IPWK6_CORE_LINEAR_REGRESSION_ELIZABETH_JOSEPHINE.ipynb

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      "name": "IPWK6_CORE_LINEAR_REGRESSION_ELIZABETH_JOSEPHINE",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/alfafimel/IPWK6-CORE/blob/master/IPWK6_CORE_LINEAR_REGRESSION_ELIZABETH_JOSEPHINE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFZSnWA70exo",
        "colab_type": "text"
      },
      "source": [
        "# Defining the question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZ-3i_jSO4-Q",
        "colab_type": "text"
      },
      "source": [
        "## **a) Specifying the Question**\n",
        "\n",
        "\n",
        "\n",
        "> You have been recruited as a football analyst in a company - Mchezopesa Ltd and tasked to accomplish the task below.A prediction result of a game between team 1  and team 2, based on who's home and who's away, and on whether or not the game is friendly (include rank in your training). \n",
        "You have two possible approaches (polynomial and logistic approaches) given the datasets that will be provided"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXZCQjSYPxoe",
        "colab_type": "text"
      },
      "source": [
        "## **b) Defining the metrics for success**\n",
        "\n",
        "\n",
        "\n",
        "> The objective for this data includes investigating whether or ot the game is friendly  while making the opredictions\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54dC2uO7QtkQ",
        "colab_type": "text"
      },
      "source": [
        "## **c) Understanding the context**\n",
        "\n",
        "\n",
        "> You have been recruited as a football analyst in a company - Mchezopesa Ltd and tasked to accomplish the task below. ---- Predict the result of a game between team 1 and team 2, based on who's home and who's away, and on whether or not the game is friendly (include rank in your training).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcxt7GgKTKik",
        "colab_type": "text"
      },
      "source": [
        "## **d) Recording the Experimental Design**\n",
        "\n",
        "1. reading in the datasets\n",
        "2. checking the data for duplicates and missinng data\n",
        "3. dropping of duplicates and removal filling in of missing data values with zero\n",
        "4. Finding  and dealing with outliers, anomalies, and missing data within the dataset.\n",
        "5. Performing of EDA recording of observations.\n",
        "6. Implementing the solution by performing the respective analysis i.e. factor analysis, principal component analysis, and discriminant analysis.\n",
        "8. Perform any necessary feature engineering \n",
        "\n",
        "9. Check of multicollinearity\n",
        "\n",
        "10. Start building the model, Cross-validate the model\n",
        "\n",
        "11. Compute RMSE\n",
        "\n",
        "12. Create residual plots for your models, and assess their heteroscedasticity using Bartlett’s test\n",
        "\n",
        "13. Perform appropriate regressions on the data including your justification\n",
        "\n",
        "14. Challenge your solution by providing insights on how you can make improvements."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrV3jgz3T5ek",
        "colab_type": "text"
      },
      "source": [
        "## **e) Relevance of the data**\n",
        "\n",
        "\n",
        "\n",
        "> The data that was used for survey was obtained from an open datasource. this data is an aggregation by day of the number of goals scored and rankings of countries in the men's FIFA team. with it is a model that determines which team gets to be ranked where and how according to the years and goals.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5AZs2SFkze5",
        "colab_type": "text"
      },
      "source": [
        "# **Checking the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMytN0aAXsOD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "##"
      ],
      "execution_count": 489,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsYJ4G5BaWpA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# reading datasets\n",
        "\n",
        "data = pd.read_csv('fifa_ranking.csv')\n",
        "data1 = pd.read_csv('results.csv')"
      ],
      "execution_count": 490,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POxsiNv0ps9y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.rename(columns= {'country_full':'country'}, inplace = True)"
      ],
      "execution_count": 491,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZe9sAo6zGdZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# droppig unnecessary columns\n",
        "to_drop = ['country_abrv', 'rank_date']\n",
        "data.drop(to_drop, axis=1, inplace=True)"
      ],
      "execution_count": 492,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqM-Go6H0EOC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "e69c6baf-d327-46fe-e7e2-3632a0edb3f1"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 493,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'country', 'total_points', 'previous_points', 'rank_change',\n",
              "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
              "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
              "       'three_year_ago_avg', 'three_year_ago_weighted', 'confederation'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 493
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLb_aP7DzRCp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c667932-b5da-4838-cfd0-14c37c67bf50"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 494,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(57793, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 494
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zOqrIETzi98",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cdf2f8eb-f281-44ae-bb4b-91394c05274f"
      },
      "source": [
        "# checking for duplicates\n",
        "data.duplicated().any()"
      ],
      "execution_count": 495,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 495
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpc7qu3c0YBL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# drop all duplicates\n",
        "data.drop_duplicates(inplace=True)"
      ],
      "execution_count": 496,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOe2ofdS0mRw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6632257-7c1c-4c40-e810-d7e0eb39b8b1"
      },
      "source": [
        "data.duplicated().sum()"
      ],
      "execution_count": 497,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 497
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4ZyNZtT0VNO",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "977fc4ea-ff68-42b0-dc42-264de484047d"
      },
      "source": [
        "# checking for null values\n",
        "data.isnull().any()"
      ],
      "execution_count": 498,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                       False\n",
              "country                    False\n",
              "total_points               False\n",
              "previous_points            False\n",
              "rank_change                False\n",
              "cur_year_avg               False\n",
              "cur_year_avg_weighted      False\n",
              "last_year_avg              False\n",
              "last_year_avg_weighted     False\n",
              "two_year_ago_avg           False\n",
              "two_year_ago_weighted      False\n",
              "three_year_ago_avg         False\n",
              "three_year_ago_weighted    False\n",
              "confederation              False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 498
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLC3yYfrznfy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a0768213-e56c-47d1-e6b9-8735ff006930"
      },
      "source": [
        "# checking for duplicates\n",
        "data1.duplicated().any()"
      ],
      "execution_count": 499,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 499
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjYjHLS40PQ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "7c42f1d9-443b-4c36-c7e6-599d43aed34f"
      },
      "source": [
        "# checking for null values\n",
        "data1.isnull().any()"
      ],
      "execution_count": 500,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date          False\n",
              "home_team     False\n",
              "away_team     False\n",
              "home_score    False\n",
              "away_score    False\n",
              "tournament    False\n",
              "city          False\n",
              "country       False\n",
              "neutral       False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 500
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KV2RMhCsrKvg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "5d81f837-4dde-4bfd-e76f-256901d1462a"
      },
      "source": [
        "#merging datasets\n",
        "frames = [data, data1]\n",
        "df = pd.concat(frames)\n",
        "df"
      ],
      "execution_count": 501,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>rank_change</th>\n",
              "      <th>cur_year_avg</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>last_year_avg</th>\n",
              "      <th>last_year_avg_weighted</th>\n",
              "      <th>two_year_ago_avg</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_avg</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40834</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Samoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-18</td>\n",
              "      <td>American Samoa</td>\n",
              "      <td>Tahiti</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Pacific Games</td>\n",
              "      <td>Apia</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40835</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Samoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-18</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Pacific Games</td>\n",
              "      <td>Apia</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40836</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-19</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>African Cup of Nations</td>\n",
              "      <td>Cairo</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40837</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-19</td>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>North Korea</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Intercontinental Cup</td>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40838</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Samoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-20</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Pacific Games</td>\n",
              "      <td>Apia</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95228 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       rank      country  ...       city  neutral\n",
              "0       1.0      Germany  ...        NaN      NaN\n",
              "1       2.0        Italy  ...        NaN      NaN\n",
              "2       3.0  Switzerland  ...        NaN      NaN\n",
              "3       4.0       Sweden  ...        NaN      NaN\n",
              "4       5.0    Argentina  ...        NaN      NaN\n",
              "...     ...          ...  ...        ...      ...\n",
              "40834   NaN        Samoa  ...       Apia     True\n",
              "40835   NaN        Samoa  ...       Apia     True\n",
              "40836   NaN        Egypt  ...      Cairo     True\n",
              "40837   NaN        India  ...  Ahmedabad     True\n",
              "40838   NaN        Samoa  ...       Apia     True\n",
              "\n",
              "[95228 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 501
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yWCf664y0RA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "24a3ed59-bbb6-461f-fcd5-fa4596328db5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 502,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>rank_change</th>\n",
              "      <th>cur_year_avg</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>last_year_avg</th>\n",
              "      <th>last_year_avg_weighted</th>\n",
              "      <th>two_year_ago_avg</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_avg</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rank      country  total_points  ...  tournament  city  neutral\n",
              "0   1.0      Germany           0.0  ...         NaN   NaN      NaN\n",
              "1   2.0        Italy           0.0  ...         NaN   NaN      NaN\n",
              "2   3.0  Switzerland           0.0  ...         NaN   NaN      NaN\n",
              "3   4.0       Sweden           0.0  ...         NaN   NaN      NaN\n",
              "4   5.0    Argentina           0.0  ...         NaN   NaN      NaN\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v98KMvrEyhUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "2483886b-5cfc-4395-c33f-7a5faf5b3c55"
      },
      "source": [
        "df.isnull().any()"
      ],
      "execution_count": 503,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                        True\n",
              "country                    False\n",
              "total_points                True\n",
              "previous_points             True\n",
              "rank_change                 True\n",
              "cur_year_avg                True\n",
              "cur_year_avg_weighted       True\n",
              "last_year_avg               True\n",
              "last_year_avg_weighted      True\n",
              "two_year_ago_avg            True\n",
              "two_year_ago_weighted       True\n",
              "three_year_ago_avg          True\n",
              "three_year_ago_weighted     True\n",
              "confederation               True\n",
              "date                        True\n",
              "home_team                   True\n",
              "away_team                   True\n",
              "home_score                  True\n",
              "away_score                  True\n",
              "tournament                  True\n",
              "city                        True\n",
              "neutral                     True\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 503
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZ3NDQYKj_xD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c7d0462b-1f9f-4de1-ae92-deb7e4eeae08"
      },
      "source": [
        "# finding shape of the dataset\n",
        "df.shape"
      ],
      "execution_count": 504,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(95228, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 504
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46YooCHEaW9A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "7cf6693b-326d-480b-a54c-2fba97e76397"
      },
      "source": [
        "# reading columns\n",
        "df.columns"
      ],
      "execution_count": 505,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'country', 'total_points', 'previous_points', 'rank_change',\n",
              "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
              "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
              "       'three_year_ago_avg', 'three_year_ago_weighted', 'confederation',\n",
              "       'date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
              "       'tournament', 'city', 'neutral'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 505
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKM0Tf3GaWWp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "23c2a360-2bb1-40e7-ffbc-3b5ee17ff356"
      },
      "source": [
        "# finding shape of the dataset\n",
        "df.shape"
      ],
      "execution_count": 506,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(95228, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 506
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnAlKszFkJzs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "34b5159a-9799-4a88-b8a1-acf49066867c"
      },
      "source": [
        "#previewing dataset\n",
        "df.head()"
      ],
      "execution_count": 507,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>rank_change</th>\n",
              "      <th>cur_year_avg</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>last_year_avg</th>\n",
              "      <th>last_year_avg_weighted</th>\n",
              "      <th>two_year_ago_avg</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_avg</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CONMEBOL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rank      country  total_points  ...  tournament  city  neutral\n",
              "0   1.0      Germany           0.0  ...         NaN   NaN      NaN\n",
              "1   2.0        Italy           0.0  ...         NaN   NaN      NaN\n",
              "2   3.0  Switzerland           0.0  ...         NaN   NaN      NaN\n",
              "3   4.0       Sweden           0.0  ...         NaN   NaN      NaN\n",
              "4   5.0    Argentina           0.0  ...         NaN   NaN      NaN\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 507
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCHnQAcUkQST",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "fca0e292-5ac5-466d-869a-78f584859db5"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 508,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>rank_change</th>\n",
              "      <th>cur_year_avg</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>last_year_avg</th>\n",
              "      <th>last_year_avg_weighted</th>\n",
              "      <th>two_year_ago_avg</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_avg</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40834</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Samoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-18</td>\n",
              "      <td>American Samoa</td>\n",
              "      <td>Tahiti</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Pacific Games</td>\n",
              "      <td>Apia</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40835</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Samoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-18</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Pacific Games</td>\n",
              "      <td>Apia</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40836</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-19</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>African Cup of Nations</td>\n",
              "      <td>Cairo</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40837</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-19</td>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>North Korea</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Intercontinental Cup</td>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40838</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Samoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-20</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Pacific Games</td>\n",
              "      <td>Apia</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       rank country  total_points  ...              tournament       city  neutral\n",
              "40834   NaN   Samoa           NaN  ...           Pacific Games       Apia     True\n",
              "40835   NaN   Samoa           NaN  ...           Pacific Games       Apia     True\n",
              "40836   NaN   Egypt           NaN  ...  African Cup of Nations      Cairo     True\n",
              "40837   NaN   India           NaN  ...    Intercontinental Cup  Ahmedabad     True\n",
              "40838   NaN   Samoa           NaN  ...           Pacific Games       Apia     True\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 508
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfFLCXstkS8j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "1a6bba6a-587c-4a66-af0b-3d9d38ceb429"
      },
      "source": [
        "df.sample(7)"
      ],
      "execution_count": 509,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>rank_change</th>\n",
              "      <th>cur_year_avg</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>last_year_avg</th>\n",
              "      <th>last_year_avg_weighted</th>\n",
              "      <th>two_year_ago_avg</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_avg</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11209</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1980-05-01</td>\n",
              "      <td>Thailand</td>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Medan</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34293</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012-08-15</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Jurong</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32091</th>\n",
              "      <td>199.0</td>\n",
              "      <td>Anguilla</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CONCACAF</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23256</th>\n",
              "      <td>10.0</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>0.0</td>\n",
              "      <td>733.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20568</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1997-06-13</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Sana'a</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38586</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Gabon</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-01-23</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>African Cup of Nations</td>\n",
              "      <td>Franceville</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38451</th>\n",
              "      <td>142.0</td>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>AFC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        rank       country  ...         city  neutral\n",
              "11209    NaN     Indonesia  ...        Medan     True\n",
              "34293    NaN     Singapore  ...       Jurong    False\n",
              "32091  199.0      Anguilla  ...          NaN      NaN\n",
              "23256   10.0        Turkey  ...          NaN      NaN\n",
              "20568    NaN         Yemen  ...       Sana'a    False\n",
              "38586    NaN         Gabon  ...  Franceville     True\n",
              "38451  142.0  Turkmenistan  ...          NaN      NaN\n",
              "\n",
              "[7 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 509
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OS9VHxJlC-T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "aecf5547-9aa1-4732-c5f0-60445494c102"
      },
      "source": [
        "# checking datatypes\n",
        "df.info()"
      ],
      "execution_count": 510,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 95228 entries, 0 to 40838\n",
            "Data columns (total 22 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   rank                     54389 non-null  float64\n",
            " 1   country                  95228 non-null  object \n",
            " 2   total_points             54389 non-null  float64\n",
            " 3   previous_points          54389 non-null  float64\n",
            " 4   rank_change              54389 non-null  float64\n",
            " 5   cur_year_avg             54389 non-null  float64\n",
            " 6   cur_year_avg_weighted    54389 non-null  float64\n",
            " 7   last_year_avg            54389 non-null  float64\n",
            " 8   last_year_avg_weighted   54389 non-null  float64\n",
            " 9   two_year_ago_avg         54389 non-null  float64\n",
            " 10  two_year_ago_weighted    54389 non-null  float64\n",
            " 11  three_year_ago_avg       54389 non-null  float64\n",
            " 12  three_year_ago_weighted  54389 non-null  float64\n",
            " 13  confederation            54389 non-null  object \n",
            " 14  date                     40839 non-null  object \n",
            " 15  home_team                40839 non-null  object \n",
            " 16  away_team                40839 non-null  object \n",
            " 17  home_score               40839 non-null  float64\n",
            " 18  away_score               40839 non-null  float64\n",
            " 19  tournament               40839 non-null  object \n",
            " 20  city                     40839 non-null  object \n",
            " 21  neutral                  40839 non-null  object \n",
            "dtypes: float64(14), object(8)\n",
            "memory usage: 16.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOqLa3VZ1PgW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8d6ab051-26c8-41fc-9100-a0fe9fbc5168"
      },
      "source": [
        "# checking for duplicates\n",
        "df.duplicated().any()"
      ],
      "execution_count": 511,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 511
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY3DyTcj1WNE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# drop all duplicates\n",
        "df.drop_duplicates(inplace=True)"
      ],
      "execution_count": 512,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZvsDbI61Xp5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e15e9e3-070f-4f40-8d60-f66ea1e7c8a0"
      },
      "source": [
        "df.duplicated().sum()"
      ],
      "execution_count": 513,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 513
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOopSS1lkWOo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "e98c4ac8-b813-459f-a288-43d960ea212f"
      },
      "source": [
        "# checking for null values\n",
        "df.isnull().any()"
      ],
      "execution_count": 514,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                        True\n",
              "country                    False\n",
              "total_points                True\n",
              "previous_points             True\n",
              "rank_change                 True\n",
              "cur_year_avg                True\n",
              "cur_year_avg_weighted       True\n",
              "last_year_avg               True\n",
              "last_year_avg_weighted      True\n",
              "two_year_ago_avg            True\n",
              "two_year_ago_weighted       True\n",
              "three_year_ago_avg          True\n",
              "three_year_ago_weighted     True\n",
              "confederation               True\n",
              "date                        True\n",
              "home_team                   True\n",
              "away_team                   True\n",
              "home_score                  True\n",
              "away_score                  True\n",
              "tournament                  True\n",
              "city                        True\n",
              "neutral                     True\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 514
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZQEBG5OoHB0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# filling null values with zero\n",
        "df.fillna(0, inplace=True)"
      ],
      "execution_count": 515,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_saAQwadoRjM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "e72b23b0-3fc4-4eaf-ee29-96a8deb41d39"
      },
      "source": [
        "# Dealing with the Missing Data\n",
        "df.isnull().sum()"
      ],
      "execution_count": 516,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                       0\n",
              "country                    0\n",
              "total_points               0\n",
              "previous_points            0\n",
              "rank_change                0\n",
              "cur_year_avg               0\n",
              "cur_year_avg_weighted      0\n",
              "last_year_avg              0\n",
              "last_year_avg_weighted     0\n",
              "two_year_ago_avg           0\n",
              "two_year_ago_weighted      0\n",
              "three_year_ago_avg         0\n",
              "three_year_ago_weighted    0\n",
              "confederation              0\n",
              "date                       0\n",
              "home_team                  0\n",
              "away_team                  0\n",
              "home_score                 0\n",
              "away_score                 0\n",
              "tournament                 0\n",
              "city                       0\n",
              "neutral                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 516
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_oMmRFO1p5u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1aa6c11f-449d-419d-c802-fb87b4cfcd03"
      },
      "source": [
        "# dealing with outliers\n",
        "# quantiles\n",
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Removing outliers based on the IQR range and stores the result in the data frame 'df_out'\n",
        "# ---\n",
        "# \n",
        "df1 = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "\n",
        "# Printing the shape of our new dataset\n",
        "# ---\n",
        "# \n",
        "print(df1.shape)\n",
        "\n",
        "# Printing the shape of our old dataset\n",
        "# ---\n",
        "#\n",
        "print(df.shape)"
      ],
      "execution_count": 578,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(34520, 22)\n",
            "(95228, 22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpTacWnIBBvN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "eaf6b625-028b-4e11-f4ed-18f3b310625f"
      },
      "source": [
        "# top football confederations\n",
        "print('Total number of confederations: {0}'.format(df1['confederation'].nunique()))\n",
        "print(df1['confederation'].value_counts().head(10))\n",
        "print('--'*40)"
      ],
      "execution_count": 518,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of confederations: 7\n",
            "0           26356\n",
            "CAF          2054\n",
            "UEFA         2035\n",
            "AFC          1759\n",
            "CONCACAF     1434\n",
            "OFC           470\n",
            "CONMEBOL      412\n",
            "Name: confederation, dtype: int64\n",
            "--------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEw9SgXM2eke",
        "colab_type": "text"
      },
      "source": [
        "# **EXPLORATORY DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qAT97ln2mNH",
        "colab_type": "text"
      },
      "source": [
        "## **Univariate analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DccLMXoP3OHY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "69a6fe65-3d7e-496f-aa0f-36885738e604"
      },
      "source": [
        "#Histogram of the ome scores of different countries\n",
        "ax = sns.countplot(df1['home_score'], order = df1['home_score'].value_counts().index)\n",
        "plt.title('Distribution of countries per their homescores')"
      ],
      "execution_count": 429,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of countries per their homescores')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 429
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHoYaC1lFj7R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "aef649db-3ffa-46f6-a31d-4bf2039dc0cd"
      },
      "source": [
        "## boxplot\n",
        "sns.boxplot(df1['home_score'], showmeans=True)"
      ],
      "execution_count": 430,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ab9dc898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 430
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hfNsuHrY3zeW0n6A5/HEwzQmroXmD7AbgdpoTiP8dsG9mbgBOBf4Q2EDz5t6pmfnEDHNtAX6+/X090j7Pqva5pZ74I2OSVMg9XUkq5DFdPW9ExOE0H5SYzrGZubZyHmkuPLwgSYU8vCBJhYyuJBUyupJUyOhKUiGjK0mF/h8UEwWyxLMxewAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4-LqhHBFo91",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "0afc8bdb-530b-4266-a98a-7e93ba3812f7"
      },
      "source": [
        "sns.boxplot(df1['away_score'], showmeans=True)"
      ],
      "execution_count": 431,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a296d978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 431
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ERMANldFtqS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "829e71dd-4d79-4a67-ce00-368c1a9e718a"
      },
      "source": [
        "df1['away_score'].value_counts().sort_index().plot.bar()"
      ],
      "execution_count": 432,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a293f320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 432
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xnF6Jqm8FcY_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "61ef4e00-4d8f-4485-ee9b-39dda9fa57d8"
      },
      "source": [
        "df1['home_score'].value_counts().sort_index().plot.bar()"
      ],
      "execution_count": 433,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a2900940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 433
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftnITM0A689J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "d584f40b-c5ff-4a44-9ff2-f325c62fabf0"
      },
      "source": [
        "print(df1.skew())"
      ],
      "execution_count": 434,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rank                       2.033130\n",
            "total_points               0.000000\n",
            "previous_points            2.654479\n",
            "rank_change                0.000000\n",
            "cur_year_avg               0.000000\n",
            "cur_year_avg_weighted      0.000000\n",
            "last_year_avg              0.000000\n",
            "last_year_avg_weighted     0.000000\n",
            "two_year_ago_avg           0.000000\n",
            "two_year_ago_weighted      0.000000\n",
            "three_year_ago_avg         0.000000\n",
            "three_year_ago_weighted    0.000000\n",
            "home_score                 0.454146\n",
            "away_score                 0.761499\n",
            "neutral                    1.599769\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uzh0rrTj7JON",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "663f76dd-d014-4696-ac8f-d1401ec5084d"
      },
      "source": [
        "print(df1.kurt())"
      ],
      "execution_count": 435,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rank                       2.643261\n",
            "total_points               0.000000\n",
            "previous_points            6.012138\n",
            "rank_change                0.000000\n",
            "cur_year_avg               0.000000\n",
            "cur_year_avg_weighted      0.000000\n",
            "last_year_avg              0.000000\n",
            "last_year_avg_weighted     0.000000\n",
            "two_year_ago_avg           0.000000\n",
            "two_year_ago_weighted      0.000000\n",
            "three_year_ago_avg         0.000000\n",
            "three_year_ago_weighted    0.000000\n",
            "home_score                -1.304573\n",
            "away_score                -0.833384\n",
            "neutral                    0.559292\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOZluE237UaM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "d7af8be1-c6ad-4735-83a9-22ebdeb4a690"
      },
      "source": [
        "df1.columns"
      ],
      "execution_count": 436,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'country', 'total_points', 'previous_points', 'rank_change',\n",
              "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
              "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
              "       'three_year_ago_avg', 'three_year_ago_weighted', 'confederation',\n",
              "       'date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
              "       'tournament', 'city', 'neutral'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 436
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBCH252_7Qh2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "to_drop = ['rank_change',\n",
        "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
        "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
        "       'three_year_ago_avg', 'three_year_ago_weighted']\n",
        "df1.drop(to_drop, axis=1, inplace=True)"
      ],
      "execution_count": 437,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkXztTZO8UMA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a10a328c-6030-4317-d580-bd2e04e71c97"
      },
      "source": [
        "df1.shape"
      ],
      "execution_count": 438,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34520, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 438
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Aie_Oar22uro"
      },
      "source": [
        "## **Bivariate analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3rNxiXI83aX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "outputId": "4967aee9-e341-434c-e30d-935bff9cd87f"
      },
      "source": [
        "# Ploting the bivariate summaries and recording our observations\n",
        "# scatterplot\n",
        "sns.pairplot(df1)\n",
        "plt.show()"
      ],
      "execution_count": 439,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 42 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAQCvNZNF5jx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9ab46036-c1b3-41c1-a7f4-c1c34e8bbadf"
      },
      "source": [
        "# Calculating the pearson coefficient\n",
        "pearson_coeff = df1[\"home_score\"].corr(df1[\"away_score\"], method=\"pearson\") \n",
        "print(pearson_coeff)\n",
        "\n",
        "# Checking whether you have to define the pearson\n",
        "coeff = df1[\"home_score\"].corr(df1[\"away_score\"])\n",
        "print(coeff)"
      ],
      "execution_count": 440,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.23873779888274616\n",
            "0.23873779888274616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwTAfyEZ8-Wf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "d75c5fac-90b7-4709-8628-5cf0a9e35be6"
      },
      "source": [
        "# heatmap\n",
        "sns.heatmap(df1.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 441,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Sezs3IrD4S6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "228b43a0-bb06-4ae9-c71c-7cabb2239575"
      },
      "source": [
        "df1.sample(6)"
      ],
      "execution_count": 442,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18776</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1995-03-29</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SAFF Cup</td>\n",
              "      <td>Colombo</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7354</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Sudan</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1970-02-10</td>\n",
              "      <td>Sudan</td>\n",
              "      <td>Cameroon</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>African Cup of Nations</td>\n",
              "      <td>Khartoum</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2471</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Netherlands Guyana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1944-08-29</td>\n",
              "      <td>Aruba</td>\n",
              "      <td>Curaçao</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Paramaribo</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9512</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1975-11-04</td>\n",
              "      <td>Kenya</td>\n",
              "      <td>Uganda</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CECAFA Cup</td>\n",
              "      <td>Ndola</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30287</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Netherlands Antilles</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2008-06-09</td>\n",
              "      <td>Curaçao</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Willemstad</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12616</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Gambia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1983-07-03</td>\n",
              "      <td>Gambia</td>\n",
              "      <td>Mali</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Banjul</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       rank               country  ...        city  neutral\n",
              "18776   0.0             Sri Lanka  ...     Colombo     True\n",
              "7354    0.0                 Sudan  ...    Khartoum    False\n",
              "2471    0.0    Netherlands Guyana  ...  Paramaribo     True\n",
              "9512    0.0                Zambia  ...       Ndola     True\n",
              "30287   0.0  Netherlands Antilles  ...  Willemstad    False\n",
              "12616   0.0                Gambia  ...      Banjul    False\n",
              "\n",
              "[6 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 442
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foq27DKx9LM8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "b35f03c2-2e93-4844-db45-ff7696eb7d32"
      },
      "source": [
        "df1.info()"
      ],
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34520 entries, 0 to 40838\n",
            "Data columns (total 13 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   rank             34520 non-null  float64\n",
            " 1   country          34520 non-null  object \n",
            " 2   total_points     34520 non-null  float64\n",
            " 3   previous_points  34520 non-null  float64\n",
            " 4   confederation    34520 non-null  object \n",
            " 5   date             34520 non-null  object \n",
            " 6   home_team        34520 non-null  object \n",
            " 7   away_team        34520 non-null  object \n",
            " 8   home_score       34520 non-null  float64\n",
            " 9   away_score       34520 non-null  float64\n",
            " 10  tournament       34520 non-null  object \n",
            " 11  city             34520 non-null  object \n",
            " 12  neutral          34520 non-null  object \n",
            "dtypes: float64(5), object(8)\n",
            "memory usage: 4.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuVirEBJ-89w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1c97259e-7453-4b62-8de8-fc95195f6c0c"
      },
      "source": [
        "df1['home_score'].corr(df1['away_score'])"
      ],
      "execution_count": 444,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.23873779888274616"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 444
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfXugOYm-iH-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "efc8a4d3-c899-4bc7-fc50-7e1cc3eebc24"
      },
      "source": [
        "# Determine correlation between numeric features\n",
        "plt.figure(figsize=(12,10))\n",
        "sns.pairplot(df1[['home_score','away_score']])\n",
        "plt.title('home_score vs away_score')"
      ],
      "execution_count": 445,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'home_score vs away_score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 445
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dHq4zyLq2u6t"
      },
      "source": [
        "## **Multivariate analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XTM05rkPG3oE",
        "colab_type": "text"
      },
      "source": [
        "### **Factor analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vUWQo53-bL8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ba04cb2b-a3a4-4f8a-ea3d-d2685fd9d448"
      },
      "source": [
        "# Installing factor analyzer \n",
        "!pip install factor_analyzer==0.2.3\n",
        "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity\n",
        "chi_square_value,p_value=calculate_bartlett_sphericity(df)\n",
        "chi_square_value, p_value\n",
        "\n",
        "#The p vlaue is 0 the observed matrix is not an identity matrix."
      ],
      "execution_count": 446,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: factor_analyzer==0.2.3 in /usr/local/lib/python3.6/dist-packages (0.2.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.0.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->factor_analyzer==0.2.3) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->factor_analyzer==0.2.3) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->factor_analyzer==0.2.3) (1.15.0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12997144.610679293, 0.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 446
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZU6IF-VoGtlk",
        "colab_type": "text"
      },
      "source": [
        "### **Principal component analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huDujxrsGd8v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(df1[['home_score','away_score']])\n",
        "scaled_data = scaler.transform(df1[['home_score','away_score']])"
      ],
      "execution_count": 447,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGObt0qhGmve",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaled_data = pd.DataFrame(scaled_data)"
      ],
      "execution_count": 448,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keOQvzf-GqRv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a90d629a-7aef-4adf-f8ee-bb4293071e2b"
      },
      "source": [
        "df2 = scaled_data.fillna(0)\n",
        "df2.head()"
      ],
      "execution_count": 449,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.950755</td>\n",
              "      <td>-0.821526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.950755</td>\n",
              "      <td>-0.821526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.950755</td>\n",
              "      <td>-0.821526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.950755</td>\n",
              "      <td>-0.821526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.950755</td>\n",
              "      <td>-0.821526</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1\n",
              "0 -0.950755 -0.821526\n",
              "1 -0.950755 -0.821526\n",
              "2 -0.950755 -0.821526\n",
              "3 -0.950755 -0.821526\n",
              "4 -0.950755 -0.821526"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 449
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2duW4RdjHIfD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "logmodel = PCA(n_components=2)\n",
        "logmodel.fit(df2)\n",
        "pca_x = logmodel.transform(df2)"
      ],
      "execution_count": 450,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjwXW7yTHNR5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e7824d44-7210-49d5-e452-ec6e62ccbcff"
      },
      "source": [
        "pca_x.shape"
      ],
      "execution_count": 451,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34520, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 451
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igVS5Dv_HRYb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df3 = pd.DataFrame(logmodel.components_, columns = ['home_score','away_score'])"
      ],
      "execution_count": 452,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4eQ8yg1HWWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "2bde1038-86bc-4672-90cb-bf38e871452a"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(df3)"
      ],
      "execution_count": 453,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a2c7ecc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 453
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVRWrWjsHeDo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "6020a032-9d70-4e0f-b46f-855441535b61"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "ty=sns.scatterplot(pca_x[:,0], pca_x[:,1])\n",
        "sns.despine(left=True)\n",
        "ty.set_title('PCA Results')\n",
        "ty.set_ylabel('Second Principle Component ')\n",
        "ty.set_xlabel('First Principle Component ')"
      ],
      "execution_count": 454,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'First Principle Component ')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 454
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZYQhRsTHjFc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f09ad623-ef69-49dc-aec5-f73777c7b777"
      },
      "source": [
        "pca_x"
      ],
      "execution_count": 455,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.25319172,  0.0913784 ],\n",
              "       [-1.25319172,  0.0913784 ],\n",
              "       [-1.25319172,  0.0913784 ],\n",
              "       ...,\n",
              "       [-0.3083254 ,  1.03624472],\n",
              "       [-0.3083254 ,  1.03624472],\n",
              "       [ 0.57347718,  0.15444214]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 455
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eK4cFQIbNJnD",
        "colab_type": "text"
      },
      "source": [
        "# **Polynomial Regression Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7x5ot84VDvZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Import libaries\n",
        "import datetime\n",
        "import operator\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact\n",
        "from IPython.display import Markdown as md\n",
        "from datetime import date, timedelta, datetime\n",
        "from numpy import sqrt\n",
        "from pandas import read_csv, to_datetime\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Import LinearRegression method from sklearn linear_model library\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 519,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVF1OwvkP56n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ea87a460-f81d-4b95-930f-077465bf5bd6"
      },
      "source": [
        "df1.shape"
      ],
      "execution_count": 553,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34520, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 553
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMYyLlV0cKDF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "426324bb-065a-411b-a6b1-e99a72868056"
      },
      "source": [
        "df1.dtypes"
      ],
      "execution_count": 579,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                       float64\n",
              "country                     object\n",
              "total_points               float64\n",
              "previous_points            float64\n",
              "rank_change                float64\n",
              "cur_year_avg               float64\n",
              "cur_year_avg_weighted      float64\n",
              "last_year_avg              float64\n",
              "last_year_avg_weighted     float64\n",
              "two_year_ago_avg           float64\n",
              "two_year_ago_weighted      float64\n",
              "three_year_ago_avg         float64\n",
              "three_year_ago_weighted    float64\n",
              "confederation               object\n",
              "date                        object\n",
              "home_team                   object\n",
              "away_team                   object\n",
              "home_score                 float64\n",
              "away_score                 float64\n",
              "tournament                  object\n",
              "city                        object\n",
              "neutral                     object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 579
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBlI6OkPhIEy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1d208667-0734-4a59-9f8a-8affd74dcbd6"
      },
      "source": [
        "df5 = df1\n",
        "df5.head()"
      ],
      "execution_count": 581,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country</th>\n",
              "      <th>total_points</th>\n",
              "      <th>previous_points</th>\n",
              "      <th>rank_change</th>\n",
              "      <th>cur_year_avg</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>last_year_avg</th>\n",
              "      <th>last_year_avg_weighted</th>\n",
              "      <th>two_year_ago_avg</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_avg</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>confederation</th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.0</td>\n",
              "      <td>Republic of Ireland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>UEFA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    rank              country  total_points  ...  tournament  city  neutral\n",
              "0    1.0              Germany           0.0  ...           0     0        0\n",
              "1    2.0                Italy           0.0  ...           0     0        0\n",
              "3    4.0               Sweden           0.0  ...           0     0        0\n",
              "5    6.0  Republic of Ireland           0.0  ...           0     0        0\n",
              "19  20.0               Poland           0.0  ...           0     0        0\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 581
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmNxNEyThh5C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "26c825bc-2394-4899-9d9f-9988f5bfea7b"
      },
      "source": [
        "df5.columns"
      ],
      "execution_count": 582,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'country', 'total_points', 'previous_points', 'rank_change',\n",
              "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
              "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
              "       'three_year_ago_avg', 'three_year_ago_weighted', 'confederation',\n",
              "       'date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
              "       'tournament', 'city', 'neutral'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 582
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53Ybi01tha07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# droppig unnecessary columns\n",
        "to_drop = ['country', 'total_points', 'previous_points', 'rank_change',\n",
        "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
        "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
        "       'three_year_ago_avg', 'three_year_ago_weighted', 'confederation',\n",
        "       'date', \n",
        "       'tournament', 'city', 'neutral']\n",
        "df5.drop(to_drop, axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6XSrjY1h40J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f28def78-6dc6-401d-bcf0-9344243cdaa2"
      },
      "source": [
        "print(df5)\n",
        "\n",
        "#Get the 2 and the last column from the dataset \n",
        "X = df5.iloc[:, 3:4].values\n",
        "y = df5.iloc[:, 4].values\n",
        "print(X)\n",
        "print(y)"
      ],
      "execution_count": 593,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       rank         home_team    away_team  home_score  away_score\n",
            "0       1.0                 0            0         0.0         0.0\n",
            "1       2.0                 0            0         0.0         0.0\n",
            "3       4.0                 0            0         0.0         0.0\n",
            "5       6.0                 0            0         0.0         0.0\n",
            "19     20.0                 0            0         0.0         0.0\n",
            "...     ...               ...          ...         ...         ...\n",
            "40829   0.0             India        Syria         1.0         1.0\n",
            "40830   0.0           Tunisia      Nigeria         0.0         1.0\n",
            "40836   0.0           Senegal      Algeria         0.0         1.0\n",
            "40837   0.0        Tajikistan  North Korea         0.0         1.0\n",
            "40838   0.0  Papua New Guinea         Fiji         1.0         1.0\n",
            "\n",
            "[34520 rows x 5 columns]\n",
            "[[0.]\n",
            " [0.]\n",
            " [0.]\n",
            " ...\n",
            " [0.]\n",
            " [0.]\n",
            " [1.]]\n",
            "[0. 0. 0. ... 1. 1. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryQ-Oqb-jgVm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split the dataset into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)"
      ],
      "execution_count": 595,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCqOXDSTlA3h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ae4c348a-8cf8-4a57-cb1e-9d0d992e25c2"
      },
      "source": [
        "# Fit Linear Regression model to the dataset(this is optional. We are doing this for the sole purpose of comparing the linear regression model to the polynomial regression model)\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)"
      ],
      "execution_count": 596,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 596
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNTBZEnXlM1I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "29329082-d174-421f-f012-340107321bf2"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg2=PolynomialFeatures(degree=2)\n",
        "X_poly=poly_reg2.fit_transform(X)\n",
        "lin_reg_2=LinearRegression()\n",
        "lin_reg_2.fit(X_poly,y)"
      ],
      "execution_count": 597,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 597
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQs9uuYQlSqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "281e1395-06b9-47ea-8e55-32c74b20e7f0"
      },
      "source": [
        "poly_reg3=PolynomialFeatures(degree=3)\n",
        "X_poly3=poly_reg3.fit_transform(X)\n",
        "lin_reg_3=LinearRegression()\n",
        "lin_reg_3.fit(X_poly3,y)"
      ],
      "execution_count": 598,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 598
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1NUOtwrlZx4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "75810f5e-8566-4723-eced-709029d4844b"
      },
      "source": [
        "# Visualize the Linear Regression results\n",
        "plt.scatter(X, y, color='blue')\n",
        "plt.plot(X, lin_reg.predict(X), color='red')\n",
        "plt.title('home score vs away_score')\n",
        "plt.xlabel('away_score')\n",
        "plt.ylabel('home_score')\n",
        "plt.show()\n"
      ],
      "execution_count": 599,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rS3Z-1qFlsNg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "180faa3e-480f-4635-ce3a-8e0cc43ad42e"
      },
      "source": [
        "# Visualize the polynomial Regression results\n",
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X,lin_reg_2.predict(poly_reg2.fit_transform(X)),color='blue')\n",
        "plt.plot(X,lin_reg_3.predict(poly_reg3.fit_transform(X)),color='green')\n",
        "plt.title('home score vs away_score')\n",
        "plt.xlabel('away_score')\n",
        "plt.ylabel('home_score')\n",
        "plt.show()"
      ],
      "execution_count": 600,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "si3ScC_TmE47",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "684aa836-32e3-476f-faa7-71885b4be073"
      },
      "source": [
        "X_grid=np.arange(min(X),max(X),0.1) # This will give us a vector.We will have to convert this into a matrix \n",
        "X_grid=X_grid.reshape((len(X_grid),1))\n",
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X_grid,lin_reg_3.predict(poly_reg3.fit_transform(X_grid)),color='blue')\n",
        "#plt.plot(X,lin_reg_3.predict(poly_reg3.fit_transform(X)),color='green')\n",
        "plt.title('home score vs away_score')\n",
        "plt.xlabel('away_score')\n",
        "plt.ylabel('home_score')\n",
        "plt.show()"
      ],
      "execution_count": 601,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--b017wTFEvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# droppig unnecessary columns\n",
        "to_drop = ['home_team', 'away_team']\n",
        "df5.drop(to_drop, axis=1, inplace=True)"
      ],
      "execution_count": 620,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmzhBb_uWlM3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "171fbe47-6418-40f7-cad2-425e15f5f8d2"
      },
      "source": [
        "# Model 1: Predict how many goals the home team scores.\n",
        "lin_reg.predict([[6.5]])"
      ],
      "execution_count": 602,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.89316118])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 602
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ul_E24r6Cq3T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "862f131f-6ff1-4070-f556-2fca9177a4a0"
      },
      "source": [
        "# multicolinearity\n",
        "correlations = df5.corr()\n",
        "correlations\n",
        "\n",
        "# the table below shows how the variables relate with each other. \n",
        "# there is very minimal correlation between the scores at home and away"
      ],
      "execution_count": 622,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.440243</td>\n",
              "      <td>-0.380404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_score</th>\n",
              "      <td>-0.440243</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.238738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>away_score</th>\n",
              "      <td>-0.380404</td>\n",
              "      <td>0.238738</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                rank  home_score  away_score\n",
              "rank        1.000000   -0.440243   -0.380404\n",
              "home_score -0.440243    1.000000    0.238738\n",
              "away_score -0.380404    0.238738    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 622
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k89kSSmMC0ty",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f2144610-dcd2-458e-c9d4-a90806ce3648"
      },
      "source": [
        "pd.DataFrame(np.linalg.inv(correlations.values), index = correlations.index, columns=correlations.columns)\n"
      ],
      "execution_count": 623,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>1.377763</td>\n",
              "      <td>0.510524</td>\n",
              "      <td>0.402226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_score</th>\n",
              "      <td>0.510524</td>\n",
              "      <td>1.249613</td>\n",
              "      <td>-0.104124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>away_score</th>\n",
              "      <td>0.402226</td>\n",
              "      <td>-0.104124</td>\n",
              "      <td>1.177867</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                rank  home_score  away_score\n",
              "rank        1.377763    0.510524    0.402226\n",
              "home_score  0.510524    1.249613   -0.104124\n",
              "away_score  0.402226   -0.104124    1.177867"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 623
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BbMKZitEWZG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bca9880c-0f04-4f55-d1f6-3fccbba12d44"
      },
      "source": [
        "print(revised.values)"
      ],
      "execution_count": 624,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0.0 0.0]\n",
            " [0 0 0.0 0.0]\n",
            " [0 0 0.0 0.0]\n",
            " ...\n",
            " ['Senegal' 'Algeria' 0.0 1.0]\n",
            " ['Tajikistan' 'North Korea' 0.0 1.0]\n",
            " ['Papua New Guinea' 'Fiji' 1.0 1.0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXTnpwKRGL3O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df5.rename(columns= {'home_score':'homescore', 'away_score':'awayscore'}, inplace = True)"
      ],
      "execution_count": 626,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WpSFQDygD3VT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ebe1a925-87ca-4d41-cbf5-614e4f8df74c"
      },
      "source": [
        "# residual plots and heteroskedasticity testing\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "X = df5.values\n",
        "y = df5['homescore'].values\n",
        "\n",
        "X_train, X_test, homescore_train, homescore_test = train_test_split(X, y, test_size=0.4, random_state=0)\n",
        "\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, homescore_train)\n",
        "\n",
        "# This is our prediction for admission based on our model\n",
        "homescore_predict = regressor.predict(X_test)\n",
        "\n",
        "# We now create the residual by substracting the test value from the predicted \n",
        "# value for each row in our dataset\n",
        "\n",
        "residuals = np.subtract(homescore_predict, homescore_test)\n",
        "\n",
        "# Let's describe our residual:\n",
        "pd.DataFrame(residuals).describe()\n",
        "\n",
        "residuals.mean()"
      ],
      "execution_count": 627,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.6193900783515012e-17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 627
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNvycvaLFrmL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2b1814b5-27b8-4c78-a366-341385b0f8d1"
      },
      "source": [
        "# residual plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(homescore_predict, residuals, color='black')\n",
        "plt.ylabel('residual')\n",
        "plt.xlabel('fitted values')\n",
        "plt.axhline(y= residuals.mean(), color='red', linewidth=1)\n",
        "plt.show()"
      ],
      "execution_count": 647,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDgRXbAgHP2k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8b83fd6b-c438-419c-d2f3-6949969a2317"
      },
      "source": [
        "import scipy as sp\n",
        "\n",
        "test_result, p_value = sp.stats.bartlett(homescore_predict, residuals)\n",
        "\n",
        "# To interpret the results we must also compute a critical value of the chi squared distribution\n",
        "degree_of_freedom = len(homescore_predict)-1\n",
        "probability = 1 - p_value\n",
        "\n",
        "critical_value = sp.stats.chi2.ppf(probability, degree_of_freedom)\n",
        "print(critical_value)\n",
        "\n",
        "# If the test_result is greater than the critical value, then we reject our null\n",
        "# hypothesis. This would mean that there are patterns to the variance of the data\n",
        "\n",
        "# Otherwise, we can identify no patterns, and we accept the null hypothesis that \n",
        "# the variance is homogeneous across our data\n",
        "\n",
        "if (test_result > critical_value):\n",
        "  print('the variances are unequal, and the model should be reassessed')\n",
        "else:\n",
        "  print('the variances are homogeneous!')\n"
      ],
      "execution_count": 648,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "inf\n",
            "the variances are homogeneous!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN7fNP3RK4Lr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training and Cross Validation\n",
        "# ---\n",
        "# \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators=300, random_state=0)\n",
        "from sklearn.model_selection import cross_val_score\n",
        "all_accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=5)"
      ],
      "execution_count": 651,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMl9QovgLBQI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c18c48c0-c083-4d30-ed1a-4bcdebd5a2e4"
      },
      "source": [
        "# Printing the accuracies returned for five folds \n",
        "# by the cross_val_score method by calling print on all_accuracies\n",
        "\n",
        "print(all_accuracies.mean())"
      ],
      "execution_count": 652,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YE3Q7AtNLbG7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# specify parameters and distributions to sample from\n",
        "from scipy.stats import randint as sp_randint\n",
        "param_dist = {\"max_depth\": [3, None],\n",
        "              \"max_features\": sp_randint(1, 11),\n",
        "              \"min_samples_split\": sp_randint(2, 11),\n",
        "              \"bootstrap\": [True, False],\n",
        "              \"criterion\": [\"gini\", \"entropy\"]}"
      ],
      "execution_count": 654,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8JlYqUSLQ99",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Instantiating RandomizedSearchCV object \n",
        "from sklearn.model_selection import RandomizedSearchCV \n",
        "random_sr = RandomizedSearchCV(classifier, param_dist, cv = 5) "
      ],
      "execution_count": 655,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITIXreU8LXQC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "34f9a6c2-aba5-43b0-86da-d3a22a15967d"
      },
      "source": [
        "#Calling the fit method\n",
        "random_sr.fit(X_train, y_train)"
      ],
      "execution_count": 656,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                    ccp_alpha=0.0,\n",
              "                                                    class_weight=None,\n",
              "                                                    criterion='gini',\n",
              "                                                    max_depth=None,\n",
              "                                                    max_features='auto',\n",
              "                                                    max_leaf_nodes=None,\n",
              "                                                    max_samples=None,\n",
              "                                                    min_impurity_decrease=0.0,\n",
              "                                                    min_impurity_split=None,\n",
              "                                                    min_samples_leaf=1,\n",
              "                                                    min_samples_split=2,\n",
              "                                                    min_weight_fraction_leaf=0.0,\n",
              "                                                    n_estimators=300,\n",
              "                                                    n_jobs...\n",
              "                   param_distributions={'bootstrap': [True, False],\n",
              "                                        'criterion': ['gini', 'entropy'],\n",
              "                                        'max_depth': [3, None],\n",
              "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fd8a7c602e8>,\n",
              "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fd8a7c60dd8>},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 656
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWTqCgaBLg5G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6506eeba-3318-47cc-df77-ccbd8ffa784e"
      },
      "source": [
        "#Checking the parameters that return the highest accuracy\n",
        "\n",
        "best_parameters = random_sr.best_params_\n",
        "print(best_parameters)"
      ],
      "execution_count": 657,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': False, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 2, 'min_samples_split': 2}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nf5-Uw1PLjJ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1af76da5-c490-4809-da18-25a6eba384d7"
      },
      "source": [
        "# Finding the obtained accuracy\n",
        "\n",
        "best_result = random_sr.best_score_\n",
        "print(best_result)\n"
      ],
      "execution_count": 658,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3_KY1_JNP7b",
        "colab_type": "text"
      },
      "source": [
        "# **Logistics Regression Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVu6joT7JKqw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "99cf08f9-5039-4c5c-ad93-7f1bfa60fdc7"
      },
      "source": [
        "# dealing with outliers\n",
        "# quantiles\n",
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Removing outliers based on the IQR range and stores the result in the data frame 'df_out'\n",
        "# ---\n",
        "# \n",
        "df6 = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "\n",
        "# Printing the shape of our new dataset\n",
        "# ---\n",
        "# \n",
        "print(df6.shape)\n",
        "\n",
        "# Printing the shape of our old dataset\n",
        "# ---\n",
        "#\n",
        "print(df.shape)"
      ],
      "execution_count": 635,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(34520, 22)\n",
            "(95228, 22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Govv_3sJSDb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "cb5c6a36-5131-4992-cd80-6c7290e7a6f2"
      },
      "source": [
        "df6.columns"
      ],
      "execution_count": 636,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'country', 'total_points', 'previous_points', 'rank_change',\n",
              "       'cur_year_avg', 'cur_year_avg_weighted', 'last_year_avg',\n",
              "       'last_year_avg_weighted', 'two_year_ago_avg', 'two_year_ago_weighted',\n",
              "       'three_year_ago_avg', 'three_year_ago_weighted', 'confederation',\n",
              "       'date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
              "       'tournament', 'city', 'neutral'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 636
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dy7Pii5YJZYF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "ead126a7-d748-480b-fc15-d14cc724ab16"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sb\n",
        "sb.countplot(x='home_score',data=df6, palette='hls')\n"
      ],
      "execution_count": 638,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a5d6a400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 638
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1LDBqPdKf4t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "7e7347de-4970-4771-b0ec-922a9ab5f65d"
      },
      "source": [
        "# Checking for independence between features¶\n",
        "# \n",
        "sb.heatmap(df6.corr())  "
      ],
      "execution_count": 646,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a7c679e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 646
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0jVUy5SJyKA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# splitting our dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3, random_state=25)"
      ],
      "execution_count": 639,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awRDnwTRJ2xC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "55bbadad-6ec8-42c4-b841-ad2e7c43e79e"
      },
      "source": [
        "# fitting our model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "LogReg = LogisticRegression()\n",
        "LogReg.fit(X_train, y_train)"
      ],
      "execution_count": 640,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 640
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stVRv0FCJ5Wf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bff8121d-5968-47ce-f975-33aa8b5be59b"
      },
      "source": [
        "# using our model to make a prediction\n",
        "y_pred = LogReg.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": 642,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 0., ..., 0., 0., 2.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 642
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ji2Bo3lsKLe8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "f0fb9109-906b-4bcf-a37c-fced5d8fc803"
      },
      "source": [
        "# Evaluating the model\n",
        "#\n",
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
        "confusion_matrix\n"
      ],
      "execution_count": 643,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4883,    0,    0],\n",
              "       [   0, 3082,    0],\n",
              "       [   0,    0, 2391]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 643
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJhSjKkzNjdf",
        "colab_type": "text"
      },
      "source": [
        "Logistic Regression\n",
        "5.0 Pts\n",
        "\n",
        "**Excellent**\n",
        "\n",
        "The students exhaustively perform Logistic regression by \n",
        "\n",
        "performing feature engineering where necessary, \n",
        "\n",
        "Training an optimal classifier, \n",
        "\n",
        "Interpret the result obtained and \n",
        "\n",
        "make a suitable prediction. \n",
        "\n",
        "The students should also challenge their solution by \n",
        "\n",
        "providing insights on how you can make improvements."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BK5CV9kBKw77",
        "colab_type": "text"
      },
      "source": [
        "# **Discriminant Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syW7KVdZKwhY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yc17b33CK4uY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "52c9854b-427a-4393-e83a-ea75200b8837"
      },
      "source": [
        "df4 = df1.fillna(0)\n",
        "df4.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34005, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0uQh5Z4L2C7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df4.iloc[:, 0:4].values\n",
        "y = df4.iloc[:2, ].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oLHwGsnL5FE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ue3_Sun3MBtB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA8PKHFEMEUH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
        "lda = LDA(n_components=1)\n",
        "X_train = lda.fit_transform(X_train, y)\n",
        "X_test = lda.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gUYH5PDMG2p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVYk4r4NMKDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "print('Accuracy' + str(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdaFZtknz2a6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}