K_Nearest_Neighbour.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtMl-8zAzhol"
      },
      "source": [
        "# We will use kNN to predict the type of Breast Cancer in the Breast Cancer Wisconsin(Diagnostic)Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ibbWoKovzhor"
      },
      "outputs": [],
      "source": [
        "#Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHVEbWyO8VKG"
      },
      "outputs": [],
      "source": [
        "# To enable plotting graphs in Jupyter notebook\n",
        "%matplotlib inline \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from scipy.stats import zscore\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-1espNa8VKV"
      },
      "source": [
        "# Load and review Data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KgyX9Z1Y8VKb"
      },
      "outputs": [],
      "source": [
        "bcData = pd.read_csv(\"wisc_bc_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L6B_uh0x8VKe",
        "outputId": "1193ac4c-c78f-440b-b474-9800cf3b3c10",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "bcData.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bcData.head(10)\n",
        "#Diagonosis Prediction- b=benign(not cancereous) m = melignent(cancereous)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "-moZ11qA6G5L",
        "outputId": "df1375b0-4845-4dbf-acbd-52fb2fd8626d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0  87139402         B        12.32         12.39           78.85      464.1   \n",
              "1   8910251         B        10.60         18.95           69.28      346.4   \n",
              "2    905520         B        11.04         16.83           70.92      373.2   \n",
              "3    868871         B        11.28         13.39           73.00      384.8   \n",
              "4   9012568         B        15.19         13.21           97.65      711.8   \n",
              "5    906539         B        11.57         19.04           74.20      409.7   \n",
              "6    925291         B        11.51         23.93           74.52      403.5   \n",
              "7     87880         M        13.81         23.75           91.56      597.8   \n",
              "8    862989         B        10.49         19.29           67.41      336.1   \n",
              "9     89827         B        11.06         14.96           71.49      373.9   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  points_mean  ...  \\\n",
              "0          0.10280           0.06981         0.03987      0.03700  ...   \n",
              "1          0.09688           0.11470         0.06387      0.02642  ...   \n",
              "2          0.10770           0.07804         0.03046      0.02480  ...   \n",
              "3          0.11640           0.11360         0.04635      0.04796  ...   \n",
              "4          0.07963           0.06934         0.03393      0.02657  ...   \n",
              "5          0.08546           0.07722         0.05485      0.01428  ...   \n",
              "6          0.09261           0.10210         0.11120      0.04105  ...   \n",
              "7          0.13230           0.17680         0.15580      0.09176  ...   \n",
              "8          0.09989           0.08578         0.02995      0.01201  ...   \n",
              "9          0.10330           0.09097         0.05397      0.03341  ...   \n",
              "\n",
              "   radius_worst  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0         13.50          15.64            86.97       549.1            0.1385   \n",
              "1         11.88          22.94            78.28       424.8            0.1213   \n",
              "2         12.41          26.44            79.93       471.4            0.1369   \n",
              "3         11.92          15.77            76.53       434.0            0.1367   \n",
              "4         16.20          15.73           104.50       819.1            0.1126   \n",
              "5         13.07          26.98            86.43       520.5            0.1249   \n",
              "6         12.48          37.16            82.28       474.2            0.1298   \n",
              "7         19.20          41.85           128.50      1153.0            0.2226   \n",
              "8         11.54          23.31            74.22       402.8            0.1219   \n",
              "9         11.92          19.90            79.76       440.0            0.1418   \n",
              "\n",
              "   compactness_worst  concavity_worst  points_worst  symmetry_worst  \\\n",
              "0             0.1266          0.12420       0.09391          0.2827   \n",
              "1             0.2515          0.19160       0.07926          0.2940   \n",
              "2             0.1482          0.10670       0.07431          0.2998   \n",
              "3             0.1822          0.08669       0.08611          0.2102   \n",
              "4             0.1737          0.13620       0.08178          0.2487   \n",
              "5             0.1937          0.25600       0.06664          0.3035   \n",
              "6             0.2517          0.36300       0.09653          0.2112   \n",
              "7             0.5209          0.46460       0.20130          0.4432   \n",
              "8             0.1486          0.07987       0.03203          0.2826   \n",
              "9             0.2210          0.22990       0.10750          0.3301   \n",
              "\n",
              "   dimension_worst  \n",
              "0          0.06771  \n",
              "1          0.07587  \n",
              "2          0.07881  \n",
              "3          0.06784  \n",
              "4          0.06766  \n",
              "5          0.08284  \n",
              "6          0.08732  \n",
              "7          0.10860  \n",
              "8          0.07552  \n",
              "9          0.09080  \n",
              "\n",
              "[10 rows x 32 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>points_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>87139402</td>\n",
              "      <td>B</td>\n",
              "      <td>12.32</td>\n",
              "      <td>12.39</td>\n",
              "      <td>78.85</td>\n",
              "      <td>464.1</td>\n",
              "      <td>0.10280</td>\n",
              "      <td>0.06981</td>\n",
              "      <td>0.03987</td>\n",
              "      <td>0.03700</td>\n",
              "      <td>...</td>\n",
              "      <td>13.50</td>\n",
              "      <td>15.64</td>\n",
              "      <td>86.97</td>\n",
              "      <td>549.1</td>\n",
              "      <td>0.1385</td>\n",
              "      <td>0.1266</td>\n",
              "      <td>0.12420</td>\n",
              "      <td>0.09391</td>\n",
              "      <td>0.2827</td>\n",
              "      <td>0.06771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8910251</td>\n",
              "      <td>B</td>\n",
              "      <td>10.60</td>\n",
              "      <td>18.95</td>\n",
              "      <td>69.28</td>\n",
              "      <td>346.4</td>\n",
              "      <td>0.09688</td>\n",
              "      <td>0.11470</td>\n",
              "      <td>0.06387</td>\n",
              "      <td>0.02642</td>\n",
              "      <td>...</td>\n",
              "      <td>11.88</td>\n",
              "      <td>22.94</td>\n",
              "      <td>78.28</td>\n",
              "      <td>424.8</td>\n",
              "      <td>0.1213</td>\n",
              "      <td>0.2515</td>\n",
              "      <td>0.19160</td>\n",
              "      <td>0.07926</td>\n",
              "      <td>0.2940</td>\n",
              "      <td>0.07587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>905520</td>\n",
              "      <td>B</td>\n",
              "      <td>11.04</td>\n",
              "      <td>16.83</td>\n",
              "      <td>70.92</td>\n",
              "      <td>373.2</td>\n",
              "      <td>0.10770</td>\n",
              "      <td>0.07804</td>\n",
              "      <td>0.03046</td>\n",
              "      <td>0.02480</td>\n",
              "      <td>...</td>\n",
              "      <td>12.41</td>\n",
              "      <td>26.44</td>\n",
              "      <td>79.93</td>\n",
              "      <td>471.4</td>\n",
              "      <td>0.1369</td>\n",
              "      <td>0.1482</td>\n",
              "      <td>0.10670</td>\n",
              "      <td>0.07431</td>\n",
              "      <td>0.2998</td>\n",
              "      <td>0.07881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>868871</td>\n",
              "      <td>B</td>\n",
              "      <td>11.28</td>\n",
              "      <td>13.39</td>\n",
              "      <td>73.00</td>\n",
              "      <td>384.8</td>\n",
              "      <td>0.11640</td>\n",
              "      <td>0.11360</td>\n",
              "      <td>0.04635</td>\n",
              "      <td>0.04796</td>\n",
              "      <td>...</td>\n",
              "      <td>11.92</td>\n",
              "      <td>15.77</td>\n",
              "      <td>76.53</td>\n",
              "      <td>434.0</td>\n",
              "      <td>0.1367</td>\n",
              "      <td>0.1822</td>\n",
              "      <td>0.08669</td>\n",
              "      <td>0.08611</td>\n",
              "      <td>0.2102</td>\n",
              "      <td>0.06784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9012568</td>\n",
              "      <td>B</td>\n",
              "      <td>15.19</td>\n",
              "      <td>13.21</td>\n",
              "      <td>97.65</td>\n",
              "      <td>711.8</td>\n",
              "      <td>0.07963</td>\n",
              "      <td>0.06934</td>\n",
              "      <td>0.03393</td>\n",
              "      <td>0.02657</td>\n",
              "      <td>...</td>\n",
              "      <td>16.20</td>\n",
              "      <td>15.73</td>\n",
              "      <td>104.50</td>\n",
              "      <td>819.1</td>\n",
              "      <td>0.1126</td>\n",
              "      <td>0.1737</td>\n",
              "      <td>0.13620</td>\n",
              "      <td>0.08178</td>\n",
              "      <td>0.2487</td>\n",
              "      <td>0.06766</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>906539</td>\n",
              "      <td>B</td>\n",
              "      <td>11.57</td>\n",
              "      <td>19.04</td>\n",
              "      <td>74.20</td>\n",
              "      <td>409.7</td>\n",
              "      <td>0.08546</td>\n",
              "      <td>0.07722</td>\n",
              "      <td>0.05485</td>\n",
              "      <td>0.01428</td>\n",
              "      <td>...</td>\n",
              "      <td>13.07</td>\n",
              "      <td>26.98</td>\n",
              "      <td>86.43</td>\n",
              "      <td>520.5</td>\n",
              "      <td>0.1249</td>\n",
              "      <td>0.1937</td>\n",
              "      <td>0.25600</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.3035</td>\n",
              "      <td>0.08284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>925291</td>\n",
              "      <td>B</td>\n",
              "      <td>11.51</td>\n",
              "      <td>23.93</td>\n",
              "      <td>74.52</td>\n",
              "      <td>403.5</td>\n",
              "      <td>0.09261</td>\n",
              "      <td>0.10210</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.04105</td>\n",
              "      <td>...</td>\n",
              "      <td>12.48</td>\n",
              "      <td>37.16</td>\n",
              "      <td>82.28</td>\n",
              "      <td>474.2</td>\n",
              "      <td>0.1298</td>\n",
              "      <td>0.2517</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.09653</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.08732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>87880</td>\n",
              "      <td>M</td>\n",
              "      <td>13.81</td>\n",
              "      <td>23.75</td>\n",
              "      <td>91.56</td>\n",
              "      <td>597.8</td>\n",
              "      <td>0.13230</td>\n",
              "      <td>0.17680</td>\n",
              "      <td>0.15580</td>\n",
              "      <td>0.09176</td>\n",
              "      <td>...</td>\n",
              "      <td>19.20</td>\n",
              "      <td>41.85</td>\n",
              "      <td>128.50</td>\n",
              "      <td>1153.0</td>\n",
              "      <td>0.2226</td>\n",
              "      <td>0.5209</td>\n",
              "      <td>0.46460</td>\n",
              "      <td>0.20130</td>\n",
              "      <td>0.4432</td>\n",
              "      <td>0.10860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>862989</td>\n",
              "      <td>B</td>\n",
              "      <td>10.49</td>\n",
              "      <td>19.29</td>\n",
              "      <td>67.41</td>\n",
              "      <td>336.1</td>\n",
              "      <td>0.09989</td>\n",
              "      <td>0.08578</td>\n",
              "      <td>0.02995</td>\n",
              "      <td>0.01201</td>\n",
              "      <td>...</td>\n",
              "      <td>11.54</td>\n",
              "      <td>23.31</td>\n",
              "      <td>74.22</td>\n",
              "      <td>402.8</td>\n",
              "      <td>0.1219</td>\n",
              "      <td>0.1486</td>\n",
              "      <td>0.07987</td>\n",
              "      <td>0.03203</td>\n",
              "      <td>0.2826</td>\n",
              "      <td>0.07552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>89827</td>\n",
              "      <td>B</td>\n",
              "      <td>11.06</td>\n",
              "      <td>14.96</td>\n",
              "      <td>71.49</td>\n",
              "      <td>373.9</td>\n",
              "      <td>0.10330</td>\n",
              "      <td>0.09097</td>\n",
              "      <td>0.05397</td>\n",
              "      <td>0.03341</td>\n",
              "      <td>...</td>\n",
              "      <td>11.92</td>\n",
              "      <td>19.90</td>\n",
              "      <td>79.76</td>\n",
              "      <td>440.0</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2210</td>\n",
              "      <td>0.22990</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.3301</td>\n",
              "      <td>0.09080</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b5af681-ebad-4a34-8b0f-aa038266c3aa');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "moTq-BtA8VKi",
        "outputId": "833f5181-7e22-403d-f471-0252d5d63d7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                      int64\n",
              "diagnosis            category\n",
              "radius_mean           float64\n",
              "texture_mean          float64\n",
              "perimeter_mean        float64\n",
              "area_mean             float64\n",
              "smoothness_mean       float64\n",
              "compactness_mean      float64\n",
              "concavity_mean        float64\n",
              "points_mean           float64\n",
              "symmetry_mean         float64\n",
              "dimension_mean        float64\n",
              "radius_se             float64\n",
              "texture_se            float64\n",
              "perimeter_se          float64\n",
              "area_se               float64\n",
              "smoothness_se         float64\n",
              "compactness_se        float64\n",
              "concavity_se          float64\n",
              "points_se             float64\n",
              "symmetry_se           float64\n",
              "dimension_se          float64\n",
              "radius_worst          float64\n",
              "texture_worst         float64\n",
              "perimeter_worst       float64\n",
              "area_worst            float64\n",
              "smoothness_worst      float64\n",
              "compactness_worst     float64\n",
              "concavity_worst       float64\n",
              "points_worst          float64\n",
              "symmetry_worst        float64\n",
              "dimension_worst       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "bcData.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lbxMlPGt8VKl"
      },
      "outputs": [],
      "source": [
        "bcData['diagnosis'] = bcData.diagnosis.astype('category')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we don't need ID\n",
        "bcData=bcData.drop(labels='id',axis=1)"
      ],
      "metadata": {
        "id": "5un0L6s57nm4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "daQVUAdR8VKo",
        "outputId": "dccc52c4-5def-48a6-e4c0-d444a0343e7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   count        mean         std         min         25%  \\\n",
              "radius_mean        569.0   14.127292    3.524049    6.981000   11.700000   \n",
              "texture_mean       569.0   19.289649    4.301036    9.710000   16.170000   \n",
              "perimeter_mean     569.0   91.969033   24.298981   43.790000   75.170000   \n",
              "area_mean          569.0  654.889104  351.914129  143.500000  420.300000   \n",
              "smoothness_mean    569.0    0.096360    0.014064    0.052630    0.086370   \n",
              "compactness_mean   569.0    0.104341    0.052813    0.019380    0.064920   \n",
              "concavity_mean     569.0    0.088799    0.079720    0.000000    0.029560   \n",
              "points_mean        569.0    0.048919    0.038803    0.000000    0.020310   \n",
              "symmetry_mean      569.0    0.181162    0.027414    0.106000    0.161900   \n",
              "dimension_mean     569.0    0.062798    0.007060    0.049960    0.057700   \n",
              "radius_se          569.0    0.405172    0.277313    0.111500    0.232400   \n",
              "texture_se         569.0    1.216853    0.551648    0.360200    0.833900   \n",
              "perimeter_se       569.0    2.866059    2.021855    0.757000    1.606000   \n",
              "area_se            569.0   40.337079   45.491006    6.802000   17.850000   \n",
              "smoothness_se      569.0    0.007041    0.003003    0.001713    0.005169   \n",
              "compactness_se     569.0    0.025478    0.017908    0.002252    0.013080   \n",
              "concavity_se       569.0    0.031894    0.030186    0.000000    0.015090   \n",
              "points_se          569.0    0.011796    0.006170    0.000000    0.007638   \n",
              "symmetry_se        569.0    0.020542    0.008266    0.007882    0.015160   \n",
              "dimension_se       569.0    0.003795    0.002646    0.000895    0.002248   \n",
              "radius_worst       569.0   16.269190    4.833242    7.930000   13.010000   \n",
              "texture_worst      569.0   25.677223    6.146258   12.020000   21.080000   \n",
              "perimeter_worst    569.0  107.261213   33.602542   50.410000   84.110000   \n",
              "area_worst         569.0  880.583128  569.356993  185.200000  515.300000   \n",
              "smoothness_worst   569.0    0.132369    0.022832    0.071170    0.116600   \n",
              "compactness_worst  569.0    0.254265    0.157336    0.027290    0.147200   \n",
              "concavity_worst    569.0    0.272188    0.208624    0.000000    0.114500   \n",
              "points_worst       569.0    0.114606    0.065732    0.000000    0.064930   \n",
              "symmetry_worst     569.0    0.290076    0.061867    0.156500    0.250400   \n",
              "dimension_worst    569.0    0.083946    0.018061    0.055040    0.071460   \n",
              "\n",
              "                          50%          75%         max  \n",
              "radius_mean         13.370000    15.780000    28.11000  \n",
              "texture_mean        18.840000    21.800000    39.28000  \n",
              "perimeter_mean      86.240000   104.100000   188.50000  \n",
              "area_mean          551.100000   782.700000  2501.00000  \n",
              "smoothness_mean      0.095870     0.105300     0.16340  \n",
              "compactness_mean     0.092630     0.130400     0.34540  \n",
              "concavity_mean       0.061540     0.130700     0.42680  \n",
              "points_mean          0.033500     0.074000     0.20120  \n",
              "symmetry_mean        0.179200     0.195700     0.30400  \n",
              "dimension_mean       0.061540     0.066120     0.09744  \n",
              "radius_se            0.324200     0.478900     2.87300  \n",
              "texture_se           1.108000     1.474000     4.88500  \n",
              "perimeter_se         2.287000     3.357000    21.98000  \n",
              "area_se             24.530000    45.190000   542.20000  \n",
              "smoothness_se        0.006380     0.008146     0.03113  \n",
              "compactness_se       0.020450     0.032450     0.13540  \n",
              "concavity_se         0.025890     0.042050     0.39600  \n",
              "points_se            0.010930     0.014710     0.05279  \n",
              "symmetry_se          0.018730     0.023480     0.07895  \n",
              "dimension_se         0.003187     0.004558     0.02984  \n",
              "radius_worst        14.970000    18.790000    36.04000  \n",
              "texture_worst       25.410000    29.720000    49.54000  \n",
              "perimeter_worst     97.660000   125.400000   251.20000  \n",
              "area_worst         686.500000  1084.000000  4254.00000  \n",
              "smoothness_worst     0.131300     0.146000     0.22260  \n",
              "compactness_worst    0.211900     0.339100     1.05800  \n",
              "concavity_worst      0.226700     0.382900     1.25200  \n",
              "points_worst         0.099930     0.161400     0.29100  \n",
              "symmetry_worst       0.282200     0.317900     0.66380  \n",
              "dimension_worst      0.080040     0.092080     0.20750  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-89cbae15-fb7e-4ba6-8c78-7a2a81158f53\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>14.127292</td>\n",
              "      <td>3.524049</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>28.11000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>39.28000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>188.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>2501.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.16340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.34540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.42680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>points_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.20120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.30400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dimension_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.09744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>2.87300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>4.88500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>21.98000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>542.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.03113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.13540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.39600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>points_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.05279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.07895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dimension_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>0.02984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>36.04000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>49.54000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>251.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>4254.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.132369</td>\n",
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              "      <td>0.071170</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.22260</td>\n",
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              "      <th>compactness_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>1.05800</td>\n",
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              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>1.25200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>points_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.29100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.66380</td>\n",
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              "    <tr>\n",
              "      <th>dimension_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>0.20750</td>\n",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "bcData.describe().transpose()\n"
      ]
    },
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            "text/plain": [
              "           radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "diagnosis                                                         \n",
              "B                  357           357             357        357   \n",
              "M                  212           212             212        212   \n",
              "\n",
              "           smoothness_mean  compactness_mean  concavity_mean  points_mean  \\\n",
              "diagnosis                                                                   \n",
              "B                      357               357             357          357   \n",
              "M                      212               212             212          212   \n",
              "\n",
              "           symmetry_mean  dimension_mean  ...  radius_worst  texture_worst  \\\n",
              "diagnosis                                 ...                                \n",
              "B                    357             357  ...           357            357   \n",
              "M                    212             212  ...           212            212   \n",
              "\n",
              "           perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
              "diagnosis                                                                     \n",
              "B                      357         357               357                357   \n",
              "M                      212         212               212                212   \n",
              "\n",
              "           concavity_worst  points_worst  symmetry_worst  dimension_worst  \n",
              "diagnosis                                                                  \n",
              "B                      357           357             357              357  \n",
              "M                      212           212             212              212  \n",
              "\n",
              "[2 rows x 30 columns]"
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              "\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "bcData.groupby([\"diagnosis\"]).count()\n",
        "\n",
        "# Class distribution among B and M is almost 2:1. The model will better predict B and M"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mexo9Mfl8VKu"
      },
      "source": [
        "drop the first column from the data frame. This is Id column which is not used in modeling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t_NzK66L8VKy",
        "outputId": "d32dbdaa-b8bf-4025-d052-5d1ef74571ad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 31)"
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          "metadata": {},
          "execution_count": 15
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          "data": {
            "text/plain": [
              "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
              "0        12.32         12.39           78.85      464.1          0.10280   \n",
              "1        10.60         18.95           69.28      346.4          0.09688   \n",
              "2        11.04         16.83           70.92      373.2          0.10770   \n",
              "3        11.28         13.39           73.00      384.8          0.11640   \n",
              "4        15.19         13.21           97.65      711.8          0.07963   \n",
              "\n",
              "   compactness_mean  concavity_mean  points_mean  symmetry_mean  \\\n",
              "0           0.06981         0.03987      0.03700         0.1959   \n",
              "1           0.11470         0.06387      0.02642         0.1922   \n",
              "2           0.07804         0.03046      0.02480         0.1714   \n",
              "3           0.11360         0.04635      0.04796         0.1771   \n",
              "4           0.06934         0.03393      0.02657         0.1721   \n",
              "\n",
              "   dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.05955  ...         13.50          15.64            86.97   \n",
              "1         0.06491  ...         11.88          22.94            78.28   \n",
              "2         0.06340  ...         12.41          26.44            79.93   \n",
              "3         0.06072  ...         11.92          15.77            76.53   \n",
              "4         0.05544  ...         16.20          15.73           104.50   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0       549.1            0.1385             0.1266          0.12420   \n",
              "1       424.8            0.1213             0.2515          0.19160   \n",
              "2       471.4            0.1369             0.1482          0.10670   \n",
              "3       434.0            0.1367             0.1822          0.08669   \n",
              "4       819.1            0.1126             0.1737          0.13620   \n",
              "\n",
              "   points_worst  symmetry_worst  dimension_worst  \n",
              "0       0.09391          0.2827          0.06771  \n",
              "1       0.07926          0.2940          0.07587  \n",
              "2       0.07431          0.2998          0.07881  \n",
              "3       0.08611          0.2102          0.06784  \n",
              "4       0.08178          0.2487          0.06766  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>dimension_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.32</td>\n",
              "      <td>12.39</td>\n",
              "      <td>78.85</td>\n",
              "      <td>464.1</td>\n",
              "      <td>0.10280</td>\n",
              "      <td>0.06981</td>\n",
              "      <td>0.03987</td>\n",
              "      <td>0.03700</td>\n",
              "      <td>0.1959</td>\n",
              "      <td>0.05955</td>\n",
              "      <td>...</td>\n",
              "      <td>13.50</td>\n",
              "      <td>15.64</td>\n",
              "      <td>86.97</td>\n",
              "      <td>549.1</td>\n",
              "      <td>0.1385</td>\n",
              "      <td>0.1266</td>\n",
              "      <td>0.12420</td>\n",
              "      <td>0.09391</td>\n",
              "      <td>0.2827</td>\n",
              "      <td>0.06771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.60</td>\n",
              "      <td>18.95</td>\n",
              "      <td>69.28</td>\n",
              "      <td>346.4</td>\n",
              "      <td>0.09688</td>\n",
              "      <td>0.11470</td>\n",
              "      <td>0.06387</td>\n",
              "      <td>0.02642</td>\n",
              "      <td>0.1922</td>\n",
              "      <td>0.06491</td>\n",
              "      <td>...</td>\n",
              "      <td>11.88</td>\n",
              "      <td>22.94</td>\n",
              "      <td>78.28</td>\n",
              "      <td>424.8</td>\n",
              "      <td>0.1213</td>\n",
              "      <td>0.2515</td>\n",
              "      <td>0.19160</td>\n",
              "      <td>0.07926</td>\n",
              "      <td>0.2940</td>\n",
              "      <td>0.07587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11.04</td>\n",
              "      <td>16.83</td>\n",
              "      <td>70.92</td>\n",
              "      <td>373.2</td>\n",
              "      <td>0.10770</td>\n",
              "      <td>0.07804</td>\n",
              "      <td>0.03046</td>\n",
              "      <td>0.02480</td>\n",
              "      <td>0.1714</td>\n",
              "      <td>0.06340</td>\n",
              "      <td>...</td>\n",
              "      <td>12.41</td>\n",
              "      <td>26.44</td>\n",
              "      <td>79.93</td>\n",
              "      <td>471.4</td>\n",
              "      <td>0.1369</td>\n",
              "      <td>0.1482</td>\n",
              "      <td>0.10670</td>\n",
              "      <td>0.07431</td>\n",
              "      <td>0.2998</td>\n",
              "      <td>0.07881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.28</td>\n",
              "      <td>13.39</td>\n",
              "      <td>73.00</td>\n",
              "      <td>384.8</td>\n",
              "      <td>0.11640</td>\n",
              "      <td>0.11360</td>\n",
              "      <td>0.04635</td>\n",
              "      <td>0.04796</td>\n",
              "      <td>0.1771</td>\n",
              "      <td>0.06072</td>\n",
              "      <td>...</td>\n",
              "      <td>11.92</td>\n",
              "      <td>15.77</td>\n",
              "      <td>76.53</td>\n",
              "      <td>434.0</td>\n",
              "      <td>0.1367</td>\n",
              "      <td>0.1822</td>\n",
              "      <td>0.08669</td>\n",
              "      <td>0.08611</td>\n",
              "      <td>0.2102</td>\n",
              "      <td>0.06784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15.19</td>\n",
              "      <td>13.21</td>\n",
              "      <td>97.65</td>\n",
              "      <td>711.8</td>\n",
              "      <td>0.07963</td>\n",
              "      <td>0.06934</td>\n",
              "      <td>0.03393</td>\n",
              "      <td>0.02657</td>\n",
              "      <td>0.1721</td>\n",
              "      <td>0.05544</td>\n",
              "      <td>...</td>\n",
              "      <td>16.20</td>\n",
              "      <td>15.73</td>\n",
              "      <td>104.50</td>\n",
              "      <td>819.1</td>\n",
              "      <td>0.1126</td>\n",
              "      <td>0.1737</td>\n",
              "      <td>0.13620</td>\n",
              "      <td>0.08178</td>\n",
              "      <td>0.2487</td>\n",
              "      <td>0.06766</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11aba48f-bf48-4370-9c7b-c2388ace9b5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-11aba48f-bf48-4370-9c7b-c2388ace9b5a');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Create a separate dataframe consisting only of the features i.e independent attributes\n",
        "\n",
        "X = bcData.drop(labels= \"diagnosis\" , axis = 1)\n",
        "y = bcData[\"diagnosis\"]\n",
        "X.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S_SwKObi8VK4",
        "outputId": "3150db23-1211-4e08-feff-1a89caacf91f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   count          mean      std       min       25%       50%  \\\n",
              "radius_mean        569.0 -1.576556e-16  1.00088 -2.029648 -0.689385 -0.215082   \n",
              "texture_mean       569.0 -7.711075e-16  1.00088 -2.229249 -0.725963 -0.104636   \n",
              "perimeter_mean     569.0 -1.186319e-16  1.00088 -1.984504 -0.691956 -0.235980   \n",
              "area_mean          569.0 -2.029230e-16  1.00088 -1.454443 -0.667195 -0.295187   \n",
              "smoothness_mean    569.0  1.717041e-16  1.00088 -3.112085 -0.710963 -0.034891   \n",
              "compactness_mean   569.0  1.935573e-16  1.00088 -1.610136 -0.747086 -0.221940   \n",
              "concavity_mean     569.0  4.058460e-17  1.00088 -1.114873 -0.743748 -0.342240   \n",
              "points_mean        569.0 -5.619407e-17  1.00088 -1.261820 -0.737944 -0.397721   \n",
              "symmetry_mean      569.0  1.638994e-16  1.00088 -2.744117 -0.703240 -0.071627   \n",
              "dimension_mean     569.0 -1.492265e-15  1.00088 -1.819865 -0.722639 -0.178279   \n",
              "radius_se          569.0  2.247763e-16  1.00088 -1.059924 -0.623571 -0.292245   \n",
              "texture_se         569.0 -1.061443e-16  1.00088 -1.554264 -0.694809 -0.197498   \n",
              "perimeter_se       569.0  1.045834e-16  1.00088 -1.044049 -0.623768 -0.286652   \n",
              "area_se            569.0 -1.342414e-16  1.00088 -0.737829 -0.494754 -0.347783   \n",
              "smoothness_se      569.0 -1.560946e-16  1.00088 -1.776065 -0.624018 -0.220335   \n",
              "compactness_se     569.0 -2.185325e-17  1.00088 -1.298098 -0.692926 -0.281020   \n",
              "concavity_se       569.0 -5.307217e-17  1.00088 -1.057501 -0.557161 -0.199065   \n",
              "points_se          569.0 -2.341419e-18  1.00088 -1.913447 -0.674490 -0.140496   \n",
              "symmetry_se        569.0  9.990056e-17  1.00088 -1.532890 -0.651681 -0.219430   \n",
              "dimension_se       569.0  0.000000e+00  1.00088 -1.096968 -0.585118 -0.229940   \n",
              "radius_worst       569.0 -9.209583e-17  1.00088 -1.726901 -0.674921 -0.269040   \n",
              "texture_worst      569.0  1.560946e-18  1.00088 -2.223994 -0.748629 -0.043516   \n",
              "perimeter_worst    569.0 -3.777490e-16  1.00088 -1.693361 -0.689578 -0.285980   \n",
              "area_worst         569.0 -1.779479e-16  1.00088 -1.222423 -0.642136 -0.341181   \n",
              "smoothness_worst   569.0 -2.239958e-16  1.00088 -2.682695 -0.691230 -0.046843   \n",
              "compactness_worst  569.0  9.365678e-18  1.00088 -1.443878 -0.681083 -0.269501   \n",
              "concavity_worst    569.0  9.053488e-17  1.00088 -1.305831 -0.756514 -0.218232   \n",
              "points_worst       569.0  2.263372e-16  1.00088 -1.745063 -0.756400 -0.223469   \n",
              "symmetry_worst     569.0  2.684828e-16  1.00088 -2.160960 -0.641864 -0.127409   \n",
              "dimension_worst    569.0  2.044840e-16  1.00088 -1.601839 -0.691912 -0.216444   \n",
              "\n",
              "                        75%        max  \n",
              "radius_mean        0.469393   3.971288  \n",
              "texture_mean       0.584176   4.651889  \n",
              "perimeter_mean     0.499677   3.976130  \n",
              "area_mean          0.363507   5.250529  \n",
              "smoothness_mean    0.636199   4.770911  \n",
              "compactness_mean   0.493857   4.568425  \n",
              "concavity_mean     0.526062   4.243589  \n",
              "points_mean        0.646935   3.927930  \n",
              "symmetry_mean      0.530779   4.484751  \n",
              "dimension_mean     0.470983   4.910919  \n",
              "radius_se          0.266100   8.906909  \n",
              "texture_se         0.466552   6.655279  \n",
              "perimeter_se       0.243031   9.461986  \n",
              "area_se            0.106773  11.041842  \n",
              "smoothness_se      0.368355   8.029999  \n",
              "compactness_se     0.389654   6.143482  \n",
              "concavity_se       0.336752  12.072680  \n",
              "points_se          0.472657   6.649601  \n",
              "symmetry_se        0.355692   7.071917  \n",
              "dimension_se       0.288642   9.851593  \n",
              "radius_worst       0.522016   4.094189  \n",
              "texture_worst      0.658341   3.885905  \n",
              "perimeter_worst    0.540279   4.287337  \n",
              "area_worst         0.357589   5.930172  \n",
              "smoothness_worst   0.597545   3.955374  \n",
              "compactness_worst  0.539669   5.112877  \n",
              "concavity_worst    0.531141   4.700669  \n",
              "points_worst       0.712510   2.685877  \n",
              "symmetry_worst     0.450138   6.046041  \n",
              "dimension_worst    0.450762   6.846856  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
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              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>569.0</td>\n",
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              "      <td>1.00088</td>\n",
              "      <td>-2.029648</td>\n",
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              "      <td>0.469393</td>\n",
              "      <td>3.971288</td>\n",
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              "      <th>texture_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-7.711075e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-2.229249</td>\n",
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              "      <td>0.584176</td>\n",
              "      <td>4.651889</td>\n",
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              "      <th>perimeter_mean</th>\n",
              "      <td>569.0</td>\n",
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              "      <td>1.00088</td>\n",
              "      <td>-1.984504</td>\n",
              "      <td>-0.691956</td>\n",
              "      <td>-0.235980</td>\n",
              "      <td>0.499677</td>\n",
              "      <td>3.976130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-2.029230e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.454443</td>\n",
              "      <td>-0.667195</td>\n",
              "      <td>-0.295187</td>\n",
              "      <td>0.363507</td>\n",
              "      <td>5.250529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.717041e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-3.112085</td>\n",
              "      <td>-0.710963</td>\n",
              "      <td>-0.034891</td>\n",
              "      <td>0.636199</td>\n",
              "      <td>4.770911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.935573e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.610136</td>\n",
              "      <td>-0.747086</td>\n",
              "      <td>-0.221940</td>\n",
              "      <td>0.493857</td>\n",
              "      <td>4.568425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>4.058460e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.114873</td>\n",
              "      <td>-0.743748</td>\n",
              "      <td>-0.342240</td>\n",
              "      <td>0.526062</td>\n",
              "      <td>4.243589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>points_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-5.619407e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.261820</td>\n",
              "      <td>-0.737944</td>\n",
              "      <td>-0.397721</td>\n",
              "      <td>0.646935</td>\n",
              "      <td>3.927930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.638994e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-2.744117</td>\n",
              "      <td>-0.703240</td>\n",
              "      <td>-0.071627</td>\n",
              "      <td>0.530779</td>\n",
              "      <td>4.484751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dimension_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-1.492265e-15</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.819865</td>\n",
              "      <td>-0.722639</td>\n",
              "      <td>-0.178279</td>\n",
              "      <td>0.470983</td>\n",
              "      <td>4.910919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.247763e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.059924</td>\n",
              "      <td>-0.623571</td>\n",
              "      <td>-0.292245</td>\n",
              "      <td>0.266100</td>\n",
              "      <td>8.906909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-1.061443e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.554264</td>\n",
              "      <td>-0.694809</td>\n",
              "      <td>-0.197498</td>\n",
              "      <td>0.466552</td>\n",
              "      <td>6.655279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.045834e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.044049</td>\n",
              "      <td>-0.623768</td>\n",
              "      <td>-0.286652</td>\n",
              "      <td>0.243031</td>\n",
              "      <td>9.461986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-1.342414e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-0.737829</td>\n",
              "      <td>-0.494754</td>\n",
              "      <td>-0.347783</td>\n",
              "      <td>0.106773</td>\n",
              "      <td>11.041842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-1.560946e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.776065</td>\n",
              "      <td>-0.624018</td>\n",
              "      <td>-0.220335</td>\n",
              "      <td>0.368355</td>\n",
              "      <td>8.029999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-2.185325e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.298098</td>\n",
              "      <td>-0.692926</td>\n",
              "      <td>-0.281020</td>\n",
              "      <td>0.389654</td>\n",
              "      <td>6.143482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-5.307217e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.057501</td>\n",
              "      <td>-0.557161</td>\n",
              "      <td>-0.199065</td>\n",
              "      <td>0.336752</td>\n",
              "      <td>12.072680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>points_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-2.341419e-18</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.913447</td>\n",
              "      <td>-0.674490</td>\n",
              "      <td>-0.140496</td>\n",
              "      <td>0.472657</td>\n",
              "      <td>6.649601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>9.990056e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.532890</td>\n",
              "      <td>-0.651681</td>\n",
              "      <td>-0.219430</td>\n",
              "      <td>0.355692</td>\n",
              "      <td>7.071917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dimension_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.096968</td>\n",
              "      <td>-0.585118</td>\n",
              "      <td>-0.229940</td>\n",
              "      <td>0.288642</td>\n",
              "      <td>9.851593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-9.209583e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.726901</td>\n",
              "      <td>-0.674921</td>\n",
              "      <td>-0.269040</td>\n",
              "      <td>0.522016</td>\n",
              "      <td>4.094189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.560946e-18</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-2.223994</td>\n",
              "      <td>-0.748629</td>\n",
              "      <td>-0.043516</td>\n",
              "      <td>0.658341</td>\n",
              "      <td>3.885905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-3.777490e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.693361</td>\n",
              "      <td>-0.689578</td>\n",
              "      <td>-0.285980</td>\n",
              "      <td>0.540279</td>\n",
              "      <td>4.287337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-1.779479e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.222423</td>\n",
              "      <td>-0.642136</td>\n",
              "      <td>-0.341181</td>\n",
              "      <td>0.357589</td>\n",
              "      <td>5.930172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>-2.239958e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-2.682695</td>\n",
              "      <td>-0.691230</td>\n",
              "      <td>-0.046843</td>\n",
              "      <td>0.597545</td>\n",
              "      <td>3.955374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>9.365678e-18</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.443878</td>\n",
              "      <td>-0.681083</td>\n",
              "      <td>-0.269501</td>\n",
              "      <td>0.539669</td>\n",
              "      <td>5.112877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>9.053488e-17</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.305831</td>\n",
              "      <td>-0.756514</td>\n",
              "      <td>-0.218232</td>\n",
              "      <td>0.531141</td>\n",
              "      <td>4.700669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>points_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.263372e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.745063</td>\n",
              "      <td>-0.756400</td>\n",
              "      <td>-0.223469</td>\n",
              "      <td>0.712510</td>\n",
              "      <td>2.685877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.684828e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-2.160960</td>\n",
              "      <td>-0.641864</td>\n",
              "      <td>-0.127409</td>\n",
              "      <td>0.450138</td>\n",
              "      <td>6.046041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dimension_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.044840e-16</td>\n",
              "      <td>1.00088</td>\n",
              "      <td>-1.601839</td>\n",
              "      <td>-0.691912</td>\n",
              "      <td>-0.216444</td>\n",
              "      <td>0.450762</td>\n",
              "      <td>6.846856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c02e220e-5188-4ef1-abdb-952fe493c606')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c02e220e-5188-4ef1-abdb-952fe493c606 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-c02e220e-5188-4ef1-abdb-952fe493c606');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# convert the features into z scores as we do not know what units / scales were used and store them in new dataframe\n",
        "# It is always adviced to scale numeric attributes in models that calculate distances.\n",
        "\n",
        "XScaled  = X.apply(zscore)  # convert all attributes to Z scale \n",
        "\n",
        "XScaled.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7wY4Qr08VLG"
      },
      "outputs": [],
      "source": [
        "# Split X and y into training and test set in 75:25 ratio\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(XScaled, y, test_size=0.40, random_state=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSyh9clCzho4"
      },
      "source": [
        "# Build kNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DPtlBrYu8VKY"
      },
      "outputs": [],
      "source": [
        "NNH = KNeighborsClassifier(n_neighbors= 5 , weights = 'distance' )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_8-LNP3j8VLM",
        "outputId": "9056a727-d5ab-4a9b-b709-43bfccd79d43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(weights='distance')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Call Nearest Neighbour algorithm\n",
        "\n",
        "NNH.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "629KeDkbzho6"
      },
      "source": [
        "# Evaluate Performance of kNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uVDCzJRM8VLO",
        "outputId": "09d6d090-f396-40b5-d747-1527b535ec92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9780701754385965"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# For every test data point, predict it's label based on 5 nearest neighbours in this model. The majority class will \n",
        "# be assigned to the test data point\n",
        "\n",
        "predicted_labels = NNH.predict(X_test)\n",
        "NNH.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DJbjqMCAKiE",
        "outputId": "d03af8fc-d9b3-4f5b-e8f8-f86b37530d99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
              "       'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'B', 'B', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'M',\n",
              "       'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'M', 'M',\n",
              "       'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B',\n",
              "       'B', 'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'B',\n",
              "       'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'M', 'M', 'B', 'B',\n",
              "       'B', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M', 'M',\n",
              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
              "       'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B', 'M', 'B',\n",
              "       'B', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B',\n",
              "       'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'M', 'M', 'B', 'M', 'B',\n",
              "       'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'M',\n",
              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B',\n",
              "       'B', 'M', 'M', 'B', 'M', 'B', 'B'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mhwaek_5zho7",
        "outputId": "3fd6d17e-7401-46e6-f603-70e0c36e6eba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2c812bd10>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# calculate accuracy measures and confusion matrix\n",
        "from sklearn import metrics\n",
        "\n",
        "print(\"Confusion Matrix\")\n",
        "cm=metrics.confusion_matrix(y_test, predicted_labels, labels=[\"M\", \"B\"])\n",
        "\n",
        "df_cm = pd.DataFrame(cm, index = [i for i in [\"M\",\"B\"]],\n",
        "                  columns = [i for i in [\"Predict M\",\"Predict B\"]])\n",
        "plt.figure(figsize = (7,5))\n",
        "sns.heatmap(df_cm, annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Choosing K Value"
      ],
      "metadata": {
        "id": "ko1GiuM7B9VV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Choosing the right k is not easy and is subjective. Usually choose as an odd number is choosen.\n",
        "\n",
        "#### A small k captures too much training noise and hence does not do well in test data. A very large k does so much smoothening that it does not manage to capture information in the training data sufficiently - and hence does not do well in test data.\n",
        "\n",
        "#### If the number of classes is 2, many suggest a rule of thumb approach(set k=sqrt(n)), that might not be the best but does well mostly."
      ],
      "metadata": {
        "id": "lerfv5qgBitB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Wtrb75-Nzho9"
      },
      "outputs": [],
      "source": [
        "scores =[]\n",
        "for k in range(1,50):\n",
        "    NNH = KNeighborsClassifier(n_neighbors = k, weights = 'distance' )\n",
        "    NNH.fit(X_train, y_train)\n",
        "    scores.append(NNH.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,50),scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "byoxh5F7Buxv",
        "outputId": "ac7f8a57-4d77-463d-a181-8984975d335e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc2c51b1bd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(40,10))\n",
        "plt.plot(range(1,50),scores,color='red',linestyle='dashed',marker='o',markerfacecolor='green',markersize=30)\n",
        "\n",
        "plt.title('Accuracy Rate VS K-Values')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('Accuracy')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "Q4TD5wh4DQcI",
        "outputId": "95ca21b0-3b37-4d1e-9638-d466fd8ce99c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}