Early_Prediction_Of_Alzheimer's_Disease.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Imort Drive**"
      ],
      "metadata": {
        "id": "H8FscQNpGy4q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3EMB5L7C7OH",
        "outputId": "fe7406c2-7b1f-4512-c421-c352a2de3c5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import Dataset**"
      ],
      "metadata": {
        "id": "6IhUEaZPGpXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file into a pandas DataFrame\n",
        "df = pd.read_csv('/content/drive/MyDrive/Franklin/oasis_longitudinal.csv')"
      ],
      "metadata": {
        "id": "QLUwvDLVG4WX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Profiling**"
      ],
      "metadata": {
        "id": "_t3EHUuQKQ4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the number of rows\n",
        "print(\"Number of Rows:\", len(df))\n",
        "\n",
        "# Print the number of columns\n",
        "print(\"Number of Columns:\", len(df.columns))\n",
        "\n",
        "# Display the first few rows of the DataFrame\n",
        "print(df.head())\n",
        "\n",
        "# Get summary statistics of the numerical columns\n",
        "print(df.describe())\n",
        "\n",
        "# Check the data types of each column\n",
        "print(df.dtypes)\n",
        "\n",
        "# check column headers\n",
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsvrHyK8KWrU",
        "outputId": "e05a2b46-1f8e-43bb-b67d-4e0c33ec3511"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Rows: 373\n",
            "Number of Columns: 15\n",
            "  Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  EDUC  \\\n",
            "0  OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87    14   \n",
            "1  OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88    14   \n",
            "2  OAS2_0002  OAS2_0002_MR1     Demented      1         0   M    R   75    12   \n",
            "3  OAS2_0002  OAS2_0002_MR2     Demented      2       560   M    R   76    12   \n",
            "4  OAS2_0002  OAS2_0002_MR3     Demented      3      1895   M    R   80    12   \n",
            "\n",
            "   SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
            "0  2.0  27.0  0.0  1987  0.696  0.883  \n",
            "1  2.0  30.0  0.0  2004  0.681  0.876  \n",
            "2  NaN  23.0  0.5  1678  0.736  1.046  \n",
            "3  NaN  28.0  0.5  1738  0.713  1.010  \n",
            "4  NaN  22.0  0.5  1698  0.701  1.034  \n",
            "            Visit     MR Delay         Age        EDUC         SES  \\\n",
            "count  373.000000   373.000000  373.000000  373.000000  354.000000   \n",
            "mean     1.882038   595.104558   77.013405   14.597855    2.460452   \n",
            "std      0.922843   635.485118    7.640957    2.876339    1.134005   \n",
            "min      1.000000     0.000000   60.000000    6.000000    1.000000   \n",
            "25%      1.000000     0.000000   71.000000   12.000000    2.000000   \n",
            "50%      2.000000   552.000000   77.000000   15.000000    2.000000   \n",
            "75%      2.000000   873.000000   82.000000   16.000000    3.000000   \n",
            "max      5.000000  2639.000000   98.000000   23.000000    5.000000   \n",
            "\n",
            "             MMSE         CDR         eTIV        nWBV         ASF  \n",
            "count  371.000000  373.000000   373.000000  373.000000  373.000000  \n",
            "mean    27.342318    0.290885  1488.128686    0.729568    1.195461  \n",
            "std      3.683244    0.374557   176.139286    0.037135    0.138092  \n",
            "min      4.000000    0.000000  1106.000000    0.644000    0.876000  \n",
            "25%     27.000000    0.000000  1357.000000    0.700000    1.099000  \n",
            "50%     29.000000    0.000000  1470.000000    0.729000    1.194000  \n",
            "75%     30.000000    0.500000  1597.000000    0.756000    1.293000  \n",
            "max     30.000000    2.000000  2004.000000    0.837000    1.587000  \n",
            "Subject ID     object\n",
            "MRI ID         object\n",
            "Group          object\n",
            "Visit           int64\n",
            "MR Delay        int64\n",
            "M/F            object\n",
            "Hand           object\n",
            "Age             int64\n",
            "EDUC            int64\n",
            "SES           float64\n",
            "MMSE          float64\n",
            "CDR           float64\n",
            "eTIV            int64\n",
            "nWBV          float64\n",
            "ASF           float64\n",
            "dtype: object\n",
            "Index(['Subject ID', 'MRI ID', 'Group', 'Visit', 'MR Delay', 'M/F', 'Hand',\n",
            "       'Age', 'EDUC', 'SES', 'MMSE', 'CDR', 'eTIV', 'nWBV', 'ASF'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Eploratory Data Analysis**"
      ],
      "metadata": {
        "id": "WSwPGDn0JCt5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Data Visualization\n",
        "# Example: Boxplot of MMSE score by gender\n",
        "sns.boxplot(x='M/F', y='MMSE', data=df)\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('MMSE Score')\n",
        "plt.title('Distribution of MMSE Score by Gender')\n",
        "plt.show()\n",
        "\n",
        "# Feature Importance\n",
        "# Example: Correlation Heatmap\n",
        "correlation = df.corr()\n",
        "sns.heatmap(correlation, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()\n",
        "\n",
        "# Plot a scatterplot of eTIV vs. nWBV\n",
        "sns.scatterplot(data=df, x='eTIV', y='nWBV')\n",
        "plt.xlabel('Estimated Total Intracranial Volume')\n",
        "plt.ylabel('Normalize Whole Brain Volume')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MOUsM4iyJttw",
        "outputId": "9abc5ffc-e354-458d-d8cc-6e6e3e3712da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-ca2b04d7f540>:14: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Clean**"
      ],
      "metadata": {
        "id": "DeJYjVXqKHOv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find Missing Data"
      ],
      "metadata": {
        "id": "WR2Ua3rXKJAv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "if missing_values.sum() == 0:\n",
        "    print(\"No missing values\")\n",
        "else:\n",
        "    print(\"Missing Values:\")\n",
        "    print(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0puBuvIRKBY2",
        "outputId": "7960de50-0fa8-4f68-d0e9-a20ed0528071"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values:\n",
            "Subject ID     0\n",
            "MRI ID         0\n",
            "Group          0\n",
            "Visit          0\n",
            "MR Delay       0\n",
            "M/F            0\n",
            "Hand           0\n",
            "Age            0\n",
            "EDUC           0\n",
            "SES           19\n",
            "MMSE           2\n",
            "CDR            0\n",
            "eTIV           0\n",
            "nWBV           0\n",
            "ASF            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing numerical data with the mean\n",
        "df['SES'].fillna(df['SES'].mean(), inplace=True)\n",
        "df['MMSE'].fillna(df['MMSE'].mean(), inplace=True)"
      ],
      "metadata": {
        "id": "OLvfZ334Lsnn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if missing data has been successfully replaced\n",
        "missing_data = df.isnull().sum()\n",
        "\n",
        "# Check if there are any missing values left\n",
        "if missing_data.sum() == 0:\n",
        "    print('No missing data.')\n",
        "else:\n",
        "    print(missing_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUgYL70dMb2i",
        "outputId": "7767839e-b2eb-41c1-ab29-ff29956d95d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No missing data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check For Irregular Data (Outliers)"
      ],
      "metadata": {
        "id": "p9ur2Me6MhzW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# check for irregular data\n",
        "irregular_data = df[~df.applymap(lambda x: isinstance(x, (int, float, np.number))).all(axis=1)]\n",
        "if not irregular_data.empty:\n",
        "    print(\"Irregular data detected:\")\n",
        "    print(irregular_data)\n",
        "else:\n",
        "    print(\"No irregular data detected.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "804D8geOMjnY",
        "outputId": "f2585e3e-6478-4f48-d4d3-7ee43d97683b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Irregular data detected:\n",
            "    Subject ID         MRI ID        Group  Visit  MR Delay M/F Hand  Age  \\\n",
            "0    OAS2_0001  OAS2_0001_MR1  Nondemented      1         0   M    R   87   \n",
            "1    OAS2_0001  OAS2_0001_MR2  Nondemented      2       457   M    R   88   \n",
            "2    OAS2_0002  OAS2_0002_MR1     Demented      1         0   M    R   75   \n",
            "3    OAS2_0002  OAS2_0002_MR2     Demented      2       560   M    R   76   \n",
            "4    OAS2_0002  OAS2_0002_MR3     Demented      3      1895   M    R   80   \n",
            "..         ...            ...          ...    ...       ...  ..  ...  ...   \n",
            "368  OAS2_0185  OAS2_0185_MR2     Demented      2       842   M    R   82   \n",
            "369  OAS2_0185  OAS2_0185_MR3     Demented      3      2297   M    R   86   \n",
            "370  OAS2_0186  OAS2_0186_MR1  Nondemented      1         0   F    R   61   \n",
            "371  OAS2_0186  OAS2_0186_MR2  Nondemented      2       763   F    R   63   \n",
            "372  OAS2_0186  OAS2_0186_MR3  Nondemented      3      1608   F    R   65   \n",
            "\n",
            "     EDUC       SES  MMSE  CDR  eTIV   nWBV    ASF  \n",
            "0      14  2.000000  27.0  0.0  1987  0.696  0.883  \n",
            "1      14  2.000000  30.0  0.0  2004  0.681  0.876  \n",
            "2      12  2.460452  23.0  0.5  1678  0.736  1.046  \n",
            "3      12  2.460452  28.0  0.5  1738  0.713  1.010  \n",
            "4      12  2.460452  22.0  0.5  1698  0.701  1.034  \n",
            "..    ...       ...   ...  ...   ...    ...    ...  \n",
            "368    16  1.000000  28.0  0.5  1693  0.694  1.037  \n",
            "369    16  1.000000  26.0  0.5  1688  0.675  1.040  \n",
            "370    13  2.000000  30.0  0.0  1319  0.801  1.331  \n",
            "371    13  2.000000  30.0  0.0  1327  0.796  1.323  \n",
            "372    13  2.000000  30.0  0.0  1333  0.801  1.317  \n",
            "\n",
            "[373 rows x 15 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "change column title capitalization"
      ],
      "metadata": {
        "id": "rjT24lkyNeBF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary to store the new column names\n",
        "columns = {}\n",
        "\n",
        "# Loop over each column\n",
        "for column in df.columns:\n",
        "    # Convert the column name to lowercase and store it in the dictionary\n",
        "    columns[column] = column.lower()\n",
        "\n",
        "# Rename the columns using the dictionary\n",
        "df.rename(columns=columns, inplace=True)"
      ],
      "metadata": {
        "id": "Flu3iWxUNAFw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check For Unneccssary Data"
      ],
      "metadata": {
        "id": "9jat_nHyNoEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for repetitive and duplicate data\n",
        "if df.duplicated().any():\n",
        "    print(\"Duplicate data detected:\")\n",
        "    print(df[df.duplicated()])\n",
        "else:\n",
        "    print(\"No duplicate data detected.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fn1kOa4ENtEa",
        "outputId": "8bacb2fc-798f-469f-e5c6-588eee051e6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No duplicate data detected.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check For Inconsistent Data"
      ],
      "metadata": {
        "id": "DK3NjleWN_iZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for inconsistent data types\n",
        "for col in df.columns:\n",
        "    if len(df[col].apply(type).value_counts()) > 1:\n",
        "        print(f\"Inconsistent data type found in column {col}\")\n",
        "\n",
        "# check for missing values\n",
        "if df.isnull().values.any():\n",
        "    print(\"Missing values found\")\n",
        "\n",
        "# check for values outside of expected range\n",
        "for col in df.columns:\n",
        "    if df[col].dtype == \"int64\":\n",
        "        if df[col].min() < 0 or df[col].max() > 100:\n",
        "            print(f\"Value outside of expected range found in column {col}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AESit3mZOAVf",
        "outputId": "808fe199-391b-4c80-c2e0-c4ca45388a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value outside of expected range found in column mr delay\n",
            "Value outside of expected range found in column etiv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Value is quite normal"
      ],
      "metadata": {
        "id": "O2urdr8cOxZP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Feature Engineering**"
      ],
      "metadata": {
        "id": "8oT2Kb0-PXnB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 'df' is your DataFrame\n",
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vup9hNkIPcPI",
        "outputId": "baf9ce88-2fe3-44ae-de44-cb8c56e22036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['subject id', 'mri id', 'group', 'visit', 'mr delay', 'm/f', 'hand',\n",
            "       'age', 'educ', 'ses', 'mmse', 'cdr', 'etiv', 'nwbv', 'asf'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert values in column 'm/f' to 1 and 0 fpr male and female respectively"
      ],
      "metadata": {
        "id": "uTMUDDfdUKF0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.reset_index(drop=True) # reset index after filtering first visit data\n",
        "df['m/f'] = df['m/f'].replace(['F','M'], [0,1]) # M/F column"
      ],
      "metadata": {
        "id": "oFh99H_-UOiw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "-aE0HqVEUNjJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a correlation matrix\n",
        "corr_matrix = df.corr().abs()\n",
        "\n",
        "# Select upper triangle of correlation matrix\n",
        "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
        "\n",
        "# Find index of feature columns with correlation greater than 0.8\n",
        "to_drop = [column for column in upper.columns if any(upper[column] > 0.8)]\n",
        "\n",
        "# Drop the correlated features from the dataset\n",
        "df = df.drop(to_drop, axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXhA2qjEP3r4",
        "outputId": "6eeca22e-0671-4a72-ebce-f01c10d14198"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-74356323b653>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr_matrix = df.corr().abs()\n",
            "<ipython-input-25-74356323b653>:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the updated dataframe\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5RBCpkBQGF4",
        "outputId": "9a007be3-78c4-452d-92c8-a73138bb51fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  subject id         mri id        group  visit  m/f hand  age  educ  \\\n",
            "0  OAS2_0001  OAS2_0001_MR1  Nondemented      1    1    R   87    14   \n",
            "1  OAS2_0001  OAS2_0001_MR2  Nondemented      2    1    R   88    14   \n",
            "2  OAS2_0002  OAS2_0002_MR1     Demented      1    1    R   75    12   \n",
            "3  OAS2_0002  OAS2_0002_MR2     Demented      2    1    R   76    12   \n",
            "4  OAS2_0002  OAS2_0002_MR3     Demented      3    1    R   80    12   \n",
            "\n",
            "        ses  mmse  cdr  etiv   nwbv  \n",
            "0  2.000000  27.0  0.0  1987  0.696  \n",
            "1  2.000000  30.0  0.0  2004  0.681  \n",
            "2  2.460452  23.0  0.5  1678  0.736  \n",
            "3  2.460452  28.0  0.5  1738  0.713  \n",
            "4  2.460452  22.0  0.5  1698  0.701  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select features and target variables\n",
        "target_col = 'group'\n",
        "feature_cols = df.columns.drop([target_col, 'subject id', 'mri id', 'visit', 'hand'])"
      ],
      "metadata": {
        "id": "8smkUmxVP_IA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Split Data Set**"
      ],
      "metadata": {
        "id": "VCTrzuThQU21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# split dataset into train and test data\n",
        "X_train, X_test, y_train, y_test = train_test_split(df[feature_cols], df[target_col], test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "uXkB1oTrQUCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Implement A Support Vector Model**"
      ],
      "metadata": {
        "id": "njJaY5N-S8ID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "\n",
        "# Create an instance of the SVM classifier\n",
        "classifier = svm.SVC()"
      ],
      "metadata": {
        "id": "kl6ql2ApTCUR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the classifier to your training data\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "UzsOLTrdpACl",
        "outputId": "13e42637-8adb-4eb3-f1f8-bb6439dd37ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on new data\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "JLqhU5wDTVvS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Performance Evaluation**"
      ],
      "metadata": {
        "id": "VnGhEfy8XoKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Print the accuracy score\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AlqhpcK3P5R",
        "outputId": "cd522b0e-4c82-49bf-9b66-323c18d05625"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.4642857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'C': [0.1, 1, 10],\n",
        "    'kernel': ['linear', 'rbf'],\n",
        "    'gamma': [0.1, 1, 10]\n",
        "}\n",
        "\n",
        "\n",
        "# Perform grid search with cross-validation\n",
        "grid_search = GridSearchCV(estimator=classifier, param_grid=param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters and best score\n",
        "print(\"Best Parameters: \", grid_search.best_params_)\n",
        "print(\"Best Score: \", grid_search.best_score_)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "accuracy = grid_search.score(X_test, y_test)\n",
        "print(\"Test Accuracy: \", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8FCEnKY7h89",
        "outputId": "e1ca50c3-2d65-4483-d52b-ab8084e8a2bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters:  {'C': 1, 'gamma': 0.1, 'kernel': 'linear'}\n",
            "Best Score:  0.9121190130624093\n",
            "Test Accuracy:  0.8571428571428571\n"
          ]
        }
      ]
    }
  ]
}