KNN.ipynb

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      "authorship_tag": "ABX9TyPTEDVuTI/iBcl9gun0YezT",
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      "name": "python3",
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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/Netesh5/Machine_Learning/blob/main/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": 1,
      "metadata": {
        "id": "eFXkHdTkZ5w_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer"
      ],
      "metadata": {
        "id": "ben5GKn_aK7e"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=load_breast_cancer()\n",
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVlkiHJHaqtn",
        "outputId": "a02d891d-b29b-49e8-f890-63679c0aebbb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data[\"DESCR\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6a34bItfawGq",
        "outputId": "24f567ae-3250-40d1-9ee4-6374850c2137"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry\n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        worst/largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
            "        10 is Radius SE, field 20 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating the DataFrame for the data**"
      ],
      "metadata": {
        "id": "-ZkLUZMnboT_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.DataFrame(data[\"data\"],columns=data[\"feature_names\"])\n",
        "dataset.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "6BSK08v5a9uj",
        "outputId": "29247e63-8ac3-4d0c-dfcd-1e5814f022c3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "5        12.45         15.70           82.57      477.1          0.12780   \n",
              "6        18.25         19.98          119.60     1040.0          0.09463   \n",
              "7        13.71         20.83           90.20      577.9          0.11890   \n",
              "8        13.00         21.82           87.50      519.8          0.12730   \n",
              "9        12.46         24.04           83.97      475.9          0.11860   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760         0.30010              0.14710         0.2419   \n",
              "1           0.07864         0.08690              0.07017         0.1812   \n",
              "2           0.15990         0.19740              0.12790         0.2069   \n",
              "3           0.28390         0.24140              0.10520         0.2597   \n",
              "4           0.13280         0.19800              0.10430         0.1809   \n",
              "5           0.17000         0.15780              0.08089         0.2087   \n",
              "6           0.10900         0.11270              0.07400         0.1794   \n",
              "7           0.16450         0.09366              0.05985         0.2196   \n",
              "8           0.19320         0.18590              0.09353         0.2350   \n",
              "9           0.23960         0.22730              0.08543         0.2030   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33           184.60   \n",
              "1                 0.05667  ...         24.99          23.41           158.80   \n",
              "2                 0.05999  ...         23.57          25.53           152.50   \n",
              "3                 0.09744  ...         14.91          26.50            98.87   \n",
              "4                 0.05883  ...         22.54          16.67           152.20   \n",
              "5                 0.07613  ...         15.47          23.75           103.40   \n",
              "6                 0.05742  ...         22.88          27.66           153.20   \n",
              "7                 0.07451  ...         17.06          28.14           110.60   \n",
              "8                 0.07389  ...         15.49          30.73           106.20   \n",
              "9                 0.08243  ...         15.09          40.68            97.65   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "5       741.6            0.1791             0.5249           0.5355   \n",
              "6      1606.0            0.1442             0.2576           0.3784   \n",
              "7       897.0            0.1654             0.3682           0.2678   \n",
              "8       739.3            0.1703             0.5401           0.5390   \n",
              "9       711.4            0.1853             1.0580           1.1050   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \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",
              "5                0.1741          0.3985                  0.12440  \n",
              "6                0.1932          0.3063                  0.08368  \n",
              "7                0.1556          0.3196                  0.11510  \n",
              "8                0.2060          0.4378                  0.10720  \n",
              "9                0.2210          0.4366                  0.20750  \n",
              "\n",
              "[10 rows x 30 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12.45</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0.17000</td>\n",
              "      <td>0.15780</td>\n",
              "      <td>0.08089</td>\n",
              "      <td>0.2087</td>\n",
              "      <td>0.07613</td>\n",
              "      <td>...</td>\n",
              "      <td>15.47</td>\n",
              "      <td>23.75</td>\n",
              "      <td>103.40</td>\n",
              "      <td>741.6</td>\n",
              "      <td>0.1791</td>\n",
              "      <td>0.5249</td>\n",
              "      <td>0.5355</td>\n",
              "      <td>0.1741</td>\n",
              "      <td>0.3985</td>\n",
              "      <td>0.12440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>18.25</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0.10900</td>\n",
              "      <td>0.11270</td>\n",
              "      <td>0.07400</td>\n",
              "      <td>0.1794</td>\n",
              "      <td>0.05742</td>\n",
              "      <td>...</td>\n",
              "      <td>22.88</td>\n",
              "      <td>27.66</td>\n",
              "      <td>153.20</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>0.1442</td>\n",
              "      <td>0.2576</td>\n",
              "      <td>0.3784</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>13.71</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>0.05985</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>0.07451</td>\n",
              "      <td>...</td>\n",
              "      <td>17.06</td>\n",
              "      <td>28.14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.1654</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.2678</td>\n",
              "      <td>0.1556</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.18590</td>\n",
              "      <td>0.09353</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>0.07389</td>\n",
              "      <td>...</td>\n",
              "      <td>15.49</td>\n",
              "      <td>30.73</td>\n",
              "      <td>106.20</td>\n",
              "      <td>739.3</td>\n",
              "      <td>0.1703</td>\n",
              "      <td>0.5401</td>\n",
              "      <td>0.5390</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>0.10720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12.46</td>\n",
              "      <td>24.04</td>\n",
              "      <td>83.97</td>\n",
              "      <td>475.9</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0.23960</td>\n",
              "      <td>0.22730</td>\n",
              "      <td>0.08543</td>\n",
              "      <td>0.2030</td>\n",
              "      <td>0.08243</td>\n",
              "      <td>...</td>\n",
              "      <td>15.09</td>\n",
              "      <td>40.68</td>\n",
              "      <td>97.65</td>\n",
              "      <td>711.4</td>\n",
              "      <td>0.1853</td>\n",
              "      <td>1.0580</td>\n",
              "      <td>1.1050</td>\n",
              "      <td>0.2210</td>\n",
              "      <td>0.4366</td>\n",
              "      <td>0.20750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 30 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23888b61-9b1b-41c8-9f47-c6b3696fa2c3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4sxHhsWbkff",
        "outputId": "b133f05f-4536-480b-e354-3a45c19ec22e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean radius                0\n",
              "mean texture               0\n",
              "mean perimeter             0\n",
              "mean area                  0\n",
              "mean smoothness            0\n",
              "mean compactness           0\n",
              "mean concavity             0\n",
              "mean concave points        0\n",
              "mean symmetry              0\n",
              "mean fractal dimension     0\n",
              "radius error               0\n",
              "texture error              0\n",
              "perimeter error            0\n",
              "area error                 0\n",
              "smoothness error           0\n",
              "compactness error          0\n",
              "concavity error            0\n",
              "concave points error       0\n",
              "symmetry error             0\n",
              "fractal dimension error    0\n",
              "worst radius               0\n",
              "worst texture              0\n",
              "worst perimeter            0\n",
              "worst area                 0\n",
              "worst smoothness           0\n",
              "worst compactness          0\n",
              "worst concavity            0\n",
              "worst concave points       0\n",
              "worst symmetry             0\n",
              "worst fractal dimension    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hZiZpUEbfAS",
        "outputId": "afe25413-850c-4e01-ab3d-4bcb7e043b5e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 30 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   mean radius              569 non-null    float64\n",
            " 1   mean texture             569 non-null    float64\n",
            " 2   mean perimeter           569 non-null    float64\n",
            " 3   mean area                569 non-null    float64\n",
            " 4   mean smoothness          569 non-null    float64\n",
            " 5   mean compactness         569 non-null    float64\n",
            " 6   mean concavity           569 non-null    float64\n",
            " 7   mean concave points      569 non-null    float64\n",
            " 8   mean symmetry            569 non-null    float64\n",
            " 9   mean fractal dimension   569 non-null    float64\n",
            " 10  radius error             569 non-null    float64\n",
            " 11  texture error            569 non-null    float64\n",
            " 12  perimeter error          569 non-null    float64\n",
            " 13  area error               569 non-null    float64\n",
            " 14  smoothness error         569 non-null    float64\n",
            " 15  compactness error        569 non-null    float64\n",
            " 16  concavity error          569 non-null    float64\n",
            " 17  concave points error     569 non-null    float64\n",
            " 18  symmetry error           569 non-null    float64\n",
            " 19  fractal dimension error  569 non-null    float64\n",
            " 20  worst radius             569 non-null    float64\n",
            " 21  worst texture            569 non-null    float64\n",
            " 22  worst perimeter          569 non-null    float64\n",
            " 23  worst area               569 non-null    float64\n",
            " 24  worst smoothness         569 non-null    float64\n",
            " 25  worst compactness        569 non-null    float64\n",
            " 26  worst concavity          569 non-null    float64\n",
            " 27  worst concave points     569 non-null    float64\n",
            " 28  worst symmetry           569 non-null    float64\n",
            " 29  worst fractal dimension  569 non-null    float64\n",
            "dtypes: float64(30)\n",
            "memory usage: 133.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhHn07pqcDyT",
        "outputId": "6c2ea1cc-58ae-44a2-d3f4-16ac6102195a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "mwd8tryNcHAl",
        "outputId": "0f52368f-8fae-4f52-c469-f165cbcc7f85"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean radius  mean texture  mean perimeter    mean area  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       mean symmetry  mean fractal dimension  ...  worst radius  \\\n",
              "count     569.000000              569.000000  ...    569.000000   \n",
              "mean        0.181162                0.062798  ...     16.269190   \n",
              "std         0.027414                0.007060  ...      4.833242   \n",
              "min         0.106000                0.049960  ...      7.930000   \n",
              "25%         0.161900                0.057700  ...     13.010000   \n",
              "50%         0.179200                0.061540  ...     14.970000   \n",
              "75%         0.195700                0.066120  ...     18.790000   \n",
              "max         0.304000                0.097440  ...     36.040000   \n",
              "\n",
              "       worst texture  worst perimeter   worst area  worst smoothness  \\\n",
              "count     569.000000       569.000000   569.000000        569.000000   \n",
              "mean       25.677223       107.261213   880.583128          0.132369   \n",
              "std         6.146258        33.602542   569.356993          0.022832   \n",
              "min        12.020000        50.410000   185.200000          0.071170   \n",
              "25%        21.080000        84.110000   515.300000          0.116600   \n",
              "50%        25.410000        97.660000   686.500000          0.131300   \n",
              "75%        29.720000       125.400000  1084.000000          0.146000   \n",
              "max        49.540000       251.200000  4254.000000          0.222600   \n",
              "\n",
              "       worst compactness  worst concavity  worst concave points  \\\n",
              "count         569.000000       569.000000            569.000000   \n",
              "mean            0.254265         0.272188              0.114606   \n",
              "std             0.157336         0.208624              0.065732   \n",
              "min             0.027290         0.000000              0.000000   \n",
              "25%             0.147200         0.114500              0.064930   \n",
              "50%             0.211900         0.226700              0.099930   \n",
              "75%             0.339100         0.382900              0.161400   \n",
              "max             1.058000         1.252000              0.291000   \n",
              "\n",
              "       worst symmetry  worst fractal dimension  \n",
              "count      569.000000               569.000000  \n",
              "mean         0.290076                 0.083946  \n",
              "std          0.061867                 0.018061  \n",
              "min          0.156500                 0.055040  \n",
              "25%          0.250400                 0.071460  \n",
              "50%          0.282200                 0.080040  \n",
              "75%          0.317900                 0.092080  \n",
              "max          0.663800                 0.207500  \n",
              "\n",
              "[8 rows x 30 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>8 rows × 30 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Scaling the feature data**"
      ],
      "metadata": {
        "id": "ULBYpe69dGpQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "\n",
        "scaled_data=sc.fit_transform(dataset)\n",
        "scaled_data\n",
        "scaled_df=pd.DataFrame(scaled_data,columns=dataset.columns)\n",
        "scaled_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "I_BCzqcJdHd0",
        "outputId": "7db98c69-b5be-40a8-aba4-44aa0cbbd877"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0       1.097064     -2.073335        1.269934   0.984375         1.568466   \n",
              "1       1.829821     -0.353632        1.685955   1.908708        -0.826962   \n",
              "2       1.579888      0.456187        1.566503   1.558884         0.942210   \n",
              "3      -0.768909      0.253732       -0.592687  -0.764464         3.283553   \n",
              "4       1.750297     -1.151816        1.776573   1.826229         0.280372   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564     2.110995      0.721473        2.060786   2.343856         1.041842   \n",
              "565     1.704854      2.085134        1.615931   1.723842         0.102458   \n",
              "566     0.702284      2.045574        0.672676   0.577953        -0.840484   \n",
              "567     1.838341      2.336457        1.982524   1.735218         1.525767   \n",
              "568    -1.808401      1.221792       -1.814389  -1.347789        -3.112085   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0            3.283515        2.652874             2.532475       2.217515   \n",
              "1           -0.487072       -0.023846             0.548144       0.001392   \n",
              "2            1.052926        1.363478             2.037231       0.939685   \n",
              "3            3.402909        1.915897             1.451707       2.867383   \n",
              "4            0.539340        1.371011             1.428493      -0.009560   \n",
              "..                ...             ...                  ...            ...   \n",
              "564          0.219060        1.947285             2.320965      -0.312589   \n",
              "565         -0.017833        0.693043             1.263669      -0.217664   \n",
              "566         -0.038680        0.046588             0.105777      -0.809117   \n",
              "567          3.272144        3.296944             2.658866       2.137194   \n",
              "568         -1.150752       -1.114873            -1.261820      -0.820070   \n",
              "\n",
              "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "0                  2.255747  ...      1.886690      -1.359293   \n",
              "1                 -0.868652  ...      1.805927      -0.369203   \n",
              "2                 -0.398008  ...      1.511870      -0.023974   \n",
              "3                  4.910919  ...     -0.281464       0.133984   \n",
              "4                 -0.562450  ...      1.298575      -1.466770   \n",
              "..                      ...  ...           ...            ...   \n",
              "564               -0.931027  ...      1.901185       0.117700   \n",
              "565               -1.058611  ...      1.536720       2.047399   \n",
              "566               -0.895587  ...      0.561361       1.374854   \n",
              "567                1.043695  ...      1.961239       2.237926   \n",
              "568               -0.561032  ...     -1.410893       0.764190   \n",
              "\n",
              "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
              "0           2.303601    2.001237          1.307686           2.616665   \n",
              "1           1.535126    1.890489         -0.375612          -0.430444   \n",
              "2           1.347475    1.456285          0.527407           1.082932   \n",
              "3          -0.249939   -0.550021          3.394275           3.893397   \n",
              "4           1.338539    1.220724          0.220556          -0.313395   \n",
              "..               ...         ...               ...                ...   \n",
              "564         1.752563    2.015301          0.378365          -0.273318   \n",
              "565         1.421940    1.494959         -0.691230          -0.394820   \n",
              "566         0.579001    0.427906         -0.809587           0.350735   \n",
              "567         2.303601    1.653171          1.430427           3.904848   \n",
              "568        -1.432735   -1.075813         -1.859019          -1.207552   \n",
              "\n",
              "     worst concavity  worst concave points  worst symmetry  \\\n",
              "0           2.109526              2.296076        2.750622   \n",
              "1          -0.146749              1.087084       -0.243890   \n",
              "2           0.854974              1.955000        1.152255   \n",
              "3           1.989588              2.175786        6.046041   \n",
              "4           0.613179              0.729259       -0.868353   \n",
              "..               ...                   ...             ...   \n",
              "564         0.664512              1.629151       -1.360158   \n",
              "565         0.236573              0.733827       -0.531855   \n",
              "566         0.326767              0.414069       -1.104549   \n",
              "567         3.197605              2.289985        1.919083   \n",
              "568        -1.305831             -1.745063       -0.048138   \n",
              "\n",
              "     worst fractal dimension  \n",
              "0                   1.937015  \n",
              "1                   0.281190  \n",
              "2                   0.201391  \n",
              "3                   4.935010  \n",
              "4                  -0.397100  \n",
              "..                       ...  \n",
              "564                -0.709091  \n",
              "565                -0.973978  \n",
              "566                -0.318409  \n",
              "567                 2.219635  \n",
              "568                -0.751207  \n",
              "\n",
              "[569 rows x 30 columns]"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.097064</td>\n",
              "      <td>-2.073335</td>\n",
              "      <td>1.269934</td>\n",
              "      <td>0.984375</td>\n",
              "      <td>1.568466</td>\n",
              "      <td>3.283515</td>\n",
              "      <td>2.652874</td>\n",
              "      <td>2.532475</td>\n",
              "      <td>2.217515</td>\n",
              "      <td>2.255747</td>\n",
              "      <td>...</td>\n",
              "      <td>1.886690</td>\n",
              "      <td>-1.359293</td>\n",
              "      <td>2.303601</td>\n",
              "      <td>2.001237</td>\n",
              "      <td>1.307686</td>\n",
              "      <td>2.616665</td>\n",
              "      <td>2.109526</td>\n",
              "      <td>2.296076</td>\n",
              "      <td>2.750622</td>\n",
              "      <td>1.937015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.829821</td>\n",
              "      <td>-0.353632</td>\n",
              "      <td>1.685955</td>\n",
              "      <td>1.908708</td>\n",
              "      <td>-0.826962</td>\n",
              "      <td>-0.487072</td>\n",
              "      <td>-0.023846</td>\n",
              "      <td>0.548144</td>\n",
              "      <td>0.001392</td>\n",
              "      <td>-0.868652</td>\n",
              "      <td>...</td>\n",
              "      <td>1.805927</td>\n",
              "      <td>-0.369203</td>\n",
              "      <td>1.535126</td>\n",
              "      <td>1.890489</td>\n",
              "      <td>-0.375612</td>\n",
              "      <td>-0.430444</td>\n",
              "      <td>-0.146749</td>\n",
              "      <td>1.087084</td>\n",
              "      <td>-0.243890</td>\n",
              "      <td>0.281190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.579888</td>\n",
              "      <td>0.456187</td>\n",
              "      <td>1.566503</td>\n",
              "      <td>1.558884</td>\n",
              "      <td>0.942210</td>\n",
              "      <td>1.052926</td>\n",
              "      <td>1.363478</td>\n",
              "      <td>2.037231</td>\n",
              "      <td>0.939685</td>\n",
              "      <td>-0.398008</td>\n",
              "      <td>...</td>\n",
              "      <td>1.511870</td>\n",
              "      <td>-0.023974</td>\n",
              "      <td>1.347475</td>\n",
              "      <td>1.456285</td>\n",
              "      <td>0.527407</td>\n",
              "      <td>1.082932</td>\n",
              "      <td>0.854974</td>\n",
              "      <td>1.955000</td>\n",
              "      <td>1.152255</td>\n",
              "      <td>0.201391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.768909</td>\n",
              "      <td>0.253732</td>\n",
              "      <td>-0.592687</td>\n",
              "      <td>-0.764464</td>\n",
              "      <td>3.283553</td>\n",
              "      <td>3.402909</td>\n",
              "      <td>1.915897</td>\n",
              "      <td>1.451707</td>\n",
              "      <td>2.867383</td>\n",
              "      <td>4.910919</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.281464</td>\n",
              "      <td>0.133984</td>\n",
              "      <td>-0.249939</td>\n",
              "      <td>-0.550021</td>\n",
              "      <td>3.394275</td>\n",
              "      <td>3.893397</td>\n",
              "      <td>1.989588</td>\n",
              "      <td>2.175786</td>\n",
              "      <td>6.046041</td>\n",
              "      <td>4.935010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.750297</td>\n",
              "      <td>-1.151816</td>\n",
              "      <td>1.776573</td>\n",
              "      <td>1.826229</td>\n",
              "      <td>0.280372</td>\n",
              "      <td>0.539340</td>\n",
              "      <td>1.371011</td>\n",
              "      <td>1.428493</td>\n",
              "      <td>-0.009560</td>\n",
              "      <td>-0.562450</td>\n",
              "      <td>...</td>\n",
              "      <td>1.298575</td>\n",
              "      <td>-1.466770</td>\n",
              "      <td>1.338539</td>\n",
              "      <td>1.220724</td>\n",
              "      <td>0.220556</td>\n",
              "      <td>-0.313395</td>\n",
              "      <td>0.613179</td>\n",
              "      <td>0.729259</td>\n",
              "      <td>-0.868353</td>\n",
              "      <td>-0.397100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>2.110995</td>\n",
              "      <td>0.721473</td>\n",
              "      <td>2.060786</td>\n",
              "      <td>2.343856</td>\n",
              "      <td>1.041842</td>\n",
              "      <td>0.219060</td>\n",
              "      <td>1.947285</td>\n",
              "      <td>2.320965</td>\n",
              "      <td>-0.312589</td>\n",
              "      <td>-0.931027</td>\n",
              "      <td>...</td>\n",
              "      <td>1.901185</td>\n",
              "      <td>0.117700</td>\n",
              "      <td>1.752563</td>\n",
              "      <td>2.015301</td>\n",
              "      <td>0.378365</td>\n",
              "      <td>-0.273318</td>\n",
              "      <td>0.664512</td>\n",
              "      <td>1.629151</td>\n",
              "      <td>-1.360158</td>\n",
              "      <td>-0.709091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>1.704854</td>\n",
              "      <td>2.085134</td>\n",
              "      <td>1.615931</td>\n",
              "      <td>1.723842</td>\n",
              "      <td>0.102458</td>\n",
              "      <td>-0.017833</td>\n",
              "      <td>0.693043</td>\n",
              "      <td>1.263669</td>\n",
              "      <td>-0.217664</td>\n",
              "      <td>-1.058611</td>\n",
              "      <td>...</td>\n",
              "      <td>1.536720</td>\n",
              "      <td>2.047399</td>\n",
              "      <td>1.421940</td>\n",
              "      <td>1.494959</td>\n",
              "      <td>-0.691230</td>\n",
              "      <td>-0.394820</td>\n",
              "      <td>0.236573</td>\n",
              "      <td>0.733827</td>\n",
              "      <td>-0.531855</td>\n",
              "      <td>-0.973978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>0.702284</td>\n",
              "      <td>2.045574</td>\n",
              "      <td>0.672676</td>\n",
              "      <td>0.577953</td>\n",
              "      <td>-0.840484</td>\n",
              "      <td>-0.038680</td>\n",
              "      <td>0.046588</td>\n",
              "      <td>0.105777</td>\n",
              "      <td>-0.809117</td>\n",
              "      <td>-0.895587</td>\n",
              "      <td>...</td>\n",
              "      <td>0.561361</td>\n",
              "      <td>1.374854</td>\n",
              "      <td>0.579001</td>\n",
              "      <td>0.427906</td>\n",
              "      <td>-0.809587</td>\n",
              "      <td>0.350735</td>\n",
              "      <td>0.326767</td>\n",
              "      <td>0.414069</td>\n",
              "      <td>-1.104549</td>\n",
              "      <td>-0.318409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>1.838341</td>\n",
              "      <td>2.336457</td>\n",
              "      <td>1.982524</td>\n",
              "      <td>1.735218</td>\n",
              "      <td>1.525767</td>\n",
              "      <td>3.272144</td>\n",
              "      <td>3.296944</td>\n",
              "      <td>2.658866</td>\n",
              "      <td>2.137194</td>\n",
              "      <td>1.043695</td>\n",
              "      <td>...</td>\n",
              "      <td>1.961239</td>\n",
              "      <td>2.237926</td>\n",
              "      <td>2.303601</td>\n",
              "      <td>1.653171</td>\n",
              "      <td>1.430427</td>\n",
              "      <td>3.904848</td>\n",
              "      <td>3.197605</td>\n",
              "      <td>2.289985</td>\n",
              "      <td>1.919083</td>\n",
              "      <td>2.219635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>-1.808401</td>\n",
              "      <td>1.221792</td>\n",
              "      <td>-1.814389</td>\n",
              "      <td>-1.347789</td>\n",
              "      <td>-3.112085</td>\n",
              "      <td>-1.150752</td>\n",
              "      <td>-1.114873</td>\n",
              "      <td>-1.261820</td>\n",
              "      <td>-0.820070</td>\n",
              "      <td>-0.561032</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.410893</td>\n",
              "      <td>0.764190</td>\n",
              "      <td>-1.432735</td>\n",
              "      <td>-1.075813</td>\n",
              "      <td>-1.859019</td>\n",
              "      <td>-1.207552</td>\n",
              "      <td>-1.305831</td>\n",
              "      <td>-1.745063</td>\n",
              "      <td>-0.048138</td>\n",
              "      <td>-0.751207</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 30 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-7f0a04f0-629f-48cd-b8d2-10dcb844b834')\"\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-7f0a04f0-629f-48cd-b8d2-10dcb844b834 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-7f0a04f0-629f-48cd-b8d2-10dcb844b834');\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-fc10f596-04c7-440e-a8c7-3ea7cbf7befd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fc10f596-04c7-440e-a8c7-3ea7cbf7befd')\"\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",
              "    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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fc10f596-04c7-440e-a8c7-3ea7cbf7befd 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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(scaled_df,np.ravel(target),test_size=0.2)"
      ],
      "metadata": {
        "id": "fFgkav0DdwXj"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "Hva-DJK4gJMU"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import neighbors\n",
        "model=KNeighborsClassifier(n_neighbors=5)\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "AJcIs94kgV9c",
        "outputId": "db121209-c25f-4a88-b955-12f4f5074286"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict=model.predict(x_test)\n",
        "predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vu3sqQ_oglWw",
        "outputId": "e51fcf8b-d095-44da-f96b-69788a6f0b09"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
              "       0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
      ],
      "metadata": {
        "id": "QBiSYhh_gqcJ"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score=accuracy_score(y_test,predict)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1J-NfPwhhOmZ",
        "outputId": "45bcd1fe-2266-427e-d763-109def08b5ef"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6052631578947368"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q489TqUlhTgx",
        "outputId": "6bf91910-fdb2-4a01-9e2e-1c5c9f01a7ad"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[20, 22],\n",
              "       [23, 49]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnnh8YSuhakh",
        "outputId": "f4dc88d2-d673-477d-b168-ab0daacaf31d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.47      0.48      0.47        42\n",
            "           1       0.69      0.68      0.69        72\n",
            "\n",
            "    accuracy                           0.61       114\n",
            "   macro avg       0.58      0.58      0.58       114\n",
            "weighted avg       0.61      0.61      0.61       114\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Selecting the best K vlaue**"
      ],
      "metadata": {
        "id": "-44udl-HidMp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,50):\n",
        "  model=KNeighborsClassifier(n_neighbors=i)\n",
        "  model.fit(x_train,y_train)\n",
        "  predict=model.predict(x_test)\n",
        "  score=accuracy_score(y_test,predict)\n",
        "  print(i,\": \", score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kbYWeTHh-5J",
        "outputId": "32457953-e80e-4357-c6ca-55093650b008"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 :  0.9473684210526315\n",
            "2 :  0.9298245614035088\n",
            "3 :  0.956140350877193\n",
            "4 :  0.9473684210526315\n",
            "5 :  0.9649122807017544\n",
            "6 :  0.956140350877193\n",
            "7 :  0.9649122807017544\n",
            "8 :  0.9649122807017544\n",
            "9 :  0.9649122807017544\n",
            "10 :  0.956140350877193\n",
            "11 :  0.9649122807017544\n",
            "12 :  0.9649122807017544\n",
            "13 :  0.956140350877193\n",
            "14 :  0.9649122807017544\n",
            "15 :  0.9473684210526315\n",
            "16 :  0.956140350877193\n",
            "17 :  0.9473684210526315\n",
            "18 :  0.9649122807017544\n",
            "19 :  0.9649122807017544\n",
            "20 :  0.9736842105263158\n",
            "21 :  0.9649122807017544\n",
            "22 :  0.9649122807017544\n",
            "23 :  0.9649122807017544\n",
            "24 :  0.9649122807017544\n",
            "25 :  0.9649122807017544\n",
            "26 :  0.9649122807017544\n",
            "27 :  0.956140350877193\n",
            "28 :  0.956140350877193\n",
            "29 :  0.956140350877193\n",
            "30 :  0.9649122807017544\n",
            "31 :  0.956140350877193\n",
            "32 :  0.956140350877193\n",
            "33 :  0.956140350877193\n",
            "34 :  0.956140350877193\n",
            "35 :  0.956140350877193\n",
            "36 :  0.956140350877193\n",
            "37 :  0.956140350877193\n",
            "38 :  0.956140350877193\n",
            "39 :  0.956140350877193\n",
            "40 :  0.956140350877193\n",
            "41 :  0.9473684210526315\n",
            "42 :  0.956140350877193\n",
            "43 :  0.9473684210526315\n",
            "44 :  0.956140350877193\n",
            "45 :  0.9473684210526315\n",
            "46 :  0.9473684210526315\n",
            "47 :  0.9385964912280702\n",
            "48 :  0.9385964912280702\n",
            "49 :  0.9385964912280702\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "92egsGB2jUn1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}