{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Loaded Pandas, mounted csv file via drive. printed head\n"
      ],
      "metadata": {
        "id": "2mbEPr8c5XA8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "99ZyG_lH7JLG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6RQQjWvBAxYu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d792128c-319f-4cce-c731-7aa6b0c1f3fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "   Data As Of  Start Date    End Date     Group  Year  Month          State  \\\n",
            "0  09/24/2023  01/01/2020  09/23/2023  By Total   NaN    NaN  United States   \n",
            "1  09/24/2023  01/01/2020  09/23/2023  By Total   NaN    NaN  United States   \n",
            "2  09/24/2023  01/01/2020  09/23/2023  By Total   NaN    NaN  United States   \n",
            "3  09/24/2023  01/01/2020  09/23/2023  By Total   NaN    NaN  United States   \n",
            "4  09/24/2023  01/01/2020  09/23/2023  By Total   NaN    NaN  United States   \n",
            "\n",
            "        Condition Group                Condition ICD10_codes Age Group  \\\n",
            "0  Respiratory diseases  Influenza and pneumonia     J09-J18      0-24   \n",
            "1  Respiratory diseases  Influenza and pneumonia     J09-J18     25-34   \n",
            "2  Respiratory diseases  Influenza and pneumonia     J09-J18     35-44   \n",
            "3  Respiratory diseases  Influenza and pneumonia     J09-J18     45-54   \n",
            "4  Respiratory diseases  Influenza and pneumonia     J09-J18     55-64   \n",
            "\n",
            "   COVID-19 Deaths  Number of Mentions Flag  \n",
            "0           1569.0              1647.0  NaN  \n",
            "1           5804.0              6029.0  NaN  \n",
            "2          15080.0             15699.0  NaN  \n",
            "3          37414.0             38878.0  NaN  \n",
            "4          82668.0             85708.0  NaN  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "file_path = '/content/drive/My Drive/Conditions_Contributing_to_COVID-19_Deaths__by_State_and_Age__Provisional_2020-2023.csv'\n",
        "\n",
        "import pandas as pd\n",
        "data_1 = pd.read_csv(file_path)\n",
        "\n",
        "print(data_1.head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "ZJqLSmuXR6CC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gathered basic statistical and descriptive information"
      ],
      "metadata": {
        "id": "Axv6TIEa5g9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_1_shape = data_1.shape\n",
        "\n",
        "# Descriptive statistics for all columns\n",
        "data_1_describe = data_1.describe(include='all')\n",
        "\n",
        "# Display the last few rows of the DataFrame\n",
        "data_1_tail = data_1.tail()\n",
        "\n",
        "# Display the data types of each column\n",
        "data_1_dtypes = data_1.dtypes\n",
        "\n",
        "data_1_shape, data_1_describe, data_1_tail, data_1_dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Shk91LTEYhv",
        "outputId": "8d4c6d64-2884-4901-e6f4-601b7a9e052f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((621000, 14),\n",
              "         Data As Of  Start Date    End Date     Group           Year  \\\n",
              " count       621000      621000      621000    621000  608580.000000   \n",
              " unique           1          45          45         3            NaN   \n",
              " top     09/24/2023  01/01/2020  09/23/2023  By Month            NaN   \n",
              " freq        621000       37260       37260    558900            NaN   \n",
              " mean           NaN         NaN         NaN       NaN    2021.408163   \n",
              " std            NaN         NaN         NaN       NaN       1.086436   \n",
              " min            NaN         NaN         NaN       NaN    2020.000000   \n",
              " 25%            NaN         NaN         NaN       NaN    2020.000000   \n",
              " 50%            NaN         NaN         NaN       NaN    2021.000000   \n",
              " 75%            NaN         NaN         NaN       NaN    2022.000000   \n",
              " max            NaN         NaN         NaN       NaN    2023.000000   \n",
              " \n",
              "                 Month          State       Condition Group  \\\n",
              " count   558900.000000         621000                621000   \n",
              " unique            NaN             54                    12   \n",
              " top               NaN  United States  Circulatory diseases   \n",
              " freq              NaN          11500                189000   \n",
              " mean         6.200000            NaN                   NaN   \n",
              " std          3.350625            NaN                   NaN   \n",
              " min          1.000000            NaN                   NaN   \n",
              " 25%          3.000000            NaN                   NaN   \n",
              " 50%          6.000000            NaN                   NaN   \n",
              " 75%          9.000000            NaN                   NaN   \n",
              " max         12.000000            NaN                   NaN   \n",
              " \n",
              "                       Condition ICD10_codes Age Group  COVID-19 Deaths  \\\n",
              " count                    621000      621000    621000     4.375510e+05   \n",
              " unique                       23          23        10              NaN   \n",
              " top     Influenza and pneumonia     J09-J18      0-24              NaN   \n",
              " freq                      27000       27000     62100              NaN   \n",
              " mean                        NaN         NaN       NaN     1.201179e+02   \n",
              " std                         NaN         NaN       NaN     2.980201e+03   \n",
              " min                         NaN         NaN       NaN     0.000000e+00   \n",
              " 25%                         NaN         NaN       NaN     0.000000e+00   \n",
              " 50%                         NaN         NaN       NaN     0.000000e+00   \n",
              " 75%                         NaN         NaN       NaN     1.800000e+01   \n",
              " max                         NaN         NaN       NaN     1.146242e+06   \n",
              " \n",
              "         Number of Mentions                                               Flag  \n",
              " count         4.434230e+05                                             183449  \n",
              " unique                 NaN                                                  1  \n",
              " top                    NaN  One or more data cells have counts between 1-9...  \n",
              " freq                   NaN                                             183449  \n",
              " mean          1.293348e+02                                                NaN  \n",
              " std           3.203936e+03                                                NaN  \n",
              " min           0.000000e+00                                                NaN  \n",
              " 25%           0.000000e+00                                                NaN  \n",
              " 50%           0.000000e+00                                                NaN  \n",
              " 75%           1.900000e+01                                                NaN  \n",
              " max           1.146242e+06                                                NaN  ,\n",
              "         Data As Of  Start Date    End Date     Group    Year  Month  \\\n",
              " 620995  09/24/2023  05/01/2023  05/31/2023  By Month  2023.0    5.0   \n",
              " 620996  09/24/2023  06/01/2023  06/30/2023  By Month  2023.0    6.0   \n",
              " 620997  09/24/2023  07/01/2023  07/31/2023  By Month  2023.0    7.0   \n",
              " 620998  09/24/2023  08/01/2023  08/31/2023  By Month  2023.0    8.0   \n",
              " 620999  09/24/2023  09/01/2023  09/23/2023  By Month  2023.0    9.0   \n",
              " \n",
              "               State Condition Group Condition ICD10_codes Age Group  \\\n",
              " 620995  Puerto Rico        COVID-19  COVID-19        U071  All Ages   \n",
              " 620996  Puerto Rico        COVID-19  COVID-19        U071  All Ages   \n",
              " 620997  Puerto Rico        COVID-19  COVID-19        U071  All Ages   \n",
              " 620998  Puerto Rico        COVID-19  COVID-19        U071  All Ages   \n",
              " 620999  Puerto Rico        COVID-19  COVID-19        U071  All Ages   \n",
              " \n",
              "         COVID-19 Deaths  Number of Mentions Flag  \n",
              " 620995             67.0                67.0  NaN  \n",
              " 620996            122.0               122.0  NaN  \n",
              " 620997            114.0               114.0  NaN  \n",
              " 620998             78.0                78.0  NaN  \n",
              " 620999             36.0                36.0  NaN  ,\n",
              " Data As Of             object\n",
              " Start Date             object\n",
              " End Date               object\n",
              " Group                  object\n",
              " Year                  float64\n",
              " Month                 float64\n",
              " State                  object\n",
              " Condition Group        object\n",
              " Condition              object\n",
              " ICD10_codes            object\n",
              " Age Group              object\n",
              " COVID-19 Deaths       float64\n",
              " Number of Mentions    float64\n",
              " Flag                   object\n",
              " dtype: object)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_1 = pd.DataFrame(data_1)\n",
        "\n",
        "data_1['Data As Of'] = pd.to_datetime(data_1['Data As Of'])\n",
        "data_1['Start Date'] = pd.to_datetime(data_1['Start Date'])\n",
        "data_1 ['End Date'] = pd.to_datetime(data_1['End Date'])\n",
        "\n",
        "print(data_1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkGv0TIwFinl",
        "outputId": "f1d3f272-8a35-437a-9304-b3263ebe0d3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Data As Of Start Date   End Date     Group    Year  Month  \\\n",
            "0      2023-09-24 2020-01-01 2023-09-23  By Total     NaN    NaN   \n",
            "1      2023-09-24 2020-01-01 2023-09-23  By Total     NaN    NaN   \n",
            "2      2023-09-24 2020-01-01 2023-09-23  By Total     NaN    NaN   \n",
            "3      2023-09-24 2020-01-01 2023-09-23  By Total     NaN    NaN   \n",
            "4      2023-09-24 2020-01-01 2023-09-23  By Total     NaN    NaN   \n",
            "...           ...        ...        ...       ...     ...    ...   \n",
            "620995 2023-09-24 2023-05-01 2023-05-31  By Month  2023.0    5.0   \n",
            "620996 2023-09-24 2023-06-01 2023-06-30  By Month  2023.0    6.0   \n",
            "620997 2023-09-24 2023-07-01 2023-07-31  By Month  2023.0    7.0   \n",
            "620998 2023-09-24 2023-08-01 2023-08-31  By Month  2023.0    8.0   \n",
            "620999 2023-09-24 2023-09-01 2023-09-23  By Month  2023.0    9.0   \n",
            "\n",
            "                State       Condition Group                Condition  \\\n",
            "0       United States  Respiratory diseases  Influenza and pneumonia   \n",
            "1       United States  Respiratory diseases  Influenza and pneumonia   \n",
            "2       United States  Respiratory diseases  Influenza and pneumonia   \n",
            "3       United States  Respiratory diseases  Influenza and pneumonia   \n",
            "4       United States  Respiratory diseases  Influenza and pneumonia   \n",
            "...               ...                   ...                      ...   \n",
            "620995    Puerto Rico              COVID-19                 COVID-19   \n",
            "620996    Puerto Rico              COVID-19                 COVID-19   \n",
            "620997    Puerto Rico              COVID-19                 COVID-19   \n",
            "620998    Puerto Rico              COVID-19                 COVID-19   \n",
            "620999    Puerto Rico              COVID-19                 COVID-19   \n",
            "\n",
            "       ICD10_codes Age Group  COVID-19 Deaths  Number of Mentions Flag  \n",
            "0          J09-J18      0-24           1569.0              1647.0  NaN  \n",
            "1          J09-J18     25-34           5804.0              6029.0  NaN  \n",
            "2          J09-J18     35-44          15080.0             15699.0  NaN  \n",
            "3          J09-J18     45-54          37414.0             38878.0  NaN  \n",
            "4          J09-J18     55-64          82668.0             85708.0  NaN  \n",
            "...            ...       ...              ...                 ...  ...  \n",
            "620995        U071  All Ages             67.0                67.0  NaN  \n",
            "620996        U071  All Ages            122.0               122.0  NaN  \n",
            "620997        U071  All Ages            114.0               114.0  NaN  \n",
            "620998        U071  All Ages             78.0                78.0  NaN  \n",
            "620999        U071  All Ages             36.0                36.0  NaN  \n",
            "\n",
            "[621000 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some Charting Below as Part of EDA"
      ],
      "metadata": {
        "id": "PWpR2o0X5nka"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "age_group_counts_data_1 = data_1['Age Group'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(age_group_counts_data_1.index, age_group_counts_data_1.values, color='skyblue')\n",
        "plt.title('Frequency Distribution of Age Groups in Data 1')\n",
        "plt.xlabel('Age Group')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels to show clearly\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Age Group', y='COVID-19 Deaths', data=data_1)\n",
        "plt.title('COVID-19 Deaths by Age Group in Data 1')\n",
        "plt.xlabel('Age Group')\n",
        "plt.ylabel('COVID-19 Deaths')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "BvTduXsuFo5T",
        "outputId": "9a6b6750-5152-40f5-e5ef-c5b3939f8180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-00a1b1d3599e>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mage_group_counts_data_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Age Group'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'data_1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Covid19 Deaths by Condition and Age Group"
      ],
      "metadata": {
        "id": "ie9yRYS05yb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "barplot1 = sns.barplot(x='Condition Group', y='COVID-19 Deaths', hue='Age Group', data=data_1)\n",
        "plt.title('COVID-19 Deaths by Condition Group and Age Group')\n",
        "plt.xlabel('Condition Group')\n",
        "plt.ylabel('COVID-19 Deaths')\n",
        "plt.legend(title='Age Group')\n",
        "barplot1.set_xticklabels(barplot1.get_xticklabels(), rotation=90)  # Rotate x-axis labels\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Eh6GOUnTFr1E",
        "outputId": "9d378baa-7d40-4ad8-bdb4-b508e5d05941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MAde a unique States data set and removed 'United States' as it was being included in the group of states and was badly skewing the data. Fixed graph is below."
      ],
      "metadata": {
        "id": "Qp_8qu0u5wFs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "unique_states = data_1['State'].unique()\n",
        "\n",
        "\n",
        "data_1_no_us = data_1[data_1['State'] != 'United States']\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "barplot_no_us = sns.barplot(x='State', y='COVID-19 Deaths', hue='Age Group', data=data_1_no_us)\n",
        "plt.title('COVID-19 Deaths by State and Age Group (excluding United States)')\n",
        "plt.xlabel('State')\n",
        "plt.ylabel('COVID-19 Deaths')\n",
        "plt.legend(title='Age Group')\n",
        "barplot_no_us.set_xticklabels(barplot_no_us.get_xticklabels(), rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "daQVl4EpFw_M",
        "outputId": "322ca968-a860-4fc8-b338-cf5c75ac2334"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checked for N/a as part pof preprocessing for ML models"
      ],
      "metadata": {
        "id": "mukoDUr96Oc6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values_count = data_1_no_us.isnull().sum()\n",
        "print(missing_values_count)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5IIKvlcSF022",
        "outputId": "5ae64060-4be0-4b62-a91d-b6f45cc8b1d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data As Of                 0\n",
            "Start Date                 0\n",
            "End Date                   0\n",
            "Group                      0\n",
            "Year                   12190\n",
            "Month                  60950\n",
            "State                      0\n",
            "Condition Group            0\n",
            "Condition                  0\n",
            "ICD10_codes                0\n",
            "Age Group                  0\n",
            "COVID-19 Deaths       183449\n",
            "Number of Mentions    177577\n",
            "Flag                  426051\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There were quite aq few N/a and bad data, which I deleted."
      ],
      "metadata": {
        "id": "rHtgsxsw6dLw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_1_cleaned = data_1.dropna(subset=['COVID-19 Deaths', 'Number of Mentions'])\n"
      ],
      "metadata": {
        "id": "TJ-lNkCJTCYv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checked to see how much of the data was removed above"
      ],
      "metadata": {
        "id": "r1Zl818D6cIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "original_row_count = data_1.shape[0]\n",
        "cleaned_row_count = data_1_cleaned.shape[0]\n",
        "rows_dropped = original_row_count - cleaned_row_count\n",
        "\n",
        "print(f\"Original number of rows: {original_row_count}\")\n",
        "print(f\"Number of rows after cleaning: {cleaned_row_count}\")\n",
        "print(f\"Number of rows dropped: {rows_dropped}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHpD344oTNNV",
        "outputId": "5e2330d5-9755-49eb-874a-b4207a83d144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original number of rows: 621000\n",
            "Number of rows after cleaning: 437551\n",
            "Number of rows dropped: 183449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While running the ML models below, I had issues with categorical columns (this data is mostly categorical) below, I hot-encoded it to make it numerical."
      ],
      "metadata": {
        "id": "aJXJHU6O7in0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "date_cols = ['Data As Of', 'Start Date', 'End Date', 'Year', 'Month']\n",
        "data_1_cleaned = data_1_cleaned.drop(columns=date_cols)\n",
        "\n",
        "categorical_cols = data_1_cleaned.select_dtypes(include=['object', 'category']).columns\n",
        "\n",
        "encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "data_1_encoded = pd.DataFrame(encoder.fit_transform(data_1_cleaned[categorical_cols]))\n",
        "\n",
        "data_1_encoded.columns = encoder.get_feature_names_out(categorical_cols)\n",
        "\n",
        "data_1_encoded.index = data_1_cleaned.index\n",
        "\n",
        "num_data_1_cleaned = data_1_cleaned.drop(categorical_cols, axis=1)\n",
        "data_1_preprocessed = pd.concat([num_data_1_cleaned, data_1_encoded], axis=1)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDOq5vQYTT3X",
        "outputId": "297abda2-0125-4d5e-e740-9c473a7cae60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "NbqNpzHt7c_k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "date_cols = ['Data As Of', 'Start Date', 'End Date', 'Year', 'Month']\n",
        "data_1_cleaned = data_1_cleaned.drop(columns=date_cols, errors='ignore')\n",
        "\n",
        "\n",
        "categorical_cols = data_1_cleaned.select_dtypes(include=['object', 'category']).columns\n",
        "\n",
        "# Applying One-Hot Encoding\n",
        "encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "data_1_encoded = pd.DataFrame(encoder.fit_transform(data_1_cleaned[categorical_cols]))\n",
        "\n",
        "data_1_encoded.index = data_1_cleaned.index\n",
        "\n",
        "num_data_1_cleaned = data_1_cleaned.drop(categorical_cols, axis=1)\n",
        "data_1_preprocessed = pd.concat([num_data_1_cleaned, data_1_encoded], axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_EzJsCLWH6Y",
        "outputId": "741ef1ca-00ac-45b6-d42f-6d6233ebb68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first Model - SKLearn Train, Test, Split"
      ],
      "metadata": {
        "id": "kFDSTAB970De"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# 'COVID-19 Deaths' is the target variable\n",
        "y = data_1_preprocessed['COVID-19 Deaths']\n",
        "X = data_1_preprocessed.drop('COVID-19 Deaths', axis=1)\n",
        "\n",
        "# Splitting the dataset into training (80%) and testing (20%) sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "print(f\"Training set size: {X_train.shape[0]} rows\")\n",
        "print(f\"Testing set size: {X_test.shape[0]} rows\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYfKT1NYTuJZ",
        "outputId": "afd020ed-cbd3-4eef-ed9f-307d4557af99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 350040 rows\n",
            "Testing set size: 87511 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just viewing my pre-processed data to see what it looks like"
      ],
      "metadata": {
        "id": "aJvafqgh75zY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_1_preprocessed.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "eN7bNZ2BWZHY",
        "outputId": "5197a41b-9084-49f7-d7bc-e856defa6039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   COVID-19 Deaths  Number of Mentions    0    1    2    3    4    5    6  \\\n",
              "0           1569.0              1647.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
              "1           5804.0              6029.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
              "2          15080.0             15699.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
              "3          37414.0             38878.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
              "4          82668.0             85708.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
              "\n",
              "     7  ...  116  117  118  119  120  121  122  123  124  125  \n",
              "0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
              "1  0.0  ...  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
              "2  0.0  ...  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
              "3  0.0  ...  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
              "4  0.0  ...  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
              "\n",
              "[5 rows x 128 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5d45725-f550-42db-9c44-24a64cbbbfe9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>COVID-19 Deaths</th>\n",
              "      <th>Number of Mentions</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>...</th>\n",
              "      <th>116</th>\n",
              "      <th>117</th>\n",
              "      <th>118</th>\n",
              "      <th>119</th>\n",
              "      <th>120</th>\n",
              "      <th>121</th>\n",
              "      <th>122</th>\n",
              "      <th>123</th>\n",
              "      <th>124</th>\n",
              "      <th>125</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1569.0</td>\n",
              "      <td>1647.0</td>\n",
              "      <td>0.0</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>...</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>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5804.0</td>\n",
              "      <td>6029.0</td>\n",
              "      <td>0.0</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>...</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>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15080.0</td>\n",
              "      <td>15699.0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>0.0</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>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37414.0</td>\n",
              "      <td>38878.0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82668.0</td>\n",
              "      <td>85708.0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 128 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5d45725-f550-42db-9c44-24a64cbbbfe9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f5d45725-f550-42db-9c44-24a64cbbbfe9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f5d45725-f550-42db-9c44-24a64cbbbfe9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-024dc821-8dad-4d33-8a4c-fcc83ea16b1b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-024dc821-8dad-4d33-8a4c-fcc83ea16b1b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-024dc821-8dad-4d33-8a4c-fcc83ea16b1b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "checking dtypes for later ML models"
      ],
      "metadata": {
        "id": "Woa6uIoa8JQo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.dtypes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czTn3hSBUT4L",
        "outputId": "36d99cdc-8df2-494b-aa98-0632f29139de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Mentions    float64\n",
            "0                     float64\n",
            "1                     float64\n",
            "2                     float64\n",
            "3                     float64\n",
            "                       ...   \n",
            "121                   float64\n",
            "122                   float64\n",
            "123                   float64\n",
            "124                   float64\n",
            "125                   float64\n",
            "Length: 127, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train/Test Split View"
      ],
      "metadata": {
        "id": "0hefGi4c8H_4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.columns)\n",
        "print(X_test.columns)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2kUy0hkUybr",
        "outputId": "894b4c96-502a-4c05-fe44-f1895f3afe0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Number of Mentions',                    0,                    1,\n",
            "                          2,                    3,                    4,\n",
            "                          5,                    6,                    7,\n",
            "                          8,\n",
            "       ...\n",
            "                        116,                  117,                  118,\n",
            "                        119,                  120,                  121,\n",
            "                        122,                  123,                  124,\n",
            "                        125],\n",
            "      dtype='object', length=127)\n",
            "Index(['Number of Mentions',                    0,                    1,\n",
            "                          2,                    3,                    4,\n",
            "                          5,                    6,                    7,\n",
            "                          8,\n",
            "       ...\n",
            "                        116,                  117,                  118,\n",
            "                        119,                  120,                  121,\n",
            "                        122,                  123,                  124,\n",
            "                        125],\n",
            "      dtype='object', length=127)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.dtypes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6Um35hiV79T",
        "outputId": "05050e39-1048-4edf-b0c5-119620b455ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Mentions    float64\n",
            "0                     float64\n",
            "1                     float64\n",
            "2                     float64\n",
            "3                     float64\n",
            "                       ...   \n",
            "121                   float64\n",
            "122                   float64\n",
            "123                   float64\n",
            "124                   float64\n",
            "125                   float64\n",
            "Length: 127, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(X_train.isnull().sum())\n",
        "\n",
        "# Check data types\n",
        "print(X_train.dtypes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "383-cyDtWkC8",
        "outputId": "334ff6b6-10ec-4f6e-b069-5ef209ab39ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Mentions    0\n",
            "0                     0\n",
            "1                     0\n",
            "2                     0\n",
            "3                     0\n",
            "                     ..\n",
            "121                   0\n",
            "122                   0\n",
            "123                   0\n",
            "124                   0\n",
            "125                   0\n",
            "Length: 127, dtype: int64\n",
            "Number of Mentions    float64\n",
            "0                     float64\n",
            "1                     float64\n",
            "2                     float64\n",
            "3                     float64\n",
            "                       ...   \n",
            "121                   float64\n",
            "122                   float64\n",
            "123                   float64\n",
            "124                   float64\n",
            "125                   float64\n",
            "Length: 127, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converted all types to str for ML modelling below"
      ],
      "metadata": {
        "id": "YKZSlyb78dNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert all column names to strings\n",
        "X.columns = X.columns.astype(str)\n"
      ],
      "metadata": {
        "id": "UQE9m8iAAXLy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.columns = X_train.columns.astype(str)\n",
        "X_test.columns = X_test.columns.astype(str)\n"
      ],
      "metadata": {
        "id": "x6ljjjsK9CdH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training Linear Regression Model on the dataset\n"
      ],
      "metadata": {
        "id": "F_ALMdhy8khY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ut8CGjrbXSof",
        "outputId": "0b502646-a1d2-4d77-9ad0-1bc23b1ab9ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking Linear Regression Model Metrics"
      ],
      "metadata": {
        "id": "esb8CfK58xx4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "csvVCN7KXaY-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R^2 Score: {r2}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCV_TN8dXdCZ",
        "outputId": "f33cfcd9-bcaa-4348-da86-2a46e20a1428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 119573.1576029768\n",
            "R^2 Score: 0.9530692937008057\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coefficients = model.coef_\n"
      ],
      "metadata": {
        "id": "WCFVYCctYX_-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training Columns and Feature Importance"
      ],
      "metadata": {
        "id": "XpckPQ4N9Gf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = X_train.columns\n",
        "feature_importance = pd.DataFrame(coefficients, index=feature_names, columns=['Coefficient'])\n"
      ],
      "metadata": {
        "id": "PpQ3p7ebYf_L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graphing Feature Importance\n"
      ],
      "metadata": {
        "id": "sujlYQP-9MLE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "feature_importance.sort_values(by='Coefficient', ascending=False).plot(kind='bar', figsize=(12,6))\n",
        "plt.title('Feature Importance in Linear Regression Model')\n",
        "plt.ylabel('Coefficient Value')\n",
        "plt.xlabel('Features')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "id": "1yQoH7csYjs4",
        "outputId": "3146884e-8751-4e55-c766-f4d7d43c812b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running Standard Scaler to fix the data before fruther training"
      ],
      "metadata": {
        "id": "j7oF8C9R9W_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "\n",
        "scaler.fit(X_train)\n",
        "\n",
        "\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "ycxwU-LcY3sD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training and gathering metrics on Linear Regression Model. Below is the same as above but with the scaled data so it is easy to visualise"
      ],
      "metadata": {
        "id": "w5u2zJXk9Vxz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred_scaled = model.predict(X_test_scaled)\n",
        "\n"
      ],
      "metadata": {
        "id": "do8f1UEjY7Ed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "\n",
        "y_pred_scaled = model.predict(X_test_scaled)\n",
        "\n",
        "\n",
        "mse_scaled = mean_squared_error(y_test, y_pred_scaled)\n",
        "print(f\"Mean Squared Error: {mse_scaled}\")\n",
        "\n",
        "\n",
        "r2_scaled = r2_score(y_test, y_pred_scaled)\n",
        "print(f\"R^2 Score: {r2_scaled}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A08nqOReZXrl",
        "outputId": "fd87c166-76f4-4737-c19f-7930d9acc463"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 119530.27704932197\n",
            "R^2 Score: 0.9530861236876511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coefficients = model.coef_\n"
      ],
      "metadata": {
        "id": "bXdvvIBPZqEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = X_train.columns\n",
        "feature_importance = pd.DataFrame(coefficients, index=feature_names, columns=['Coefficient'])\n"
      ],
      "metadata": {
        "id": "npjyCaOsZsX3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance.sort_values(by='Coefficient', ascending=False).plot(kind='bar', figsize=(12,6))\n",
        "plt.title('Feature Importance in Linear Regression Model')\n",
        "plt.ylabel('Coefficient Value')\n",
        "plt.xlabel('Features')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "id": "kKuQLNsBZt5b",
        "outputId": "9bd8accc-7d37-4049-ae16-c1a90b247ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coefficients = model.coef_\n",
        "\n",
        "feature_names = X_train.columns\n",
        "feature_importance = pd.DataFrame(coefficients, index=feature_names, columns=['Coefficient'])\n"
      ],
      "metadata": {
        "id": "KNFNkgkmZ_wW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_features = feature_importance.sort_values(by='Coefficient', ascending=False)\n",
        "\n",
        "sorted_features.plot(kind='bar', figsize=(12,6))\n",
        "plt.title('Feature Importance in Linear Regression Model')\n",
        "plt.ylabel('Coefficient Value')\n",
        "plt.xlabel('Features')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "id": "K3zT99JaaCPs",
        "outputId": "b44393f8-edf6-4fa8-8f4f-95f798aeac62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Viewing Feature Importance Data"
      ],
      "metadata": {
        "id": "fsQ1fy6k9-Eq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coefficients = model.coef_\n",
        "\n",
        "feature_names = X_train.columns\n",
        "feature_importance = pd.DataFrame(coefficients, index=feature_names, columns=['Coefficient'])\n",
        "\n",
        "print(feature_importance)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vl1u2aj2aSHO",
        "outputId": "3e4d45fc-6068-4e60-a9c8-39d41c8504b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     Coefficient\n",
            "Number of Mentions  3.193643e+03\n",
            "0                  -3.228707e+14\n",
            "1                  -1.584457e+14\n",
            "2                  -2.892308e+14\n",
            "3                  -2.441330e+14\n",
            "...                          ...\n",
            "121                -6.542653e+14\n",
            "122                -6.586453e+14\n",
            "123                -7.226933e+14\n",
            "124                -8.236515e+14\n",
            "125                 0.000000e+00\n",
            "\n",
            "[127 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of rows to display at a time\n",
        "chunk_size = 10\n",
        "\n",
        "# Iterate over the DataFrame in chunks\n",
        "for start in range(0, len(feature_importance), chunk_size):\n",
        "    end = start + chunk_size\n",
        "    print(feature_importance.iloc[start:end])\n",
        "    print(\"\\n\")  # Print a newline for better separation between chunks\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcflDSzMar6F",
        "outputId": "f9f8e37c-afe1-4a0f-e1e9-4a9b53f99234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     Coefficient\n",
            "Number of Mentions  3.193643e+03\n",
            "0                  -3.228707e+14\n",
            "1                  -1.584457e+14\n",
            "2                  -2.892308e+14\n",
            "3                  -2.441330e+14\n",
            "4                  -2.605345e+14\n",
            "5                  -2.471668e+14\n",
            "6                  -2.453000e+14\n",
            "7                  -2.624680e+14\n",
            "8                  -2.473037e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "9  -2.503349e+14\n",
            "10 -2.508170e+14\n",
            "11 -2.583988e+14\n",
            "12 -2.614571e+14\n",
            "13 -2.499485e+14\n",
            "14 -2.540121e+14\n",
            "15 -2.525443e+14\n",
            "16 -2.523530e+14\n",
            "17 -2.497357e+14\n",
            "18 -2.481822e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "19 -2.465004e+14\n",
            "20 -2.544298e+14\n",
            "21 -2.459108e+14\n",
            "22 -2.563755e+14\n",
            "23 -2.485521e+14\n",
            "24 -2.499291e+14\n",
            "25 -2.539170e+14\n",
            "26 -2.504892e+14\n",
            "27 -2.471276e+14\n",
            "28 -2.482990e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "29 -2.551684e+14\n",
            "30 -2.484353e+14\n",
            "31 -2.449841e+14\n",
            "32 -2.551495e+14\n",
            "33 -2.527545e+14\n",
            "34 -2.426008e+14\n",
            "35 -2.530982e+14\n",
            "36 -2.476750e+14\n",
            "37 -2.518932e+14\n",
            "38 -2.543539e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "39 -2.578759e+14\n",
            "40 -2.469122e+14\n",
            "41 -2.459305e+14\n",
            "42 -2.565072e+14\n",
            "43 -2.445688e+14\n",
            "44 -2.552629e+14\n",
            "45 -2.481432e+14\n",
            "46 -2.518932e+14\n",
            "47 -2.523339e+14\n",
            "48 -2.624313e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "49 -3.005830e+14\n",
            "50 -2.419610e+14\n",
            "51 -2.645681e+14\n",
            "52 -2.498711e+14\n",
            "53 -2.474406e+14\n",
            "54 -2.529073e+14\n",
            "55 -2.492513e+14\n",
            "56 -2.615676e+14\n",
            "57  3.310441e+14\n",
            "58  1.508162e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "59  1.371026e+14\n",
            "60  2.154090e+14\n",
            "61 -3.856187e+14\n",
            "62 -5.358547e+14\n",
            "63 -4.984364e+14\n",
            "64 -5.795386e+14\n",
            "65 -2.881679e+12\n",
            "66  1.210663e+14\n",
            "67  6.967718e+14\n",
            "68  3.919754e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "69 -4.097677e+13\n",
            "70 -3.093303e+14\n",
            "71 -4.052706e+14\n",
            "72 -6.728743e+14\n",
            "73 -2.441636e+14\n",
            "74 -3.476412e+14\n",
            "75  9.645447e+13\n",
            "76 -7.996895e+14\n",
            "77  2.970814e+14\n",
            "78  2.487471e+14\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "79  1.988275e+14\n",
            "80 -2.272504e+14\n",
            "81  1.396508e+14\n",
            "82 -1.633230e+14\n",
            "83  1.031200e+14\n",
            "84 -1.893600e+14\n",
            "85 -1.537223e+14\n",
            "86 -9.015754e+13\n",
            "87 -3.362053e+14\n",
            "88  2.154295e+13\n",
            "\n",
            "\n",
            "     Coefficient\n",
            "89 -1.625299e+14\n",
            "90 -8.376282e+14\n",
            "91 -3.045707e+14\n",
            "92 -3.263975e+14\n",
            "93 -1.573358e+14\n",
            "94  9.804019e+13\n",
            "95 -2.107130e+14\n",
            "96  4.728852e+14\n",
            "97 -4.112718e+14\n",
            "98 -6.801835e+13\n",
            "\n",
            "\n",
            "      Coefficient\n",
            "99  -2.302176e+14\n",
            "100 -6.023744e+14\n",
            "101 -2.371293e+14\n",
            "102 -5.434325e+13\n",
            "103 -1.477647e+14\n",
            "104 -6.502466e+14\n",
            "105 -4.921786e+14\n",
            "106 -2.558928e+14\n",
            "107 -1.370602e+14\n",
            "108  4.421373e+14\n",
            "\n",
            "\n",
            "      Coefficient\n",
            "109 -3.167915e+14\n",
            "110 -2.010348e+14\n",
            "111  3.930446e+13\n",
            "112 -3.888962e+14\n",
            "113  1.032215e+14\n",
            "114  2.224110e+14\n",
            "115 -7.720316e+14\n",
            "116 -7.419122e+14\n",
            "117 -7.079868e+14\n",
            "118 -6.761242e+14\n",
            "\n",
            "\n",
            "      Coefficient\n",
            "119 -6.560650e+14\n",
            "120 -6.505726e+14\n",
            "121 -6.542653e+14\n",
            "122 -6.586453e+14\n",
            "123 -7.226933e+14\n",
            "124 -8.236515e+14\n",
            "125  0.000000e+00\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running Lasso ML Model and Running Metrics"
      ],
      "metadata": {
        "id": "hLmEpdBK-Lxo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n"
      ],
      "metadata": {
        "id": "wqX_EH4ObU_I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_model = Lasso(alpha=1.0)\n"
      ],
      "metadata": {
        "id": "-Y9HW6h6bcvq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "oZPeYdR2be3C",
        "outputId": "82b219bb-ed2f-43f8-f4ac-4855d189a90b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "y_pred = lasso_model.predict(X_test)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R^2 Score: {r2}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-L-IugxbgAk",
        "outputId": "c0df712e-0cd5-4992-a548-ab5664067ba8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 119629.68971020046\n",
            "R^2 Score: 0.9530471056799006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "lasso_model = Lasso(alpha=1.0)\n",
        "\n",
        "lasso_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = lasso_model.predict(X_test)\n",
        "\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R^2 Score: {r2}\")\n"
      ],
      "metadata": {
        "id": "G5HWpCNB2EG1",
        "outputId": "9f9fbe7e-06a5-4d73-9dae-ffbc4344e272",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 119629.68971020046\n",
            "R^2 Score: 0.9530471056799006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating Metrics"
      ],
      "metadata": {
        "id": "-n3HIt1i-ZzZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
        "print(\"Mean Absolute Error (MAE):\", mae)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIdm95eO_kad",
        "outputId": "accc6b7c-2144-4a7a-9448-82df2f66f008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE): 345.8752516590345\n",
            "Mean Absolute Error (MAE): 18.54945519148683\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running Residual Plot"
      ],
      "metadata": {
        "id": "vM_veNUQ-mEA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "residuals = y_test - y_pred\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(y_pred, residuals)\n",
        "plt.hlines(y=0, xmin=y_pred.min(), xmax=y_pred.max(), colors='red')\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residual Plot')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "temjdt4v_rrv",
        "outputId": "f2460056-fa96-4b42-bc43-f9f33c4a6c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Running Cross Val Score"
      ],
      "metadata": {
        "id": "7rBw74WM_3cO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "rmse_scores = np.sqrt(-scores)\n",
        "\n",
        "print(\"Cross-validated RMSE scores:\", rmse_scores)\n",
        "print(\"Mean RMSE:\", rmse_scores.mean())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0za_EY3_3eo",
        "outputId": "701d3118-4065-4cb7-9420-d5528707d239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validated RMSE scores: [1094.62584254   41.30816971   36.25026322   36.84615      41.14088855]\n",
            "Mean RMSE: 250.03426280424463\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running KFold ML Model"
      ],
      "metadata": {
        "id": "Q4r5X6rK-wW7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "folds = list(kf.split(X))\n",
        "\n",
        "train_indices, test_indices = folds[0]\n"
      ],
      "metadata": {
        "id": "BmUgiRBbJgaZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_fold1, y_fold1 = X.iloc[test_indices], y.iloc[test_indices]\n",
        "\n",
        "\n",
        "print(X_fold1.describe())\n",
        "print(y_fold1.describe())\n",
        "\n",
        "print(X.describe())\n",
        "print(y.describe())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzeJdmuvJi8Z",
        "outputId": "cfbfb7d3-0a89-4dde-81e7-cfd3c238c1c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Number of Mentions             0             1             2  \\\n",
            "count        87511.000000  87511.000000  87511.000000  87511.000000   \n",
            "mean           115.520952      0.888700      0.023951      0.087349   \n",
            "std           1855.304203      0.314505      0.152898      0.282347   \n",
            "min              0.000000      0.000000      0.000000      0.000000   \n",
            "25%              0.000000      1.000000      0.000000      0.000000   \n",
            "50%              0.000000      1.000000      0.000000      0.000000   \n",
            "75%             19.000000      1.000000      0.000000      0.000000   \n",
            "max         298663.000000      1.000000      1.000000      1.000000   \n",
            "\n",
            "                  3             4             5             6             7  \\\n",
            "count  87511.000000  87511.000000  87511.000000  87511.000000  87511.000000   \n",
            "mean       0.017643      0.020249      0.017609      0.018386      0.020455   \n",
            "std        0.131653      0.140851      0.131527      0.134344      0.141550   \n",
            "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
            "\n",
            "                  8  ...           116           117           118  \\\n",
            "count  87511.000000  ...  87511.000000  87511.000000  87511.000000   \n",
            "mean       0.017723  ...      0.111449      0.099325      0.089372   \n",
            "std        0.131945  ...      0.314689      0.299099      0.285281   \n",
            "min        0.000000  ...      0.000000      0.000000      0.000000   \n",
            "25%        0.000000  ...      0.000000      0.000000      0.000000   \n",
            "50%        0.000000  ...      0.000000      0.000000      0.000000   \n",
            "75%        0.000000  ...      0.000000      0.000000      0.000000   \n",
            "max        1.000000  ...      1.000000      1.000000      1.000000   \n",
            "\n",
            "                119           120           121           122           123  \\\n",
            "count  87511.000000  87511.000000  87511.000000  87511.000000  87511.000000   \n",
            "mean       0.082024      0.081795      0.083270      0.084389      0.104890   \n",
            "std        0.274403      0.274054      0.276291      0.277972      0.306413   \n",
            "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
            "\n",
            "                124      125  \n",
            "count  87511.000000  87511.0  \n",
            "mean       0.142851      1.0  \n",
            "std        0.349922      0.0  \n",
            "min        0.000000      1.0  \n",
            "25%        0.000000      1.0  \n",
            "50%        0.000000      1.0  \n",
            "75%        0.000000      1.0  \n",
            "max        1.000000      1.0  \n",
            "\n",
            "[8 rows x 127 columns]\n",
            "count     87511.000000\n",
            "mean        105.524231\n",
            "std        1596.212786\n",
            "min           0.000000\n",
            "25%           0.000000\n",
            "50%           0.000000\n",
            "75%          18.000000\n",
            "max      194736.000000\n",
            "Name: COVID-19 Deaths, dtype: float64\n",
            "       Number of Mentions              0              1              2  \\\n",
            "count        4.375510e+05  437551.000000  437551.000000  437551.000000   \n",
            "mean         1.309027e+02       0.887896       0.024482       0.087622   \n",
            "std          3.225334e+03       0.315494       0.154539       0.282744   \n",
            "min          0.000000e+00       0.000000       0.000000       0.000000   \n",
            "25%          0.000000e+00       1.000000       0.000000       0.000000   \n",
            "50%          0.000000e+00       1.000000       0.000000       0.000000   \n",
            "75%          1.900000e+01       1.000000       0.000000       0.000000   \n",
            "max          1.146242e+06       1.000000       1.000000       1.000000   \n",
            "\n",
            "                   3              4              5              6  \\\n",
            "count  437551.000000  437551.000000  437551.000000  437551.000000   \n",
            "mean        0.017349       0.019829       0.017694       0.017632   \n",
            "std         0.130568       0.139411       0.131837       0.131611   \n",
            "min         0.000000       0.000000       0.000000       0.000000   \n",
            "25%         0.000000       0.000000       0.000000       0.000000   \n",
            "50%         0.000000       0.000000       0.000000       0.000000   \n",
            "75%         0.000000       0.000000       0.000000       0.000000   \n",
            "max         1.000000       1.000000       1.000000       1.000000   \n",
            "\n",
            "                   7              8  ...            116            117  \\\n",
            "count  437551.000000  437551.000000  ...  437551.000000  437551.000000   \n",
            "mean        0.020110       0.017733  ...       0.110684       0.099367   \n",
            "std         0.140376       0.131979  ...       0.313741       0.299154   \n",
            "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
            "25%         0.000000       0.000000  ...       0.000000       0.000000   \n",
            "50%         0.000000       0.000000  ...       0.000000       0.000000   \n",
            "75%         0.000000       0.000000  ...       0.000000       0.000000   \n",
            "max         1.000000       1.000000  ...       1.000000       1.000000   \n",
            "\n",
            "                 118            119            120            121  \\\n",
            "count  437551.000000  437551.000000  437551.000000  437551.000000   \n",
            "mean        0.089608       0.083519       0.082244       0.083364   \n",
            "std         0.285619       0.276666       0.274737       0.276432   \n",
            "min         0.000000       0.000000       0.000000       0.000000   \n",
            "25%         0.000000       0.000000       0.000000       0.000000   \n",
            "50%         0.000000       0.000000       0.000000       0.000000   \n",
            "75%         0.000000       0.000000       0.000000       0.000000   \n",
            "max         1.000000       1.000000       1.000000       1.000000   \n",
            "\n",
            "                 122            123            124       125  \n",
            "count  437551.000000  437551.000000  437551.000000  437551.0  \n",
            "mean        0.084575       0.104253       0.141387       1.0  \n",
            "std         0.278249       0.305589       0.348421       0.0  \n",
            "min         0.000000       0.000000       0.000000       1.0  \n",
            "25%         0.000000       0.000000       0.000000       1.0  \n",
            "50%         0.000000       0.000000       0.000000       1.0  \n",
            "75%         0.000000       0.000000       0.000000       1.0  \n",
            "max         1.000000       1.000000       1.000000       1.0  \n",
            "\n",
            "[8 rows x 127 columns]\n",
            "count    4.375510e+05\n",
            "mean     1.201179e+02\n",
            "std      2.980201e+03\n",
            "min      0.000000e+00\n",
            "25%      0.000000e+00\n",
            "50%      0.000000e+00\n",
            "75%      1.800000e+01\n",
            "max      1.146242e+06\n",
            "Name: COVID-19 Deaths, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest"
      ],
      "metadata": {
        "id": "i5lBEcHYKewe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "rf_predictions = rf_model.predict(X_test)\n",
        "\n",
        "rf_mse = mean_squared_error(y_test, rf_predictions)\n",
        "rf_r2 = r2_score(y_test, rf_predictions)\n",
        "\n",
        "print(f\"Random Forest - Mean Squared Error: {rf_mse}\")\n",
        "print(f\"Random Forest - R^2 Score: {rf_r2}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBq9LY3KKbVi",
        "outputId": "67460a2c-7569-4221-c898-07e3cdab2d1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest - Mean Squared Error: 230110.6785765412\n",
            "Random Forest - R^2 Score: 0.9096849419295162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gradient Boosting"
      ],
      "metadata": {
        "id": "YZjfe0sxK1do"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "\n",
        "xgb_model = XGBRegressor(random_state=42)\n",
        "\n",
        "\n",
        "xgb_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "xgb_predictions = xgb_model.predict(X_test)\n",
        "\n",
        "\n",
        "xgb_mse = mean_squared_error(y_test, xgb_predictions)\n",
        "xgb_r2 = r2_score(y_test, xgb_predictions)\n",
        "\n",
        "print(f\"XGBoost - Mean Squared Error: {xgb_mse}\")\n",
        "print(f\"XGBoost - R^2 Score: {xgb_r2}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99_fHpMSK2Kn",
        "outputId": "6a77ab10-a663-45f1-c070-419461b8e178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost - Mean Squared Error: 1435663.2412159576\n",
            "XGBoost - R^2 Score: 0.4365232860892677\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ridge Regression"
      ],
      "metadata": {
        "id": "JYrfOe1LLIZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "\n",
        "data_1_preprocessed.columns = data_1_preprocessed.columns.astype(str)\n",
        "\n",
        "# Sample 3% of the data\n",
        "sampled_data = data_1_preprocessed.sample(frac=0.03, random_state=42)\n",
        "\n",
        "# Split the Sample\n",
        "X_sample = sampled_data.drop('COVID-19 Deaths', axis=1)\n",
        "y_sample = sampled_data['COVID-19 Deaths']\n",
        "X_train_sample, X_test_sample, y_train_sample, y_test_sample = train_test_split(X_sample, y_sample, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "ridge_model = Ridge(random_state=42)\n",
        "ridge_model.fit(X_train_sample, y_train_sample)\n",
        "\n",
        "\n",
        "ridge_predictions = ridge_model.predict(X_test_sample)\n",
        "ridge_mse = mean_squared_error(y_test_sample, ridge_predictions)\n",
        "ridge_r2 = r2_score(y_test_sample, ridge_predictions)\n",
        "\n",
        "print(f\"Ridge Regression - Mean Squared Error: {ridge_mse}\")\n",
        "print(f\"Ridge Regression - R^2 Score: {ridge_r2}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnuxWA1PLLwJ",
        "outputId": "47158734-1c7e-4a66-af11-a75e94c5eba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge Regression - Mean Squared Error: 69549.19693577621\n",
            "Ridge Regression - R^2 Score: 0.8998919383066677\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVR Model (on 3% of the data as 100% was taking hours to process"
      ],
      "metadata": {
        "id": "QPrK47hoxE2w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "sampled_data = data_1_preprocessed.sample(frac=0.03, random_state=42)\n",
        "\n",
        "X_sample = sampled_data.drop('COVID-19 Deaths', axis=1)\n",
        "y_sample = sampled_data['COVID-19 Deaths']\n",
        "X_train_sample, X_test_sample, y_train_sample, y_test_sample = train_test_split(X_sample, y_sample, test_size=0.2, random_state=42)\n",
        "\n",
        "svr_model = SVR()\n",
        "svr_model.fit(X_train_sample, y_train_sample)\n",
        "\n",
        "y_pred_sample = svr_model.predict(X_test_sample)\n",
        "mse = mean_squared_error(y_test_sample, y_pred_sample)\n",
        "r2 = r2_score(y_test_sample, y_pred_sample)\n",
        "\n",
        "print(\"Mean Squared Error:\", mse)\n",
        "print(\"R^2 Score:\", r2)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDWQzB57s2bo",
        "outputId": "305ce40c-53b9-4e61-f50e-fad061163abd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 684977.500927637\n",
            "R^2 Score: 0.014053749826476558\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running Ridge Regression Model"
      ],
      "metadata": {
        "id": "nR5XHq-P_DQx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Sample 3% of the data\n",
        "sampled_data = data_1_preprocessed.sample(frac=0.03, random_state=42)\n",
        "\n",
        "# Split the Sample\n",
        "X_sample = sampled_data.drop('COVID-19 Deaths', axis=1)\n",
        "y_sample = sampled_data['COVID-19 Deaths']\n",
        "X_train_sample, X_test_sample, y_train_sample, y_test_sample = train_test_split(X_sample, y_sample, test_size=0.2, random_state=42)\n",
        "\n",
        "ridge_model = Ridge(random_state=42)\n",
        "ridge_model.fit(X_train_sample, y_train_sample)\n",
        "\n",
        "\n",
        "ridge_predictions = ridge_model.predict(X_test_sample)\n",
        "ridge_mse = mean_squared_error(y_test_sample, ridge_predictions)\n",
        "ridge_r2 = r2_score(y_test_sample, ridge_predictions)\n",
        "\n",
        "print(f\"Ridge Regression - Mean Squared Error: {ridge_mse}\")\n",
        "print(f\"Ridge Regression - R^2 Score: {ridge_r2}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HsIEzmNuQ2J",
        "outputId": "0d928816-b181-4c38-f17a-b4163f4cdaf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge Regression - Mean Squared Error: 69549.19693577621\n",
            "Ridge Regression - R^2 Score: 0.8998919383066677\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QwA0sUDB_CcV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}