL9-"
      },
      "source": [
        "## **3.1 Modeling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mR6QgtMB1QDV"
      },
      "source": [
        "We're going to create two dataframes to separate goalkeeping skills from player skills. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "bJ2pJnEU0daA"
      },
      "outputs": [],
      "source": [
        "player_skills = MASTER_players_and_skills_df.drop(columns=['gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning', 'gk_reflexes'])\n",
        "goalkeeper_skills = MASTER_players_and_skills_df[['gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning', 'gk_reflexes']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMKSsBu4Epv2"
      },
      "source": [
        "After separating player's and goalkeeper's skills, we're going to separate our final player dataset that we will test on after training or models. \n",
        "\n",
        "The rows we're going to separate have `peak_flg_2` column as 'None', which basically means that there is no data for that particular player after this point in time. \n",
        "\n",
        "We will resolve this issue via our ML models by training our it on historical values and tehn predicting their future performance.   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "u3fRV8wv9JXr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "outputId": "21d02dec-a082-4358-f0db-a47a72942ade"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  player_fifa_api_id                 date  overall_rating  \\\n",
              "0            6              189615  2016-04-21 00:00:00            74.0   \n",
              "32          39              186170  2016-01-07 00:00:00            65.0   \n",
              "57          65              140161  2016-04-21 00:00:00            69.0   \n",
              "79          88               17725  2015-12-24 00:00:00            70.0   \n",
              "103        113              158138  2016-04-28 00:00:00            77.0   \n",
              "...        ...                 ...                  ...             ...   \n",
              "165028  183896              195840  2016-01-21 00:00:00            64.0   \n",
              "165035  183904              205705  2016-02-04 00:00:00            75.0   \n",
              "165055  183925                2488  2015-03-06 00:00:00            73.0   \n",
              "165067  183938              164680  2014-09-18 00:00:00            63.0   \n",
              "165082  183969              102359  2012-08-31 00:00:00            78.0   \n",
              "\n",
              "        potential  attacking_work_rate  defensive_work_rate  crossing  \\\n",
              "0            76.0                    9                    6      80.0   \n",
              "32           67.0                    6                    6      64.0   \n",
              "57           69.0                    6                    6      57.0   \n",
              "79           70.0                    6                    6      46.0   \n",
              "103          77.0                    6                    6      77.0   \n",
              "...           ...                  ...                  ...       ...   \n",
              "165028       64.0                    6                    6      65.0   \n",
              "165035       76.0                    6                    6      57.0   \n",
              "165055       73.0                    3                    6      42.0   \n",
              "165067       67.0                    9                    6      65.0   \n",
              "165082       78.0                    6                    3      77.0   \n",
              "\n",
              "        finishing  heading_accuracy  ...  standing_tackle  sliding_tackle  \\\n",
              "0            53.0              58.0  ...             75.0            78.0   \n",
              "32           58.0              60.0  ...             22.0            24.0   \n",
              "57           21.0              68.0  ...             71.0            68.0   \n",
              "79           34.0              73.0  ...             73.0            71.0   \n",
              "103          72.0              64.0  ...             34.0            35.0   \n",
              "...           ...               ...  ...              ...             ...   \n",
              "165028       60.0              42.0  ...             26.0            24.0   \n",
              "165035       40.0              73.0  ...             79.0            71.0   \n",
              "165055       27.0              74.0  ...             77.0            72.0   \n",
              "165067       44.0              59.0  ...             61.0            61.0   \n",
              "165082       70.0              59.0  ...             31.0            30.0   \n",
              "\n",
              "        player_api_id         player_name             birthday  height  \\\n",
              "0              155782     Aaron Cresswell  1989-12-15 00:00:00  170.18   \n",
              "32             162549         Aaron Doran  1991-05-13 00:00:00  170.18   \n",
              "57              30572       Aaron Galindo  1982-05-08 00:00:00  182.88   \n",
              "79              23780        Aaron Hughes  1979-11-08 00:00:00  182.88   \n",
              "103             27316          Aaron Hunt  1986-09-04 00:00:00  182.88   \n",
              "...               ...                 ...                  ...     ...   \n",
              "165028         192132  Zouhaier Dhaouadhi  1988-01-01 00:00:00  180.34   \n",
              "165035         282473      Zouhair Feddal  1989-01-01 00:00:00  190.50   \n",
              "165055          26357      Zoumana Camara  1979-04-03 00:00:00  182.88   \n",
              "165067         111182        Zsolt Laczko  1986-12-18 00:00:00  182.88   \n",
              "165082          39902  Zvjezdan Misimovic  1982-06-05 00:00:00  180.34   \n",
              "\n",
              "        weight  age  peak_flg  peak_flg_2  \n",
              "0          146   27      RISE        None  \n",
              "32         163   25   DECLINE        None  \n",
              "57         198   34   DECLINE        None  \n",
              "79         154   36   DECLINE        None  \n",
              "103        161   30   DECLINE        None  \n",
              "...        ...  ...       ...         ...  \n",
              "165028     159   28      RISE        None  \n",
              "165035     172   27      RISE        None  \n",
              "165055     168   36   DECLINE        None  \n",
              "165067     176   28   DECLINE        None  \n",
              "165082     176   30   DECLINE        None  \n",
              "\n",
              "[9361 rows x 43 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>player_fifa_api_id</th>\n",
              "      <th>date</th>\n",
              "      <th>overall_rating</th>\n",
              "      <th>potential</th>\n",
              "      <th>attacking_work_rate</th>\n",
              "      <th>defensive_work_rate</th>\n",
              "      <th>crossing</th>\n",
              "      <th>finishing</th>\n",
              "      <th>heading_accuracy</th>\n",
              "      <th>...</th>\n",
              "      <th>standing_tackle</th>\n",
              "      <th>sliding_tackle</th>\n",
              "      <th>player_api_id</th>\n",
              "      <th>player_name</th>\n",
              "      <th>birthday</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>peak_flg</th>\n",
              "      <th>peak_flg_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>189615</td>\n",
              "      <td>2016-04-21 00:00:00</td>\n",
              "      <td>74.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>80.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>...</td>\n",
              "      <td>75.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>155782</td>\n",
              "      <td>Aaron Cresswell</td>\n",
              "      <td>1989-12-15 00:00:00</td>\n",
              "      <td>170.18</td>\n",
              "      <td>146</td>\n",
              "      <td>27</td>\n",
              "      <td>RISE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>39</td>\n",
              "      <td>186170</td>\n",
              "      <td>2016-01-07 00:00:00</td>\n",
              "      <td>65.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>64.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>162549</td>\n",
              "      <td>Aaron Doran</td>\n",
              "      <td>1991-05-13 00:00:00</td>\n",
              "      <td>170.18</td>\n",
              "      <td>163</td>\n",
              "      <td>25</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>65</td>\n",
              "      <td>140161</td>\n",
              "      <td>2016-04-21 00:00:00</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>57.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>...</td>\n",
              "      <td>71.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>30572</td>\n",
              "      <td>Aaron Galindo</td>\n",
              "      <td>1982-05-08 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>198</td>\n",
              "      <td>34</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>88</td>\n",
              "      <td>17725</td>\n",
              "      <td>2015-12-24 00:00:00</td>\n",
              "      <td>70.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>46.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>...</td>\n",
              "      <td>73.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>23780</td>\n",
              "      <td>Aaron Hughes</td>\n",
              "      <td>1979-11-08 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>154</td>\n",
              "      <td>36</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>113</td>\n",
              "      <td>158138</td>\n",
              "      <td>2016-04-28 00:00:00</td>\n",
              "      <td>77.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>77.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>...</td>\n",
              "      <td>34.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>27316</td>\n",
              "      <td>Aaron Hunt</td>\n",
              "      <td>1986-09-04 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>161</td>\n",
              "      <td>30</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>165028</th>\n",
              "      <td>183896</td>\n",
              "      <td>195840</td>\n",
              "      <td>2016-01-21 00:00:00</td>\n",
              "      <td>64.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>65.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>...</td>\n",
              "      <td>26.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>192132</td>\n",
              "      <td>Zouhaier Dhaouadhi</td>\n",
              "      <td>1988-01-01 00:00:00</td>\n",
              "      <td>180.34</td>\n",
              "      <td>159</td>\n",
              "      <td>28</td>\n",
              "      <td>RISE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165035</th>\n",
              "      <td>183904</td>\n",
              "      <td>205705</td>\n",
              "      <td>2016-02-04 00:00:00</td>\n",
              "      <td>75.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>57.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>...</td>\n",
              "      <td>79.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>282473</td>\n",
              "      <td>Zouhair Feddal</td>\n",
              "      <td>1989-01-01 00:00:00</td>\n",
              "      <td>190.50</td>\n",
              "      <td>172</td>\n",
              "      <td>27</td>\n",
              "      <td>RISE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165055</th>\n",
              "      <td>183925</td>\n",
              "      <td>2488</td>\n",
              "      <td>2015-03-06 00:00:00</td>\n",
              "      <td>73.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>...</td>\n",
              "      <td>77.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>26357</td>\n",
              "      <td>Zoumana Camara</td>\n",
              "      <td>1979-04-03 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>168</td>\n",
              "      <td>36</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165067</th>\n",
              "      <td>183938</td>\n",
              "      <td>164680</td>\n",
              "      <td>2014-09-18 00:00:00</td>\n",
              "      <td>63.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>65.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>...</td>\n",
              "      <td>61.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>111182</td>\n",
              "      <td>Zsolt Laczko</td>\n",
              "      <td>1986-12-18 00:00:00</td>\n",
              "      <td>182.88</td>\n",
              "      <td>176</td>\n",
              "      <td>28</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165082</th>\n",
              "      <td>183969</td>\n",
              "      <td>102359</td>\n",
              "      <td>2012-08-31 00:00:00</td>\n",
              "      <td>78.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>77.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>39902</td>\n",
              "      <td>Zvjezdan Misimovic</td>\n",
              "      <td>1982-06-05 00:00:00</td>\n",
              "      <td>180.34</td>\n",
              "      <td>176</td>\n",
              "      <td>30</td>\n",
              "      <td>DECLINE</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>9361 rows × 43 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "final_player_df = player_skills[player_skills['peak_flg_2'].isnull()]\n",
        "final_player_df "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSwdC_8L14B1"
      },
      "source": [
        "Let's separate all rows that have `peak_flg_2` as None value. This is because `peak_flg_2` column predicts rise/decline based on next(future) datapoint's attribute and since these rows are last in the dataset, they can't be predicted on. \n",
        "\n",
        "We will be predicting the future performace on those rows once our model is created. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "CxuEZQiq2fse",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "outputId": "e435ea20-80de-47a3-f081-ded9d00326d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  player_fifa_api_id                 date  overall_rating  \\\n",
              "1            7              189615  2016-04-07 00:00:00            74.0   \n",
              "2            8              189615  2016-01-07 00:00:00            73.0   \n",
              "3            9              189615  2015-12-24 00:00:00            73.0   \n",
              "4           10              189615  2015-12-17 00:00:00            73.0   \n",
              "5           11              189615  2015-10-16 00:00:00            73.0   \n",
              "...        ...                 ...                  ...             ...   \n",
              "165086  183973              102359  2010-08-30 00:00:00            83.0   \n",
              "165087  183974              102359  2009-08-30 00:00:00            83.0   \n",
              "165088  183975              102359  2009-02-22 00:00:00            78.0   \n",
              "165089  183976              102359  2008-08-30 00:00:00            77.0   \n",
              "165090  183977              102359  2007-08-30 00:00:00            78.0   \n",
              "\n",
              "        potential  attacking_work_rate  defensive_work_rate  crossing  \\\n",
              "1            76.0                    9                    6      80.0   \n",
              "2            75.0                    9                    6      79.0   \n",
              "3            75.0                    9                    6      79.0   \n",
              "4            75.0                    9                    6      79.0   \n",
              "5            77.0                    9                    6      79.0   \n",
              "...           ...                  ...                  ...       ...   \n",
              "165086       85.0                    6                    3      84.0   \n",
              "165087       85.0                    6                    3      84.0   \n",
              "165088       80.0                    6                    3      74.0   \n",
              "165089       80.0                    6                    3      74.0   \n",
              "165090       81.0                    6                    3      74.0   \n",
              "\n",
              "        finishing  heading_accuracy  ...  standing_tackle  sliding_tackle  \\\n",
              "1            53.0              58.0  ...             75.0            78.0   \n",
              "2            52.0              57.0  ...             75.0            78.0   \n",
              "3            51.0              57.0  ...             75.0            78.0   \n",
              "4            51.0              57.0  ...             75.0            78.0   \n",
              "5            51.0              57.0  ...             75.0            78.0   \n",
              "...           ...               ...  ...              ...             ...   \n",
              "165086       78.0              59.0  ...             31.0            30.0   \n",
              "165087       77.0              59.0  ...             31.0            30.0   \n",
              "165088       76.0              53.0  ...             31.0            30.0   \n",
              "165089       71.0              53.0  ...             31.0            30.0   \n",
              "165090       64.0              57.0  ...             32.0            30.0   \n",
              "\n",
              "        player_api_id         player_name             birthday  height  \\\n",
              "1              155782     Aaron Cresswell  1989-12-15 00:00:00  170.18   \n",
              "2              155782     Aaron Cresswell  1989-12-15 00:00:00  170.18   \n",
              "3              155782     Aaron Cresswell  1989-12-15 00:00:00  170.18   \n",
              "4              155782     Aaron Cresswell  1989-12-15 00:00:00  170.18   \n",
              "5              155782     Aaron Cresswell  1989-12-15 00:00:00  170.18   \n",
              "...               ...                 ...                  ...     ...   \n",
              "165086          39902  Zvjezdan Misimovic  1982-06-05 00:00:00  180.34   \n",
              "165087          39902  Zvjezdan Misimovic  1982-06-05 00:00:00  180.34   \n",
              "165088          39902  Zvjezdan Misimovic  1982-06-05 00:00:00  180.34   \n",
              "165089          39902  Zvjezdan Misimovic  1982-06-05 00:00:00  180.34   \n",
              "165090          39902  Zvjezdan Misimovic  1982-06-05 00:00:00  180.34   \n",
              "\n",
              "        weight  age  peak_flg  peak_flg_2  \n",
              "1          146   27      RISE        RISE  \n",
              "2          146   27      RISE        RISE  \n",
              "3          146   26      RISE        RISE  \n",
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          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "player_skills = player_skills[(player_skills['peak_flg_2'] == 'RISE') | (player_skills['peak_flg_2'] == 'DECLINE')]\n",
        "player_skills"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrYTJWzuEdht"
      },
      "source": [
        "### 3.1.1 Creating the classification labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NLCUWAN2NlB"
      },
      "source": [
        "To run machine learning models all our column values need to be integer or float. Since our `peak flag` column is a categorical value, we will change it to a binary value. If the `peak flag` is `RISE`, we will change it to 1 and if it is `DECLINE`, we will change it to 0. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "AwsR5WFd2OMI"
      },
      "outputs": [],
      "source": [
        "player_skills['peak_boolean'] = player_skills['peak_flg_2'].apply(lambda x: 0 if x == 'DECLINE' else 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUbk-Vu31ZQ-"
      },
      "source": [
        "Since we need to start classifying data into features and labels to train and test, we create two dataframes for the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "EnWi7rc6MjHq"
      },
      "outputs": [],
      "source": [
        "player_skills_features = player_skills.drop(columns=['id', 'player_fifa_api_id', 'player_api_id', 'date', 'potential', 'player_name', 'date', 'birthday', 'height', 'weight', 'peak_flg', 'peak_flg_2', 'peak_boolean'])\n",
        "player_rating_label = player_skills['peak_boolean']\n",
        "player_rating_label = pd.Series(player_rating_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "zozW38nU2H_E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "8733fdd4-62fd-4e6c-d293-d6adddbc11b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        overall_rating  attacking_work_rate  defensive_work_rate  crossing  \\\n",
              "1                 74.0                    9                    6      80.0   \n",
              "2                 73.0                    9                    6      79.0   \n",
              "3                 73.0                    9                    6      79.0   \n",
              "4                 73.0                    9                    6      79.0   \n",
              "5                 73.0                    9                    6      79.0   \n",
              "...                ...                  ...                  ...       ...   \n",
              "165086            83.0                    6                    3      84.0   \n",
              "165087            83.0                    6                    3      84.0   \n",
              "165088            78.0                    6                    3      74.0   \n",
              "165089            77.0                    6                    3      74.0   \n",
              "165090            78.0                    6                    3      74.0   \n",
              "\n",
              "        finishing  heading_accuracy  short_passing  volleys  dribbling  curve  \\\n",
              "1            53.0              58.0           71.0     32.0       73.0   70.0   \n",
              "2            52.0              57.0           70.0     29.0       71.0   68.0   \n",
              "3            51.0              57.0           70.0     29.0       71.0   68.0   \n",
              "4            51.0              57.0           70.0     29.0       71.0   68.0   \n",
              "5            51.0              57.0           70.0     29.0       71.0   68.0   \n",
              "...           ...               ...            ...      ...        ...    ...   \n",
              "165086       78.0              59.0           87.0     77.0       84.0   86.0   \n",
              "165087       77.0              59.0           89.0     77.0       84.0   86.0   \n",
              "165088       76.0              53.0           84.0     77.0       85.0   86.0   \n",
              "165089       71.0              53.0           84.0     77.0       85.0   86.0   \n",
              "165090       64.0              57.0           86.0     77.0       87.0   86.0   \n",
              "\n",
              "        ...  long_shots  aggression  interceptions  positioning  vision  \\\n",
              "1       ...        60.0        68.0           67.0         60.0    66.0   \n",
              "2       ...        59.0        67.0           66.0         58.0    65.0   \n",
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              "\n",
              "        penalties  marking  standing_tackle  sliding_tackle  age  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "player_skills_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "BS3BQ4AW3lDx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3cc325f-e344-47ca-c5f2-8de2f3bcaa76"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1         1\n",
              "2         1\n",
              "3         1\n",
              "4         1\n",
              "5         1\n",
              "         ..\n",
              "165086    0\n",
              "165087    1\n",
              "165088    1\n",
              "165089    1\n",
              "165090    1\n",
              "Name: peak_boolean, Length: 155730, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "player_rating_label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsYyXN_RMmHV"
      },
      "source": [
        "### 3.1.2 Split data into test and train "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "gUtX1bihMo2c"
      },
      "outputs": [],
      "source": [
        "seed = 42\n",
        "x_train, x_test, y_train, y_test = train_test_split(player_skills_features, player_rating_label, test_size=0.2, random_state=seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyC54krbpnDA"
      },
      "source": [
        "### 3.1.3 Scaling the data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "n-h-AUjcpp2P"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "GrNZNBwQ7gDc"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "pca_scaled = scaler.fit(x_train).transform(x_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8JZXnimDx3I"
      },
      "source": [
        "##**3.2 Logistic Regression Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "QelCzolREDYy"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# intermediate step to address scale-invariance\n",
        "scaler = StandardScaler()\n",
        "x_train_sc = scaler.fit_transform(x_train)\n",
        "x_test_sc = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "ehFYmdW9lNmu"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "zyK6de-9GgbW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69d37758-5490-4b91-e315-03cc859077e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "# train the logistic regression model\n",
        "lr = LogisticRegression(max_iter=100, solver='liblinear')\n",
        "lr.fit(x_train_sc,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "hqeC-jGndbP_"
      },
      "outputs": [],
      "source": [
        "train_pred_lr = lr.predict(x_train_sc)\n",
        "test_pred_lr = lr.predict(x_test_sc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "FU6ABcXeognd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "fb7b35d1-2e9b-4d6f-af72-e0056ae0a0cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true=y_train, y_pred=train_pred_lr)\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Logistic Regression model (Training dataset)', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "ZLuW1gDAdUBd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "663089ee-e15e-416c-b1dd-e18fdc45fc68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for Training dataset:\n",
            "\n",
            "precision: 0.7971041355473903\n",
            "accuracy: 0.7719289796442561\n",
            "recall: 0.8817435142860589\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Training dataset:\\n\")\n",
        "print(\"precision: \"+str(metrics.precision_score(y_train, train_pred_lr)))\n",
        "print(\"accuracy: \"+str(metrics.accuracy_score(y_train, train_pred_lr)))\n",
        "print(\"recall: \"+str(metrics.recall_score(y_train, train_pred_lr)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "_-rgeM_0ouTx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "b499e4ab-f756-4a4d-ec5a-749a4f9ea5dd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=test_pred_lr)\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Logistic Regression model (Test dataset)', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "7GK5gOepdUee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14691858-12e5-4ba0-9b25-a9ccc6aa9fe4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for Test dataset:\n",
            "\n",
            "precision: 0.7942088483207407\n",
            "accuracy: 0.7690875232774674\n",
            "recall: 0.8799903218001451\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Test dataset:\\n\")\n",
        "print(\"precision: \"+str(metrics.precision_score(y_test, test_pred_lr)))\n",
        "print(\"accuracy: \"+str(metrics.accuracy_score(y_test, test_pred_lr)))\n",
        "print(\"recall: \"+str(metrics.recall_score(y_test, test_pred_lr)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G88UQNpAJLMf"
      },
      "source": [
        "##**3.3 SVM's LinearSVC classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "3Nm-XjWpJO8q"
      },
      "outputs": [],
      "source": [
        "from sklearn import svm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "FHbq71WVJpTy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bef8246d-9b31-4a16-f142-44eff3f8e5ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC(random_state=0, tol=1e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "# train the Linear SVC model\n",
        "svmmodel = svm.LinearSVC(random_state=0, tol=1e-5)\n",
        "svmmodel.fit(x_train_sc,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "8Y9tnsOkQcR2"
      },
      "outputs": [],
      "source": [
        "train_pred_svm = svmmodel.predict(x_train_sc)\n",
        "test_pred_svm = svmmodel.predict(x_test_sc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "TKX7C7lIpDBa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a1a1c602-5329-4c4d-ae42-4e40c7859930"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_train, y_pred=train_pred_svm)\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for LinearSVC (Training dataset)', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "w03ZjMDPaqBz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa758925-7573-4c9d-ee3e-46a710edea2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for Training dataset:\n",
            "\n",
            "precision: 0.7954727434049644\n",
            "accuracy: 0.7728520516278173\n",
            "recall: 0.8866643349052622\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Training dataset:\\n\")\n",
        "print(\"precision: \"+str(metrics.precision_score(y_train, train_pred_svm)))\n",
        "print(\"accuracy: \"+str(metrics.accuracy_score(y_train, train_pred_svm)))\n",
        "print(\"recall: \"+str(metrics.recall_score(y_train, train_pred_svm)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "WIl8fc3JpD6o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "adad6c77-b2ff-4063-b2ef-17d744b366d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=test_pred_svm)\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        " \n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for LinearSVC (Test dataset)', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "qS-j_mE-b2vq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2271786-552d-408d-e254-cfd737a0df49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for Test dataset:\n",
            "\n",
            "precision: 0.7924724655277079\n",
            "accuracy: 0.7696333397547036\n",
            "recall: 0.8843939027340915\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Test dataset:\\n\")\n",
        "print(\"precision: \"+str(metrics.precision_score(y_test, test_pred_svm)))\n",
        "print(\"accuracy: \"+str(metrics.accuracy_score(y_test, test_pred_svm)))\n",
        "print(\"recall: \"+str(metrics.recall_score(y_test, test_pred_svm)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lq7DQ08urhTG"
      },
      "source": [
        "##**3.4 Random Forest classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "sf_R41vCrtpC"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "hPuN6n3Mr3Qg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25d0adfe-4182-4144-c3c9-016c6841551c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=28, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "rf = RandomForestClassifier(max_depth=28,random_state=42)\n",
        "rf.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "Ng_7oB2W8yP5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "eea25b0e-b73c-4c7c-97ec-ea5c42fb37fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        overall_rating  attacking_work_rate  defensive_work_rate  crossing  \\\n",
              "111078            72.0                    6                    6      44.0   \n",
              "161955            72.0                    6                    6       8.0   \n",
              "159061            72.0                    9                    9      71.0   \n",
              "104033            79.0                    6                    6      57.0   \n",
              "74601             73.0                    6                    6      52.0   \n",
              "...                ...                  ...                  ...       ...   \n",
              "127076            57.0                    9                    6      52.0   \n",
              "109900            78.0                    6                    9      57.0   \n",
              "139864            74.0                    9                    6      82.0   \n",
              "155675            64.0                    6                    6      62.0   \n",
              "129285            69.0                    9                    9      59.0   \n",
              "\n",
              "        finishing  heading_accuracy  short_passing  volleys  dribbling  curve  \\\n",
              "111078       26.0              73.0           61.0     39.0       54.0   31.0   \n",
              "161955       13.0              11.0           22.0      7.0       11.0   12.0   \n",
              "159061       63.0              37.0           80.0     68.0       66.0   73.0   \n",
              "104033       33.0              76.0           74.0     31.0       61.0   46.0   \n",
              "74601        42.0              61.0           79.0     26.0       56.0   51.0   \n",
              "...           ...               ...            ...      ...        ...    ...   \n",
              "127076       35.0              45.0           52.0     47.0       60.0   51.0   \n",
              "109900       59.0              70.0           74.0     56.0       65.0   60.0   \n",
              "139864       48.0              58.0           76.0     56.0       64.0   85.0   \n",
              "155675       33.0              59.0           68.0     54.0       60.0   55.0   \n",
              "129285       47.0              44.0           74.0     60.0       71.0   53.0   \n",
              "\n",
              "        ...  long_shots  aggression  interceptions  positioning  vision  \\\n",
              "111078  ...        21.0        68.0           71.0         31.0    55.0   \n",
              "161955  ...        11.0        20.0           21.0         14.0    29.0   \n",
              "159061  ...        70.0        28.0           43.0         71.0    78.0   \n",
              "104033  ...        35.0        80.0           80.0         40.0    45.0   \n",
              "74601   ...        51.0        75.0           69.0         47.0    71.0   \n",
              "...     ...         ...         ...            ...          ...     ...   \n",
              "127076  ...        47.0        51.0           56.0         54.0    50.0   \n",
              "109900  ...        75.0        82.0           81.0         52.0    78.0   \n",
              "139864  ...        79.0        76.0           71.0         65.0    73.0   \n",
              "155675  ...        53.0        62.0           64.0         53.0    64.0   \n",
              "129285  ...        74.0        60.0           56.0         63.0    68.0   \n",
              "\n",
              "        penalties  marking  standing_tackle  sliding_tackle  age  \n",
              "111078       40.0     70.0             74.0            70.0   25  \n",
              "161955       22.0     10.0             13.0            16.0   27  \n",
              "159061       74.0     24.0             31.0            21.0   26  \n",
              "104033       31.0     80.0             80.0            84.0   21  \n",
              "74601        42.0     70.0             76.0            71.0   23  \n",
              "...           ...      ...              ...             ...  ...  \n",
              "127076       38.0     53.0             46.0            55.0   32  \n",
              "109900       61.0     72.0             83.0            80.0   25  \n",
              "139864       35.0     71.0             75.0            77.0   28  \n",
              "155675       54.0     55.0             62.0            61.0   22  \n",
              "129285       71.0     37.0             41.0            40.0   30  \n",
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          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "396XLJ75r3ys"
      },
      "outputs": [],
      "source": [
        "train_pred_rf = rf.predict(x_train)\n",
        "test_pred_rf = rf.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "nhNHNvGYr4Jr",
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          "height": 0
        },
        "outputId": "634d94cb-6061-4945-961f-5f47ab481575"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_train, y_pred=train_pred_rf)\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Random Forest model (Training dataset)', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "_pqrzsrzr4ir",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d532700-de06-477c-9529-37b671a01df0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for Training dataset:\n",
            "\n",
            "precision: 0.9973030569368024\n",
            "accuracy: 0.997559879278238\n",
            "recall: 0.999035133211921\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Training dataset:\\n\")\n",
        "print(\"precision: \"+str(metrics.precision_score(y_train, train_pred_rf)))\n",
        "print(\"accuracy: \"+str(metrics.accuracy_score(y_train, train_pred_rf)))\n",
        "print(\"recall: \"+str(metrics.recall_score(y_train, train_pred_rf)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "kM7OZ2jDsRrj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cf8f8e20-dac7-4709-ebcb-43f924aa66e1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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zHvgW+bk4577BbwyY2e74bO8e/P3m/PwO7G1mZeKvFoJqpiZs+/kKMxRfvRn7Oz/74u+H93PO3RXfw8zCGscVmnFGcD/+ntyNwee1oKFhkRthxUnJOnDOZZrZ7fhl3ht/Txp8FfMuQMf42w5BTNXw7SKiWIZvW9Q0pF/YFdjv+IPPPuRtJBUrH3alnCq/4999Utk5l3jl3AyflC3PO1gevwEnAH8658JqYXIJku4xwQcz2xffbuF2fHsMKJ59IRlF2bZ/BxqbWen4Cwkzq0PeGsZYw95cifHWcs5tNrMZRLvlHItpbbIxOeem4y9qHwoalk4GHjCzQcGtoqKut9j+kOw+dh4+aTgm/oLKzMKGLyiWbvjE40TnXM4+aWY7E1KbkcQ2OzuYXtkkl2Uyy2kvfFuKQhuhJ5M5PReM6Hoz6xRWwMwONP9EBvjqsbXAf82sYlyZivjHRdewpQptW4lVc+a6r2dmZ+NbssZ3C2sxPh9/0C4so34H3/gs8SR8cdB9W9dihHkN/wzv1S5vq/94sQNLrisdM2tBeLuBNUH/KFcVeZjZifiT7XDn3EP42o8m+Oq5rZHKdTACf/C+Pu7ebH7L+WK2vOuiyIIDxwdAazM7Jm68RvhL3d4Jvm8JysTKt8A3TBsftHlIF+/gjz83x3cMtpv98e1nNicxnhHB933m3wGSi5nVivs7bN//FX+VFr/db9N9oQiKsm2/i6+iPj+h7E0h4x2JT2b7mn+/Sy7BPfmMiDGPIVr7qk/xDQFvDlvO5tt9VQz+rprYPiZIPP/At/GKtW2LPUWQ1Hpzzi3BPyHRycyaxE27LP7Ylegf/Am4VFxZw5/MExUUS+gxA3+Rnms+k9lmnX+S4yPgVDP7T2Jh8+JvfySznP4DTHVJvIel0JoI59w6M+uAf4zmHTP7DJ8ErMBv2Mfgq6wfDMqvNLMb8Q2zJtuW9/t3x2c3lzr/qMk245ybaWajgUuDlToN/4hQF3yWtlNc8dvNrD3+AP0HfkWegs9GwxqsxXsQ/9jRIDM7AF97sT/+XtTMJIbfakED1T5JFJ2Br8a90cxiT2Q0wT9K+BNwYEL5SfhHFQeb2Yf42pvJ8ZlysoKroeH4q41eQdwfmNkTwNVm9qlz7rWijjeQsnXgnNtkZvfjHzG8Gn/74mN8NfsIMxuIv096OHAS/mmkrWnAdzu++vYDM3sSn+yegt/vEmMbZWYj8Y+nVQnaaMQe8czGPw2QTobhHze+yfxrisfijw9X4BsMJlPriXPuW/OvTu4DTDOz1/HV4HXw2/hJ+Nb2AM+aWT18VfJc/KOQZ+LbX8Q3UNxm+0IRFWXbfhB/RfusmR2I39fb4BsK5qrBcc7NN7PL8ReEM8y/+GkufjtqiW+k2Yxob3l8Hb+NnYBPVpLinFtrZufjE6eZZjYUf6yujD8Wn4o/fo/BJ0q9g1uKs/Hr42j8eWdk7NYi8C3+9spt5l+bvxb4wzk3uYBQrg2mMcHMBrHlEc+w/fYNfHu0L8zsRfx5pTM+kUmcvxVmNhs4y8z+h9+m1zrn3scng72Bj8xsCL6dVTt87XFi7Vuy2+zl+Ea8Y4PYvscnJHsCnYKyfYKysdcY9Df//06ygelBTQ9m1gjfjuL6fJdawswm+yhMhWDGx+MPlBuDBfMhvpqndEL5LvjHZ9YGn6+BziHjnUP4S0jakPA4Z1i3uH618Rv03/hM62N81e4Ycj/i2QZ/X2sOPpvLxFeL9ST3I1jdCX/EsAa+kcv8YBnMxydM1RPKhQ4f9MsVUwHLvGEwjoFJlA17xLNBsEyW4U903wTrpQ8Jj/jgN7iHg/mJZdzdg37DKOCRN+IeNwzGMxq/YbZKKFcW/+z7KmCPQuanoOW31eugkGk7Ql42FfTbCb8z/wXsGnQ7ii0vOlqJ3ydahK1nirC9B91b4g8gRXnZ1Az8lWcm/iCd78umkt02i7IsKfrLpn7HH0iX4msWGiSzbBLKnIy/us0M5n0e/hhwWVyZU/Et2OcHZZbhX+LTNWFc+e4LBUw/v/Xah5DH6fJbB8lu20HZ2AvB/ia5l00djj+BLWXLy4i+xLcvKFfYNlDAvP8MvJ/EPhUWUwv8vfzYS5eW4M8VdwBVgzKt8Bcls/H7wd/4Jxmuw799Nn58F+AbRW4gZB/JJ7ajgmlmB9MfRMjLpoKyF7PlZW6L8E/SVA2bFv5lf7EXLjpyn4c6429JxBq8vxasz1zrLtltNihbHXgIXzOfjT8W/YRvE9csoeyN+P1uY+J84p+kyybkBWdhHwsGEhERKTIzOwufCDR3zkV9kZukAfOvPfgdeM05d21SwyiJEBGRrWFmE/G3DrqlOhaJzsyuwf9DtUYu/PHrvMMoiRAREZEoivSeAJEdmZmdYGYzzWy2md1c+BDyb2JmQ81sqZlNT3UsIulCSYQIOf8ifBD+aYhmwNm29W/Ekx3LMPxTCCISUBIh4h0MzHbO/e78u/Nfwz8aJQKAc24s0f5PhMgOS0mEiLcbwb+WDswPuomISD6URIiIiEgkSiJEvAXA7nG/6wXdREQkH0oiRLxv8f/QaI/g3fln4d8UJyIi+VASIYL/vxj4/5fwKf610SOdcz+nNipJJ2b2KjAR/58255tZj1THJJJqetmUiIiIRKKaCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRBJYGaXpDoGSV/aPkS2UBIhkpdOElIQbR8iASURIiIiEoneEyFUr1rZNaxXJ9VhpI1lmSupUbVyqsNIHzuVT3UEaWXZ8uXUqF491WGkjanffb/cOVcj1XFIapRJdQCSeg3r1eHb919MdRiSruq2SHUEksZKla0wN9UxSOrodoaIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKIkQERGRSJREiIiISCRKIkRERCQSJREiIiISiZIIERERiURJhIiIiESiJEJEREQiURIhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKIkQERGRSJREiIiISCRKIkRERCQSJREiIiISiZIIERERiURJhIiIiESiJEJEREQiURIhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKImQ7d7c+Ys49+o7qHlAe8o3OYJWJ3Rj2OsfFDjMF19/S6mGB1Oq4cHMnjMvT/+167K49u7H2O3gkyjf5AgOOOlcXnvvs9BxLc9cyUU33E3NA9qzc9MjOfzUHowe/802mTfZemvWrOGuvndzcsfO1Kxbn1JlK3D7nX1Cy27atIm7772fPZvsQ/mKVdinRSsGDn4K51yesjNm/MqZZ59L/T0bs/Ou1di72b7cdMvtZGZm5io3c+Ysrr3hJo5scxy7VK5OqbIVGP35F8UxqyIlrkyqAxDZGgsWL+U/nS8ke/0GenU/gzo1q/PB6HFcdEM/Vv69mmt6nJ1nmI0bN9HrjofYuUJ51q7LytPfOUfXS2/ky0lTuerCs2iyR31e/3A03a66nQ0bN3J+15NzymZnr+e4blcwZ/4iru3ZjZrVqjB05Huc1P1qPn3xSY45rHWxzr8UbvnyFdx97/3Uq7cb+7faj1GjP8+37OW9ruL5ocPo2eNCDj6oNaNGfc5V11xHZuZf3Hn7rTnlZs6cxcGHHUnVqlW47JKLqVGjOlOmfsejjz/BqNGj+XbSBEqXLg3AxMmTGfDkIPZu0oQWzZvxzbdTin2eRUqKkgjZrj3w1HCWrviL8W88y6EH7gvAFeedRuee13PHI09z3qknUq1K5VzDPDzkJTJX/U3PszrxxNDX8ozzvVFj+WzcZAb0vZ5eF5wBQI8zO3LkaRdz4/1PcmaHdmRklAXgmVfe5qdfZ/Pe84/Q4bgjAbjgtA60bH8Wvfs9yrRPXinO2Zck1KlTm/lzZlO3bl3mzJnLnk32CS03bdoPPD90GNf2vpqH+98PQM+LLsS6ncf9/R/i4h4XUqdOHQCef2E4a9eu5euxX9KyZQsALu5xETvvvDOPP/Ek30+bRusDDwSgY4eTyVy6kEqVKjHsxRFKImSHotsZsl0bN/l7GjWol5NAxJzT5QTWrsvinc++ytX9zwWLuXfgUO6/8Up2rbhL6DhHfjCK8uUy6Hlmp5xupUqV4orzT2fp8ky++HpKrrJ77F43J4EAqFC+HBed2ZEff53NjNl/bIvZlK2QkZFB3bp1Cy038o03Abjqyitydf9vrytYv34977z3fk63v1f/DfgEJV6d2v53hfIVcrpVrVqVSpUqRQteJM0pidgBmdkJZjbTzGab2c2pjqc4rd+wkQrlMvJ037lCeQCm/DgjV/dr+j5Cy733ovvpHfId55SfZrDfPo0plzDeQ1o1B2DqdD/OzZs38/3PszikVYs844h1m/rTr0WYG0mlqd99T+3atahff/dc3Q9qfSClSpVi6nff53Q7tk0bAC7qeSnfff898+fP5+133uXhRx+na5fONGsWXtshsqPR7YwdjJmVBgYB7YD5wLdm9p5z7pfURlY8mjZqwCdfTWLx0uXUrlk9p/uXE6cCsHDJspxuH34xnvdGj2PSO0Mxs3zHuXDJclruvVee7nVr1QBgwWI/zsyVf5O9fj11alUPKVs9KLs0wlxJKixcuIi6we2KeGXLlqVatWosXLgwp9vpp53Kz7/8wiOPPcEHH32c071njwt5auCAEolXJB0oidjxHAzMds79DmBmrwGdgB0yibji/NN5d9RYTrv8Zh689Srq1KzG+6PH88zLbwGwLisb8A0gr+7zCBeecQqt921W4DizsteTUXanPN3LBe0gsrPX55QDyChbNqRsRq4ykv6ysrOoVKliaL9y5TLICrYlADOjYcMGtDn6SE7pcDK1atZk4qRveOLJgWSty+LFYc8XmKiK7CiUROx4dgPin1mcDxySWMjMLgEuAai/W+3E3tuNdkcewrP9b+OGewdwRNeeAFSuVJFBd9/IBdf2oeLO/t70/YOH89eq1dx/45WFjrN8uQzWb9iYp3v2+g0AObc5ygff6zdsCCm7PlcZSX/ly5Vn/frwpC87ez3ly5fL+T1g4GDu6ns3v06fRq1atQDo3KkjDRrsTq+retP11M507tSxROIWSSW1ifiXcs4Ncc61ds61rlG1cuEDpLEeZ3Zi0bcfM/ndYUx48zkWfPMhBwW1DY33qM+ipct58OkXuaRbF9asXceceQuZM28hK/9eA/jbE/MWLskZX91a1XPdBomJdYvd1qhauRLlMjJYtGR5SNnlucpK+qtbtw4LFy3K033Dhg2sWLEi58kMgMcHPMlhhx6Sk0DEdO3SGYCvxo0v3mBF0oRqInY8C4D4lmH1gm47tIyMshy035bbFJ+NmwxA+6MOYcmyTNZv2ED/p4bT/6nheYY95qzLqFZlV5Z9PwqAA1vsw7ujviI7e32uxpWTp/3s+7dsCvgnNlo1a8zkadPzjDPWLVZW0t8B++/PqNGf8+ef83I1rvx2ylQ2b97MgQfsn9Nt4cJFNGncOM84Nm3aBMDGjXlrskR2RKqJ2PF8CzQ2sz3MrCxwFvBeimMqUYuWLqf/U8M5sGVTjj3sIPbYvS5vPfNgns+ZHdoBMPiem3jx0b45w5/RoS1Z2et57v/ezem2efNmBr/4OtWrVubYQ1vHlW3HH/MW8sHn43K6rcvKZuj/vUeLvRvRrPGeJTDHsi2ccdqpAAwYNDhX9ycHDqZs2bJ07nhKTreme+/N+AlfM2fO3FxlR7z8KgCtDzygmKMVSQ+qidjBOOc2mVkv4FOgNDDUOfdzisMqNouXLuek7tfQqf3R1KtTkz8XLGHIK2/hgBGP9cPM2LXSLnQ+vk2eYaf9MqZ3sQQAABj9SURBVAuAtkcczF4Nt1x5dmp/NMcdfhDX3fM4fy5cTOOG/o2VE7/7iaEP3ZmrduLSc7rw/P+9yzlX38m1PbtRq3pVho58jz8XLuHj4U8U9+xLkgYOfoqVK1exctUqACZ8PZF77nsA8C+D2nffluy/fysu7H4+jz0+gNWrV+e8sXLkG29y5+235nrXxG233MSZ3c7l0COP5rJLLqZ27VpMnDiZES+/QvNmzTjrjNNzyq5atYonBz0F+BdaAbzy6mtMmuxfjf7fKy9n1113LZHlILKtWdg74eXfpfW++7hv338x1WFEsmbtOrpf15fJ035m6YpMqlepzInHHEafay6mXp1aBQ7b57Eh9HviOWaNeTNXEhEb7+0PP83ID0bx16rVNG3UgBsuO49unU7IM56lyzO56f4nef/zcazLWk+rZo3p0/sS2h/1n206rylTN+97MLY3ezRuyty5f4b2G/rcM3Q//zzA34a474EHGfbiCBYtWkzDBg244vJL+e+Vl+d52mLc+Anc3/9BfvxpOsuWLadOndp0OPkk+t11B1WrVs0pV9BbMgF+nzWDhg0bbIO5TI1SZStMdc7p/e7/UkoiZLtOIqQE7ABJhBQfJRH/bmoTISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKIkQERGRSJREiIiISCRKIkRERCQSJREiIiISiZIIERERiURJhIiIiESiJEJEREQiURIhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREpEGzOxgM7s4oVsnM/vJzBaY2X2pik1ERCQ/SiLSw11Ax9gPM6sPvArUBlYBN5nZhSmKTUREJJSSiPSwHzA+7vdZgAGtnHPNgM+AS1IRmIiISH6URKSHasCSuN/HA2OdcwuC3+8BjUs8KhERkQIoiUgPK4FaAGaWAfwHGBvX3wHlUxCXiIhIvsqkOgABYBrQ08xGA12AcsCncf33IHdNhYiISMopiUgPd+PbPXyDbwsxyjk3Ja5/B2ByKgITERHJj5KINOCc+9rMDsC3hVgFvBbrZ2bV8AnG2ykKT0REJJSSiDThnJsFzArpvgLoXfIRiYiIFEwNK0VERCQS1USkgJl9EWEw55w7bpsHIyIiEpGSiNTYE//YpoiIyHZLSUQKOOcapjoGERGRraU2ESIiIhKJkggRERGJRLcz0oSZVQF6AIcAVcib4KlhpYiIpBUlEWnAzBoAE4C6+JdNVQIy2ZJMLAfWpixAERGRELqdkR7uASoDx+H/W6cBZ+KTifuB1cCRKYtOREQkhJKI9HAc8Kxz7ku2PPppzrl1zrnbgJ+A/imLTkREJISSiPRQDZge/L0x+I7/19+jgHYlGpGIiEghlESkh2VA1eDv1UA20DCuf1lyJxUiIiIppyQiPfwM7Af+EQz8vwS/wszqm1lD4BLg15RFJyIiEkJPZ6SHd4HrzKy8cy4L6Ad8CvwR9HfAqakKTkREJIySiDTgnBsMDI77/YWZHQp0A/4B3nbOfZ2q+ERERMIoiUhTzrkpwJRUxyEiIpIftYkQERGRSFQTkQbMbGgSxZxzrkexByMiIpIkJRHpoXsSZRz+f2uIiIikBd3OSAPOuVKJH2AnYG/gWWAS/v9oiIiIpA3VRKQp59w/wG/ApWb2Pv6115cXy8TKVsDq7Vcso5bt38yxo1IdgoikKdVEbB8+AbqmOggREZF4SiK2D1WBXVIdhIiISDzdzkhjZlYZaAv0BqamOBwREZFclESkATPbzJZ/AZ6nN5AJXFtyEYmIiBROSUR6eJG8SYTDJw+zgFedc6tLPCoREZECKIlIA8657qmOQUREpKjUsDINmNmdZtaigP7NzezOkoxJRESkMEoi0kMfYN8C+rcA7iqZUERERJKjJGL7UA7YlOogRERE4qlNRIqYWSWgclynamZWP6RoVeAcYF6JBCYiIpIkJRGp0xuItXNwwOPBJ4wBN5ZEUCIiIslSEpE6Y4JvwycTbwM/JpRxwBpgknPu65ILTUREpHBKIlLEOfcV8BWAmTUAnnbOTU5tVCIiIslTEpEGnHMXpjoGERGRotLTGWnAzK40s9EF9P/MzC4tyZhEREQKoyQiPXQHfiug/yzgopIJRUREJDlKItJDY+CnAvr/HJQRERFJG0oi0sNO+BdK5adcIf1FRERKnJKI9DALaFdA//bA/0ooFhERkaQoiUgPrwLtzexuMysb62hmO5lZX3wS8UrKohMREQmhRzzTw2PAicBtwOVm9mvQvSn+tdfjgEdSFJuIiEgo1USkAefcRnxtw83AfGD/4DMP/7rr4/BvthQREUkbSiLShHNuo3PuQedcK+fczsFnf+BLYACwMMUhioiI5KLbGWnIzKoC5+LfDdESXwsxK6VBiYiIJFBNRBoxs+PN7P+ABfh2EhlAX6Clc65pSoMTERFJoJqIFDOzhvgahwuAesBy4A2gG3Cbc+6tlAUnIiJSANVEpIiZnWNmnwOzgZuAKUAXYDegD2pIKSIiaU41EakzAvgduAZ41Tm3ItbDTPmDiIikP9VEpM56oCHQCTjBzMqnNhwREZGiURKROnXwtRDV8LUSi83seTM7Ct3KEBGR7YCSiBRxzq10zg10zh0AtAZewreJ+BIYDzhg1xSGKCIiUiAlEWnAOfedc+5KfO3Eefh//Q3wnJlNM7Pbzax56iIUERHJS0lEGnHOrXfOveKcOw5oBNwLVAH6AT+kNDgREZEESiLSlHNujnPuTnzjy5MAvS9CRETSih7xTHPOOQd8EnxERETShmoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKIkQERGRSJREiIiISCRKIkRERCQSJREiIiISiZIIERERiURJhIiIiESiJEJEREQiURIhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlEbJDmjNnDlYmI/TT85LLcspNmTKVa669jn33P5CKlatRe7f6tG1/Al988WWeca5Zs4a7+vTjpA4dqVF7N6xMBrffcVdJzpYU4KeZv3HfwCF07NmLA04+nSO6nsuF19/GpO9+yFN20z//MHjEqxzXrQf7Ht+FEy+4jJfefh/nXK5yS1dk8siQYXS/7lZan3IGTY/twOsffppUPG9+/BlNj+1A02M7sOmffwosO+CFl2h6bAeOPuOC5GdYJA2USXUAIsWpU8dTOK3rqbm67dWoUc7fDzz4EF+NHUfXUzvT64rLWbNmDS8Mf5Hj2p/AM08N4pKLe+aUXb58Of3uuZd69eqxf6v9GDX68xKbDyncs6++zrc/TKf9UYdxTucOrMvK4q1PRtP9+tvoe20vzuxwQk7Zvo8N4vWPPuP0k49n36ZNmDDle+558hlWrV7DleefnVPuj3nzefa1N6hftw5NG+3JlB+nJxXLyr9X8/CQYVQoV4512dkFlp27YCHPvfYmFcqVizbjIimkJEJ2aC2aN+fcc7rl27/31Vfx8ojhZGRk5HS7/LJLaXXgQdxy2x1cdGF3ypTxu0mdOnVY8Ocf1K1blzlz5rDHXnsXe/ySvO6ndebhW2+gbNmdcrqd3fEkOl98FY8+N5yuJ7ajTOnSzJj9O69/9BkXnt6Fmy7vAcDpJx9P7379eeblkZx+8vHUrFYVgOZN9mLi269QZddKTJ72Ixdce2tSsTz63HCqV6lM07325P3RYwose/eApzm4VUvWb9jAnwsWRZt5kRTR7QzZ4WVlZZGVlRXa7/DDD8uVQACUL1+eDiedRGZmJosXL87pnpGRQd26dYs1VonugBbNciUQAOUyMmhz6EGs+ns1yzP/AuDjMeMAOO/UU3KVPe/UU9iwcSOfj5+U022XChWosmulIsXx44yZvPHRZ9zW61JKly5dYNlPvhrP5O9/5NYrLynSNETShZKIHYyZDTWzpWaWXL3rDu6JJwdSoWJlKlSsTOOmzRj81NNJDbdw0SLKlClD5cqVizlCKW5Ll2dSpnRpKu6yMwDTZ86mRtUq1K1VM1e5Fns3plSpUvz82+zI09q8eTN9n3iK9kcexn8O2K/Asuuysnlg8HOc37Uje9avF3maIqmk2xk7nmHAQODFFMeRUqVKleK4Y4+hS+dO1N99dxYuWsRzQ1/gyv9ezR9/zOGhBx/Id9gZM2bw1tvv0PGUDuyyyy4lGLVsa/+bO49R47/mmMMOYefy5QFYtmIFNYLbFfHK7rQTlStVZMnyFZGn9+p7H/H7n/MY2O+2QssOHP4Kmzdv5oq4Nhgi2xslETsY59xYM2uY6jhSrX79+oz+7JNc3Xr2uIhj2x7Po48/wWWXXkyjuAaWMatWraLrGWdRoUIFHnvkoZIKV4rB6jVruarPfZTPyOCWK7Y0kM1ev4GdK1QIHSajbFmy16+PNL0Vf63kiaEjuLTbGdSpWaPAsr/9MZcX33yX+2+6Jie5Edke6XbGv5SZXWJmU8xsyrJly1MdTokoXbo01197DZs3b+bzkEc4s7KyOKXTqfz++x+8/cZI6tevn4IoZVvIXr+ey2/rx7yFi3my3225bl2UyyjLho0bQ4dbv2ED5RLayCTrwWeGUrlSJS4649RCy/Yb8BStmjXllLbHRJqWSLpQEvEv5Zwb4pxr7ZxrXaNG9VSHU2IaNGgAwPKEKusNGzbQpevpTJw0iZGvvUybNkenIjzZBjZs3EivO+9l2i+/8vidN3NIq31z9a9RrRrLVmSGDrfy79U5T2YUxc+zZvPuZ19wwWmdWJqZyfzFS5i/eAnrgga9CxcvZWkwzVHjvubbH6Zz4RldcsrNX7yEDRs2snnzZuYvXkLmylUR5lyk5Ol2hvyrzP7f/wCoGVfdvGnTJs44qxujRn/OiOEv0PGUU/IbXNLcpn/+oXe//nw9dRr9b7mWYw8/JE+Z5k0a8fXU71m4ZGmuGorpM39j8+bNNG+yV5Gnu2jpMsA/rnn3gLyNd9ufdzHNm+zFm08/zsIlvuyVd9wTOq623XrQ/qjDGNAnucdJRVJJSYTskJYuXUrNmrlb32dnZ3PfA/0pU6YM7du1BXxr+nPP7867773PkKcH0+3ss1IRrmwDmzdv5sb7HubzCZPod20vTjmuTWi5E9scybOvvsGIt97PeU8EwIi33menncpw3OH/KfK0991n79DGlCPeep/J035kQJ9bqbxrRQCOOfRgdqtdM0/ZJ4aOYFnmSu65/r/Uql6tyDGIpIKSiB2Mmb0KtAGqm9l84C7n3POpjark3XjzrcycOYt2bY9j993rsXjxEka8/DK//Tabe/r1zWnvcP0NN/F/I1/n6KOOonz58rz08iu5xtOu7XHUqlUr5/fAQYNZuXIVK1euBGD8hK+55977Aeh4Sgf23bdlCc2hJOr/9PN89OU4DtqvBeUyMnhvVO52L4cd2IrqVavQrHEjup7YjmFvvMParKycN1Z+PGYcV55/dp4T+FMjXgNg/uIlAHw16ducd050bHcsu9WuSc1qVWl7xKF5Yho9wb9z4tjDD6FM8M6I+rvVof5udfKUHf7mu/y9Zm3oeETSlZKIHYxzTs+LAce3b8fcuX8y5LnnyczMpEKFCuzfqhUP3Hcvp3bpnFPuu++nAfDV2LF8NXZsnvF8OfqzXEnEw48+zty5c3N+xw9Xr95uSiJS6Jff/K2qb3+Yzrc/5H1NyvBH76N61SoA9Ol9JXVq1uCtT0bz9qej2a1WLW7tdQnndcl7K+uJF17K9Xv0hEk5ycEBLZuF1iqI/FtY4j+ckX+f1q0PdFMmT0x1GJKmZo4dleoQJI01PbbDVOdc61THIamhpzNEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKIkQERGRSJREiIiISCRKIkRERCQSJREiIiISiZIIERERiURJhIiIiESiJEJEREQiURIhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCJREiEiIiKRKIkQERGRSJREiIiISCRKIkRERCQSJREiIiISiZIIERERiURJhIiIiESiJEJEREQiURIhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpEoiRAREZFIlESIiIhIJEoiREREJBIlESIiIhKJkggRERGJREmEiIiIRKIkQkRERCIx51yqY5AUM7NlwNxUx5FGqgPLUx2EpC1tH7k1cM7VSHUQkhpKIkQSmNkU51zrVMch6Unbh8gWup0hIiIikSiJEBERkUiURIjkNSTVAUha0/YhElASIZLAObdDniTMrKGZOTPrU1C34prWjmJH3T5EolASIVLMzKxNcEKN/6wxs6lmdrWZlU51jFEEiUIfM2uV6lhEJDXKpDoAkX+RV4GPAAPqAt2Bx4HmwCUpimkuUB7YFGHYhsBdwBxg2jYcr4hsJ5REiJSc75xzL8V+mNlTwAygp5nd4ZxbkjiAmVV0zq0uroCcf8Y7e3sZr4ikF93OEEkR59zfwER8zcSeZjbHzMaY2f5m9qmZrQJ+jJU3s8ZmNsLMFpnZhqD8Q2a2c+K4zewIM5tgZllmtsTMBgK7hJTLt+2CmXUN4llpZuvMbKaZDTCzsmbWHfgyKPpC3G2aMQWN18zKmNlNZvaLmWWb2Qoze9vMWuYXl5l1MLNvg/KLgnkuk1C+uZm9bmYLzGy9mS02sy/N7OQkVoWIRKSaCJEUMTMD9gp+xt6AWB/4AngdeJPgxG9mBwbdVwLPAAuA/YCrgMPN7Gjn3Mag7CHAaGA10D8Y5izgxSLEdi9wK/AL8BiwCGgEdAXuBMYC9wVlhgDjgkHz1KYkeBk4AxgFPAXUBq4EJprZkc657xPKnwRcATwNDAU6AdcDfwXTx8yqBcuGoNxc/FslWwOHAB8mO98iUjRKIkRKTgUzq46veagD/BefCExyzv3mcwr2AC52zj2XMOxQ/In8oPjbG2b2OfAWcA4wLOj8GL6W8XDn3Kyg3GBgfDJBmtnB+OTgS+Ak51x2XL+bAZxzK81sVFBuYvxtmgLG2w6fQIwEzgpueWBmI4GpwADgyITBmgPNnXNzgrJPAz/hl919QZnDgZrAmc65kcnMo4hsG7qdIVJy+gLLgKXAD8BFwHtA57gymcAL8QMFVf37Aq8AGWZWPfbBJwZrgfZB2ZrAocC7sQQCwDm3AZ9cJOOc4PuW+AQiGI+Lnfwj6BJ83xs/DufcD8D7wBFmlvg/GN6JJRCx6eOTm9pmFrs9syr4PtHMKkWMTUQiUBIhUnKGAO2AtvgTfQ3nXKeEBpX/c879kzDcPsF3LAmJ/ywFdgZqBWX2DL5/DZn+L0nG2Rhw+ERnW9oD2IxvTJro57gy8X4PKbsi+K4G4Jz7Cn+rpjuwPGgL0tfMmm11xCJSIN3OECk5vznnRhdSZl1INwu+HwE+yWe4vyJHFc4Fn1RLTKjixZYLzrkLzOwh4ET8LZHrgNvM7Brn3MBijlHkX0tJhEj6+y34/ieJJOSP4LtpSL9kr8xn4U/G+wHfFFCuqEnG7/jaz32Ie+okIbY/iMg5Nx2YDjxkZpWBycADZjZoK27BiEgBdDtDJP19jz85XmZmeyb2DB6brAoQ3BqZBHQysyZxZcoCvZOc3ivB933BcInTi9UArAm+qyY53neC71vixoGZtQA6AuOdc8uSHFd8PFXNLNexzDm3Ep+QVADKFXWcIpIc1USIpDnnnDOz8/CPMf5oZkPxbQgq4B8RPRW4hS1PZ1wLjAEmmNkgtjzimdT+7pz7xsz6AzcB35nZ/wGL8e0VTgMODsb5C/4x0ivMbF3Qbalz7ot8xjsqeBLjLKCKmX3Alkc8s/GPq0ZxPtDbzN4GZgMbgaOB44GRzrmsiOMVkUIoiRDZDjjnppnZ/vhkoSNwGf4EPgefPHweV3Zi8DjlA8DN+KcX3sC/l+GnJKd3s5n9APQCbsTXWs7Dv7Z7XVAmy8zOAu7Bv747A/iKLe9sCHMO8B2+EeQj+CdLvgLucM4lFVuIMcD+QAf8o7P/4GshrgfUHkKkGJluFYqIiEgUahMhIiIikSiJEBERkUiURIiIiEgkSiJEREQkEiURIiIiEomSCBEREYlESYSIiIhEoiRCREREIlESISIiIpH8P+Kay/wAbkhIAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "conf_matrix = confusion_matrix(y_true=y_test, y_pred=test_pred_rf)\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
        "for i in range(conf_matrix.shape[0]):\n",
        "    for j in range(conf_matrix.shape[1]):\n",
        "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
        "\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix for Random Forest model (Test dataset)', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "TPT68q-GsXuQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43308500-6e06-4fb5-ca7c-ac3872636847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for Test dataset:\n",
            "\n",
            "precision: 0.9490694935217904\n",
            "accuracy: 0.9485648237333847\n",
            "recall: 0.9747882893781756\n"
          ]
        }
      ],
      "source": [
        "print(\"Metrics for Test dataset:\\n\")\n",
        "print(\"precision: \"+str(metrics.precision_score(y_test, test_pred_rf)))\n",
        "print(\"accuracy: \"+str(metrics.accuracy_score(y_test, test_pred_rf)))\n",
        "print(\"recall: \"+str(metrics.recall_score(y_test, test_pred_rf)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIu9H27ODmfB"
      },
      "source": [
        "Comparing the models, we can see that Random Forest performs the best and give us an accuracy of 94%. \n",
        "\n",
        "Now, We will use our Random Forest model to predict on all player's for the latest season. "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.5 Model Performance Review"
      ],
      "metadata": {
        "id": "ka3L7KGrP6NQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xlr = [1,2,3]\n",
        "xsvm  = [1,2,3]\n",
        "xrf  = [1,2,3]\n",
        "\n",
        "\n",
        "ylr = [(metrics.precision_score(y_test, test_pred_lr)),(metrics.accuracy_score(y_test, test_pred_lr)),(metrics.recall_score(y_test, test_pred_lr))]\n",
        "ysvm = [(metrics.precision_score(y_test, test_pred_svm)),(metrics.accuracy_score(y_test, test_pred_svm)),(metrics.recall_score(y_test, test_pred_svm))]\n",
        "yrf = [(metrics.precision_score(y_test, test_pred_rf)),(metrics.accuracy_score(y_test, test_pred_rf)),(metrics.recall_score(y_test, test_pred_rf))]\n",
        "\n",
        "xticks=['precision','accuracy','recall']\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(xlr,ylr,'bo', label = 'LogReg', marker = 's')\n",
        "plt.plot(xsvm,ysvm,'go', label = 'SVM', marker = 'o')\n",
        "plt.plot(xrf,yrf,'ro', label = 'Random Forest', marker = '^')\n",
        "plt.xticks(xlr,xticks )\n",
        "plt.title(\"Comparison of Model Metrics\",fontsize=18)\n",
        "plt.xlabel('Measure', fontsize=14)\n",
        "plt.ylabel('Percentage', fontsize=14)\n",
        "plt.legend(bbox_to_anchor=(1.02, 1),loc = 'upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "uWh-vzzHQAAx",
        "outputId": "91eacfe3-3b67-4415-af76-b376a7a48f25"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HD2bZnys7ZMu"
      },
      "source": [
        "# **4. Predictions on Final dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boSszrRB7elW"
      },
      "source": [
        "Now that we are done fine-tuning our model, it is ready to be used on our main and final dataset.\n",
        "To recall, our dataset had values from 2007-2016. We have trained our dataset to predict on player's future performance. This section will predict on all the player's (mentioned in the dataset) future performance in the next period/season of data. \n",
        "\n",
        "Previously we had separated out a final_players_df which had `peak_flg_2` column 'None'. Now we will fill this solumn based on our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "ypfSNbrI8MaQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "df0a9500-7aa6-4f68-cefc-1bf549a7a846"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        overall_rating  attacking_work_rate  defensive_work_rate  crossing  \\\n",
              "0                 74.0                    9                    6      80.0   \n",
              "32                65.0                    6                    6      64.0   \n",
              "57                69.0                    6                    6      57.0   \n",
              "79                70.0                    6                    6      46.0   \n",
              "103               77.0                    6                    6      77.0   \n",
              "...                ...                  ...                  ...       ...   \n",
              "165028            64.0                    6                    6      65.0   \n",
              "165035            75.0                    6                    6      57.0   \n",
              "165055            73.0                    3                    6      42.0   \n",
              "165067            63.0                    9                    6      65.0   \n",
              "165082            78.0                    6                    3      77.0   \n",
              "\n",
              "        finishing  heading_accuracy  short_passing  volleys  dribbling  curve  \\\n",
              "0            53.0              58.0           71.0     40.0       73.0   70.0   \n",
              "32           58.0              60.0           66.0     52.0       66.0   67.0   \n",
              "57           21.0              68.0           65.0     48.0       56.0   38.0   \n",
              "79           34.0              73.0           65.0     33.0       47.0   42.0   \n",
              "103          72.0              64.0           79.0     77.0       78.0   77.0   \n",
              "...           ...               ...            ...      ...        ...    ...   \n",
              "165028       60.0              42.0           68.0     34.0       75.0   53.0   \n",
              "165035       40.0              73.0           66.0     39.0       56.0   36.0   \n",
              "165055       27.0              74.0           70.0     30.0       54.0   49.0   \n",
              "165067       44.0              59.0           63.0     49.0       57.0   67.0   \n",
              "165082       70.0              59.0           80.0     77.0       78.0   84.0   \n",
              "\n",
              "        ...  long_shots  aggression  interceptions  positioning  vision  \\\n",
              "0       ...        62.0        68.0           67.0         60.0    66.0   \n",
              "32      ...        57.0        60.0           55.0         64.0    68.0   \n",
              "57      ...        29.0        70.0           62.0         26.0    54.0   \n",
              "79      ...        26.0        66.0           68.0         29.0    41.0   \n",
              "103     ...        76.0        59.0           55.0         77.0    76.0   \n",
              "...     ...         ...         ...            ...          ...     ...   \n",
              "165028  ...        78.0        75.0           26.0         54.0    35.0   \n",
              "165035  ...        56.0        79.0           81.0         44.0    47.0   \n",
              "165055  ...        30.0        75.0           66.0         22.0    52.0   \n",
              "165067  ...        31.0        67.0           61.0         49.0    49.0   \n",
              "165082  ...        79.0        51.0           32.0         74.0    82.0   \n",
              "\n",
              "        penalties  marking  standing_tackle  sliding_tackle  age  \n",
              "0            59.0     76.0             75.0            78.0   27  \n",
              "32           61.0     23.0             22.0            24.0   25  \n",
              "57           37.0     72.0             71.0            68.0   34  \n",
              "79           45.0     75.0             73.0            71.0   36  \n",
              "103          81.0     28.0             34.0            35.0   30  \n",
              "...           ...      ...              ...             ...  ...  \n",
              "165028       76.0     39.0             26.0            24.0   28  \n",
              "165035       48.0     78.0             79.0            71.0   27  \n",
              "165055       50.0     76.0             77.0            72.0   36  \n",
              "165067       53.0     57.0             61.0            61.0   28  \n",
              "165082       78.0     22.0             31.0            30.0   30  \n",
              "\n",
              "[9361 rows x 32 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>overall_rating</th>\n",
              "      <th>attacking_work_rate</th>\n",
              "      <th>defensive_work_rate</th>\n",
              "      <th>crossing</th>\n",
              "      <th>finishing</th>\n",
              "      <th>heading_accuracy</th>\n",
              "      <th>short_passing</th>\n",
              "      <th>volleys</th>\n",
              "      <th>dribbling</th>\n",
              "      <th>curve</th>\n",
              "      <th>...</th>\n",
              "      <th>long_shots</th>\n",
              "      <th>aggression</th>\n",
              "      <th>interceptions</th>\n",
              "      <th>positioning</th>\n",
              "      <th>vision</th>\n",
              "      <th>penalties</th>\n",
              "      <th>marking</th>\n",
              "      <th>standing_tackle</th>\n",
              "      <th>sliding_tackle</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>74.0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>80.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>...</td>\n",
              "      <td>62.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>65.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>64.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>...</td>\n",
              "      <td>57.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>69.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>57.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>...</td>\n",
              "      <td>29.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>70.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>46.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>...</td>\n",
              "      <td>26.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>77.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>77.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>...</td>\n",
              "      <td>76.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165028</th>\n",
              "      <td>64.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>65.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>...</td>\n",
              "      <td>78.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165035</th>\n",
              "      <td>75.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>57.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>...</td>\n",
              "      <td>56.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165055</th>\n",
              "      <td>73.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>42.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>...</td>\n",
              "      <td>30.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165067</th>\n",
              "      <td>63.0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>65.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165082</th>\n",
              "      <td>78.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>77.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>...</td>\n",
              "      <td>79.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9361 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ef48905-e2ec-4ae3-bcd7-ca2e5946e6e1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ef48905-e2ec-4ae3-bcd7-ca2e5946e6e1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ef48905-e2ec-4ae3-bcd7-ca2e5946e6e1');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "# final_player_df\n",
        "final_player_test_df = final_player_df.drop(columns=['id', 'player_fifa_api_id', 'player_api_id', 'date', 'potential', 'player_name', 'date', 'birthday', 'height', 'weight', 'peak_flg', 'peak_flg_2'])\n",
        "final_player_test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "NNPxEYHr8oQA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f7bb6e2-db03-415e-c040-7efd899c2fdc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "rf.predict(final_player_test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHn_ZNIlB9D8"
      },
      "source": [
        "Let's convert the predictions into categorical values for better undertsanding. Like previously done, we change 1 to `RISE` and 0 to `DECLINE`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "UdEE-C9L_mRL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "outputId": "4cda8fdc-25fd-41ed-b157-2af170cf55fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  player_fifa_api_id         player_name  age  \\\n",
              "0            6              189615     Aaron Cresswell   27   \n",
              "32          39              186170         Aaron Doran   25   \n",
              "57          65              140161       Aaron Galindo   34   \n",
              "79          88               17725        Aaron Hughes   36   \n",
              "103        113              158138          Aaron Hunt   30   \n",
              "...        ...                 ...                 ...  ...   \n",
              "165028  183896              195840  Zouhaier Dhaouadhi   28   \n",
              "165035  183904              205705      Zouhair Feddal   27   \n",
              "165055  183925                2488      Zoumana Camara   36   \n",
              "165067  183938              164680        Zsolt Laczko   28   \n",
              "165082  183969              102359  Zvjezdan Misimovic   30   \n",
              "\n",
              "                       date peak_flg peak_flg_2  overall_rating  potential  \\\n",
              "0       2016-04-21 00:00:00     RISE       RISE            74.0       76.0   \n",
              "32      2016-01-07 00:00:00  DECLINE    DECLINE            65.0       67.0   \n",
              "57      2016-04-21 00:00:00  DECLINE    DECLINE            69.0       69.0   \n",
              "79      2015-12-24 00:00:00  DECLINE    DECLINE            70.0       70.0   \n",
              "103     2016-04-28 00:00:00  DECLINE    DECLINE            77.0       77.0   \n",
              "...                     ...      ...        ...             ...        ...   \n",
              "165028  2016-01-21 00:00:00     RISE       RISE            64.0       64.0   \n",
              "165035  2016-02-04 00:00:00     RISE       RISE            75.0       76.0   \n",
              "165055  2015-03-06 00:00:00  DECLINE    DECLINE            73.0       73.0   \n",
              "165067  2014-09-18 00:00:00  DECLINE    DECLINE            63.0       67.0   \n",
              "165082  2012-08-31 00:00:00  DECLINE    DECLINE            78.0       78.0   \n",
              "\n",
              "        attacking_work_rate  ...  vision  penalties  marking  standing_tackle  \\\n",
              "0                         9  ...    66.0       59.0     76.0             75.0   \n",
              "32                        6  ...    68.0       61.0     23.0             22.0   \n",
              "57                        6  ...    54.0       37.0     72.0             71.0   \n",
              "79                        6  ...    41.0       45.0     75.0             73.0   \n",
              "103                       6  ...    76.0       81.0     28.0             34.0   \n",
              "...                     ...  ...     ...        ...      ...              ...   \n",
              "165028                    6  ...    35.0       76.0     39.0             26.0   \n",
              "165035                    6  ...    47.0       48.0     78.0             79.0   \n",
              "165055                    3  ...    52.0       50.0     76.0             77.0   \n",
              "165067                    9  ...    49.0       53.0     57.0             61.0   \n",
              "165082                    6  ...    82.0       78.0     22.0             31.0   \n",
              "\n",
              "        sliding_tackle  player_api_id             birthday  height  weight  \\\n",
              "0                 78.0         155782  1989-12-15 00:00:00  170.18     146   \n",
              "32                24.0         162549  1991-05-13 00:00:00  170.18     163   \n",
              "57                68.0          30572  1982-05-08 00:00:00  182.88     198   \n",
              "79                71.0          23780  1979-11-08 00:00:00  182.88     154   \n",
              "103               35.0          27316  1986-09-04 00:00:00  182.88     161   \n",
              "...                ...            ...                  ...     ...     ...   \n",
              "165028            24.0         192132  1988-01-01 00:00:00  180.34     159   \n",
              "165035            71.0         282473  1989-01-01 00:00:00  190.50     172   \n",
              "165055            72.0          26357  1979-04-03 00:00:00  182.88     168   \n",
              "165067            61.0         111182  1986-12-18 00:00:00  182.88     176   \n",
              "165082            30.0          39902  1982-06-05 00:00:00  180.34     176   \n",
              "\n",
              "        predictions  \n",
              "0                 1  \n",
              "32                0  \n",
              "57                0  \n",
              "79                0  \n",
              "103               0  \n",
              "...             ...  \n",
              "165028            1  \n",
              "165035            1  \n",
              "165055            0  \n",
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          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "final_player_df.loc[:, ('predictions')] = rf.predict(final_player_test_df)\n",
        "final_player_df.loc[:, ('peak_flg_2')] = final_player_df['predictions'].apply(lambda x: 'RISE' if x == 1 else 'DECLINE')\n",
        "final_player_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iFaqK1qCNeH"
      },
      "source": [
        "Re-ordering column names so it's clearer to understand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "1K4PDbjYCSKJ"
      },
      "outputs": [],
      "source": [
        "final_player_df = final_player_df[['id', 'player_fifa_api_id', 'player_name', 'age', 'date', 'peak_flg', 'peak_flg_2', 'overall_rating', 'potential',\n",
        "       'attacking_work_rate', 'defensive_work_rate', 'crossing', 'finishing',\n",
        "       'heading_accuracy', 'short_passing', 'volleys', 'dribbling', 'curve',\n",
        "       'free_kick_accuracy', 'long_passing', 'ball_control', 'acceleration',\n",
        "       'sprint_speed', 'agility', 'reactions', 'balance', 'shot_power',\n",
        "       'jumping', 'stamina', 'strength', 'long_shots', 'aggression',\n",
        "       'interceptions', 'positioning', 'vision', 'penalties', 'marking',\n",
        "       'standing_tackle', 'sliding_tackle', 'player_api_id', \n",
        "       'birthday', 'height', 'weight', 'predictions']]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now see predictions for players that we had earlier picked out in section 2.4.\n",
        "\n",
        "`peak_flg` and `peak_flg_2` respectively represent a player's last recorded performance and predicted future performance (in the next period). \n",
        "\n",
        "For example, **Eden Hazard** was on the declining curve (see plot on section 2.4) but as of January 2016, has started to rise back. This makes a lot of sense since his Potential Rating (given by FIFA) is greater than his current Rating.\n",
        "\n",
        "Another notable case is **Virgil Van Dijk**, whose Potential Rating is greater than his current Rating, and whose skills started to raise (see plot in section 2.4). We can see that our model predicts that he will continue in the RISE stage over the next period.\n",
        "\n",
        "Finally, there are some cases such as **Santi Cazorla** and **Sergio Garcia**. They are currently in their RISE stage (as we can see in section 2.4), but our model predicts that their curve will change their slopes to DECLINE. \n",
        "\n",
        "*Thus, a scout studying the acquision of those players at that time should be cautious about the signing!*"
      ],
      "metadata": {
        "id": "ePYTu-AAJaa0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_player_df[final_player_df['player_name'].isin(['Lionel Messi', 'Santi Cazorla', 'Sergio Garcia','Gerard Pique', 'Cristiano Ronaldo', 'Edinson Cavani', 'Eden Hazard', 'Virgil van Dijk', 'Andres Iniesta'])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "tweFaAjeJqGW",
        "outputId": "976c3566-166e-4280-92ba-a46f8c8e161f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  player_fifa_api_id        player_name  age  \\\n",
              "11425    12774                  41     Andres Iniesta   31   \n",
              "29861    33331               20801  Cristiano Ronaldo   30   \n",
              "42322    47248              183277        Eden Hazard   25   \n",
              "42761    47726              179813     Edinson Cavani   29   \n",
              "56519    62978              152729       Gerard Pique   28   \n",
              "92008   102483              158023       Lionel Messi   28   \n",
              "141600  157740              146562      Santi Cazorla   32   \n",
              "144682  161136              135697      Sergio Garcia   32   \n",
              "159684  177844              203376    Virgil van Dijk   25   \n",
              "\n",
              "                       date peak_flg peak_flg_2  overall_rating  potential  \\\n",
              "11425   2015-10-16 00:00:00  DECLINE    DECLINE            88.0       88.0   \n",
              "29861   2015-10-16 00:00:00     RISE       RISE            93.0       93.0   \n",
              "42322   2016-01-28 00:00:00  DECLINE       RISE            88.0       90.0   \n",
              "42761   2016-03-17 00:00:00  DECLINE    DECLINE            85.0       85.0   \n",
              "56519   2015-10-16 00:00:00  DECLINE    DECLINE            85.0       85.0   \n",
              "92008   2015-12-17 00:00:00     RISE       RISE            94.0       94.0   \n",
              "141600  2016-01-28 00:00:00     RISE    DECLINE            85.0       85.0   \n",
              "144682  2015-02-13 00:00:00     RISE    DECLINE            79.0       79.0   \n",
              "159684  2016-03-17 00:00:00     RISE       RISE            77.0       83.0   \n",
              "\n",
              "        attacking_work_rate  ...  vision  penalties  marking  standing_tackle  \\\n",
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              "92008                     6  ...    90.0       74.0     13.0             23.0   \n",
              "141600                    6  ...    85.0       80.0     56.0             62.0   \n",
              "144682                    6  ...    76.0       77.0     25.0             29.0   \n",
              "159684                    9  ...    59.0       64.0     77.0             81.0   \n",
              "\n",
              "        sliding_tackle  player_api_id             birthday  height  weight  \\\n",
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