PL_KNN.ipynb

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      "authorship_tag": "ABX9TyPFkC/kysD51twZELJycta4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dhruvi013/Machine-Learning/blob/main/PL_KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ABg4CDSMR-4m"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from ucimlrepo import fetch_ucirepo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install ucimlrepo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-hP5z7yR__F",
        "outputId": "3ea09157-a655-4490-bbee-f906515613c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.7-py3-none-any.whl.metadata (5.5 kB)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2.2.2)\n",
            "Requirement already satisfied: certifi>=2020.12.5 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2024.8.30)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.16.0)\n",
            "Downloading ucimlrepo-0.0.7-py3-none-any.whl (8.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "breast_cancer_wisconsin_diagnostic = fetch_ucirepo(id=17)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = breast_cancer_wisconsin_diagnostic.data.features\n",
        "y = breast_cancer_wisconsin_diagnostic.data.targets\n",
        "\n",
        "# metadata\n",
        "print(breast_cancer_wisconsin_diagnostic.metadata)\n",
        "\n",
        "# variable information\n",
        "print(breast_cancer_wisconsin_diagnostic.variables)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slROFqBWSJ_6",
        "outputId": "00505137-6a62-4aaf-a458-a8f96ba6c070"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'uci_id': 17, 'name': 'Breast Cancer Wisconsin (Diagnostic)', 'repository_url': 'https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic', 'data_url': 'https://archive.ics.uci.edu/static/public/17/data.csv', 'abstract': 'Diagnostic Wisconsin Breast Cancer Database.', 'area': 'Health and Medicine', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 569, 'num_features': 30, 'feature_types': ['Real'], 'demographics': [], 'target_col': ['Diagnosis'], 'index_col': ['ID'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1993, 'last_updated': 'Fri Nov 03 2023', 'dataset_doi': '10.24432/C5DW2B', 'creators': ['William Wolberg', 'Olvi Mangasarian', 'Nick Street', 'W. Street'], 'intro_paper': {'ID': 230, 'type': 'NATIVE', 'title': 'Nuclear feature extraction for breast tumor diagnosis', 'authors': 'W. Street, W. Wolberg, O. Mangasarian', 'venue': 'Electronic imaging', 'year': 1993, 'journal': None, 'DOI': '10.1117/12.148698', 'URL': 'https://www.semanticscholar.org/paper/53f0fbb425bc14468eb3bf96b2e1d41ba8087f36', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': None, 'pmcid': None}, 'additional_info': {'summary': 'Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.  They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/\\r\\n\\r\\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree.  Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\\r\\n\\r\\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\\r\\n\\r\\nThis database is also available through the UW CS ftp server:\\r\\nftp ftp.cs.wisc.edu\\r\\ncd math-prog/cpo-dataset/machine-learn/WDBC/', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': '1) ID number\\r\\n2) Diagnosis (M = malignant, B = benign)\\r\\n3-32)\\r\\n\\r\\nTen real-valued features are computed for each cell nucleus:\\r\\n\\r\\n\\ta) radius (mean of distances from center to points on the perimeter)\\r\\n\\tb) texture (standard deviation of gray-scale values)\\r\\n\\tc) perimeter\\r\\n\\td) area\\r\\n\\te) smoothness (local variation in radius lengths)\\r\\n\\tf) compactness (perimeter^2 / area - 1.0)\\r\\n\\tg) concavity (severity of concave portions of the contour)\\r\\n\\th) concave points (number of concave portions of the contour)\\r\\n\\ti) symmetry \\r\\n\\tj) fractal dimension (\"coastline approximation\" - 1)', 'citation': None}}\n",
            "                  name     role         type demographic description units  \\\n",
            "0                   ID       ID  Categorical        None        None  None   \n",
            "1            Diagnosis   Target  Categorical        None        None  None   \n",
            "2              radius1  Feature   Continuous        None        None  None   \n",
            "3             texture1  Feature   Continuous        None        None  None   \n",
            "4           perimeter1  Feature   Continuous        None        None  None   \n",
            "5                area1  Feature   Continuous        None        None  None   \n",
            "6          smoothness1  Feature   Continuous        None        None  None   \n",
            "7         compactness1  Feature   Continuous        None        None  None   \n",
            "8           concavity1  Feature   Continuous        None        None  None   \n",
            "9      concave_points1  Feature   Continuous        None        None  None   \n",
            "10           symmetry1  Feature   Continuous        None        None  None   \n",
            "11  fractal_dimension1  Feature   Continuous        None        None  None   \n",
            "12             radius2  Feature   Continuous        None        None  None   \n",
            "13            texture2  Feature   Continuous        None        None  None   \n",
            "14          perimeter2  Feature   Continuous        None        None  None   \n",
            "15               area2  Feature   Continuous        None        None  None   \n",
            "16         smoothness2  Feature   Continuous        None        None  None   \n",
            "17        compactness2  Feature   Continuous        None        None  None   \n",
            "18          concavity2  Feature   Continuous        None        None  None   \n",
            "19     concave_points2  Feature   Continuous        None        None  None   \n",
            "20           symmetry2  Feature   Continuous        None        None  None   \n",
            "21  fractal_dimension2  Feature   Continuous        None        None  None   \n",
            "22             radius3  Feature   Continuous        None        None  None   \n",
            "23            texture3  Feature   Continuous        None        None  None   \n",
            "24          perimeter3  Feature   Continuous        None        None  None   \n",
            "25               area3  Feature   Continuous        None        None  None   \n",
            "26         smoothness3  Feature   Continuous        None        None  None   \n",
            "27        compactness3  Feature   Continuous        None        None  None   \n",
            "28          concavity3  Feature   Continuous        None        None  None   \n",
            "29     concave_points3  Feature   Continuous        None        None  None   \n",
            "30           symmetry3  Feature   Continuous        None        None  None   \n",
            "31  fractal_dimension3  Feature   Continuous        None        None  None   \n",
            "\n",
            "   missing_values  \n",
            "0              no  \n",
            "1              no  \n",
            "2              no  \n",
            "3              no  \n",
            "4              no  \n",
            "5              no  \n",
            "6              no  \n",
            "7              no  \n",
            "8              no  \n",
            "9              no  \n",
            "10             no  \n",
            "11             no  \n",
            "12             no  \n",
            "13             no  \n",
            "14             no  \n",
            "15             no  \n",
            "16             no  \n",
            "17             no  \n",
            "18             no  \n",
            "19             no  \n",
            "20             no  \n",
            "21             no  \n",
            "22             no  \n",
            "23             no  \n",
            "24             no  \n",
            "25             no  \n",
            "26             no  \n",
            "27             no  \n",
            "28             no  \n",
            "29             no  \n",
            "30             no  \n",
            "31             no  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset Metadata:\\n\", breast_cancer_wisconsin_diagnostic.metadata)\n",
        "print(\"\\nVariable Information:\\n\", breast_cancer_wisconsin_diagnostic.variables)\n",
        "print(\"\\nFirst five rows of the dataset:\\n\", X.head())\n",
        "print(\"\\nTarget distribution:\\n\", y.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bL5hM4N8SRkm",
        "outputId": "cb7b8a38-fdee-428b-92eb-08d274da946b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Metadata:\n",
            " {'uci_id': 17, 'name': 'Breast Cancer Wisconsin (Diagnostic)', 'repository_url': 'https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic', 'data_url': 'https://archive.ics.uci.edu/static/public/17/data.csv', 'abstract': 'Diagnostic Wisconsin Breast Cancer Database.', 'area': 'Health and Medicine', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 569, 'num_features': 30, 'feature_types': ['Real'], 'demographics': [], 'target_col': ['Diagnosis'], 'index_col': ['ID'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1993, 'last_updated': 'Fri Nov 03 2023', 'dataset_doi': '10.24432/C5DW2B', 'creators': ['William Wolberg', 'Olvi Mangasarian', 'Nick Street', 'W. Street'], 'intro_paper': {'ID': 230, 'type': 'NATIVE', 'title': 'Nuclear feature extraction for breast tumor diagnosis', 'authors': 'W. Street, W. Wolberg, O. Mangasarian', 'venue': 'Electronic imaging', 'year': 1993, 'journal': None, 'DOI': '10.1117/12.148698', 'URL': 'https://www.semanticscholar.org/paper/53f0fbb425bc14468eb3bf96b2e1d41ba8087f36', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': None, 'pmcid': None}, 'additional_info': {'summary': 'Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.  They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/\\r\\n\\r\\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree.  Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\\r\\n\\r\\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\\r\\n\\r\\nThis database is also available through the UW CS ftp server:\\r\\nftp ftp.cs.wisc.edu\\r\\ncd math-prog/cpo-dataset/machine-learn/WDBC/', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': '1) ID number\\r\\n2) Diagnosis (M = malignant, B = benign)\\r\\n3-32)\\r\\n\\r\\nTen real-valued features are computed for each cell nucleus:\\r\\n\\r\\n\\ta) radius (mean of distances from center to points on the perimeter)\\r\\n\\tb) texture (standard deviation of gray-scale values)\\r\\n\\tc) perimeter\\r\\n\\td) area\\r\\n\\te) smoothness (local variation in radius lengths)\\r\\n\\tf) compactness (perimeter^2 / area - 1.0)\\r\\n\\tg) concavity (severity of concave portions of the contour)\\r\\n\\th) concave points (number of concave portions of the contour)\\r\\n\\ti) symmetry \\r\\n\\tj) fractal dimension (\"coastline approximation\" - 1)', 'citation': None}}\n",
            "\n",
            "Variable Information:\n",
            "                   name     role         type demographic description units  \\\n",
            "0                   ID       ID  Categorical        None        None  None   \n",
            "1            Diagnosis   Target  Categorical        None        None  None   \n",
            "2              radius1  Feature   Continuous        None        None  None   \n",
            "3             texture1  Feature   Continuous        None        None  None   \n",
            "4           perimeter1  Feature   Continuous        None        None  None   \n",
            "5                area1  Feature   Continuous        None        None  None   \n",
            "6          smoothness1  Feature   Continuous        None        None  None   \n",
            "7         compactness1  Feature   Continuous        None        None  None   \n",
            "8           concavity1  Feature   Continuous        None        None  None   \n",
            "9      concave_points1  Feature   Continuous        None        None  None   \n",
            "10           symmetry1  Feature   Continuous        None        None  None   \n",
            "11  fractal_dimension1  Feature   Continuous        None        None  None   \n",
            "12             radius2  Feature   Continuous        None        None  None   \n",
            "13            texture2  Feature   Continuous        None        None  None   \n",
            "14          perimeter2  Feature   Continuous        None        None  None   \n",
            "15               area2  Feature   Continuous        None        None  None   \n",
            "16         smoothness2  Feature   Continuous        None        None  None   \n",
            "17        compactness2  Feature   Continuous        None        None  None   \n",
            "18          concavity2  Feature   Continuous        None        None  None   \n",
            "19     concave_points2  Feature   Continuous        None        None  None   \n",
            "20           symmetry2  Feature   Continuous        None        None  None   \n",
            "21  fractal_dimension2  Feature   Continuous        None        None  None   \n",
            "22             radius3  Feature   Continuous        None        None  None   \n",
            "23            texture3  Feature   Continuous        None        None  None   \n",
            "24          perimeter3  Feature   Continuous        None        None  None   \n",
            "25               area3  Feature   Continuous        None        None  None   \n",
            "26         smoothness3  Feature   Continuous        None        None  None   \n",
            "27        compactness3  Feature   Continuous        None        None  None   \n",
            "28          concavity3  Feature   Continuous        None        None  None   \n",
            "29     concave_points3  Feature   Continuous        None        None  None   \n",
            "30           symmetry3  Feature   Continuous        None        None  None   \n",
            "31  fractal_dimension3  Feature   Continuous        None        None  None   \n",
            "\n",
            "   missing_values  \n",
            "0              no  \n",
            "1              no  \n",
            "2              no  \n",
            "3              no  \n",
            "4              no  \n",
            "5              no  \n",
            "6              no  \n",
            "7              no  \n",
            "8              no  \n",
            "9              no  \n",
            "10             no  \n",
            "11             no  \n",
            "12             no  \n",
            "13             no  \n",
            "14             no  \n",
            "15             no  \n",
            "16             no  \n",
            "17             no  \n",
            "18             no  \n",
            "19             no  \n",
            "20             no  \n",
            "21             no  \n",
            "22             no  \n",
            "23             no  \n",
            "24             no  \n",
            "25             no  \n",
            "26             no  \n",
            "27             no  \n",
            "28             no  \n",
            "29             no  \n",
            "30             no  \n",
            "31             no  \n",
            "\n",
            "First five rows of the dataset:\n",
            "    radius1  texture1  perimeter1   area1  smoothness1  compactness1  \\\n",
            "0    17.99     10.38      122.80  1001.0      0.11840       0.27760   \n",
            "1    20.57     17.77      132.90  1326.0      0.08474       0.07864   \n",
            "2    19.69     21.25      130.00  1203.0      0.10960       0.15990   \n",
            "3    11.42     20.38       77.58   386.1      0.14250       0.28390   \n",
            "4    20.29     14.34      135.10  1297.0      0.10030       0.13280   \n",
            "\n",
            "   concavity1  concave_points1  symmetry1  fractal_dimension1  ...  radius3  \\\n",
            "0      0.3001          0.14710     0.2419             0.07871  ...    25.38   \n",
            "1      0.0869          0.07017     0.1812             0.05667  ...    24.99   \n",
            "2      0.1974          0.12790     0.2069             0.05999  ...    23.57   \n",
            "3      0.2414          0.10520     0.2597             0.09744  ...    14.91   \n",
            "4      0.1980          0.10430     0.1809             0.05883  ...    22.54   \n",
            "\n",
            "   texture3  perimeter3   area3  smoothness3  compactness3  concavity3  \\\n",
            "0     17.33      184.60  2019.0       0.1622        0.6656      0.7119   \n",
            "1     23.41      158.80  1956.0       0.1238        0.1866      0.2416   \n",
            "2     25.53      152.50  1709.0       0.1444        0.4245      0.4504   \n",
            "3     26.50       98.87   567.7       0.2098        0.8663      0.6869   \n",
            "4     16.67      152.20  1575.0       0.1374        0.2050      0.4000   \n",
            "\n",
            "   concave_points3  symmetry3  fractal_dimension3  \n",
            "0           0.2654     0.4601             0.11890  \n",
            "1           0.1860     0.2750             0.08902  \n",
            "2           0.2430     0.3613             0.08758  \n",
            "3           0.2575     0.6638             0.17300  \n",
            "4           0.1625     0.2364             0.07678  \n",
            "\n",
            "[5 rows x 30 columns]\n",
            "\n",
            "Target distribution:\n",
            " Diagnosis\n",
            "B            357\n",
            "M            212\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in dataset:\\n\", X.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33OeGVv9SVJx",
        "outputId": "0361e8aa-5f85-480c-945d-7bbfa59c66bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in dataset:\n",
            " radius1               0\n",
            "texture1              0\n",
            "perimeter1            0\n",
            "area1                 0\n",
            "smoothness1           0\n",
            "compactness1          0\n",
            "concavity1            0\n",
            "concave_points1       0\n",
            "symmetry1             0\n",
            "fractal_dimension1    0\n",
            "radius2               0\n",
            "texture2              0\n",
            "perimeter2            0\n",
            "area2                 0\n",
            "smoothness2           0\n",
            "compactness2          0\n",
            "concavity2            0\n",
            "concave_points2       0\n",
            "symmetry2             0\n",
            "fractal_dimension2    0\n",
            "radius3               0\n",
            "texture3              0\n",
            "perimeter3            0\n",
            "area3                 0\n",
            "smoothness3           0\n",
            "compactness3          0\n",
            "concavity3            0\n",
            "concave_points3       0\n",
            "symmetry3             0\n",
            "fractal_dimension3    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the feature data to improve KNN performance\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "V-tirN7sSZNC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(pd.DataFrame(X_scaled, columns=X.columns).corr(), annot=False, cmap=\"coolwarm\")\n",
        "plt.title(\"Feature Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "PfdlOLi6ScnP",
        "outputId": "de260d7b-0e57-4af5-c144-7aa238bd09ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "MFJ3V4N_Sfgt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Select the number K of neighbors\n",
        "k = 5"
      ],
      "metadata": {
        "id": "vCXERuvSSjwm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the KNN model with chosen k\n",
        "knn_model = KNeighborsClassifier(n_neighbors=k)\n",
        "knn_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "id": "qQXybja8Sp1q",
        "outputId": "c6333aec-c792-45ac-926b-4467e5631bf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict and assign category based on majority of neighbors\n",
        "y_pred = knn_model.predict(X_test)"
      ],
      "metadata": {
        "id": "812JMVi_SsN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model evaluation\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"\\nModel Accuracy with K={k}: {accuracy:.2f}\")\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "at_2J6hnSwC6",
        "outputId": "f64d009e-6f86-46bf-da34-d46123dc1334"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model Accuracy with K=5: 0.95\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           B       0.96      0.96      0.96        71\n",
            "           M       0.93      0.93      0.93        43\n",
            "\n",
            "    accuracy                           0.95       114\n",
            "   macro avg       0.94      0.94      0.94       114\n",
            "weighted avg       0.95      0.95      0.95       114\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            " [[68  3]\n",
            " [ 3 40]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization of accuracy for different k values (optional)\n",
        "k_values = range(1, 21)\n",
        "accuracies = []\n",
        "\n",
        "for k in k_values:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred_k = knn.predict(X_test)\n",
        "    accuracies.append(accuracy_score(y_test, y_pred_k))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDuyeAL3SzSM",
        "outputId": "e67fe7c9-193b-434e-9e40-0dbdaa2ff422"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(k_values, accuracies, marker='o')\n",
        "plt.title(\"K-Value vs Accuracy\")\n",
        "plt.xlabel(\"Number of Neighbors K\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "6VxQsPjlS4Gq",
        "outputId": "0b8958c6-6eab-4f9a-a4df-16536d144e0d"
      },
      "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": [],
      "metadata": {
        "id": "XyfpF5esS6xd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}