CA3Notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "WorkonLater.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ultan-Kearns/LYIT-Machine-Learning-Project/blob/main/CA3Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaYS6bFNmWuN"
      },
      "source": [
        "# Loading files and importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "8ySUe3n2zXfN",
        "outputId": "bfca78e8-dec7-49d9-8644-493bc0e7b5f1"
      },
      "source": [
        "#import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "import matplotlib \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from google.colab import files\n",
        "import io\n",
        "\n",
        "uploaded = files.upload()\n",
        "df = pd.read_csv(io.BytesIO(uploaded['wdbcwh.csv']), header = 0)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-84ca0221-b966-45c4-9277-96df0452e1b1\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-84ca0221-b966-45c4-9277-96df0452e1b1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving wdbcwh.csv to wdbcwh.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N963plximUXS"
      },
      "source": [
        "# Prepping and analyzing the data\n",
        "In this section we prep and analyze the data we do this by dividing the data into two groups training and testing.  We will train the models with the training data so that we can predict the values within the testing dataset.  We randomize the data to produce a more accurate model and to show us the variation in performance of our models as the data is different everytime. We could also use a random seed to produce the same \"random\" set of rows everytime, although we wanted to avoid this to reduce our models bias towards the data when training and testing our models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6Qw95tszgIg"
      },
      "source": [
        "# Show dataset\n",
        "# split data into training and test datasets\n",
        "X = df[0:570]\n",
        "y = X\n",
        "# Drop ID from the dataframe, it is not useful for our purposes except for identifying unique files\n",
        "X = X.drop(['ID'],axis=1)\n",
        "y = y.drop(['ID'],axis=1)\n",
        "from sklearn.model_selection import train_test_split\n",
        "# could use random state 42 for same results every time\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.9)\n"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65JKC0E6WSeX"
      },
      "source": [
        "Here we are showing some of the values in our training sets and our test sets, by doing this we can see that we have a decent amount of randomness in our data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPHeQ96EpAo8",
        "outputId": "1c7d35de-b356-425a-8774-11894b913982"
      },
      "source": [
        "print('X_train ',X_train)\n",
        "print('y_train ',y_train)\n",
        "print('X_test ',X_test)\n",
        "print('y_test ',y_test)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train      Diagnosis  Mean_Radius  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
            "198         M       19.180  ...          0.3193                  0.09221\n",
            "476         B       14.200  ...          0.2534                  0.07858\n",
            "563         M       20.920  ...          0.2929                  0.09873\n",
            "350         B       11.660  ...          0.2731                  0.06825\n",
            "193         M       12.340  ...          0.3215                  0.12050\n",
            "94          M       15.060  ...          0.2834                  0.08234\n",
            "539         B        7.691  ...          0.2790                  0.10660\n",
            "95          M       20.260  ...          0.3689                  0.08368\n",
            "342         B       11.060  ...          0.3301                  0.09080\n",
            "59          B        8.618  ...          0.3220                  0.09026\n",
            "531         B       11.670  ...          0.3206                  0.08950\n",
            "428         B       11.130  ...          0.2383                  0.07083\n",
            "298         B       14.260  ...          0.2636                  0.07676\n",
            "124         B       13.370  ...          0.2048                  0.07628\n",
            "117         M       14.870  ...          0.3585                  0.10650\n",
            "299         B       10.510  ...          0.2227                  0.06777\n",
            "15          M       14.540  ...          0.4218                  0.13410\n",
            "91          M       15.370  ...          0.2556                  0.06828\n",
            "467         B        9.668  ...          0.3057                  0.07875\n",
            "62          M       14.250  ...          0.2844                  0.11320\n",
            "39          M       13.480  ...          0.2807                  0.10710\n",
            "493         B       12.460  ...          0.2280                  0.07028\n",
            "358         B        8.878  ...          0.2434                  0.07431\n",
            "491         B       17.850  ...          0.1783                  0.05871\n",
            "398         B       11.060  ...          0.2590                  0.09158\n",
            "389         M       19.550  ...          0.2576                  0.07602\n",
            "220         B       13.650  ...          0.2380                  0.08718\n",
            "170         B       12.320  ...          0.2827                  0.06771\n",
            "397         B       12.800  ...          0.1988                  0.07053\n",
            "362         B       12.760  ...          0.2744                  0.07238\n",
            "537         B       11.690  ...          0.2803                  0.09970\n",
            "25          M       17.140  ...          0.4066                  0.10590\n",
            "270         B       14.290  ...          0.2458                  0.06120\n",
            "278         B       13.590  ...          0.2335                  0.06263\n",
            "366         M       20.200  ...          0.3271                  0.07632\n",
            "58          B       13.050  ...          0.2439                  0.06289\n",
            "318         B        9.042  ...          0.3135                  0.10550\n",
            "233         M       20.510  ...          0.2437                  0.08328\n",
            "200         B       12.230  ...          0.2668                  0.08174\n",
            "215         M       13.860  ...          0.3630                  0.10590\n",
            "100         M       13.610  ...          0.2651                  0.07397\n",
            "349         B       11.950  ...          0.3124                  0.07590\n",
            "280         M       19.160  ...          0.3258                  0.09720\n",
            "436         B       12.870  ...          0.3313                  0.07735\n",
            "5           M       12.450  ...          0.3985                  0.12440\n",
            "492         M       18.010  ...          0.3251                  0.07625\n",
            "258         M       15.660  ...          0.3277                  0.10190\n",
            "222         B       10.180  ...          0.3055                  0.08797\n",
            "160         B       11.750  ...          0.3168                  0.07987\n",
            "449         M       21.100  ...          0.2268                  0.07425\n",
            "2           M       19.690  ...          0.3613                  0.08758\n",
            "357         B       13.870  ...          0.2362                  0.07113\n",
            "446         M       17.750  ...          0.2972                  0.09075\n",
            "72          M       17.200  ...          0.3313                  0.13390\n",
            "21          B        9.504  ...          0.2450                  0.07773\n",
            "66          B        9.465  ...          0.2878                  0.09211\n",
            "\n",
            "[56 rows x 31 columns]\n",
            "y_train      Diagnosis  Mean_Radius  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
            "198         M       19.180  ...          0.3193                  0.09221\n",
            "476         B       14.200  ...          0.2534                  0.07858\n",
            "563         M       20.920  ...          0.2929                  0.09873\n",
            "350         B       11.660  ...          0.2731                  0.06825\n",
            "193         M       12.340  ...          0.3215                  0.12050\n",
            "94          M       15.060  ...          0.2834                  0.08234\n",
            "539         B        7.691  ...          0.2790                  0.10660\n",
            "95          M       20.260  ...          0.3689                  0.08368\n",
            "342         B       11.060  ...          0.3301                  0.09080\n",
            "59          B        8.618  ...          0.3220                  0.09026\n",
            "531         B       11.670  ...          0.3206                  0.08950\n",
            "428         B       11.130  ...          0.2383                  0.07083\n",
            "298         B       14.260  ...          0.2636                  0.07676\n",
            "124         B       13.370  ...          0.2048                  0.07628\n",
            "117         M       14.870  ...          0.3585                  0.10650\n",
            "299         B       10.510  ...          0.2227                  0.06777\n",
            "15          M       14.540  ...          0.4218                  0.13410\n",
            "91          M       15.370  ...          0.2556                  0.06828\n",
            "467         B        9.668  ...          0.3057                  0.07875\n",
            "62          M       14.250  ...          0.2844                  0.11320\n",
            "39          M       13.480  ...          0.2807                  0.10710\n",
            "493         B       12.460  ...          0.2280                  0.07028\n",
            "358         B        8.878  ...          0.2434                  0.07431\n",
            "491         B       17.850  ...          0.1783                  0.05871\n",
            "398         B       11.060  ...          0.2590                  0.09158\n",
            "389         M       19.550  ...          0.2576                  0.07602\n",
            "220         B       13.650  ...          0.2380                  0.08718\n",
            "170         B       12.320  ...          0.2827                  0.06771\n",
            "397         B       12.800  ...          0.1988                  0.07053\n",
            "362         B       12.760  ...          0.2744                  0.07238\n",
            "537         B       11.690  ...          0.2803                  0.09970\n",
            "25          M       17.140  ...          0.4066                  0.10590\n",
            "270         B       14.290  ...          0.2458                  0.06120\n",
            "278         B       13.590  ...          0.2335                  0.06263\n",
            "366         M       20.200  ...          0.3271                  0.07632\n",
            "58          B       13.050  ...          0.2439                  0.06289\n",
            "318         B        9.042  ...          0.3135                  0.10550\n",
            "233         M       20.510  ...          0.2437                  0.08328\n",
            "200         B       12.230  ...          0.2668                  0.08174\n",
            "215         M       13.860  ...          0.3630                  0.10590\n",
            "100         M       13.610  ...          0.2651                  0.07397\n",
            "349         B       11.950  ...          0.3124                  0.07590\n",
            "280         M       19.160  ...          0.3258                  0.09720\n",
            "436         B       12.870  ...          0.3313                  0.07735\n",
            "5           M       12.450  ...          0.3985                  0.12440\n",
            "492         M       18.010  ...          0.3251                  0.07625\n",
            "258         M       15.660  ...          0.3277                  0.10190\n",
            "222         B       10.180  ...          0.3055                  0.08797\n",
            "160         B       11.750  ...          0.3168                  0.07987\n",
            "449         M       21.100  ...          0.2268                  0.07425\n",
            "2           M       19.690  ...          0.3613                  0.08758\n",
            "357         B       13.870  ...          0.2362                  0.07113\n",
            "446         M       17.750  ...          0.2972                  0.09075\n",
            "72          M       17.200  ...          0.3313                  0.13390\n",
            "21          B        9.504  ...          0.2450                  0.07773\n",
            "66          B        9.465  ...          0.2878                  0.09211\n",
            "\n",
            "[56 rows x 31 columns]\n",
            "X_test      Diagnosis  Mean_Radius  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
            "257         M        15.32  ...          0.3258                  0.11910\n",
            "561         B        11.20  ...          0.1566                  0.05905\n",
            "326         B        14.11  ...          0.2100                  0.07083\n",
            "477         B        13.90  ...          0.2679                  0.07698\n",
            "255         M        13.96  ...          0.3068                  0.07957\n",
            "..        ...          ...  ...             ...                      ...\n",
            "121         M        18.66  ...          0.2894                  0.08456\n",
            "385         M        14.60  ...          0.2477                  0.06836\n",
            "515         B        11.34  ...          0.3060                  0.06783\n",
            "471         B        12.04  ...          0.2404                  0.06639\n",
            "304         B        11.46  ...          0.2208                  0.07638\n",
            "\n",
            "[513 rows x 31 columns]\n",
            "y_test      Diagnosis  Mean_Radius  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
            "257         M        15.32  ...          0.3258                  0.11910\n",
            "561         B        11.20  ...          0.1566                  0.05905\n",
            "326         B        14.11  ...          0.2100                  0.07083\n",
            "477         B        13.90  ...          0.2679                  0.07698\n",
            "255         M        13.96  ...          0.3068                  0.07957\n",
            "..        ...          ...  ...             ...                      ...\n",
            "121         M        18.66  ...          0.2894                  0.08456\n",
            "385         M        14.60  ...          0.2477                  0.06836\n",
            "515         B        11.34  ...          0.3060                  0.06783\n",
            "471         B        12.04  ...          0.2404                  0.06639\n",
            "304         B        11.46  ...          0.2208                  0.07638\n",
            "\n",
            "[513 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHiGaozoahwT"
      },
      "source": [
        "We show the shape of our test and training sets here, when using different split values we will get differing results.  It is important to know the shape of our sets as we can reshape them if needed to fit our models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "razAblX4qtPl",
        "outputId": "d3136de5-d0c5-48ae-c6e1-800517e339fe"
      },
      "source": [
        "# print test sets shape will be same as training sets shape\n",
        "print('X_test set shape: ',X_test.shape)\n",
        "print('y_test set shape: ',y_test.shape)\n",
        "print('X_train shape: ', X_train.shape)\n",
        "print('y_train shape: ', y_train.shape)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_test set shape:  (513, 31)\n",
            "y_test set shape:  (513, 31)\n",
            "X_train shape:  (56, 31)\n",
            "y_train shape:  (56, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6m3wXanraw-v"
      },
      "source": [
        "Here we are replacing diagnosis values with a 1 for malignant and a 0 for benign, we do this because it makes it easier to perform numerical calculations on the sets. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hCqV87N0k7x"
      },
      "source": [
        "# Disabled pandas warning due to false positive\n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "\n",
        "\n",
        "# Replace malignant with 1 and benign with 0\n",
        "X_train['Diagnosis'] = X_train['Diagnosis'].replace({'M':1,'B':0})\n",
        "y_train['Diagnosis'] = y_train['Diagnosis'].replace({'M':1,'B':0})\n",
        "X_test['Diagnosis'] = X_test['Diagnosis'].replace({'M':1,'B':0})\n",
        "y_test['Diagnosis'] = y_test['Diagnosis'].replace({'M':1,'B':0})\n",
        "\n",
        "# resetting pandas warning\n",
        "pd.options.mode.chained_assignment = 'warn'  # default='warn'\n"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esjXl_W26F7C"
      },
      "source": [
        "## Show standard deviation and variance between sets\n",
        "\n",
        "Here we are showing both the standard deviation and variance of the sets, these are good methods for detecting outliers in our set, as we can see both sets have a somewhat high level of standard deviation.  We may try dropping columns from our dataframe to try and reduce this.  We can also see from these variables the amount of variation in certain features or throughout the overall dataset, these are useful metrics when training the models as we can ensure the models are trained with a large variety of different values.\n",
        "\n",
        "Standard deviation is calculated by calculating the sum of each element in our data set then subtracting the population mean from the element and squaring we then divide by n and get the square root\n",
        "\n",
        "Variance is calculated by essentially the same way but without getting the square root of our end result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BVX2LRv51ZB",
        "outputId": "40f00637-c60b-4707-b2f3-5bcf6c909c56"
      },
      "source": [
        "print('Standard deviation of values in x training set ',X_train.std())\n",
        "print('Variance of diagnosis values in x training set: ', X_train.var())"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Standard deviation of values in x training set  Diagnosis                    0.499350\n",
            "Mean_Radius                  3.525902\n",
            "Mean_Texture                 4.386342\n",
            "Mean_Perimeter              24.275622\n",
            "Mean_Area                  328.394373\n",
            "Mean_Smoothness              0.014057\n",
            "Mean_Compactness             0.057211\n",
            "Mean_Concavity               0.079702\n",
            "Mean_Concave_Points          0.039960\n",
            "Mean_Symmetry                0.031804\n",
            "Mean_Fractal_Dimension       0.007187\n",
            "Radius_SE                    0.246520\n",
            "Texture_SE                   0.457157\n",
            "Perimeter_SE                 1.919288\n",
            "Area_SE                     31.193676\n",
            "Smoothness_SE                0.002623\n",
            "Compactness_SE               0.016270\n",
            "Concavity_SE                 0.023071\n",
            "Concave_Points_SE            0.006713\n",
            "Symmetry_SE                  0.006561\n",
            "Fractal_Dimension_SE         0.001637\n",
            "Worst_Radius                 4.656183\n",
            "Worst_Texture                6.290715\n",
            "Worst_Perimeter             32.946698\n",
            "Worst_Area                 497.021246\n",
            "Worst_Smoothness             0.025460\n",
            "Worst_Compactness            0.166428\n",
            "Worst_Concavity              0.207343\n",
            "Worst_Concave_Points         0.070098\n",
            "Worst_Symmetry               0.052244\n",
            "Worst_Fractal_Dimension      0.017872\n",
            "dtype: float64\n",
            "Variance of diagnosis values in x training set:  Diagnosis                       0.249351\n",
            "Mean_Radius                    12.431986\n",
            "Mean_Texture                   19.239996\n",
            "Mean_Perimeter                589.305846\n",
            "Mean_Area                  107842.863987\n",
            "Mean_Smoothness                 0.000198\n",
            "Mean_Compactness                0.003273\n",
            "Mean_Concavity                  0.006352\n",
            "Mean_Concave_Points             0.001597\n",
            "Mean_Symmetry                   0.001011\n",
            "Mean_Fractal_Dimension          0.000052\n",
            "Radius_SE                       0.060772\n",
            "Texture_SE                      0.208993\n",
            "Perimeter_SE                    3.683667\n",
            "Area_SE                       973.045407\n",
            "Smoothness_SE                   0.000007\n",
            "Compactness_SE                  0.000265\n",
            "Concavity_SE                    0.000532\n",
            "Concave_Points_SE               0.000045\n",
            "Symmetry_SE                     0.000043\n",
            "Fractal_Dimension_SE            0.000003\n",
            "Worst_Radius                   21.680038\n",
            "Worst_Texture                  39.573100\n",
            "Worst_Perimeter              1085.484887\n",
            "Worst_Area                 247030.119244\n",
            "Worst_Smoothness                0.000648\n",
            "Worst_Compactness               0.027698\n",
            "Worst_Concavity                 0.042991\n",
            "Worst_Concave_Points            0.004914\n",
            "Worst_Symmetry                  0.002729\n",
            "Worst_Fractal_Dimension         0.000319\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L63BMNQJZFWg",
        "outputId": "754427c3-f444-4ef2-c890-ba8f551eb314"
      },
      "source": [
        "\n",
        "print('Standard deviation of values in y training set ',y_train.std())\n",
        "print('Variance of values in y training set: ', y_train.var())"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Standard deviation of values in y training set  Diagnosis                    0.499350\n",
            "Mean_Radius                  3.525902\n",
            "Mean_Texture                 4.386342\n",
            "Mean_Perimeter              24.275622\n",
            "Mean_Area                  328.394373\n",
            "Mean_Smoothness              0.014057\n",
            "Mean_Compactness             0.057211\n",
            "Mean_Concavity               0.079702\n",
            "Mean_Concave_Points          0.039960\n",
            "Mean_Symmetry                0.031804\n",
            "Mean_Fractal_Dimension       0.007187\n",
            "Radius_SE                    0.246520\n",
            "Texture_SE                   0.457157\n",
            "Perimeter_SE                 1.919288\n",
            "Area_SE                     31.193676\n",
            "Smoothness_SE                0.002623\n",
            "Compactness_SE               0.016270\n",
            "Concavity_SE                 0.023071\n",
            "Concave_Points_SE            0.006713\n",
            "Symmetry_SE                  0.006561\n",
            "Fractal_Dimension_SE         0.001637\n",
            "Worst_Radius                 4.656183\n",
            "Worst_Texture                6.290715\n",
            "Worst_Perimeter             32.946698\n",
            "Worst_Area                 497.021246\n",
            "Worst_Smoothness             0.025460\n",
            "Worst_Compactness            0.166428\n",
            "Worst_Concavity              0.207343\n",
            "Worst_Concave_Points         0.070098\n",
            "Worst_Symmetry               0.052244\n",
            "Worst_Fractal_Dimension      0.017872\n",
            "dtype: float64\n",
            "Variance of values in y training set:  Diagnosis                       0.249351\n",
            "Mean_Radius                    12.431986\n",
            "Mean_Texture                   19.239996\n",
            "Mean_Perimeter                589.305846\n",
            "Mean_Area                  107842.863987\n",
            "Mean_Smoothness                 0.000198\n",
            "Mean_Compactness                0.003273\n",
            "Mean_Concavity                  0.006352\n",
            "Mean_Concave_Points             0.001597\n",
            "Mean_Symmetry                   0.001011\n",
            "Mean_Fractal_Dimension          0.000052\n",
            "Radius_SE                       0.060772\n",
            "Texture_SE                      0.208993\n",
            "Perimeter_SE                    3.683667\n",
            "Area_SE                       973.045407\n",
            "Smoothness_SE                   0.000007\n",
            "Compactness_SE                  0.000265\n",
            "Concavity_SE                    0.000532\n",
            "Concave_Points_SE               0.000045\n",
            "Symmetry_SE                     0.000043\n",
            "Fractal_Dimension_SE            0.000003\n",
            "Worst_Radius                   21.680038\n",
            "Worst_Texture                  39.573100\n",
            "Worst_Perimeter              1085.484887\n",
            "Worst_Area                 247030.119244\n",
            "Worst_Smoothness                0.000648\n",
            "Worst_Compactness               0.027698\n",
            "Worst_Concavity                 0.042991\n",
            "Worst_Concave_Points            0.004914\n",
            "Worst_Symmetry                  0.002729\n",
            "Worst_Fractal_Dimension         0.000319\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POHC2Qe-51pY",
        "outputId": "0f5f0718-8ec3-4b42-f593-9399b584b788"
      },
      "source": [
        "print('Standard deviation of values in x testing set ',X_test.std())\n",
        "print('Variance of values in x testing set: ', X_test.var())\n"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Standard deviation of values in x testing set  Diagnosis                    0.482311\n",
            "Mean_Radius                  3.527101\n",
            "Mean_Texture                 4.293656\n",
            "Mean_Perimeter              24.324535\n",
            "Mean_Area                  354.671021\n",
            "Mean_Smoothness              0.014079\n",
            "Mean_Compactness             0.052312\n",
            "Mean_Concavity               0.079783\n",
            "Mean_Concave_Points          0.038705\n",
            "Mean_Symmetry                0.026896\n",
            "Mean_Fractal_Dimension       0.007042\n",
            "Radius_SE                    0.280622\n",
            "Texture_SE                   0.561287\n",
            "Perimeter_SE                 2.033595\n",
            "Area_SE                     46.810722\n",
            "Smoothness_SE                0.003041\n",
            "Compactness_SE               0.018090\n",
            "Concavity_SE                 0.030882\n",
            "Concave_Points_SE            0.006109\n",
            "Symmetry_SE                  0.008436\n",
            "Fractal_Dimension_SE         0.002735\n",
            "Worst_Radius                 4.856571\n",
            "Worst_Texture                6.134417\n",
            "Worst_Perimeter             33.704859\n",
            "Worst_Area                 577.129767\n",
            "Worst_Smoothness             0.022545\n",
            "Worst_Compactness            0.156302\n",
            "Worst_Concavity              0.208914\n",
            "Worst_Concave_Points         0.065282\n",
            "Worst_Symmetry               0.062872\n",
            "Worst_Fractal_Dimension      0.018079\n",
            "dtype: float64\n",
            "Variance of values in x testing set:  Diagnosis                       0.232624\n",
            "Mean_Radius                    12.440438\n",
            "Mean_Texture                   18.435480\n",
            "Mean_Perimeter                591.683021\n",
            "Mean_Area                  125791.533014\n",
            "Mean_Smoothness                 0.000198\n",
            "Mean_Compactness                0.002737\n",
            "Mean_Concavity                  0.006365\n",
            "Mean_Concave_Points             0.001498\n",
            "Mean_Symmetry                   0.000723\n",
            "Mean_Fractal_Dimension          0.000050\n",
            "Radius_SE                       0.078749\n",
            "Texture_SE                      0.315043\n",
            "Perimeter_SE                    4.135508\n",
            "Area_SE                      2191.243739\n",
            "Smoothness_SE                   0.000009\n",
            "Compactness_SE                  0.000327\n",
            "Concavity_SE                    0.000954\n",
            "Concave_Points_SE               0.000037\n",
            "Symmetry_SE                     0.000071\n",
            "Fractal_Dimension_SE            0.000007\n",
            "Worst_Radius                   23.586279\n",
            "Worst_Texture                  37.631067\n",
            "Worst_Perimeter              1136.017491\n",
            "Worst_Area                 333078.768283\n",
            "Worst_Smoothness                0.000508\n",
            "Worst_Compactness               0.024430\n",
            "Worst_Concavity                 0.043645\n",
            "Worst_Concave_Points            0.004262\n",
            "Worst_Symmetry                  0.003953\n",
            "Worst_Fractal_Dimension         0.000327\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOwCIZ2yZMhv",
        "outputId": "c207ef4b-0dac-4f11-f71c-ae2d737fab44"
      },
      "source": [
        "print('Standard deviation of values in y testing set ',y_test.std())\n",
        "print('Variance of values in y testing set: ', y_test.var())"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Standard deviation of values in y testing set  Diagnosis                    0.482311\n",
            "Mean_Radius                  3.527101\n",
            "Mean_Texture                 4.293656\n",
            "Mean_Perimeter              24.324535\n",
            "Mean_Area                  354.671021\n",
            "Mean_Smoothness              0.014079\n",
            "Mean_Compactness             0.052312\n",
            "Mean_Concavity               0.079783\n",
            "Mean_Concave_Points          0.038705\n",
            "Mean_Symmetry                0.026896\n",
            "Mean_Fractal_Dimension       0.007042\n",
            "Radius_SE                    0.280622\n",
            "Texture_SE                   0.561287\n",
            "Perimeter_SE                 2.033595\n",
            "Area_SE                     46.810722\n",
            "Smoothness_SE                0.003041\n",
            "Compactness_SE               0.018090\n",
            "Concavity_SE                 0.030882\n",
            "Concave_Points_SE            0.006109\n",
            "Symmetry_SE                  0.008436\n",
            "Fractal_Dimension_SE         0.002735\n",
            "Worst_Radius                 4.856571\n",
            "Worst_Texture                6.134417\n",
            "Worst_Perimeter             33.704859\n",
            "Worst_Area                 577.129767\n",
            "Worst_Smoothness             0.022545\n",
            "Worst_Compactness            0.156302\n",
            "Worst_Concavity              0.208914\n",
            "Worst_Concave_Points         0.065282\n",
            "Worst_Symmetry               0.062872\n",
            "Worst_Fractal_Dimension      0.018079\n",
            "dtype: float64\n",
            "Variance of values in y testing set:  Diagnosis                       0.232624\n",
            "Mean_Radius                    12.440438\n",
            "Mean_Texture                   18.435480\n",
            "Mean_Perimeter                591.683021\n",
            "Mean_Area                  125791.533014\n",
            "Mean_Smoothness                 0.000198\n",
            "Mean_Compactness                0.002737\n",
            "Mean_Concavity                  0.006365\n",
            "Mean_Concave_Points             0.001498\n",
            "Mean_Symmetry                   0.000723\n",
            "Mean_Fractal_Dimension          0.000050\n",
            "Radius_SE                       0.078749\n",
            "Texture_SE                      0.315043\n",
            "Perimeter_SE                    4.135508\n",
            "Area_SE                      2191.243739\n",
            "Smoothness_SE                   0.000009\n",
            "Compactness_SE                  0.000327\n",
            "Concavity_SE                    0.000954\n",
            "Concave_Points_SE               0.000037\n",
            "Symmetry_SE                     0.000071\n",
            "Fractal_Dimension_SE            0.000007\n",
            "Worst_Radius                   23.586279\n",
            "Worst_Texture                  37.631067\n",
            "Worst_Perimeter              1136.017491\n",
            "Worst_Area                 333078.768283\n",
            "Worst_Smoothness                0.000508\n",
            "Worst_Compactness               0.024430\n",
            "Worst_Concavity                 0.043645\n",
            "Worst_Concave_Points            0.004262\n",
            "Worst_Symmetry                  0.003953\n",
            "Worst_Fractal_Dimension         0.000327\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTKjzh8miMId"
      },
      "source": [
        "## Show correlation\n",
        "Here we are showing the correlation between each feature within the dataset.  Correlation is determined by analyzing the values of different features and comparing them with each other.  A 1 means that the values of the feature match exactly a value of -1 means that the values have very little in common.  It is important that we use a wide variety of features which have low correlation as it will yield more accurate results.  I have shown correlations of features for both the X and Y triaining and test sets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GanMBBm4ZXul"
      },
      "source": [
        "### Training Set Correlation values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ftfJEYaBeJUe",
        "outputId": "9616e6e0-5fe3-4c43-984f-efe35943e81d"
      },
      "source": [
        "# Show correlation of our training set\n",
        "X_train.corr(method='pearson')"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Diagnosis</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>Radius_SE</th>\n",
              "      <th>Texture_SE</th>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <th>Area_SE</th>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <th>Compactness_SE</th>\n",
              "      <th>Concavity_SE</th>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <th>Fractal_Dimension_SE</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>Diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.718963</td>\n",
              "      <td>0.590164</td>\n",
              "      <td>0.748290</td>\n",
              "      <td>0.714016</td>\n",
              "      <td>0.450624</td>\n",
              "      <td>0.691589</td>\n",
              "      <td>0.806572</td>\n",
              "      <td>0.848798</td>\n",
              "      <td>0.368120</td>\n",
              "      <td>0.061748</td>\n",
              "      <td>0.620258</td>\n",
              "      <td>0.147192</td>\n",
              "      <td>0.595459</td>\n",
              "      <td>0.697258</td>\n",
              "      <td>-0.023649</td>\n",
              "      <td>0.492419</td>\n",
              "      <td>0.485526</td>\n",
              "      <td>0.543528</td>\n",
              "      <td>-0.083942</td>\n",
              "      <td>0.337851</td>\n",
              "      <td>0.788719</td>\n",
              "      <td>0.622984</td>\n",
              "      <td>0.806620</td>\n",
              "      <td>0.778064</td>\n",
              "      <td>0.474074</td>\n",
              "      <td>0.732958</td>\n",
              "      <td>0.829992</td>\n",
              "      <td>0.877711</td>\n",
              "      <td>0.494937</td>\n",
              "      <td>0.491492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Radius</th>\n",
              "      <td>0.718963</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.461645</td>\n",
              "      <td>0.997222</td>\n",
              "      <td>0.992897</td>\n",
              "      <td>0.100541</td>\n",
              "      <td>0.425790</td>\n",
              "      <td>0.621734</td>\n",
              "      <td>0.777105</td>\n",
              "      <td>0.062836</td>\n",
              "      <td>-0.418678</td>\n",
              "      <td>0.639325</td>\n",
              "      <td>0.093109</td>\n",
              "      <td>0.611552</td>\n",
              "      <td>0.783412</td>\n",
              "      <td>-0.361386</td>\n",
              "      <td>0.212260</td>\n",
              "      <td>0.244194</td>\n",
              "      <td>0.411916</td>\n",
              "      <td>-0.193701</td>\n",
              "      <td>0.028003</td>\n",
              "      <td>0.978696</td>\n",
              "      <td>0.471728</td>\n",
              "      <td>0.968769</td>\n",
              "      <td>0.968042</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>0.412658</td>\n",
              "      <td>0.533238</td>\n",
              "      <td>0.717917</td>\n",
              "      <td>0.139212</td>\n",
              "      <td>0.017384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Texture</th>\n",
              "      <td>0.590164</td>\n",
              "      <td>0.461645</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.480277</td>\n",
              "      <td>0.477899</td>\n",
              "      <td>0.252809</td>\n",
              "      <td>0.472125</td>\n",
              "      <td>0.537598</td>\n",
              "      <td>0.496728</td>\n",
              "      <td>0.188051</td>\n",
              "      <td>0.047373</td>\n",
              "      <td>0.413561</td>\n",
              "      <td>0.402565</td>\n",
              "      <td>0.418557</td>\n",
              "      <td>0.456982</td>\n",
              "      <td>0.131524</td>\n",
              "      <td>0.480066</td>\n",
              "      <td>0.477579</td>\n",
              "      <td>0.382308</td>\n",
              "      <td>-0.001915</td>\n",
              "      <td>0.316852</td>\n",
              "      <td>0.507594</td>\n",
              "      <td>0.926055</td>\n",
              "      <td>0.526952</td>\n",
              "      <td>0.519918</td>\n",
              "      <td>0.318822</td>\n",
              "      <td>0.521537</td>\n",
              "      <td>0.594230</td>\n",
              "      <td>0.506841</td>\n",
              "      <td>0.242014</td>\n",
              "      <td>0.361987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Perimeter</th>\n",
              "      <td>0.748290</td>\n",
              "      <td>0.997222</td>\n",
              "      <td>0.480277</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990425</td>\n",
              "      <td>0.147277</td>\n",
              "      <td>0.488296</td>\n",
              "      <td>0.672794</td>\n",
              "      <td>0.815552</td>\n",
              "      <td>0.113736</td>\n",
              "      <td>-0.359612</td>\n",
              "      <td>0.677583</td>\n",
              "      <td>0.114407</td>\n",
              "      <td>0.655441</td>\n",
              "      <td>0.814313</td>\n",
              "      <td>-0.329495</td>\n",
              "      <td>0.265181</td>\n",
              "      <td>0.292193</td>\n",
              "      <td>0.458639</td>\n",
              "      <td>-0.179359</td>\n",
              "      <td>0.079156</td>\n",
              "      <td>0.983951</td>\n",
              "      <td>0.488351</td>\n",
              "      <td>0.980911</td>\n",
              "      <td>0.973372</td>\n",
              "      <td>0.053225</td>\n",
              "      <td>0.460293</td>\n",
              "      <td>0.578346</td>\n",
              "      <td>0.758052</td>\n",
              "      <td>0.176434</td>\n",
              "      <td>0.066931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Area</th>\n",
              "      <td>0.714016</td>\n",
              "      <td>0.992897</td>\n",
              "      <td>0.477899</td>\n",
              "      <td>0.990425</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.098499</td>\n",
              "      <td>0.426872</td>\n",
              "      <td>0.636764</td>\n",
              "      <td>0.784396</td>\n",
              "      <td>0.065203</td>\n",
              "      <td>-0.391319</td>\n",
              "      <td>0.656323</td>\n",
              "      <td>0.115531</td>\n",
              "      <td>0.630211</td>\n",
              "      <td>0.799085</td>\n",
              "      <td>-0.312470</td>\n",
              "      <td>0.233174</td>\n",
              "      <td>0.280425</td>\n",
              "      <td>0.430333</td>\n",
              "      <td>-0.163784</td>\n",
              "      <td>0.056821</td>\n",
              "      <td>0.973454</td>\n",
              "      <td>0.480905</td>\n",
              "      <td>0.964036</td>\n",
              "      <td>0.976528</td>\n",
              "      <td>0.020302</td>\n",
              "      <td>0.403074</td>\n",
              "      <td>0.535307</td>\n",
              "      <td>0.711491</td>\n",
              "      <td>0.135912</td>\n",
              "      <td>0.022775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Smoothness</th>\n",
              "      <td>0.450624</td>\n",
              "      <td>0.100541</td>\n",
              "      <td>0.252809</td>\n",
              "      <td>0.147277</td>\n",
              "      <td>0.098499</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.687499</td>\n",
              "      <td>0.503398</td>\n",
              "      <td>0.530112</td>\n",
              "      <td>0.681009</td>\n",
              "      <td>0.612929</td>\n",
              "      <td>0.354027</td>\n",
              "      <td>0.195963</td>\n",
              "      <td>0.358325</td>\n",
              "      <td>0.322634</td>\n",
              "      <td>0.304354</td>\n",
              "      <td>0.421365</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>0.484582</td>\n",
              "      <td>0.207641</td>\n",
              "      <td>0.470341</td>\n",
              "      <td>0.187081</td>\n",
              "      <td>0.277978</td>\n",
              "      <td>0.230673</td>\n",
              "      <td>0.184278</td>\n",
              "      <td>0.802873</td>\n",
              "      <td>0.556804</td>\n",
              "      <td>0.482328</td>\n",
              "      <td>0.563082</td>\n",
              "      <td>0.651191</td>\n",
              "      <td>0.615967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Compactness</th>\n",
              "      <td>0.691589</td>\n",
              "      <td>0.425790</td>\n",
              "      <td>0.472125</td>\n",
              "      <td>0.488296</td>\n",
              "      <td>0.426872</td>\n",
              "      <td>0.687499</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.902124</td>\n",
              "      <td>0.830360</td>\n",
              "      <td>0.718561</td>\n",
              "      <td>0.582449</td>\n",
              "      <td>0.750683</td>\n",
              "      <td>0.360917</td>\n",
              "      <td>0.774404</td>\n",
              "      <td>0.720159</td>\n",
              "      <td>0.258740</td>\n",
              "      <td>0.825208</td>\n",
              "      <td>0.738344</td>\n",
              "      <td>0.776709</td>\n",
              "      <td>0.134836</td>\n",
              "      <td>0.713343</td>\n",
              "      <td>0.521641</td>\n",
              "      <td>0.467758</td>\n",
              "      <td>0.584863</td>\n",
              "      <td>0.517321</td>\n",
              "      <td>0.579309</td>\n",
              "      <td>0.822263</td>\n",
              "      <td>0.809953</td>\n",
              "      <td>0.833016</td>\n",
              "      <td>0.589464</td>\n",
              "      <td>0.671134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concavity</th>\n",
              "      <td>0.806572</td>\n",
              "      <td>0.621734</td>\n",
              "      <td>0.537598</td>\n",
              "      <td>0.672794</td>\n",
              "      <td>0.636764</td>\n",
              "      <td>0.503398</td>\n",
              "      <td>0.902124</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.928459</td>\n",
              "      <td>0.534929</td>\n",
              "      <td>0.361920</td>\n",
              "      <td>0.790334</td>\n",
              "      <td>0.297141</td>\n",
              "      <td>0.814762</td>\n",
              "      <td>0.809463</td>\n",
              "      <td>0.146029</td>\n",
              "      <td>0.742701</td>\n",
              "      <td>0.793914</td>\n",
              "      <td>0.780787</td>\n",
              "      <td>0.036355</td>\n",
              "      <td>0.611230</td>\n",
              "      <td>0.690657</td>\n",
              "      <td>0.516125</td>\n",
              "      <td>0.741989</td>\n",
              "      <td>0.698801</td>\n",
              "      <td>0.446839</td>\n",
              "      <td>0.756279</td>\n",
              "      <td>0.893141</td>\n",
              "      <td>0.891423</td>\n",
              "      <td>0.451734</td>\n",
              "      <td>0.545869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concave_Points</th>\n",
              "      <td>0.848798</td>\n",
              "      <td>0.777105</td>\n",
              "      <td>0.496728</td>\n",
              "      <td>0.815552</td>\n",
              "      <td>0.784396</td>\n",
              "      <td>0.530112</td>\n",
              "      <td>0.830360</td>\n",
              "      <td>0.928459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.501594</td>\n",
              "      <td>0.161314</td>\n",
              "      <td>0.838759</td>\n",
              "      <td>0.253658</td>\n",
              "      <td>0.833207</td>\n",
              "      <td>0.887914</td>\n",
              "      <td>0.060994</td>\n",
              "      <td>0.586157</td>\n",
              "      <td>0.607598</td>\n",
              "      <td>0.770256</td>\n",
              "      <td>0.043561</td>\n",
              "      <td>0.458322</td>\n",
              "      <td>0.826039</td>\n",
              "      <td>0.482996</td>\n",
              "      <td>0.857212</td>\n",
              "      <td>0.825035</td>\n",
              "      <td>0.404239</td>\n",
              "      <td>0.667295</td>\n",
              "      <td>0.782109</td>\n",
              "      <td>0.942427</td>\n",
              "      <td>0.445199</td>\n",
              "      <td>0.390418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Symmetry</th>\n",
              "      <td>0.368120</td>\n",
              "      <td>0.062836</td>\n",
              "      <td>0.188051</td>\n",
              "      <td>0.113736</td>\n",
              "      <td>0.065203</td>\n",
              "      <td>0.681009</td>\n",
              "      <td>0.718561</td>\n",
              "      <td>0.534929</td>\n",
              "      <td>0.501594</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.635357</td>\n",
              "      <td>0.545343</td>\n",
              "      <td>0.278579</td>\n",
              "      <td>0.539909</td>\n",
              "      <td>0.452065</td>\n",
              "      <td>0.390588</td>\n",
              "      <td>0.558194</td>\n",
              "      <td>0.439631</td>\n",
              "      <td>0.579665</td>\n",
              "      <td>0.406263</td>\n",
              "      <td>0.692370</td>\n",
              "      <td>0.149323</td>\n",
              "      <td>0.152772</td>\n",
              "      <td>0.200160</td>\n",
              "      <td>0.145554</td>\n",
              "      <td>0.500745</td>\n",
              "      <td>0.474324</td>\n",
              "      <td>0.392309</td>\n",
              "      <td>0.457268</td>\n",
              "      <td>0.743554</td>\n",
              "      <td>0.524186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Fractal_Dimension</th>\n",
              "      <td>0.061748</td>\n",
              "      <td>-0.418678</td>\n",
              "      <td>0.047373</td>\n",
              "      <td>-0.359612</td>\n",
              "      <td>-0.391319</td>\n",
              "      <td>0.612929</td>\n",
              "      <td>0.582449</td>\n",
              "      <td>0.361920</td>\n",
              "      <td>0.161314</td>\n",
              "      <td>0.635357</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.165735</td>\n",
              "      <td>0.261846</td>\n",
              "      <td>0.211624</td>\n",
              "      <td>0.029894</td>\n",
              "      <td>0.593192</td>\n",
              "      <td>0.580716</td>\n",
              "      <td>0.523295</td>\n",
              "      <td>0.402934</td>\n",
              "      <td>0.338910</td>\n",
              "      <td>0.693909</td>\n",
              "      <td>-0.319845</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>-0.253514</td>\n",
              "      <td>-0.296290</td>\n",
              "      <td>0.593672</td>\n",
              "      <td>0.403685</td>\n",
              "      <td>0.327684</td>\n",
              "      <td>0.184825</td>\n",
              "      <td>0.490931</td>\n",
              "      <td>0.673239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radius_SE</th>\n",
              "      <td>0.620258</td>\n",
              "      <td>0.639325</td>\n",
              "      <td>0.413561</td>\n",
              "      <td>0.677583</td>\n",
              "      <td>0.656323</td>\n",
              "      <td>0.354027</td>\n",
              "      <td>0.750683</td>\n",
              "      <td>0.790334</td>\n",
              "      <td>0.838759</td>\n",
              "      <td>0.545343</td>\n",
              "      <td>0.165735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.410953</td>\n",
              "      <td>0.978585</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>0.191905</td>\n",
              "      <td>0.552037</td>\n",
              "      <td>0.535923</td>\n",
              "      <td>0.724420</td>\n",
              "      <td>0.278701</td>\n",
              "      <td>0.482608</td>\n",
              "      <td>0.697271</td>\n",
              "      <td>0.354318</td>\n",
              "      <td>0.722825</td>\n",
              "      <td>0.702233</td>\n",
              "      <td>0.169746</td>\n",
              "      <td>0.450534</td>\n",
              "      <td>0.528337</td>\n",
              "      <td>0.690131</td>\n",
              "      <td>0.341622</td>\n",
              "      <td>0.199023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Texture_SE</th>\n",
              "      <td>0.147192</td>\n",
              "      <td>0.093109</td>\n",
              "      <td>0.402565</td>\n",
              "      <td>0.114407</td>\n",
              "      <td>0.115531</td>\n",
              "      <td>0.195963</td>\n",
              "      <td>0.360917</td>\n",
              "      <td>0.297141</td>\n",
              "      <td>0.253658</td>\n",
              "      <td>0.278579</td>\n",
              "      <td>0.261846</td>\n",
              "      <td>0.410953</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.442794</td>\n",
              "      <td>0.335502</td>\n",
              "      <td>0.560069</td>\n",
              "      <td>0.433429</td>\n",
              "      <td>0.466527</td>\n",
              "      <td>0.573731</td>\n",
              "      <td>0.409346</td>\n",
              "      <td>0.509468</td>\n",
              "      <td>0.085145</td>\n",
              "      <td>0.434059</td>\n",
              "      <td>0.103227</td>\n",
              "      <td>0.091174</td>\n",
              "      <td>0.079307</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.132785</td>\n",
              "      <td>0.158551</td>\n",
              "      <td>0.001641</td>\n",
              "      <td>0.073064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <td>0.595459</td>\n",
              "      <td>0.611552</td>\n",
              "      <td>0.418557</td>\n",
              "      <td>0.655441</td>\n",
              "      <td>0.630211</td>\n",
              "      <td>0.358325</td>\n",
              "      <td>0.774404</td>\n",
              "      <td>0.814762</td>\n",
              "      <td>0.833207</td>\n",
              "      <td>0.539909</td>\n",
              "      <td>0.211624</td>\n",
              "      <td>0.978585</td>\n",
              "      <td>0.442794</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.944080</td>\n",
              "      <td>0.219937</td>\n",
              "      <td>0.580687</td>\n",
              "      <td>0.597051</td>\n",
              "      <td>0.779833</td>\n",
              "      <td>0.288514</td>\n",
              "      <td>0.507384</td>\n",
              "      <td>0.655865</td>\n",
              "      <td>0.345206</td>\n",
              "      <td>0.701482</td>\n",
              "      <td>0.661942</td>\n",
              "      <td>0.161297</td>\n",
              "      <td>0.454788</td>\n",
              "      <td>0.545117</td>\n",
              "      <td>0.689611</td>\n",
              "      <td>0.304805</td>\n",
              "      <td>0.206003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area_SE</th>\n",
              "      <td>0.697258</td>\n",
              "      <td>0.783412</td>\n",
              "      <td>0.456982</td>\n",
              "      <td>0.814313</td>\n",
              "      <td>0.799085</td>\n",
              "      <td>0.322634</td>\n",
              "      <td>0.720159</td>\n",
              "      <td>0.809463</td>\n",
              "      <td>0.887914</td>\n",
              "      <td>0.452065</td>\n",
              "      <td>0.029894</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>0.335502</td>\n",
              "      <td>0.944080</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.058436</td>\n",
              "      <td>0.491773</td>\n",
              "      <td>0.495157</td>\n",
              "      <td>0.684411</td>\n",
              "      <td>0.146471</td>\n",
              "      <td>0.396571</td>\n",
              "      <td>0.826329</td>\n",
              "      <td>0.413038</td>\n",
              "      <td>0.845135</td>\n",
              "      <td>0.833629</td>\n",
              "      <td>0.162537</td>\n",
              "      <td>0.474866</td>\n",
              "      <td>0.572335</td>\n",
              "      <td>0.756040</td>\n",
              "      <td>0.320292</td>\n",
              "      <td>0.180651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <td>-0.023649</td>\n",
              "      <td>-0.361386</td>\n",
              "      <td>0.131524</td>\n",
              "      <td>-0.329495</td>\n",
              "      <td>-0.312470</td>\n",
              "      <td>0.304354</td>\n",
              "      <td>0.258740</td>\n",
              "      <td>0.146029</td>\n",
              "      <td>0.060994</td>\n",
              "      <td>0.390588</td>\n",
              "      <td>0.593192</td>\n",
              "      <td>0.191905</td>\n",
              "      <td>0.560069</td>\n",
              "      <td>0.219937</td>\n",
              "      <td>0.058436</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.397698</td>\n",
              "      <td>0.415695</td>\n",
              "      <td>0.443756</td>\n",
              "      <td>0.545801</td>\n",
              "      <td>0.547568</td>\n",
              "      <td>-0.328185</td>\n",
              "      <td>0.031605</td>\n",
              "      <td>-0.297221</td>\n",
              "      <td>-0.290679</td>\n",
              "      <td>0.350120</td>\n",
              "      <td>0.006986</td>\n",
              "      <td>-0.006906</td>\n",
              "      <td>-0.032487</td>\n",
              "      <td>0.102525</td>\n",
              "      <td>0.173192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Compactness_SE</th>\n",
              "      <td>0.492419</td>\n",
              "      <td>0.212260</td>\n",
              "      <td>0.480066</td>\n",
              "      <td>0.265181</td>\n",
              "      <td>0.233174</td>\n",
              "      <td>0.421365</td>\n",
              "      <td>0.825208</td>\n",
              "      <td>0.742701</td>\n",
              "      <td>0.586157</td>\n",
              "      <td>0.558194</td>\n",
              "      <td>0.580716</td>\n",
              "      <td>0.552037</td>\n",
              "      <td>0.433429</td>\n",
              "      <td>0.580687</td>\n",
              "      <td>0.491773</td>\n",
              "      <td>0.397698</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.882924</td>\n",
              "      <td>0.722649</td>\n",
              "      <td>0.199823</td>\n",
              "      <td>0.835100</td>\n",
              "      <td>0.293871</td>\n",
              "      <td>0.430129</td>\n",
              "      <td>0.342119</td>\n",
              "      <td>0.305131</td>\n",
              "      <td>0.418847</td>\n",
              "      <td>0.772767</td>\n",
              "      <td>0.725132</td>\n",
              "      <td>0.589257</td>\n",
              "      <td>0.397320</td>\n",
              "      <td>0.649174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concavity_SE</th>\n",
              "      <td>0.485526</td>\n",
              "      <td>0.244194</td>\n",
              "      <td>0.477579</td>\n",
              "      <td>0.292193</td>\n",
              "      <td>0.280425</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>0.738344</td>\n",
              "      <td>0.793914</td>\n",
              "      <td>0.607598</td>\n",
              "      <td>0.439631</td>\n",
              "      <td>0.523295</td>\n",
              "      <td>0.535923</td>\n",
              "      <td>0.466527</td>\n",
              "      <td>0.597051</td>\n",
              "      <td>0.495157</td>\n",
              "      <td>0.415695</td>\n",
              "      <td>0.882924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.784106</td>\n",
              "      <td>0.245830</td>\n",
              "      <td>0.745728</td>\n",
              "      <td>0.283000</td>\n",
              "      <td>0.399767</td>\n",
              "      <td>0.337590</td>\n",
              "      <td>0.306270</td>\n",
              "      <td>0.302940</td>\n",
              "      <td>0.582957</td>\n",
              "      <td>0.718665</td>\n",
              "      <td>0.570656</td>\n",
              "      <td>0.256936</td>\n",
              "      <td>0.474993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <td>0.543528</td>\n",
              "      <td>0.411916</td>\n",
              "      <td>0.382308</td>\n",
              "      <td>0.458639</td>\n",
              "      <td>0.430333</td>\n",
              "      <td>0.484582</td>\n",
              "      <td>0.776709</td>\n",
              "      <td>0.780787</td>\n",
              "      <td>0.770256</td>\n",
              "      <td>0.579665</td>\n",
              "      <td>0.402934</td>\n",
              "      <td>0.724420</td>\n",
              "      <td>0.573731</td>\n",
              "      <td>0.779833</td>\n",
              "      <td>0.684411</td>\n",
              "      <td>0.443756</td>\n",
              "      <td>0.722649</td>\n",
              "      <td>0.784106</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.366776</td>\n",
              "      <td>0.646480</td>\n",
              "      <td>0.418885</td>\n",
              "      <td>0.282416</td>\n",
              "      <td>0.471306</td>\n",
              "      <td>0.420944</td>\n",
              "      <td>0.262816</td>\n",
              "      <td>0.457468</td>\n",
              "      <td>0.547825</td>\n",
              "      <td>0.697906</td>\n",
              "      <td>0.245800</td>\n",
              "      <td>0.265116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <td>-0.083942</td>\n",
              "      <td>-0.193701</td>\n",
              "      <td>-0.001915</td>\n",
              "      <td>-0.179359</td>\n",
              "      <td>-0.163784</td>\n",
              "      <td>0.207641</td>\n",
              "      <td>0.134836</td>\n",
              "      <td>0.036355</td>\n",
              "      <td>0.043561</td>\n",
              "      <td>0.406263</td>\n",
              "      <td>0.338910</td>\n",
              "      <td>0.278701</td>\n",
              "      <td>0.409346</td>\n",
              "      <td>0.288514</td>\n",
              "      <td>0.146471</td>\n",
              "      <td>0.545801</td>\n",
              "      <td>0.199823</td>\n",
              "      <td>0.245830</td>\n",
              "      <td>0.366776</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344375</td>\n",
              "      <td>-0.216756</td>\n",
              "      <td>-0.183768</td>\n",
              "      <td>-0.202425</td>\n",
              "      <td>-0.198325</td>\n",
              "      <td>-0.089892</td>\n",
              "      <td>-0.175653</td>\n",
              "      <td>-0.189033</td>\n",
              "      <td>-0.131871</td>\n",
              "      <td>0.242389</td>\n",
              "      <td>-0.094488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fractal_Dimension_SE</th>\n",
              "      <td>0.337851</td>\n",
              "      <td>0.028003</td>\n",
              "      <td>0.316852</td>\n",
              "      <td>0.079156</td>\n",
              "      <td>0.056821</td>\n",
              "      <td>0.470341</td>\n",
              "      <td>0.713343</td>\n",
              "      <td>0.611230</td>\n",
              "      <td>0.458322</td>\n",
              "      <td>0.692370</td>\n",
              "      <td>0.693909</td>\n",
              "      <td>0.482608</td>\n",
              "      <td>0.509468</td>\n",
              "      <td>0.507384</td>\n",
              "      <td>0.396571</td>\n",
              "      <td>0.547568</td>\n",
              "      <td>0.835100</td>\n",
              "      <td>0.745728</td>\n",
              "      <td>0.646480</td>\n",
              "      <td>0.344375</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.109922</td>\n",
              "      <td>0.282047</td>\n",
              "      <td>0.160395</td>\n",
              "      <td>0.128170</td>\n",
              "      <td>0.439688</td>\n",
              "      <td>0.589495</td>\n",
              "      <td>0.534881</td>\n",
              "      <td>0.425602</td>\n",
              "      <td>0.470119</td>\n",
              "      <td>0.684865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Radius</th>\n",
              "      <td>0.788719</td>\n",
              "      <td>0.978696</td>\n",
              "      <td>0.507594</td>\n",
              "      <td>0.983951</td>\n",
              "      <td>0.973454</td>\n",
              "      <td>0.187081</td>\n",
              "      <td>0.521641</td>\n",
              "      <td>0.690657</td>\n",
              "      <td>0.826039</td>\n",
              "      <td>0.149323</td>\n",
              "      <td>-0.319845</td>\n",
              "      <td>0.697271</td>\n",
              "      <td>0.085145</td>\n",
              "      <td>0.655865</td>\n",
              "      <td>0.826329</td>\n",
              "      <td>-0.328185</td>\n",
              "      <td>0.293871</td>\n",
              "      <td>0.283000</td>\n",
              "      <td>0.418885</td>\n",
              "      <td>-0.216756</td>\n",
              "      <td>0.109922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.541598</td>\n",
              "      <td>0.992425</td>\n",
              "      <td>0.992325</td>\n",
              "      <td>0.136779</td>\n",
              "      <td>0.533010</td>\n",
              "      <td>0.626929</td>\n",
              "      <td>0.785420</td>\n",
              "      <td>0.250176</td>\n",
              "      <td>0.158266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Texture</th>\n",
              "      <td>0.622984</td>\n",
              "      <td>0.471728</td>\n",
              "      <td>0.926055</td>\n",
              "      <td>0.488351</td>\n",
              "      <td>0.480905</td>\n",
              "      <td>0.277978</td>\n",
              "      <td>0.467758</td>\n",
              "      <td>0.516125</td>\n",
              "      <td>0.482996</td>\n",
              "      <td>0.152772</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.354318</td>\n",
              "      <td>0.434059</td>\n",
              "      <td>0.345206</td>\n",
              "      <td>0.413038</td>\n",
              "      <td>0.031605</td>\n",
              "      <td>0.430129</td>\n",
              "      <td>0.399767</td>\n",
              "      <td>0.282416</td>\n",
              "      <td>-0.183768</td>\n",
              "      <td>0.282047</td>\n",
              "      <td>0.541598</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.552795</td>\n",
              "      <td>0.547225</td>\n",
              "      <td>0.424210</td>\n",
              "      <td>0.590353</td>\n",
              "      <td>0.635756</td>\n",
              "      <td>0.539389</td>\n",
              "      <td>0.303465</td>\n",
              "      <td>0.452119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Perimeter</th>\n",
              "      <td>0.806620</td>\n",
              "      <td>0.968769</td>\n",
              "      <td>0.526952</td>\n",
              "      <td>0.980911</td>\n",
              "      <td>0.964036</td>\n",
              "      <td>0.230673</td>\n",
              "      <td>0.584863</td>\n",
              "      <td>0.741989</td>\n",
              "      <td>0.857212</td>\n",
              "      <td>0.200160</td>\n",
              "      <td>-0.253514</td>\n",
              "      <td>0.722825</td>\n",
              "      <td>0.103227</td>\n",
              "      <td>0.701482</td>\n",
              "      <td>0.845135</td>\n",
              "      <td>-0.297221</td>\n",
              "      <td>0.342119</td>\n",
              "      <td>0.337590</td>\n",
              "      <td>0.471306</td>\n",
              "      <td>-0.202425</td>\n",
              "      <td>0.160395</td>\n",
              "      <td>0.992425</td>\n",
              "      <td>0.552795</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.985087</td>\n",
              "      <td>0.173521</td>\n",
              "      <td>0.577892</td>\n",
              "      <td>0.671010</td>\n",
              "      <td>0.821865</td>\n",
              "      <td>0.282905</td>\n",
              "      <td>0.207687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Area</th>\n",
              "      <td>0.778064</td>\n",
              "      <td>0.968042</td>\n",
              "      <td>0.519918</td>\n",
              "      <td>0.973372</td>\n",
              "      <td>0.976528</td>\n",
              "      <td>0.184278</td>\n",
              "      <td>0.517321</td>\n",
              "      <td>0.698801</td>\n",
              "      <td>0.825035</td>\n",
              "      <td>0.145554</td>\n",
              "      <td>-0.296290</td>\n",
              "      <td>0.702233</td>\n",
              "      <td>0.091174</td>\n",
              "      <td>0.661942</td>\n",
              "      <td>0.833629</td>\n",
              "      <td>-0.290679</td>\n",
              "      <td>0.305131</td>\n",
              "      <td>0.306270</td>\n",
              "      <td>0.420944</td>\n",
              "      <td>-0.198325</td>\n",
              "      <td>0.128170</td>\n",
              "      <td>0.992325</td>\n",
              "      <td>0.547225</td>\n",
              "      <td>0.985087</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.144603</td>\n",
              "      <td>0.523951</td>\n",
              "      <td>0.626568</td>\n",
              "      <td>0.773350</td>\n",
              "      <td>0.242290</td>\n",
              "      <td>0.164742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Smoothness</th>\n",
              "      <td>0.474074</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>0.318822</td>\n",
              "      <td>0.053225</td>\n",
              "      <td>0.020302</td>\n",
              "      <td>0.802873</td>\n",
              "      <td>0.579309</td>\n",
              "      <td>0.446839</td>\n",
              "      <td>0.404239</td>\n",
              "      <td>0.500745</td>\n",
              "      <td>0.593672</td>\n",
              "      <td>0.169746</td>\n",
              "      <td>0.079307</td>\n",
              "      <td>0.161297</td>\n",
              "      <td>0.162537</td>\n",
              "      <td>0.350120</td>\n",
              "      <td>0.418847</td>\n",
              "      <td>0.302940</td>\n",
              "      <td>0.262816</td>\n",
              "      <td>-0.089892</td>\n",
              "      <td>0.439688</td>\n",
              "      <td>0.136779</td>\n",
              "      <td>0.424210</td>\n",
              "      <td>0.173521</td>\n",
              "      <td>0.144603</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.648209</td>\n",
              "      <td>0.569676</td>\n",
              "      <td>0.520635</td>\n",
              "      <td>0.654306</td>\n",
              "      <td>0.774750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Compactness</th>\n",
              "      <td>0.732958</td>\n",
              "      <td>0.412658</td>\n",
              "      <td>0.521537</td>\n",
              "      <td>0.460293</td>\n",
              "      <td>0.403074</td>\n",
              "      <td>0.556804</td>\n",
              "      <td>0.822263</td>\n",
              "      <td>0.756279</td>\n",
              "      <td>0.667295</td>\n",
              "      <td>0.474324</td>\n",
              "      <td>0.403685</td>\n",
              "      <td>0.450534</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.454788</td>\n",
              "      <td>0.474866</td>\n",
              "      <td>0.006986</td>\n",
              "      <td>0.772767</td>\n",
              "      <td>0.582957</td>\n",
              "      <td>0.457468</td>\n",
              "      <td>-0.175653</td>\n",
              "      <td>0.589495</td>\n",
              "      <td>0.533010</td>\n",
              "      <td>0.590353</td>\n",
              "      <td>0.577892</td>\n",
              "      <td>0.523951</td>\n",
              "      <td>0.648209</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904890</td>\n",
              "      <td>0.784110</td>\n",
              "      <td>0.616021</td>\n",
              "      <td>0.850462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concavity</th>\n",
              "      <td>0.829992</td>\n",
              "      <td>0.533238</td>\n",
              "      <td>0.594230</td>\n",
              "      <td>0.578346</td>\n",
              "      <td>0.535307</td>\n",
              "      <td>0.482328</td>\n",
              "      <td>0.809953</td>\n",
              "      <td>0.893141</td>\n",
              "      <td>0.782109</td>\n",
              "      <td>0.392309</td>\n",
              "      <td>0.327684</td>\n",
              "      <td>0.528337</td>\n",
              "      <td>0.132785</td>\n",
              "      <td>0.545117</td>\n",
              "      <td>0.572335</td>\n",
              "      <td>-0.006906</td>\n",
              "      <td>0.725132</td>\n",
              "      <td>0.718665</td>\n",
              "      <td>0.547825</td>\n",
              "      <td>-0.189033</td>\n",
              "      <td>0.534881</td>\n",
              "      <td>0.626929</td>\n",
              "      <td>0.635756</td>\n",
              "      <td>0.671010</td>\n",
              "      <td>0.626568</td>\n",
              "      <td>0.569676</td>\n",
              "      <td>0.904890</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.858649</td>\n",
              "      <td>0.503534</td>\n",
              "      <td>0.729406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concave_Points</th>\n",
              "      <td>0.877711</td>\n",
              "      <td>0.717917</td>\n",
              "      <td>0.506841</td>\n",
              "      <td>0.758052</td>\n",
              "      <td>0.711491</td>\n",
              "      <td>0.563082</td>\n",
              "      <td>0.833016</td>\n",
              "      <td>0.891423</td>\n",
              "      <td>0.942427</td>\n",
              "      <td>0.457268</td>\n",
              "      <td>0.184825</td>\n",
              "      <td>0.690131</td>\n",
              "      <td>0.158551</td>\n",
              "      <td>0.689611</td>\n",
              "      <td>0.756040</td>\n",
              "      <td>-0.032487</td>\n",
              "      <td>0.589257</td>\n",
              "      <td>0.570656</td>\n",
              "      <td>0.697906</td>\n",
              "      <td>-0.131871</td>\n",
              "      <td>0.425602</td>\n",
              "      <td>0.785420</td>\n",
              "      <td>0.539389</td>\n",
              "      <td>0.821865</td>\n",
              "      <td>0.773350</td>\n",
              "      <td>0.520635</td>\n",
              "      <td>0.784110</td>\n",
              "      <td>0.858649</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.484958</td>\n",
              "      <td>0.521362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Symmetry</th>\n",
              "      <td>0.494937</td>\n",
              "      <td>0.139212</td>\n",
              "      <td>0.242014</td>\n",
              "      <td>0.176434</td>\n",
              "      <td>0.135912</td>\n",
              "      <td>0.651191</td>\n",
              "      <td>0.589464</td>\n",
              "      <td>0.451734</td>\n",
              "      <td>0.445199</td>\n",
              "      <td>0.743554</td>\n",
              "      <td>0.490931</td>\n",
              "      <td>0.341622</td>\n",
              "      <td>0.001641</td>\n",
              "      <td>0.304805</td>\n",
              "      <td>0.320292</td>\n",
              "      <td>0.102525</td>\n",
              "      <td>0.397320</td>\n",
              "      <td>0.256936</td>\n",
              "      <td>0.245800</td>\n",
              "      <td>0.242389</td>\n",
              "      <td>0.470119</td>\n",
              "      <td>0.250176</td>\n",
              "      <td>0.303465</td>\n",
              "      <td>0.282905</td>\n",
              "      <td>0.242290</td>\n",
              "      <td>0.654306</td>\n",
              "      <td>0.616021</td>\n",
              "      <td>0.503534</td>\n",
              "      <td>0.484958</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.685794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Fractal_Dimension</th>\n",
              "      <td>0.491492</td>\n",
              "      <td>0.017384</td>\n",
              "      <td>0.361987</td>\n",
              "      <td>0.066931</td>\n",
              "      <td>0.022775</td>\n",
              "      <td>0.615967</td>\n",
              "      <td>0.671134</td>\n",
              "      <td>0.545869</td>\n",
              "      <td>0.390418</td>\n",
              "      <td>0.524186</td>\n",
              "      <td>0.673239</td>\n",
              "      <td>0.199023</td>\n",
              "      <td>0.073064</td>\n",
              "      <td>0.206003</td>\n",
              "      <td>0.180651</td>\n",
              "      <td>0.173192</td>\n",
              "      <td>0.649174</td>\n",
              "      <td>0.474993</td>\n",
              "      <td>0.265116</td>\n",
              "      <td>-0.094488</td>\n",
              "      <td>0.684865</td>\n",
              "      <td>0.158266</td>\n",
              "      <td>0.452119</td>\n",
              "      <td>0.207687</td>\n",
              "      <td>0.164742</td>\n",
              "      <td>0.774750</td>\n",
              "      <td>0.850462</td>\n",
              "      <td>0.729406</td>\n",
              "      <td>0.521362</td>\n",
              "      <td>0.685794</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Diagnosis  ...  Worst_Fractal_Dimension\n",
              "Diagnosis                 1.000000  ...                 0.491492\n",
              "Mean_Radius               0.718963  ...                 0.017384\n",
              "Mean_Texture              0.590164  ...                 0.361987\n",
              "Mean_Perimeter            0.748290  ...                 0.066931\n",
              "Mean_Area                 0.714016  ...                 0.022775\n",
              "Mean_Smoothness           0.450624  ...                 0.615967\n",
              "Mean_Compactness          0.691589  ...                 0.671134\n",
              "Mean_Concavity            0.806572  ...                 0.545869\n",
              "Mean_Concave_Points       0.848798  ...                 0.390418\n",
              "Mean_Symmetry             0.368120  ...                 0.524186\n",
              "Mean_Fractal_Dimension    0.061748  ...                 0.673239\n",
              "Radius_SE                 0.620258  ...                 0.199023\n",
              "Texture_SE                0.147192  ...                 0.073064\n",
              "Perimeter_SE              0.595459  ...                 0.206003\n",
              "Area_SE                   0.697258  ...                 0.180651\n",
              "Smoothness_SE            -0.023649  ...                 0.173192\n",
              "Compactness_SE            0.492419  ...                 0.649174\n",
              "Concavity_SE              0.485526  ...                 0.474993\n",
              "Concave_Points_SE         0.543528  ...                 0.265116\n",
              "Symmetry_SE              -0.083942  ...                -0.094488\n",
              "Fractal_Dimension_SE      0.337851  ...                 0.684865\n",
              "Worst_Radius              0.788719  ...                 0.158266\n",
              "Worst_Texture             0.622984  ...                 0.452119\n",
              "Worst_Perimeter           0.806620  ...                 0.207687\n",
              "Worst_Area                0.778064  ...                 0.164742\n",
              "Worst_Smoothness          0.474074  ...                 0.774750\n",
              "Worst_Compactness         0.732958  ...                 0.850462\n",
              "Worst_Concavity           0.829992  ...                 0.729406\n",
              "Worst_Concave_Points      0.877711  ...                 0.521362\n",
              "Worst_Symmetry            0.494937  ...                 0.685794\n",
              "Worst_Fractal_Dimension   0.491492  ...                 1.000000\n",
              "\n",
              "[31 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hxUuBZh7d68k",
        "outputId": "05ad75a6-56b9-4646-e2bc-aaf214eaaa45"
      },
      "source": [
        "y_train.corr(method='pearson')\n"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diagnosis</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>Radius_SE</th>\n",
              "      <th>Texture_SE</th>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <th>Area_SE</th>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <th>Compactness_SE</th>\n",
              "      <th>Concavity_SE</th>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <th>Fractal_Dimension_SE</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>Diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.718963</td>\n",
              "      <td>0.590164</td>\n",
              "      <td>0.748290</td>\n",
              "      <td>0.714016</td>\n",
              "      <td>0.450624</td>\n",
              "      <td>0.691589</td>\n",
              "      <td>0.806572</td>\n",
              "      <td>0.848798</td>\n",
              "      <td>0.368120</td>\n",
              "      <td>0.061748</td>\n",
              "      <td>0.620258</td>\n",
              "      <td>0.147192</td>\n",
              "      <td>0.595459</td>\n",
              "      <td>0.697258</td>\n",
              "      <td>-0.023649</td>\n",
              "      <td>0.492419</td>\n",
              "      <td>0.485526</td>\n",
              "      <td>0.543528</td>\n",
              "      <td>-0.083942</td>\n",
              "      <td>0.337851</td>\n",
              "      <td>0.788719</td>\n",
              "      <td>0.622984</td>\n",
              "      <td>0.806620</td>\n",
              "      <td>0.778064</td>\n",
              "      <td>0.474074</td>\n",
              "      <td>0.732958</td>\n",
              "      <td>0.829992</td>\n",
              "      <td>0.877711</td>\n",
              "      <td>0.494937</td>\n",
              "      <td>0.491492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Radius</th>\n",
              "      <td>0.718963</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.461645</td>\n",
              "      <td>0.997222</td>\n",
              "      <td>0.992897</td>\n",
              "      <td>0.100541</td>\n",
              "      <td>0.425790</td>\n",
              "      <td>0.621734</td>\n",
              "      <td>0.777105</td>\n",
              "      <td>0.062836</td>\n",
              "      <td>-0.418678</td>\n",
              "      <td>0.639325</td>\n",
              "      <td>0.093109</td>\n",
              "      <td>0.611552</td>\n",
              "      <td>0.783412</td>\n",
              "      <td>-0.361386</td>\n",
              "      <td>0.212260</td>\n",
              "      <td>0.244194</td>\n",
              "      <td>0.411916</td>\n",
              "      <td>-0.193701</td>\n",
              "      <td>0.028003</td>\n",
              "      <td>0.978696</td>\n",
              "      <td>0.471728</td>\n",
              "      <td>0.968769</td>\n",
              "      <td>0.968042</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>0.412658</td>\n",
              "      <td>0.533238</td>\n",
              "      <td>0.717917</td>\n",
              "      <td>0.139212</td>\n",
              "      <td>0.017384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Texture</th>\n",
              "      <td>0.590164</td>\n",
              "      <td>0.461645</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.480277</td>\n",
              "      <td>0.477899</td>\n",
              "      <td>0.252809</td>\n",
              "      <td>0.472125</td>\n",
              "      <td>0.537598</td>\n",
              "      <td>0.496728</td>\n",
              "      <td>0.188051</td>\n",
              "      <td>0.047373</td>\n",
              "      <td>0.413561</td>\n",
              "      <td>0.402565</td>\n",
              "      <td>0.418557</td>\n",
              "      <td>0.456982</td>\n",
              "      <td>0.131524</td>\n",
              "      <td>0.480066</td>\n",
              "      <td>0.477579</td>\n",
              "      <td>0.382308</td>\n",
              "      <td>-0.001915</td>\n",
              "      <td>0.316852</td>\n",
              "      <td>0.507594</td>\n",
              "      <td>0.926055</td>\n",
              "      <td>0.526952</td>\n",
              "      <td>0.519918</td>\n",
              "      <td>0.318822</td>\n",
              "      <td>0.521537</td>\n",
              "      <td>0.594230</td>\n",
              "      <td>0.506841</td>\n",
              "      <td>0.242014</td>\n",
              "      <td>0.361987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Perimeter</th>\n",
              "      <td>0.748290</td>\n",
              "      <td>0.997222</td>\n",
              "      <td>0.480277</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990425</td>\n",
              "      <td>0.147277</td>\n",
              "      <td>0.488296</td>\n",
              "      <td>0.672794</td>\n",
              "      <td>0.815552</td>\n",
              "      <td>0.113736</td>\n",
              "      <td>-0.359612</td>\n",
              "      <td>0.677583</td>\n",
              "      <td>0.114407</td>\n",
              "      <td>0.655441</td>\n",
              "      <td>0.814313</td>\n",
              "      <td>-0.329495</td>\n",
              "      <td>0.265181</td>\n",
              "      <td>0.292193</td>\n",
              "      <td>0.458639</td>\n",
              "      <td>-0.179359</td>\n",
              "      <td>0.079156</td>\n",
              "      <td>0.983951</td>\n",
              "      <td>0.488351</td>\n",
              "      <td>0.980911</td>\n",
              "      <td>0.973372</td>\n",
              "      <td>0.053225</td>\n",
              "      <td>0.460293</td>\n",
              "      <td>0.578346</td>\n",
              "      <td>0.758052</td>\n",
              "      <td>0.176434</td>\n",
              "      <td>0.066931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Area</th>\n",
              "      <td>0.714016</td>\n",
              "      <td>0.992897</td>\n",
              "      <td>0.477899</td>\n",
              "      <td>0.990425</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.098499</td>\n",
              "      <td>0.426872</td>\n",
              "      <td>0.636764</td>\n",
              "      <td>0.784396</td>\n",
              "      <td>0.065203</td>\n",
              "      <td>-0.391319</td>\n",
              "      <td>0.656323</td>\n",
              "      <td>0.115531</td>\n",
              "      <td>0.630211</td>\n",
              "      <td>0.799085</td>\n",
              "      <td>-0.312470</td>\n",
              "      <td>0.233174</td>\n",
              "      <td>0.280425</td>\n",
              "      <td>0.430333</td>\n",
              "      <td>-0.163784</td>\n",
              "      <td>0.056821</td>\n",
              "      <td>0.973454</td>\n",
              "      <td>0.480905</td>\n",
              "      <td>0.964036</td>\n",
              "      <td>0.976528</td>\n",
              "      <td>0.020302</td>\n",
              "      <td>0.403074</td>\n",
              "      <td>0.535307</td>\n",
              "      <td>0.711491</td>\n",
              "      <td>0.135912</td>\n",
              "      <td>0.022775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Smoothness</th>\n",
              "      <td>0.450624</td>\n",
              "      <td>0.100541</td>\n",
              "      <td>0.252809</td>\n",
              "      <td>0.147277</td>\n",
              "      <td>0.098499</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.687499</td>\n",
              "      <td>0.503398</td>\n",
              "      <td>0.530112</td>\n",
              "      <td>0.681009</td>\n",
              "      <td>0.612929</td>\n",
              "      <td>0.354027</td>\n",
              "      <td>0.195963</td>\n",
              "      <td>0.358325</td>\n",
              "      <td>0.322634</td>\n",
              "      <td>0.304354</td>\n",
              "      <td>0.421365</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>0.484582</td>\n",
              "      <td>0.207641</td>\n",
              "      <td>0.470341</td>\n",
              "      <td>0.187081</td>\n",
              "      <td>0.277978</td>\n",
              "      <td>0.230673</td>\n",
              "      <td>0.184278</td>\n",
              "      <td>0.802873</td>\n",
              "      <td>0.556804</td>\n",
              "      <td>0.482328</td>\n",
              "      <td>0.563082</td>\n",
              "      <td>0.651191</td>\n",
              "      <td>0.615967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Compactness</th>\n",
              "      <td>0.691589</td>\n",
              "      <td>0.425790</td>\n",
              "      <td>0.472125</td>\n",
              "      <td>0.488296</td>\n",
              "      <td>0.426872</td>\n",
              "      <td>0.687499</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.902124</td>\n",
              "      <td>0.830360</td>\n",
              "      <td>0.718561</td>\n",
              "      <td>0.582449</td>\n",
              "      <td>0.750683</td>\n",
              "      <td>0.360917</td>\n",
              "      <td>0.774404</td>\n",
              "      <td>0.720159</td>\n",
              "      <td>0.258740</td>\n",
              "      <td>0.825208</td>\n",
              "      <td>0.738344</td>\n",
              "      <td>0.776709</td>\n",
              "      <td>0.134836</td>\n",
              "      <td>0.713343</td>\n",
              "      <td>0.521641</td>\n",
              "      <td>0.467758</td>\n",
              "      <td>0.584863</td>\n",
              "      <td>0.517321</td>\n",
              "      <td>0.579309</td>\n",
              "      <td>0.822263</td>\n",
              "      <td>0.809953</td>\n",
              "      <td>0.833016</td>\n",
              "      <td>0.589464</td>\n",
              "      <td>0.671134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concavity</th>\n",
              "      <td>0.806572</td>\n",
              "      <td>0.621734</td>\n",
              "      <td>0.537598</td>\n",
              "      <td>0.672794</td>\n",
              "      <td>0.636764</td>\n",
              "      <td>0.503398</td>\n",
              "      <td>0.902124</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.928459</td>\n",
              "      <td>0.534929</td>\n",
              "      <td>0.361920</td>\n",
              "      <td>0.790334</td>\n",
              "      <td>0.297141</td>\n",
              "      <td>0.814762</td>\n",
              "      <td>0.809463</td>\n",
              "      <td>0.146029</td>\n",
              "      <td>0.742701</td>\n",
              "      <td>0.793914</td>\n",
              "      <td>0.780787</td>\n",
              "      <td>0.036355</td>\n",
              "      <td>0.611230</td>\n",
              "      <td>0.690657</td>\n",
              "      <td>0.516125</td>\n",
              "      <td>0.741989</td>\n",
              "      <td>0.698801</td>\n",
              "      <td>0.446839</td>\n",
              "      <td>0.756279</td>\n",
              "      <td>0.893141</td>\n",
              "      <td>0.891423</td>\n",
              "      <td>0.451734</td>\n",
              "      <td>0.545869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concave_Points</th>\n",
              "      <td>0.848798</td>\n",
              "      <td>0.777105</td>\n",
              "      <td>0.496728</td>\n",
              "      <td>0.815552</td>\n",
              "      <td>0.784396</td>\n",
              "      <td>0.530112</td>\n",
              "      <td>0.830360</td>\n",
              "      <td>0.928459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.501594</td>\n",
              "      <td>0.161314</td>\n",
              "      <td>0.838759</td>\n",
              "      <td>0.253658</td>\n",
              "      <td>0.833207</td>\n",
              "      <td>0.887914</td>\n",
              "      <td>0.060994</td>\n",
              "      <td>0.586157</td>\n",
              "      <td>0.607598</td>\n",
              "      <td>0.770256</td>\n",
              "      <td>0.043561</td>\n",
              "      <td>0.458322</td>\n",
              "      <td>0.826039</td>\n",
              "      <td>0.482996</td>\n",
              "      <td>0.857212</td>\n",
              "      <td>0.825035</td>\n",
              "      <td>0.404239</td>\n",
              "      <td>0.667295</td>\n",
              "      <td>0.782109</td>\n",
              "      <td>0.942427</td>\n",
              "      <td>0.445199</td>\n",
              "      <td>0.390418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Symmetry</th>\n",
              "      <td>0.368120</td>\n",
              "      <td>0.062836</td>\n",
              "      <td>0.188051</td>\n",
              "      <td>0.113736</td>\n",
              "      <td>0.065203</td>\n",
              "      <td>0.681009</td>\n",
              "      <td>0.718561</td>\n",
              "      <td>0.534929</td>\n",
              "      <td>0.501594</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.635357</td>\n",
              "      <td>0.545343</td>\n",
              "      <td>0.278579</td>\n",
              "      <td>0.539909</td>\n",
              "      <td>0.452065</td>\n",
              "      <td>0.390588</td>\n",
              "      <td>0.558194</td>\n",
              "      <td>0.439631</td>\n",
              "      <td>0.579665</td>\n",
              "      <td>0.406263</td>\n",
              "      <td>0.692370</td>\n",
              "      <td>0.149323</td>\n",
              "      <td>0.152772</td>\n",
              "      <td>0.200160</td>\n",
              "      <td>0.145554</td>\n",
              "      <td>0.500745</td>\n",
              "      <td>0.474324</td>\n",
              "      <td>0.392309</td>\n",
              "      <td>0.457268</td>\n",
              "      <td>0.743554</td>\n",
              "      <td>0.524186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Fractal_Dimension</th>\n",
              "      <td>0.061748</td>\n",
              "      <td>-0.418678</td>\n",
              "      <td>0.047373</td>\n",
              "      <td>-0.359612</td>\n",
              "      <td>-0.391319</td>\n",
              "      <td>0.612929</td>\n",
              "      <td>0.582449</td>\n",
              "      <td>0.361920</td>\n",
              "      <td>0.161314</td>\n",
              "      <td>0.635357</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.165735</td>\n",
              "      <td>0.261846</td>\n",
              "      <td>0.211624</td>\n",
              "      <td>0.029894</td>\n",
              "      <td>0.593192</td>\n",
              "      <td>0.580716</td>\n",
              "      <td>0.523295</td>\n",
              "      <td>0.402934</td>\n",
              "      <td>0.338910</td>\n",
              "      <td>0.693909</td>\n",
              "      <td>-0.319845</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>-0.253514</td>\n",
              "      <td>-0.296290</td>\n",
              "      <td>0.593672</td>\n",
              "      <td>0.403685</td>\n",
              "      <td>0.327684</td>\n",
              "      <td>0.184825</td>\n",
              "      <td>0.490931</td>\n",
              "      <td>0.673239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radius_SE</th>\n",
              "      <td>0.620258</td>\n",
              "      <td>0.639325</td>\n",
              "      <td>0.413561</td>\n",
              "      <td>0.677583</td>\n",
              "      <td>0.656323</td>\n",
              "      <td>0.354027</td>\n",
              "      <td>0.750683</td>\n",
              "      <td>0.790334</td>\n",
              "      <td>0.838759</td>\n",
              "      <td>0.545343</td>\n",
              "      <td>0.165735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.410953</td>\n",
              "      <td>0.978585</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>0.191905</td>\n",
              "      <td>0.552037</td>\n",
              "      <td>0.535923</td>\n",
              "      <td>0.724420</td>\n",
              "      <td>0.278701</td>\n",
              "      <td>0.482608</td>\n",
              "      <td>0.697271</td>\n",
              "      <td>0.354318</td>\n",
              "      <td>0.722825</td>\n",
              "      <td>0.702233</td>\n",
              "      <td>0.169746</td>\n",
              "      <td>0.450534</td>\n",
              "      <td>0.528337</td>\n",
              "      <td>0.690131</td>\n",
              "      <td>0.341622</td>\n",
              "      <td>0.199023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Texture_SE</th>\n",
              "      <td>0.147192</td>\n",
              "      <td>0.093109</td>\n",
              "      <td>0.402565</td>\n",
              "      <td>0.114407</td>\n",
              "      <td>0.115531</td>\n",
              "      <td>0.195963</td>\n",
              "      <td>0.360917</td>\n",
              "      <td>0.297141</td>\n",
              "      <td>0.253658</td>\n",
              "      <td>0.278579</td>\n",
              "      <td>0.261846</td>\n",
              "      <td>0.410953</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.442794</td>\n",
              "      <td>0.335502</td>\n",
              "      <td>0.560069</td>\n",
              "      <td>0.433429</td>\n",
              "      <td>0.466527</td>\n",
              "      <td>0.573731</td>\n",
              "      <td>0.409346</td>\n",
              "      <td>0.509468</td>\n",
              "      <td>0.085145</td>\n",
              "      <td>0.434059</td>\n",
              "      <td>0.103227</td>\n",
              "      <td>0.091174</td>\n",
              "      <td>0.079307</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.132785</td>\n",
              "      <td>0.158551</td>\n",
              "      <td>0.001641</td>\n",
              "      <td>0.073064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <td>0.595459</td>\n",
              "      <td>0.611552</td>\n",
              "      <td>0.418557</td>\n",
              "      <td>0.655441</td>\n",
              "      <td>0.630211</td>\n",
              "      <td>0.358325</td>\n",
              "      <td>0.774404</td>\n",
              "      <td>0.814762</td>\n",
              "      <td>0.833207</td>\n",
              "      <td>0.539909</td>\n",
              "      <td>0.211624</td>\n",
              "      <td>0.978585</td>\n",
              "      <td>0.442794</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.944080</td>\n",
              "      <td>0.219937</td>\n",
              "      <td>0.580687</td>\n",
              "      <td>0.597051</td>\n",
              "      <td>0.779833</td>\n",
              "      <td>0.288514</td>\n",
              "      <td>0.507384</td>\n",
              "      <td>0.655865</td>\n",
              "      <td>0.345206</td>\n",
              "      <td>0.701482</td>\n",
              "      <td>0.661942</td>\n",
              "      <td>0.161297</td>\n",
              "      <td>0.454788</td>\n",
              "      <td>0.545117</td>\n",
              "      <td>0.689611</td>\n",
              "      <td>0.304805</td>\n",
              "      <td>0.206003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area_SE</th>\n",
              "      <td>0.697258</td>\n",
              "      <td>0.783412</td>\n",
              "      <td>0.456982</td>\n",
              "      <td>0.814313</td>\n",
              "      <td>0.799085</td>\n",
              "      <td>0.322634</td>\n",
              "      <td>0.720159</td>\n",
              "      <td>0.809463</td>\n",
              "      <td>0.887914</td>\n",
              "      <td>0.452065</td>\n",
              "      <td>0.029894</td>\n",
              "      <td>0.968399</td>\n",
              "      <td>0.335502</td>\n",
              "      <td>0.944080</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.058436</td>\n",
              "      <td>0.491773</td>\n",
              "      <td>0.495157</td>\n",
              "      <td>0.684411</td>\n",
              "      <td>0.146471</td>\n",
              "      <td>0.396571</td>\n",
              "      <td>0.826329</td>\n",
              "      <td>0.413038</td>\n",
              "      <td>0.845135</td>\n",
              "      <td>0.833629</td>\n",
              "      <td>0.162537</td>\n",
              "      <td>0.474866</td>\n",
              "      <td>0.572335</td>\n",
              "      <td>0.756040</td>\n",
              "      <td>0.320292</td>\n",
              "      <td>0.180651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <td>-0.023649</td>\n",
              "      <td>-0.361386</td>\n",
              "      <td>0.131524</td>\n",
              "      <td>-0.329495</td>\n",
              "      <td>-0.312470</td>\n",
              "      <td>0.304354</td>\n",
              "      <td>0.258740</td>\n",
              "      <td>0.146029</td>\n",
              "      <td>0.060994</td>\n",
              "      <td>0.390588</td>\n",
              "      <td>0.593192</td>\n",
              "      <td>0.191905</td>\n",
              "      <td>0.560069</td>\n",
              "      <td>0.219937</td>\n",
              "      <td>0.058436</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.397698</td>\n",
              "      <td>0.415695</td>\n",
              "      <td>0.443756</td>\n",
              "      <td>0.545801</td>\n",
              "      <td>0.547568</td>\n",
              "      <td>-0.328185</td>\n",
              "      <td>0.031605</td>\n",
              "      <td>-0.297221</td>\n",
              "      <td>-0.290679</td>\n",
              "      <td>0.350120</td>\n",
              "      <td>0.006986</td>\n",
              "      <td>-0.006906</td>\n",
              "      <td>-0.032487</td>\n",
              "      <td>0.102525</td>\n",
              "      <td>0.173192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Compactness_SE</th>\n",
              "      <td>0.492419</td>\n",
              "      <td>0.212260</td>\n",
              "      <td>0.480066</td>\n",
              "      <td>0.265181</td>\n",
              "      <td>0.233174</td>\n",
              "      <td>0.421365</td>\n",
              "      <td>0.825208</td>\n",
              "      <td>0.742701</td>\n",
              "      <td>0.586157</td>\n",
              "      <td>0.558194</td>\n",
              "      <td>0.580716</td>\n",
              "      <td>0.552037</td>\n",
              "      <td>0.433429</td>\n",
              "      <td>0.580687</td>\n",
              "      <td>0.491773</td>\n",
              "      <td>0.397698</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.882924</td>\n",
              "      <td>0.722649</td>\n",
              "      <td>0.199823</td>\n",
              "      <td>0.835100</td>\n",
              "      <td>0.293871</td>\n",
              "      <td>0.430129</td>\n",
              "      <td>0.342119</td>\n",
              "      <td>0.305131</td>\n",
              "      <td>0.418847</td>\n",
              "      <td>0.772767</td>\n",
              "      <td>0.725132</td>\n",
              "      <td>0.589257</td>\n",
              "      <td>0.397320</td>\n",
              "      <td>0.649174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concavity_SE</th>\n",
              "      <td>0.485526</td>\n",
              "      <td>0.244194</td>\n",
              "      <td>0.477579</td>\n",
              "      <td>0.292193</td>\n",
              "      <td>0.280425</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>0.738344</td>\n",
              "      <td>0.793914</td>\n",
              "      <td>0.607598</td>\n",
              "      <td>0.439631</td>\n",
              "      <td>0.523295</td>\n",
              "      <td>0.535923</td>\n",
              "      <td>0.466527</td>\n",
              "      <td>0.597051</td>\n",
              "      <td>0.495157</td>\n",
              "      <td>0.415695</td>\n",
              "      <td>0.882924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.784106</td>\n",
              "      <td>0.245830</td>\n",
              "      <td>0.745728</td>\n",
              "      <td>0.283000</td>\n",
              "      <td>0.399767</td>\n",
              "      <td>0.337590</td>\n",
              "      <td>0.306270</td>\n",
              "      <td>0.302940</td>\n",
              "      <td>0.582957</td>\n",
              "      <td>0.718665</td>\n",
              "      <td>0.570656</td>\n",
              "      <td>0.256936</td>\n",
              "      <td>0.474993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <td>0.543528</td>\n",
              "      <td>0.411916</td>\n",
              "      <td>0.382308</td>\n",
              "      <td>0.458639</td>\n",
              "      <td>0.430333</td>\n",
              "      <td>0.484582</td>\n",
              "      <td>0.776709</td>\n",
              "      <td>0.780787</td>\n",
              "      <td>0.770256</td>\n",
              "      <td>0.579665</td>\n",
              "      <td>0.402934</td>\n",
              "      <td>0.724420</td>\n",
              "      <td>0.573731</td>\n",
              "      <td>0.779833</td>\n",
              "      <td>0.684411</td>\n",
              "      <td>0.443756</td>\n",
              "      <td>0.722649</td>\n",
              "      <td>0.784106</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.366776</td>\n",
              "      <td>0.646480</td>\n",
              "      <td>0.418885</td>\n",
              "      <td>0.282416</td>\n",
              "      <td>0.471306</td>\n",
              "      <td>0.420944</td>\n",
              "      <td>0.262816</td>\n",
              "      <td>0.457468</td>\n",
              "      <td>0.547825</td>\n",
              "      <td>0.697906</td>\n",
              "      <td>0.245800</td>\n",
              "      <td>0.265116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <td>-0.083942</td>\n",
              "      <td>-0.193701</td>\n",
              "      <td>-0.001915</td>\n",
              "      <td>-0.179359</td>\n",
              "      <td>-0.163784</td>\n",
              "      <td>0.207641</td>\n",
              "      <td>0.134836</td>\n",
              "      <td>0.036355</td>\n",
              "      <td>0.043561</td>\n",
              "      <td>0.406263</td>\n",
              "      <td>0.338910</td>\n",
              "      <td>0.278701</td>\n",
              "      <td>0.409346</td>\n",
              "      <td>0.288514</td>\n",
              "      <td>0.146471</td>\n",
              "      <td>0.545801</td>\n",
              "      <td>0.199823</td>\n",
              "      <td>0.245830</td>\n",
              "      <td>0.366776</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344375</td>\n",
              "      <td>-0.216756</td>\n",
              "      <td>-0.183768</td>\n",
              "      <td>-0.202425</td>\n",
              "      <td>-0.198325</td>\n",
              "      <td>-0.089892</td>\n",
              "      <td>-0.175653</td>\n",
              "      <td>-0.189033</td>\n",
              "      <td>-0.131871</td>\n",
              "      <td>0.242389</td>\n",
              "      <td>-0.094488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fractal_Dimension_SE</th>\n",
              "      <td>0.337851</td>\n",
              "      <td>0.028003</td>\n",
              "      <td>0.316852</td>\n",
              "      <td>0.079156</td>\n",
              "      <td>0.056821</td>\n",
              "      <td>0.470341</td>\n",
              "      <td>0.713343</td>\n",
              "      <td>0.611230</td>\n",
              "      <td>0.458322</td>\n",
              "      <td>0.692370</td>\n",
              "      <td>0.693909</td>\n",
              "      <td>0.482608</td>\n",
              "      <td>0.509468</td>\n",
              "      <td>0.507384</td>\n",
              "      <td>0.396571</td>\n",
              "      <td>0.547568</td>\n",
              "      <td>0.835100</td>\n",
              "      <td>0.745728</td>\n",
              "      <td>0.646480</td>\n",
              "      <td>0.344375</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.109922</td>\n",
              "      <td>0.282047</td>\n",
              "      <td>0.160395</td>\n",
              "      <td>0.128170</td>\n",
              "      <td>0.439688</td>\n",
              "      <td>0.589495</td>\n",
              "      <td>0.534881</td>\n",
              "      <td>0.425602</td>\n",
              "      <td>0.470119</td>\n",
              "      <td>0.684865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Radius</th>\n",
              "      <td>0.788719</td>\n",
              "      <td>0.978696</td>\n",
              "      <td>0.507594</td>\n",
              "      <td>0.983951</td>\n",
              "      <td>0.973454</td>\n",
              "      <td>0.187081</td>\n",
              "      <td>0.521641</td>\n",
              "      <td>0.690657</td>\n",
              "      <td>0.826039</td>\n",
              "      <td>0.149323</td>\n",
              "      <td>-0.319845</td>\n",
              "      <td>0.697271</td>\n",
              "      <td>0.085145</td>\n",
              "      <td>0.655865</td>\n",
              "      <td>0.826329</td>\n",
              "      <td>-0.328185</td>\n",
              "      <td>0.293871</td>\n",
              "      <td>0.283000</td>\n",
              "      <td>0.418885</td>\n",
              "      <td>-0.216756</td>\n",
              "      <td>0.109922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.541598</td>\n",
              "      <td>0.992425</td>\n",
              "      <td>0.992325</td>\n",
              "      <td>0.136779</td>\n",
              "      <td>0.533010</td>\n",
              "      <td>0.626929</td>\n",
              "      <td>0.785420</td>\n",
              "      <td>0.250176</td>\n",
              "      <td>0.158266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Texture</th>\n",
              "      <td>0.622984</td>\n",
              "      <td>0.471728</td>\n",
              "      <td>0.926055</td>\n",
              "      <td>0.488351</td>\n",
              "      <td>0.480905</td>\n",
              "      <td>0.277978</td>\n",
              "      <td>0.467758</td>\n",
              "      <td>0.516125</td>\n",
              "      <td>0.482996</td>\n",
              "      <td>0.152772</td>\n",
              "      <td>0.038090</td>\n",
              "      <td>0.354318</td>\n",
              "      <td>0.434059</td>\n",
              "      <td>0.345206</td>\n",
              "      <td>0.413038</td>\n",
              "      <td>0.031605</td>\n",
              "      <td>0.430129</td>\n",
              "      <td>0.399767</td>\n",
              "      <td>0.282416</td>\n",
              "      <td>-0.183768</td>\n",
              "      <td>0.282047</td>\n",
              "      <td>0.541598</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.552795</td>\n",
              "      <td>0.547225</td>\n",
              "      <td>0.424210</td>\n",
              "      <td>0.590353</td>\n",
              "      <td>0.635756</td>\n",
              "      <td>0.539389</td>\n",
              "      <td>0.303465</td>\n",
              "      <td>0.452119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Perimeter</th>\n",
              "      <td>0.806620</td>\n",
              "      <td>0.968769</td>\n",
              "      <td>0.526952</td>\n",
              "      <td>0.980911</td>\n",
              "      <td>0.964036</td>\n",
              "      <td>0.230673</td>\n",
              "      <td>0.584863</td>\n",
              "      <td>0.741989</td>\n",
              "      <td>0.857212</td>\n",
              "      <td>0.200160</td>\n",
              "      <td>-0.253514</td>\n",
              "      <td>0.722825</td>\n",
              "      <td>0.103227</td>\n",
              "      <td>0.701482</td>\n",
              "      <td>0.845135</td>\n",
              "      <td>-0.297221</td>\n",
              "      <td>0.342119</td>\n",
              "      <td>0.337590</td>\n",
              "      <td>0.471306</td>\n",
              "      <td>-0.202425</td>\n",
              "      <td>0.160395</td>\n",
              "      <td>0.992425</td>\n",
              "      <td>0.552795</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.985087</td>\n",
              "      <td>0.173521</td>\n",
              "      <td>0.577892</td>\n",
              "      <td>0.671010</td>\n",
              "      <td>0.821865</td>\n",
              "      <td>0.282905</td>\n",
              "      <td>0.207687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Area</th>\n",
              "      <td>0.778064</td>\n",
              "      <td>0.968042</td>\n",
              "      <td>0.519918</td>\n",
              "      <td>0.973372</td>\n",
              "      <td>0.976528</td>\n",
              "      <td>0.184278</td>\n",
              "      <td>0.517321</td>\n",
              "      <td>0.698801</td>\n",
              "      <td>0.825035</td>\n",
              "      <td>0.145554</td>\n",
              "      <td>-0.296290</td>\n",
              "      <td>0.702233</td>\n",
              "      <td>0.091174</td>\n",
              "      <td>0.661942</td>\n",
              "      <td>0.833629</td>\n",
              "      <td>-0.290679</td>\n",
              "      <td>0.305131</td>\n",
              "      <td>0.306270</td>\n",
              "      <td>0.420944</td>\n",
              "      <td>-0.198325</td>\n",
              "      <td>0.128170</td>\n",
              "      <td>0.992325</td>\n",
              "      <td>0.547225</td>\n",
              "      <td>0.985087</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.144603</td>\n",
              "      <td>0.523951</td>\n",
              "      <td>0.626568</td>\n",
              "      <td>0.773350</td>\n",
              "      <td>0.242290</td>\n",
              "      <td>0.164742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Smoothness</th>\n",
              "      <td>0.474074</td>\n",
              "      <td>0.011199</td>\n",
              "      <td>0.318822</td>\n",
              "      <td>0.053225</td>\n",
              "      <td>0.020302</td>\n",
              "      <td>0.802873</td>\n",
              "      <td>0.579309</td>\n",
              "      <td>0.446839</td>\n",
              "      <td>0.404239</td>\n",
              "      <td>0.500745</td>\n",
              "      <td>0.593672</td>\n",
              "      <td>0.169746</td>\n",
              "      <td>0.079307</td>\n",
              "      <td>0.161297</td>\n",
              "      <td>0.162537</td>\n",
              "      <td>0.350120</td>\n",
              "      <td>0.418847</td>\n",
              "      <td>0.302940</td>\n",
              "      <td>0.262816</td>\n",
              "      <td>-0.089892</td>\n",
              "      <td>0.439688</td>\n",
              "      <td>0.136779</td>\n",
              "      <td>0.424210</td>\n",
              "      <td>0.173521</td>\n",
              "      <td>0.144603</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.648209</td>\n",
              "      <td>0.569676</td>\n",
              "      <td>0.520635</td>\n",
              "      <td>0.654306</td>\n",
              "      <td>0.774750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Compactness</th>\n",
              "      <td>0.732958</td>\n",
              "      <td>0.412658</td>\n",
              "      <td>0.521537</td>\n",
              "      <td>0.460293</td>\n",
              "      <td>0.403074</td>\n",
              "      <td>0.556804</td>\n",
              "      <td>0.822263</td>\n",
              "      <td>0.756279</td>\n",
              "      <td>0.667295</td>\n",
              "      <td>0.474324</td>\n",
              "      <td>0.403685</td>\n",
              "      <td>0.450534</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.454788</td>\n",
              "      <td>0.474866</td>\n",
              "      <td>0.006986</td>\n",
              "      <td>0.772767</td>\n",
              "      <td>0.582957</td>\n",
              "      <td>0.457468</td>\n",
              "      <td>-0.175653</td>\n",
              "      <td>0.589495</td>\n",
              "      <td>0.533010</td>\n",
              "      <td>0.590353</td>\n",
              "      <td>0.577892</td>\n",
              "      <td>0.523951</td>\n",
              "      <td>0.648209</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904890</td>\n",
              "      <td>0.784110</td>\n",
              "      <td>0.616021</td>\n",
              "      <td>0.850462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concavity</th>\n",
              "      <td>0.829992</td>\n",
              "      <td>0.533238</td>\n",
              "      <td>0.594230</td>\n",
              "      <td>0.578346</td>\n",
              "      <td>0.535307</td>\n",
              "      <td>0.482328</td>\n",
              "      <td>0.809953</td>\n",
              "      <td>0.893141</td>\n",
              "      <td>0.782109</td>\n",
              "      <td>0.392309</td>\n",
              "      <td>0.327684</td>\n",
              "      <td>0.528337</td>\n",
              "      <td>0.132785</td>\n",
              "      <td>0.545117</td>\n",
              "      <td>0.572335</td>\n",
              "      <td>-0.006906</td>\n",
              "      <td>0.725132</td>\n",
              "      <td>0.718665</td>\n",
              "      <td>0.547825</td>\n",
              "      <td>-0.189033</td>\n",
              "      <td>0.534881</td>\n",
              "      <td>0.626929</td>\n",
              "      <td>0.635756</td>\n",
              "      <td>0.671010</td>\n",
              "      <td>0.626568</td>\n",
              "      <td>0.569676</td>\n",
              "      <td>0.904890</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.858649</td>\n",
              "      <td>0.503534</td>\n",
              "      <td>0.729406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concave_Points</th>\n",
              "      <td>0.877711</td>\n",
              "      <td>0.717917</td>\n",
              "      <td>0.506841</td>\n",
              "      <td>0.758052</td>\n",
              "      <td>0.711491</td>\n",
              "      <td>0.563082</td>\n",
              "      <td>0.833016</td>\n",
              "      <td>0.891423</td>\n",
              "      <td>0.942427</td>\n",
              "      <td>0.457268</td>\n",
              "      <td>0.184825</td>\n",
              "      <td>0.690131</td>\n",
              "      <td>0.158551</td>\n",
              "      <td>0.689611</td>\n",
              "      <td>0.756040</td>\n",
              "      <td>-0.032487</td>\n",
              "      <td>0.589257</td>\n",
              "      <td>0.570656</td>\n",
              "      <td>0.697906</td>\n",
              "      <td>-0.131871</td>\n",
              "      <td>0.425602</td>\n",
              "      <td>0.785420</td>\n",
              "      <td>0.539389</td>\n",
              "      <td>0.821865</td>\n",
              "      <td>0.773350</td>\n",
              "      <td>0.520635</td>\n",
              "      <td>0.784110</td>\n",
              "      <td>0.858649</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.484958</td>\n",
              "      <td>0.521362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Symmetry</th>\n",
              "      <td>0.494937</td>\n",
              "      <td>0.139212</td>\n",
              "      <td>0.242014</td>\n",
              "      <td>0.176434</td>\n",
              "      <td>0.135912</td>\n",
              "      <td>0.651191</td>\n",
              "      <td>0.589464</td>\n",
              "      <td>0.451734</td>\n",
              "      <td>0.445199</td>\n",
              "      <td>0.743554</td>\n",
              "      <td>0.490931</td>\n",
              "      <td>0.341622</td>\n",
              "      <td>0.001641</td>\n",
              "      <td>0.304805</td>\n",
              "      <td>0.320292</td>\n",
              "      <td>0.102525</td>\n",
              "      <td>0.397320</td>\n",
              "      <td>0.256936</td>\n",
              "      <td>0.245800</td>\n",
              "      <td>0.242389</td>\n",
              "      <td>0.470119</td>\n",
              "      <td>0.250176</td>\n",
              "      <td>0.303465</td>\n",
              "      <td>0.282905</td>\n",
              "      <td>0.242290</td>\n",
              "      <td>0.654306</td>\n",
              "      <td>0.616021</td>\n",
              "      <td>0.503534</td>\n",
              "      <td>0.484958</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.685794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Fractal_Dimension</th>\n",
              "      <td>0.491492</td>\n",
              "      <td>0.017384</td>\n",
              "      <td>0.361987</td>\n",
              "      <td>0.066931</td>\n",
              "      <td>0.022775</td>\n",
              "      <td>0.615967</td>\n",
              "      <td>0.671134</td>\n",
              "      <td>0.545869</td>\n",
              "      <td>0.390418</td>\n",
              "      <td>0.524186</td>\n",
              "      <td>0.673239</td>\n",
              "      <td>0.199023</td>\n",
              "      <td>0.073064</td>\n",
              "      <td>0.206003</td>\n",
              "      <td>0.180651</td>\n",
              "      <td>0.173192</td>\n",
              "      <td>0.649174</td>\n",
              "      <td>0.474993</td>\n",
              "      <td>0.265116</td>\n",
              "      <td>-0.094488</td>\n",
              "      <td>0.684865</td>\n",
              "      <td>0.158266</td>\n",
              "      <td>0.452119</td>\n",
              "      <td>0.207687</td>\n",
              "      <td>0.164742</td>\n",
              "      <td>0.774750</td>\n",
              "      <td>0.850462</td>\n",
              "      <td>0.729406</td>\n",
              "      <td>0.521362</td>\n",
              "      <td>0.685794</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Diagnosis  ...  Worst_Fractal_Dimension\n",
              "Diagnosis                 1.000000  ...                 0.491492\n",
              "Mean_Radius               0.718963  ...                 0.017384\n",
              "Mean_Texture              0.590164  ...                 0.361987\n",
              "Mean_Perimeter            0.748290  ...                 0.066931\n",
              "Mean_Area                 0.714016  ...                 0.022775\n",
              "Mean_Smoothness           0.450624  ...                 0.615967\n",
              "Mean_Compactness          0.691589  ...                 0.671134\n",
              "Mean_Concavity            0.806572  ...                 0.545869\n",
              "Mean_Concave_Points       0.848798  ...                 0.390418\n",
              "Mean_Symmetry             0.368120  ...                 0.524186\n",
              "Mean_Fractal_Dimension    0.061748  ...                 0.673239\n",
              "Radius_SE                 0.620258  ...                 0.199023\n",
              "Texture_SE                0.147192  ...                 0.073064\n",
              "Perimeter_SE              0.595459  ...                 0.206003\n",
              "Area_SE                   0.697258  ...                 0.180651\n",
              "Smoothness_SE            -0.023649  ...                 0.173192\n",
              "Compactness_SE            0.492419  ...                 0.649174\n",
              "Concavity_SE              0.485526  ...                 0.474993\n",
              "Concave_Points_SE         0.543528  ...                 0.265116\n",
              "Symmetry_SE              -0.083942  ...                -0.094488\n",
              "Fractal_Dimension_SE      0.337851  ...                 0.684865\n",
              "Worst_Radius              0.788719  ...                 0.158266\n",
              "Worst_Texture             0.622984  ...                 0.452119\n",
              "Worst_Perimeter           0.806620  ...                 0.207687\n",
              "Worst_Area                0.778064  ...                 0.164742\n",
              "Worst_Smoothness          0.474074  ...                 0.774750\n",
              "Worst_Compactness         0.732958  ...                 0.850462\n",
              "Worst_Concavity           0.829992  ...                 0.729406\n",
              "Worst_Concave_Points      0.877711  ...                 0.521362\n",
              "Worst_Symmetry            0.494937  ...                 0.685794\n",
              "Worst_Fractal_Dimension   0.491492  ...                 1.000000\n",
              "\n",
              "[31 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYDI3zYOZec8"
      },
      "source": [
        "### Test set correlation values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kEhxeH7seAAl",
        "outputId": "ffe47baf-a55a-415d-e228-ed0c8fef2dfa"
      },
      "source": [
        "X_test.corr(method='pearson')"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diagnosis</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>Radius_SE</th>\n",
              "      <th>Texture_SE</th>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <th>Area_SE</th>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <th>Compactness_SE</th>\n",
              "      <th>Concavity_SE</th>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <th>Fractal_Dimension_SE</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>Diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.732356</td>\n",
              "      <td>0.394542</td>\n",
              "      <td>0.742983</td>\n",
              "      <td>0.709895</td>\n",
              "      <td>0.348769</td>\n",
              "      <td>0.584313</td>\n",
              "      <td>0.684041</td>\n",
              "      <td>0.768244</td>\n",
              "      <td>0.324597</td>\n",
              "      <td>-0.023644</td>\n",
              "      <td>0.562344</td>\n",
              "      <td>-0.021672</td>\n",
              "      <td>0.551723</td>\n",
              "      <td>0.541411</td>\n",
              "      <td>-0.072968</td>\n",
              "      <td>0.272885</td>\n",
              "      <td>0.235707</td>\n",
              "      <td>0.390464</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>0.061546</td>\n",
              "      <td>0.776062</td>\n",
              "      <td>0.437429</td>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.731475</td>\n",
              "      <td>0.414404</td>\n",
              "      <td>0.573433</td>\n",
              "      <td>0.640670</td>\n",
              "      <td>0.783377</td>\n",
              "      <td>0.410486</td>\n",
              "      <td>0.304205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Radius</th>\n",
              "      <td>0.732356</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.309198</td>\n",
              "      <td>0.997927</td>\n",
              "      <td>0.987067</td>\n",
              "      <td>0.178071</td>\n",
              "      <td>0.516843</td>\n",
              "      <td>0.683061</td>\n",
              "      <td>0.828096</td>\n",
              "      <td>0.159431</td>\n",
              "      <td>-0.299837</td>\n",
              "      <td>0.683750</td>\n",
              "      <td>-0.114350</td>\n",
              "      <td>0.681281</td>\n",
              "      <td>0.736570</td>\n",
              "      <td>-0.209729</td>\n",
              "      <td>0.205723</td>\n",
              "      <td>0.190821</td>\n",
              "      <td>0.373024</td>\n",
              "      <td>-0.097291</td>\n",
              "      <td>-0.047544</td>\n",
              "      <td>0.968712</td>\n",
              "      <td>0.278169</td>\n",
              "      <td>0.964859</td>\n",
              "      <td>0.939486</td>\n",
              "      <td>0.133208</td>\n",
              "      <td>0.414634</td>\n",
              "      <td>0.526622</td>\n",
              "      <td>0.748107</td>\n",
              "      <td>0.166336</td>\n",
              "      <td>0.006463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Texture</th>\n",
              "      <td>0.394542</td>\n",
              "      <td>0.309198</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.313463</td>\n",
              "      <td>0.305815</td>\n",
              "      <td>-0.053572</td>\n",
              "      <td>0.207347</td>\n",
              "      <td>0.276192</td>\n",
              "      <td>0.269925</td>\n",
              "      <td>0.054891</td>\n",
              "      <td>-0.092349</td>\n",
              "      <td>0.262373</td>\n",
              "      <td>0.386603</td>\n",
              "      <td>0.266864</td>\n",
              "      <td>0.247254</td>\n",
              "      <td>-0.006484</td>\n",
              "      <td>0.163232</td>\n",
              "      <td>0.116490</td>\n",
              "      <td>0.136297</td>\n",
              "      <td>0.010619</td>\n",
              "      <td>0.037794</td>\n",
              "      <td>0.336573</td>\n",
              "      <td>0.910412</td>\n",
              "      <td>0.340065</td>\n",
              "      <td>0.327659</td>\n",
              "      <td>0.046780</td>\n",
              "      <td>0.248266</td>\n",
              "      <td>0.268615</td>\n",
              "      <td>0.269750</td>\n",
              "      <td>0.092974</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Perimeter</th>\n",
              "      <td>0.742983</td>\n",
              "      <td>0.997927</td>\n",
              "      <td>0.313463</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986371</td>\n",
              "      <td>0.213686</td>\n",
              "      <td>0.566204</td>\n",
              "      <td>0.721102</td>\n",
              "      <td>0.855356</td>\n",
              "      <td>0.192677</td>\n",
              "      <td>-0.250755</td>\n",
              "      <td>0.693927</td>\n",
              "      <td>-0.104625</td>\n",
              "      <td>0.697611</td>\n",
              "      <td>0.744135</td>\n",
              "      <td>-0.191024</td>\n",
              "      <td>0.249648</td>\n",
              "      <td>0.223668</td>\n",
              "      <td>0.402142</td>\n",
              "      <td>-0.073806</td>\n",
              "      <td>-0.011012</td>\n",
              "      <td>0.968077</td>\n",
              "      <td>0.282986</td>\n",
              "      <td>0.969352</td>\n",
              "      <td>0.939479</td>\n",
              "      <td>0.162731</td>\n",
              "      <td>0.456249</td>\n",
              "      <td>0.562656</td>\n",
              "      <td>0.773543</td>\n",
              "      <td>0.190466</td>\n",
              "      <td>0.049737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Area</th>\n",
              "      <td>0.709895</td>\n",
              "      <td>0.987067</td>\n",
              "      <td>0.305815</td>\n",
              "      <td>0.986371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.184854</td>\n",
              "      <td>0.508014</td>\n",
              "      <td>0.691439</td>\n",
              "      <td>0.828194</td>\n",
              "      <td>0.162558</td>\n",
              "      <td>-0.272146</td>\n",
              "      <td>0.739731</td>\n",
              "      <td>-0.081250</td>\n",
              "      <td>0.736365</td>\n",
              "      <td>0.802887</td>\n",
              "      <td>-0.154053</td>\n",
              "      <td>0.210969</td>\n",
              "      <td>0.202597</td>\n",
              "      <td>0.367392</td>\n",
              "      <td>-0.065685</td>\n",
              "      <td>-0.024577</td>\n",
              "      <td>0.961810</td>\n",
              "      <td>0.268279</td>\n",
              "      <td>0.958844</td>\n",
              "      <td>0.957940</td>\n",
              "      <td>0.135704</td>\n",
              "      <td>0.390384</td>\n",
              "      <td>0.510847</td>\n",
              "      <td>0.724547</td>\n",
              "      <td>0.144212</td>\n",
              "      <td>0.002342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Smoothness</th>\n",
              "      <td>0.348769</td>\n",
              "      <td>0.178071</td>\n",
              "      <td>-0.053572</td>\n",
              "      <td>0.213686</td>\n",
              "      <td>0.184854</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.656942</td>\n",
              "      <td>0.524147</td>\n",
              "      <td>0.556546</td>\n",
              "      <td>0.543816</td>\n",
              "      <td>0.582842</td>\n",
              "      <td>0.296862</td>\n",
              "      <td>0.057337</td>\n",
              "      <td>0.290060</td>\n",
              "      <td>0.242471</td>\n",
              "      <td>0.335643</td>\n",
              "      <td>0.309318</td>\n",
              "      <td>0.244146</td>\n",
              "      <td>0.369149</td>\n",
              "      <td>0.200667</td>\n",
              "      <td>0.273901</td>\n",
              "      <td>0.215811</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>0.239724</td>\n",
              "      <td>0.208972</td>\n",
              "      <td>0.806685</td>\n",
              "      <td>0.463619</td>\n",
              "      <td>0.430050</td>\n",
              "      <td>0.496579</td>\n",
              "      <td>0.371951</td>\n",
              "      <td>0.487634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Compactness</th>\n",
              "      <td>0.584313</td>\n",
              "      <td>0.516843</td>\n",
              "      <td>0.207347</td>\n",
              "      <td>0.566204</td>\n",
              "      <td>0.508014</td>\n",
              "      <td>0.656942</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.881458</td>\n",
              "      <td>0.831441</td>\n",
              "      <td>0.585863</td>\n",
              "      <td>0.562328</td>\n",
              "      <td>0.472103</td>\n",
              "      <td>0.017192</td>\n",
              "      <td>0.523927</td>\n",
              "      <td>0.439633</td>\n",
              "      <td>0.121548</td>\n",
              "      <td>0.731025</td>\n",
              "      <td>0.559763</td>\n",
              "      <td>0.624128</td>\n",
              "      <td>0.240811</td>\n",
              "      <td>0.499654</td>\n",
              "      <td>0.537986</td>\n",
              "      <td>0.220847</td>\n",
              "      <td>0.591737</td>\n",
              "      <td>0.510953</td>\n",
              "      <td>0.563269</td>\n",
              "      <td>0.870988</td>\n",
              "      <td>0.817473</td>\n",
              "      <td>0.813259</td>\n",
              "      <td>0.505076</td>\n",
              "      <td>0.688946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concavity</th>\n",
              "      <td>0.684041</td>\n",
              "      <td>0.683061</td>\n",
              "      <td>0.276192</td>\n",
              "      <td>0.721102</td>\n",
              "      <td>0.691439</td>\n",
              "      <td>0.524147</td>\n",
              "      <td>0.881458</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.920623</td>\n",
              "      <td>0.496806</td>\n",
              "      <td>0.333480</td>\n",
              "      <td>0.617278</td>\n",
              "      <td>0.057446</td>\n",
              "      <td>0.644674</td>\n",
              "      <td>0.607270</td>\n",
              "      <td>0.093549</td>\n",
              "      <td>0.663497</td>\n",
              "      <td>0.685358</td>\n",
              "      <td>0.672002</td>\n",
              "      <td>0.190646</td>\n",
              "      <td>0.442651</td>\n",
              "      <td>0.688221</td>\n",
              "      <td>0.275716</td>\n",
              "      <td>0.728383</td>\n",
              "      <td>0.674761</td>\n",
              "      <td>0.449117</td>\n",
              "      <td>0.755013</td>\n",
              "      <td>0.883090</td>\n",
              "      <td>0.858034</td>\n",
              "      <td>0.406493</td>\n",
              "      <td>0.511359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concave_Points</th>\n",
              "      <td>0.768244</td>\n",
              "      <td>0.828096</td>\n",
              "      <td>0.269925</td>\n",
              "      <td>0.855356</td>\n",
              "      <td>0.828194</td>\n",
              "      <td>0.556546</td>\n",
              "      <td>0.831441</td>\n",
              "      <td>0.920623</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.457422</td>\n",
              "      <td>0.166575</td>\n",
              "      <td>0.685003</td>\n",
              "      <td>0.000983</td>\n",
              "      <td>0.697917</td>\n",
              "      <td>0.680514</td>\n",
              "      <td>0.023643</td>\n",
              "      <td>0.481100</td>\n",
              "      <td>0.427087</td>\n",
              "      <td>0.596685</td>\n",
              "      <td>0.100514</td>\n",
              "      <td>0.246793</td>\n",
              "      <td>0.831240</td>\n",
              "      <td>0.270695</td>\n",
              "      <td>0.856072</td>\n",
              "      <td>0.809646</td>\n",
              "      <td>0.458687</td>\n",
              "      <td>0.667358</td>\n",
              "      <td>0.749064</td>\n",
              "      <td>0.906347</td>\n",
              "      <td>0.370343</td>\n",
              "      <td>0.365750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Symmetry</th>\n",
              "      <td>0.324597</td>\n",
              "      <td>0.159431</td>\n",
              "      <td>0.054891</td>\n",
              "      <td>0.192677</td>\n",
              "      <td>0.162558</td>\n",
              "      <td>0.543816</td>\n",
              "      <td>0.585863</td>\n",
              "      <td>0.496806</td>\n",
              "      <td>0.457422</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.458964</td>\n",
              "      <td>0.277106</td>\n",
              "      <td>0.114372</td>\n",
              "      <td>0.286699</td>\n",
              "      <td>0.207923</td>\n",
              "      <td>0.164448</td>\n",
              "      <td>0.407379</td>\n",
              "      <td>0.336963</td>\n",
              "      <td>0.366068</td>\n",
              "      <td>0.458266</td>\n",
              "      <td>0.310421</td>\n",
              "      <td>0.190892</td>\n",
              "      <td>0.081532</td>\n",
              "      <td>0.222095</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>0.415406</td>\n",
              "      <td>0.472140</td>\n",
              "      <td>0.439229</td>\n",
              "      <td>0.426124</td>\n",
              "      <td>0.700508</td>\n",
              "      <td>0.427166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Fractal_Dimension</th>\n",
              "      <td>-0.023644</td>\n",
              "      <td>-0.299837</td>\n",
              "      <td>-0.092349</td>\n",
              "      <td>-0.250755</td>\n",
              "      <td>-0.272146</td>\n",
              "      <td>0.582842</td>\n",
              "      <td>0.562328</td>\n",
              "      <td>0.333480</td>\n",
              "      <td>0.166575</td>\n",
              "      <td>0.458964</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017073</td>\n",
              "      <td>0.157139</td>\n",
              "      <td>0.020418</td>\n",
              "      <td>-0.099559</td>\n",
              "      <td>0.383091</td>\n",
              "      <td>0.558162</td>\n",
              "      <td>0.442705</td>\n",
              "      <td>0.332145</td>\n",
              "      <td>0.348023</td>\n",
              "      <td>0.695081</td>\n",
              "      <td>-0.247106</td>\n",
              "      <td>-0.062924</td>\n",
              "      <td>-0.200464</td>\n",
              "      <td>-0.226155</td>\n",
              "      <td>0.493655</td>\n",
              "      <td>0.463808</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.172914</td>\n",
              "      <td>0.321228</td>\n",
              "      <td>0.776946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radius_SE</th>\n",
              "      <td>0.562344</td>\n",
              "      <td>0.683750</td>\n",
              "      <td>0.262373</td>\n",
              "      <td>0.693927</td>\n",
              "      <td>0.739731</td>\n",
              "      <td>0.296862</td>\n",
              "      <td>0.472103</td>\n",
              "      <td>0.617278</td>\n",
              "      <td>0.685003</td>\n",
              "      <td>0.277106</td>\n",
              "      <td>-0.017073</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.198579</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.953203</td>\n",
              "      <td>0.161605</td>\n",
              "      <td>0.339197</td>\n",
              "      <td>0.318366</td>\n",
              "      <td>0.492332</td>\n",
              "      <td>0.238315</td>\n",
              "      <td>0.214353</td>\n",
              "      <td>0.717105</td>\n",
              "      <td>0.179111</td>\n",
              "      <td>0.719858</td>\n",
              "      <td>0.755857</td>\n",
              "      <td>0.138863</td>\n",
              "      <td>0.270523</td>\n",
              "      <td>0.366695</td>\n",
              "      <td>0.515648</td>\n",
              "      <td>0.075343</td>\n",
              "      <td>0.034701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Texture_SE</th>\n",
              "      <td>-0.021672</td>\n",
              "      <td>-0.114350</td>\n",
              "      <td>0.386603</td>\n",
              "      <td>-0.104625</td>\n",
              "      <td>-0.081250</td>\n",
              "      <td>0.057337</td>\n",
              "      <td>0.017192</td>\n",
              "      <td>0.057446</td>\n",
              "      <td>0.000983</td>\n",
              "      <td>0.114372</td>\n",
              "      <td>0.157139</td>\n",
              "      <td>0.198579</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.205918</td>\n",
              "      <td>0.098625</td>\n",
              "      <td>0.386096</td>\n",
              "      <td>0.216309</td>\n",
              "      <td>0.177332</td>\n",
              "      <td>0.199304</td>\n",
              "      <td>0.411635</td>\n",
              "      <td>0.268378</td>\n",
              "      <td>-0.128365</td>\n",
              "      <td>0.408348</td>\n",
              "      <td>-0.119920</td>\n",
              "      <td>-0.096448</td>\n",
              "      <td>-0.088646</td>\n",
              "      <td>-0.110653</td>\n",
              "      <td>-0.086205</td>\n",
              "      <td>-0.145696</td>\n",
              "      <td>-0.137802</td>\n",
              "      <td>-0.055185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <td>0.551723</td>\n",
              "      <td>0.681281</td>\n",
              "      <td>0.266864</td>\n",
              "      <td>0.697611</td>\n",
              "      <td>0.736365</td>\n",
              "      <td>0.290060</td>\n",
              "      <td>0.523927</td>\n",
              "      <td>0.644674</td>\n",
              "      <td>0.697917</td>\n",
              "      <td>0.286699</td>\n",
              "      <td>0.020418</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.205918</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.941315</td>\n",
              "      <td>0.144100</td>\n",
              "      <td>0.401096</td>\n",
              "      <td>0.345571</td>\n",
              "      <td>0.531447</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>0.229798</td>\n",
              "      <td>0.701595</td>\n",
              "      <td>0.184740</td>\n",
              "      <td>0.723266</td>\n",
              "      <td>0.737454</td>\n",
              "      <td>0.125946</td>\n",
              "      <td>0.329070</td>\n",
              "      <td>0.405875</td>\n",
              "      <td>0.540290</td>\n",
              "      <td>0.093832</td>\n",
              "      <td>0.072072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area_SE</th>\n",
              "      <td>0.541411</td>\n",
              "      <td>0.736570</td>\n",
              "      <td>0.247254</td>\n",
              "      <td>0.744135</td>\n",
              "      <td>0.802887</td>\n",
              "      <td>0.242471</td>\n",
              "      <td>0.439633</td>\n",
              "      <td>0.607270</td>\n",
              "      <td>0.680514</td>\n",
              "      <td>0.207923</td>\n",
              "      <td>-0.099559</td>\n",
              "      <td>0.953203</td>\n",
              "      <td>0.098625</td>\n",
              "      <td>0.941315</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.076448</td>\n",
              "      <td>0.272369</td>\n",
              "      <td>0.258992</td>\n",
              "      <td>0.398951</td>\n",
              "      <td>0.133497</td>\n",
              "      <td>0.115554</td>\n",
              "      <td>0.755887</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>0.759038</td>\n",
              "      <td>0.811598</td>\n",
              "      <td>0.123809</td>\n",
              "      <td>0.271281</td>\n",
              "      <td>0.373973</td>\n",
              "      <td>0.526130</td>\n",
              "      <td>0.059577</td>\n",
              "      <td>0.006182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <td>-0.072968</td>\n",
              "      <td>-0.209729</td>\n",
              "      <td>-0.006484</td>\n",
              "      <td>-0.191024</td>\n",
              "      <td>-0.154053</td>\n",
              "      <td>0.335643</td>\n",
              "      <td>0.121548</td>\n",
              "      <td>0.093549</td>\n",
              "      <td>0.023643</td>\n",
              "      <td>0.164448</td>\n",
              "      <td>0.383091</td>\n",
              "      <td>0.161605</td>\n",
              "      <td>0.386096</td>\n",
              "      <td>0.144100</td>\n",
              "      <td>0.076448</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.331255</td>\n",
              "      <td>0.258884</td>\n",
              "      <td>0.316369</td>\n",
              "      <td>0.405105</td>\n",
              "      <td>0.422608</td>\n",
              "      <td>-0.222252</td>\n",
              "      <td>-0.086065</td>\n",
              "      <td>-0.210476</td>\n",
              "      <td>-0.173489</td>\n",
              "      <td>0.311070</td>\n",
              "      <td>-0.063834</td>\n",
              "      <td>-0.063996</td>\n",
              "      <td>-0.110423</td>\n",
              "      <td>-0.123336</td>\n",
              "      <td>0.093343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Compactness_SE</th>\n",
              "      <td>0.272885</td>\n",
              "      <td>0.205723</td>\n",
              "      <td>0.163232</td>\n",
              "      <td>0.249648</td>\n",
              "      <td>0.210969</td>\n",
              "      <td>0.309318</td>\n",
              "      <td>0.731025</td>\n",
              "      <td>0.663497</td>\n",
              "      <td>0.481100</td>\n",
              "      <td>0.407379</td>\n",
              "      <td>0.558162</td>\n",
              "      <td>0.339197</td>\n",
              "      <td>0.216309</td>\n",
              "      <td>0.401096</td>\n",
              "      <td>0.272369</td>\n",
              "      <td>0.331255</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.796424</td>\n",
              "      <td>0.747876</td>\n",
              "      <td>0.410036</td>\n",
              "      <td>0.805266</td>\n",
              "      <td>0.196434</td>\n",
              "      <td>0.114272</td>\n",
              "      <td>0.252884</td>\n",
              "      <td>0.190706</td>\n",
              "      <td>0.206712</td>\n",
              "      <td>0.670046</td>\n",
              "      <td>0.631038</td>\n",
              "      <td>0.472684</td>\n",
              "      <td>0.268518</td>\n",
              "      <td>0.585553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concavity_SE</th>\n",
              "      <td>0.235707</td>\n",
              "      <td>0.190821</td>\n",
              "      <td>0.116490</td>\n",
              "      <td>0.223668</td>\n",
              "      <td>0.202597</td>\n",
              "      <td>0.244146</td>\n",
              "      <td>0.559763</td>\n",
              "      <td>0.685358</td>\n",
              "      <td>0.427087</td>\n",
              "      <td>0.336963</td>\n",
              "      <td>0.442705</td>\n",
              "      <td>0.318366</td>\n",
              "      <td>0.177332</td>\n",
              "      <td>0.345571</td>\n",
              "      <td>0.258992</td>\n",
              "      <td>0.258884</td>\n",
              "      <td>0.796424</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.776207</td>\n",
              "      <td>0.313454</td>\n",
              "      <td>0.727315</td>\n",
              "      <td>0.179927</td>\n",
              "      <td>0.076003</td>\n",
              "      <td>0.218579</td>\n",
              "      <td>0.180329</td>\n",
              "      <td>0.157563</td>\n",
              "      <td>0.479425</td>\n",
              "      <td>0.660230</td>\n",
              "      <td>0.431751</td>\n",
              "      <td>0.193919</td>\n",
              "      <td>0.438233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <td>0.390464</td>\n",
              "      <td>0.373024</td>\n",
              "      <td>0.136297</td>\n",
              "      <td>0.402142</td>\n",
              "      <td>0.367392</td>\n",
              "      <td>0.369149</td>\n",
              "      <td>0.624128</td>\n",
              "      <td>0.672002</td>\n",
              "      <td>0.596685</td>\n",
              "      <td>0.366068</td>\n",
              "      <td>0.332145</td>\n",
              "      <td>0.492332</td>\n",
              "      <td>0.199304</td>\n",
              "      <td>0.531447</td>\n",
              "      <td>0.398951</td>\n",
              "      <td>0.316369</td>\n",
              "      <td>0.747876</td>\n",
              "      <td>0.776207</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.310494</td>\n",
              "      <td>0.616870</td>\n",
              "      <td>0.352040</td>\n",
              "      <td>0.061980</td>\n",
              "      <td>0.386775</td>\n",
              "      <td>0.335802</td>\n",
              "      <td>0.208040</td>\n",
              "      <td>0.451124</td>\n",
              "      <td>0.549797</td>\n",
              "      <td>0.589912</td>\n",
              "      <td>0.133997</td>\n",
              "      <td>0.314660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <td>0.000782</td>\n",
              "      <td>-0.097291</td>\n",
              "      <td>0.010619</td>\n",
              "      <td>-0.073806</td>\n",
              "      <td>-0.065685</td>\n",
              "      <td>0.200667</td>\n",
              "      <td>0.240811</td>\n",
              "      <td>0.190646</td>\n",
              "      <td>0.100514</td>\n",
              "      <td>0.458266</td>\n",
              "      <td>0.348023</td>\n",
              "      <td>0.238315</td>\n",
              "      <td>0.411635</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>0.133497</td>\n",
              "      <td>0.405105</td>\n",
              "      <td>0.410036</td>\n",
              "      <td>0.313454</td>\n",
              "      <td>0.310494</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.370941</td>\n",
              "      <td>-0.121271</td>\n",
              "      <td>-0.068224</td>\n",
              "      <td>-0.095874</td>\n",
              "      <td>-0.104143</td>\n",
              "      <td>-0.005014</td>\n",
              "      <td>0.082315</td>\n",
              "      <td>0.056315</td>\n",
              "      <td>-0.020998</td>\n",
              "      <td>0.399618</td>\n",
              "      <td>0.093332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fractal_Dimension_SE</th>\n",
              "      <td>0.061546</td>\n",
              "      <td>-0.047544</td>\n",
              "      <td>0.037794</td>\n",
              "      <td>-0.011012</td>\n",
              "      <td>-0.024577</td>\n",
              "      <td>0.273901</td>\n",
              "      <td>0.499654</td>\n",
              "      <td>0.442651</td>\n",
              "      <td>0.246793</td>\n",
              "      <td>0.310421</td>\n",
              "      <td>0.695081</td>\n",
              "      <td>0.214353</td>\n",
              "      <td>0.268378</td>\n",
              "      <td>0.229798</td>\n",
              "      <td>0.115554</td>\n",
              "      <td>0.422608</td>\n",
              "      <td>0.805266</td>\n",
              "      <td>0.727315</td>\n",
              "      <td>0.616870</td>\n",
              "      <td>0.370941</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.046821</td>\n",
              "      <td>-0.021988</td>\n",
              "      <td>-0.011145</td>\n",
              "      <td>-0.031178</td>\n",
              "      <td>0.153533</td>\n",
              "      <td>0.381267</td>\n",
              "      <td>0.373183</td>\n",
              "      <td>0.203247</td>\n",
              "      <td>0.092214</td>\n",
              "      <td>0.590613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Radius</th>\n",
              "      <td>0.776062</td>\n",
              "      <td>0.968712</td>\n",
              "      <td>0.336573</td>\n",
              "      <td>0.968077</td>\n",
              "      <td>0.961810</td>\n",
              "      <td>0.215811</td>\n",
              "      <td>0.537986</td>\n",
              "      <td>0.688221</td>\n",
              "      <td>0.831240</td>\n",
              "      <td>0.190892</td>\n",
              "      <td>-0.247106</td>\n",
              "      <td>0.717105</td>\n",
              "      <td>-0.128365</td>\n",
              "      <td>0.701595</td>\n",
              "      <td>0.755887</td>\n",
              "      <td>-0.222252</td>\n",
              "      <td>0.196434</td>\n",
              "      <td>0.179927</td>\n",
              "      <td>0.352040</td>\n",
              "      <td>-0.121271</td>\n",
              "      <td>-0.046821</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340978</td>\n",
              "      <td>0.993863</td>\n",
              "      <td>0.983745</td>\n",
              "      <td>0.226289</td>\n",
              "      <td>0.470428</td>\n",
              "      <td>0.568768</td>\n",
              "      <td>0.788533</td>\n",
              "      <td>0.243152</td>\n",
              "      <td>0.087078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Texture</th>\n",
              "      <td>0.437429</td>\n",
              "      <td>0.278169</td>\n",
              "      <td>0.910412</td>\n",
              "      <td>0.282986</td>\n",
              "      <td>0.268279</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>0.220847</td>\n",
              "      <td>0.275716</td>\n",
              "      <td>0.270695</td>\n",
              "      <td>0.081532</td>\n",
              "      <td>-0.062924</td>\n",
              "      <td>0.179111</td>\n",
              "      <td>0.408348</td>\n",
              "      <td>0.184740</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>-0.086065</td>\n",
              "      <td>0.114272</td>\n",
              "      <td>0.076003</td>\n",
              "      <td>0.061980</td>\n",
              "      <td>-0.068224</td>\n",
              "      <td>-0.021988</td>\n",
              "      <td>0.340978</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344989</td>\n",
              "      <td>0.327447</td>\n",
              "      <td>0.200238</td>\n",
              "      <td>0.333210</td>\n",
              "      <td>0.338788</td>\n",
              "      <td>0.338052</td>\n",
              "      <td>0.227270</td>\n",
              "      <td>0.192847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Perimeter</th>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.964859</td>\n",
              "      <td>0.340065</td>\n",
              "      <td>0.969352</td>\n",
              "      <td>0.958844</td>\n",
              "      <td>0.239724</td>\n",
              "      <td>0.591737</td>\n",
              "      <td>0.728383</td>\n",
              "      <td>0.856072</td>\n",
              "      <td>0.222095</td>\n",
              "      <td>-0.200464</td>\n",
              "      <td>0.719858</td>\n",
              "      <td>-0.119920</td>\n",
              "      <td>0.723266</td>\n",
              "      <td>0.759038</td>\n",
              "      <td>-0.210476</td>\n",
              "      <td>0.252884</td>\n",
              "      <td>0.218579</td>\n",
              "      <td>0.386775</td>\n",
              "      <td>-0.095874</td>\n",
              "      <td>-0.011145</td>\n",
              "      <td>0.993863</td>\n",
              "      <td>0.344989</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977577</td>\n",
              "      <td>0.244560</td>\n",
              "      <td>0.524669</td>\n",
              "      <td>0.612958</td>\n",
              "      <td>0.816340</td>\n",
              "      <td>0.268631</td>\n",
              "      <td>0.131858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Area</th>\n",
              "      <td>0.731475</td>\n",
              "      <td>0.939486</td>\n",
              "      <td>0.327659</td>\n",
              "      <td>0.939479</td>\n",
              "      <td>0.957940</td>\n",
              "      <td>0.208972</td>\n",
              "      <td>0.510953</td>\n",
              "      <td>0.674761</td>\n",
              "      <td>0.809646</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>-0.226155</td>\n",
              "      <td>0.755857</td>\n",
              "      <td>-0.096448</td>\n",
              "      <td>0.737454</td>\n",
              "      <td>0.811598</td>\n",
              "      <td>-0.173489</td>\n",
              "      <td>0.190706</td>\n",
              "      <td>0.180329</td>\n",
              "      <td>0.335802</td>\n",
              "      <td>-0.104143</td>\n",
              "      <td>-0.031178</td>\n",
              "      <td>0.983745</td>\n",
              "      <td>0.327447</td>\n",
              "      <td>0.977577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216841</td>\n",
              "      <td>0.431484</td>\n",
              "      <td>0.536393</td>\n",
              "      <td>0.746961</td>\n",
              "      <td>0.206582</td>\n",
              "      <td>0.072245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Smoothness</th>\n",
              "      <td>0.414404</td>\n",
              "      <td>0.133208</td>\n",
              "      <td>0.046780</td>\n",
              "      <td>0.162731</td>\n",
              "      <td>0.135704</td>\n",
              "      <td>0.806685</td>\n",
              "      <td>0.563269</td>\n",
              "      <td>0.449117</td>\n",
              "      <td>0.458687</td>\n",
              "      <td>0.415406</td>\n",
              "      <td>0.493655</td>\n",
              "      <td>0.138863</td>\n",
              "      <td>-0.088646</td>\n",
              "      <td>0.125946</td>\n",
              "      <td>0.123809</td>\n",
              "      <td>0.311070</td>\n",
              "      <td>0.206712</td>\n",
              "      <td>0.157563</td>\n",
              "      <td>0.208040</td>\n",
              "      <td>-0.005014</td>\n",
              "      <td>0.153533</td>\n",
              "      <td>0.226289</td>\n",
              "      <td>0.200238</td>\n",
              "      <td>0.244560</td>\n",
              "      <td>0.216841</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.557487</td>\n",
              "      <td>0.512539</td>\n",
              "      <td>0.550918</td>\n",
              "      <td>0.480191</td>\n",
              "      <td>0.598972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Compactness</th>\n",
              "      <td>0.573433</td>\n",
              "      <td>0.414634</td>\n",
              "      <td>0.248266</td>\n",
              "      <td>0.456249</td>\n",
              "      <td>0.390384</td>\n",
              "      <td>0.463619</td>\n",
              "      <td>0.870988</td>\n",
              "      <td>0.755013</td>\n",
              "      <td>0.667358</td>\n",
              "      <td>0.472140</td>\n",
              "      <td>0.463808</td>\n",
              "      <td>0.270523</td>\n",
              "      <td>-0.110653</td>\n",
              "      <td>0.329070</td>\n",
              "      <td>0.271281</td>\n",
              "      <td>-0.063834</td>\n",
              "      <td>0.670046</td>\n",
              "      <td>0.479425</td>\n",
              "      <td>0.451124</td>\n",
              "      <td>0.082315</td>\n",
              "      <td>0.381267</td>\n",
              "      <td>0.470428</td>\n",
              "      <td>0.333210</td>\n",
              "      <td>0.524669</td>\n",
              "      <td>0.431484</td>\n",
              "      <td>0.557487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.891236</td>\n",
              "      <td>0.803026</td>\n",
              "      <td>0.616957</td>\n",
              "      <td>0.805732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concavity</th>\n",
              "      <td>0.640670</td>\n",
              "      <td>0.526622</td>\n",
              "      <td>0.268615</td>\n",
              "      <td>0.562656</td>\n",
              "      <td>0.510847</td>\n",
              "      <td>0.430050</td>\n",
              "      <td>0.817473</td>\n",
              "      <td>0.883090</td>\n",
              "      <td>0.749064</td>\n",
              "      <td>0.439229</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.366695</td>\n",
              "      <td>-0.086205</td>\n",
              "      <td>0.405875</td>\n",
              "      <td>0.373973</td>\n",
              "      <td>-0.063996</td>\n",
              "      <td>0.631038</td>\n",
              "      <td>0.660230</td>\n",
              "      <td>0.549797</td>\n",
              "      <td>0.056315</td>\n",
              "      <td>0.373183</td>\n",
              "      <td>0.568768</td>\n",
              "      <td>0.338788</td>\n",
              "      <td>0.612958</td>\n",
              "      <td>0.536393</td>\n",
              "      <td>0.512539</td>\n",
              "      <td>0.891236</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855267</td>\n",
              "      <td>0.536054</td>\n",
              "      <td>0.681879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concave_Points</th>\n",
              "      <td>0.783377</td>\n",
              "      <td>0.748107</td>\n",
              "      <td>0.269750</td>\n",
              "      <td>0.773543</td>\n",
              "      <td>0.724547</td>\n",
              "      <td>0.496579</td>\n",
              "      <td>0.813259</td>\n",
              "      <td>0.858034</td>\n",
              "      <td>0.906347</td>\n",
              "      <td>0.426124</td>\n",
              "      <td>0.172914</td>\n",
              "      <td>0.515648</td>\n",
              "      <td>-0.145696</td>\n",
              "      <td>0.540290</td>\n",
              "      <td>0.526130</td>\n",
              "      <td>-0.110423</td>\n",
              "      <td>0.472684</td>\n",
              "      <td>0.431751</td>\n",
              "      <td>0.589912</td>\n",
              "      <td>-0.020998</td>\n",
              "      <td>0.203247</td>\n",
              "      <td>0.788533</td>\n",
              "      <td>0.338052</td>\n",
              "      <td>0.816340</td>\n",
              "      <td>0.746961</td>\n",
              "      <td>0.550918</td>\n",
              "      <td>0.803026</td>\n",
              "      <td>0.855267</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.506077</td>\n",
              "      <td>0.509532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Symmetry</th>\n",
              "      <td>0.410486</td>\n",
              "      <td>0.166336</td>\n",
              "      <td>0.092974</td>\n",
              "      <td>0.190466</td>\n",
              "      <td>0.144212</td>\n",
              "      <td>0.371951</td>\n",
              "      <td>0.505076</td>\n",
              "      <td>0.406493</td>\n",
              "      <td>0.370343</td>\n",
              "      <td>0.700508</td>\n",
              "      <td>0.321228</td>\n",
              "      <td>0.075343</td>\n",
              "      <td>-0.137802</td>\n",
              "      <td>0.093832</td>\n",
              "      <td>0.059577</td>\n",
              "      <td>-0.123336</td>\n",
              "      <td>0.268518</td>\n",
              "      <td>0.193919</td>\n",
              "      <td>0.133997</td>\n",
              "      <td>0.399618</td>\n",
              "      <td>0.092214</td>\n",
              "      <td>0.243152</td>\n",
              "      <td>0.227270</td>\n",
              "      <td>0.268631</td>\n",
              "      <td>0.206582</td>\n",
              "      <td>0.480191</td>\n",
              "      <td>0.616957</td>\n",
              "      <td>0.536054</td>\n",
              "      <td>0.506077</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.526349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Fractal_Dimension</th>\n",
              "      <td>0.304205</td>\n",
              "      <td>0.006463</td>\n",
              "      <td>0.091530</td>\n",
              "      <td>0.049737</td>\n",
              "      <td>0.002342</td>\n",
              "      <td>0.487634</td>\n",
              "      <td>0.688946</td>\n",
              "      <td>0.511359</td>\n",
              "      <td>0.365750</td>\n",
              "      <td>0.427166</td>\n",
              "      <td>0.776946</td>\n",
              "      <td>0.034701</td>\n",
              "      <td>-0.055185</td>\n",
              "      <td>0.072072</td>\n",
              "      <td>0.006182</td>\n",
              "      <td>0.093343</td>\n",
              "      <td>0.585553</td>\n",
              "      <td>0.438233</td>\n",
              "      <td>0.314660</td>\n",
              "      <td>0.093332</td>\n",
              "      <td>0.590613</td>\n",
              "      <td>0.087078</td>\n",
              "      <td>0.192847</td>\n",
              "      <td>0.131858</td>\n",
              "      <td>0.072245</td>\n",
              "      <td>0.598972</td>\n",
              "      <td>0.805732</td>\n",
              "      <td>0.681879</td>\n",
              "      <td>0.509532</td>\n",
              "      <td>0.526349</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Diagnosis  ...  Worst_Fractal_Dimension\n",
              "Diagnosis                 1.000000  ...                 0.304205\n",
              "Mean_Radius               0.732356  ...                 0.006463\n",
              "Mean_Texture              0.394542  ...                 0.091530\n",
              "Mean_Perimeter            0.742983  ...                 0.049737\n",
              "Mean_Area                 0.709895  ...                 0.002342\n",
              "Mean_Smoothness           0.348769  ...                 0.487634\n",
              "Mean_Compactness          0.584313  ...                 0.688946\n",
              "Mean_Concavity            0.684041  ...                 0.511359\n",
              "Mean_Concave_Points       0.768244  ...                 0.365750\n",
              "Mean_Symmetry             0.324597  ...                 0.427166\n",
              "Mean_Fractal_Dimension   -0.023644  ...                 0.776946\n",
              "Radius_SE                 0.562344  ...                 0.034701\n",
              "Texture_SE               -0.021672  ...                -0.055185\n",
              "Perimeter_SE              0.551723  ...                 0.072072\n",
              "Area_SE                   0.541411  ...                 0.006182\n",
              "Smoothness_SE            -0.072968  ...                 0.093343\n",
              "Compactness_SE            0.272885  ...                 0.585553\n",
              "Concavity_SE              0.235707  ...                 0.438233\n",
              "Concave_Points_SE         0.390464  ...                 0.314660\n",
              "Symmetry_SE               0.000782  ...                 0.093332\n",
              "Fractal_Dimension_SE      0.061546  ...                 0.590613\n",
              "Worst_Radius              0.776062  ...                 0.087078\n",
              "Worst_Texture             0.437429  ...                 0.192847\n",
              "Worst_Perimeter           0.781000  ...                 0.131858\n",
              "Worst_Area                0.731475  ...                 0.072245\n",
              "Worst_Smoothness          0.414404  ...                 0.598972\n",
              "Worst_Compactness         0.573433  ...                 0.805732\n",
              "Worst_Concavity           0.640670  ...                 0.681879\n",
              "Worst_Concave_Points      0.783377  ...                 0.509532\n",
              "Worst_Symmetry            0.410486  ...                 0.526349\n",
              "Worst_Fractal_Dimension   0.304205  ...                 1.000000\n",
              "\n",
              "[31 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sb6uKc0yeAPm",
        "outputId": "67104022-161a-4634-d0d2-abb59ad49069"
      },
      "source": [
        "y_test.corr(method='pearson')"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diagnosis</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>Radius_SE</th>\n",
              "      <th>Texture_SE</th>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <th>Area_SE</th>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <th>Compactness_SE</th>\n",
              "      <th>Concavity_SE</th>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <th>Fractal_Dimension_SE</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>Diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.732356</td>\n",
              "      <td>0.394542</td>\n",
              "      <td>0.742983</td>\n",
              "      <td>0.709895</td>\n",
              "      <td>0.348769</td>\n",
              "      <td>0.584313</td>\n",
              "      <td>0.684041</td>\n",
              "      <td>0.768244</td>\n",
              "      <td>0.324597</td>\n",
              "      <td>-0.023644</td>\n",
              "      <td>0.562344</td>\n",
              "      <td>-0.021672</td>\n",
              "      <td>0.551723</td>\n",
              "      <td>0.541411</td>\n",
              "      <td>-0.072968</td>\n",
              "      <td>0.272885</td>\n",
              "      <td>0.235707</td>\n",
              "      <td>0.390464</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>0.061546</td>\n",
              "      <td>0.776062</td>\n",
              "      <td>0.437429</td>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.731475</td>\n",
              "      <td>0.414404</td>\n",
              "      <td>0.573433</td>\n",
              "      <td>0.640670</td>\n",
              "      <td>0.783377</td>\n",
              "      <td>0.410486</td>\n",
              "      <td>0.304205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Radius</th>\n",
              "      <td>0.732356</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.309198</td>\n",
              "      <td>0.997927</td>\n",
              "      <td>0.987067</td>\n",
              "      <td>0.178071</td>\n",
              "      <td>0.516843</td>\n",
              "      <td>0.683061</td>\n",
              "      <td>0.828096</td>\n",
              "      <td>0.159431</td>\n",
              "      <td>-0.299837</td>\n",
              "      <td>0.683750</td>\n",
              "      <td>-0.114350</td>\n",
              "      <td>0.681281</td>\n",
              "      <td>0.736570</td>\n",
              "      <td>-0.209729</td>\n",
              "      <td>0.205723</td>\n",
              "      <td>0.190821</td>\n",
              "      <td>0.373024</td>\n",
              "      <td>-0.097291</td>\n",
              "      <td>-0.047544</td>\n",
              "      <td>0.968712</td>\n",
              "      <td>0.278169</td>\n",
              "      <td>0.964859</td>\n",
              "      <td>0.939486</td>\n",
              "      <td>0.133208</td>\n",
              "      <td>0.414634</td>\n",
              "      <td>0.526622</td>\n",
              "      <td>0.748107</td>\n",
              "      <td>0.166336</td>\n",
              "      <td>0.006463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Texture</th>\n",
              "      <td>0.394542</td>\n",
              "      <td>0.309198</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.313463</td>\n",
              "      <td>0.305815</td>\n",
              "      <td>-0.053572</td>\n",
              "      <td>0.207347</td>\n",
              "      <td>0.276192</td>\n",
              "      <td>0.269925</td>\n",
              "      <td>0.054891</td>\n",
              "      <td>-0.092349</td>\n",
              "      <td>0.262373</td>\n",
              "      <td>0.386603</td>\n",
              "      <td>0.266864</td>\n",
              "      <td>0.247254</td>\n",
              "      <td>-0.006484</td>\n",
              "      <td>0.163232</td>\n",
              "      <td>0.116490</td>\n",
              "      <td>0.136297</td>\n",
              "      <td>0.010619</td>\n",
              "      <td>0.037794</td>\n",
              "      <td>0.336573</td>\n",
              "      <td>0.910412</td>\n",
              "      <td>0.340065</td>\n",
              "      <td>0.327659</td>\n",
              "      <td>0.046780</td>\n",
              "      <td>0.248266</td>\n",
              "      <td>0.268615</td>\n",
              "      <td>0.269750</td>\n",
              "      <td>0.092974</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Perimeter</th>\n",
              "      <td>0.742983</td>\n",
              "      <td>0.997927</td>\n",
              "      <td>0.313463</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986371</td>\n",
              "      <td>0.213686</td>\n",
              "      <td>0.566204</td>\n",
              "      <td>0.721102</td>\n",
              "      <td>0.855356</td>\n",
              "      <td>0.192677</td>\n",
              "      <td>-0.250755</td>\n",
              "      <td>0.693927</td>\n",
              "      <td>-0.104625</td>\n",
              "      <td>0.697611</td>\n",
              "      <td>0.744135</td>\n",
              "      <td>-0.191024</td>\n",
              "      <td>0.249648</td>\n",
              "      <td>0.223668</td>\n",
              "      <td>0.402142</td>\n",
              "      <td>-0.073806</td>\n",
              "      <td>-0.011012</td>\n",
              "      <td>0.968077</td>\n",
              "      <td>0.282986</td>\n",
              "      <td>0.969352</td>\n",
              "      <td>0.939479</td>\n",
              "      <td>0.162731</td>\n",
              "      <td>0.456249</td>\n",
              "      <td>0.562656</td>\n",
              "      <td>0.773543</td>\n",
              "      <td>0.190466</td>\n",
              "      <td>0.049737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Area</th>\n",
              "      <td>0.709895</td>\n",
              "      <td>0.987067</td>\n",
              "      <td>0.305815</td>\n",
              "      <td>0.986371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.184854</td>\n",
              "      <td>0.508014</td>\n",
              "      <td>0.691439</td>\n",
              "      <td>0.828194</td>\n",
              "      <td>0.162558</td>\n",
              "      <td>-0.272146</td>\n",
              "      <td>0.739731</td>\n",
              "      <td>-0.081250</td>\n",
              "      <td>0.736365</td>\n",
              "      <td>0.802887</td>\n",
              "      <td>-0.154053</td>\n",
              "      <td>0.210969</td>\n",
              "      <td>0.202597</td>\n",
              "      <td>0.367392</td>\n",
              "      <td>-0.065685</td>\n",
              "      <td>-0.024577</td>\n",
              "      <td>0.961810</td>\n",
              "      <td>0.268279</td>\n",
              "      <td>0.958844</td>\n",
              "      <td>0.957940</td>\n",
              "      <td>0.135704</td>\n",
              "      <td>0.390384</td>\n",
              "      <td>0.510847</td>\n",
              "      <td>0.724547</td>\n",
              "      <td>0.144212</td>\n",
              "      <td>0.002342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Smoothness</th>\n",
              "      <td>0.348769</td>\n",
              "      <td>0.178071</td>\n",
              "      <td>-0.053572</td>\n",
              "      <td>0.213686</td>\n",
              "      <td>0.184854</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.656942</td>\n",
              "      <td>0.524147</td>\n",
              "      <td>0.556546</td>\n",
              "      <td>0.543816</td>\n",
              "      <td>0.582842</td>\n",
              "      <td>0.296862</td>\n",
              "      <td>0.057337</td>\n",
              "      <td>0.290060</td>\n",
              "      <td>0.242471</td>\n",
              "      <td>0.335643</td>\n",
              "      <td>0.309318</td>\n",
              "      <td>0.244146</td>\n",
              "      <td>0.369149</td>\n",
              "      <td>0.200667</td>\n",
              "      <td>0.273901</td>\n",
              "      <td>0.215811</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>0.239724</td>\n",
              "      <td>0.208972</td>\n",
              "      <td>0.806685</td>\n",
              "      <td>0.463619</td>\n",
              "      <td>0.430050</td>\n",
              "      <td>0.496579</td>\n",
              "      <td>0.371951</td>\n",
              "      <td>0.487634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Compactness</th>\n",
              "      <td>0.584313</td>\n",
              "      <td>0.516843</td>\n",
              "      <td>0.207347</td>\n",
              "      <td>0.566204</td>\n",
              "      <td>0.508014</td>\n",
              "      <td>0.656942</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.881458</td>\n",
              "      <td>0.831441</td>\n",
              "      <td>0.585863</td>\n",
              "      <td>0.562328</td>\n",
              "      <td>0.472103</td>\n",
              "      <td>0.017192</td>\n",
              "      <td>0.523927</td>\n",
              "      <td>0.439633</td>\n",
              "      <td>0.121548</td>\n",
              "      <td>0.731025</td>\n",
              "      <td>0.559763</td>\n",
              "      <td>0.624128</td>\n",
              "      <td>0.240811</td>\n",
              "      <td>0.499654</td>\n",
              "      <td>0.537986</td>\n",
              "      <td>0.220847</td>\n",
              "      <td>0.591737</td>\n",
              "      <td>0.510953</td>\n",
              "      <td>0.563269</td>\n",
              "      <td>0.870988</td>\n",
              "      <td>0.817473</td>\n",
              "      <td>0.813259</td>\n",
              "      <td>0.505076</td>\n",
              "      <td>0.688946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concavity</th>\n",
              "      <td>0.684041</td>\n",
              "      <td>0.683061</td>\n",
              "      <td>0.276192</td>\n",
              "      <td>0.721102</td>\n",
              "      <td>0.691439</td>\n",
              "      <td>0.524147</td>\n",
              "      <td>0.881458</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.920623</td>\n",
              "      <td>0.496806</td>\n",
              "      <td>0.333480</td>\n",
              "      <td>0.617278</td>\n",
              "      <td>0.057446</td>\n",
              "      <td>0.644674</td>\n",
              "      <td>0.607270</td>\n",
              "      <td>0.093549</td>\n",
              "      <td>0.663497</td>\n",
              "      <td>0.685358</td>\n",
              "      <td>0.672002</td>\n",
              "      <td>0.190646</td>\n",
              "      <td>0.442651</td>\n",
              "      <td>0.688221</td>\n",
              "      <td>0.275716</td>\n",
              "      <td>0.728383</td>\n",
              "      <td>0.674761</td>\n",
              "      <td>0.449117</td>\n",
              "      <td>0.755013</td>\n",
              "      <td>0.883090</td>\n",
              "      <td>0.858034</td>\n",
              "      <td>0.406493</td>\n",
              "      <td>0.511359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Concave_Points</th>\n",
              "      <td>0.768244</td>\n",
              "      <td>0.828096</td>\n",
              "      <td>0.269925</td>\n",
              "      <td>0.855356</td>\n",
              "      <td>0.828194</td>\n",
              "      <td>0.556546</td>\n",
              "      <td>0.831441</td>\n",
              "      <td>0.920623</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.457422</td>\n",
              "      <td>0.166575</td>\n",
              "      <td>0.685003</td>\n",
              "      <td>0.000983</td>\n",
              "      <td>0.697917</td>\n",
              "      <td>0.680514</td>\n",
              "      <td>0.023643</td>\n",
              "      <td>0.481100</td>\n",
              "      <td>0.427087</td>\n",
              "      <td>0.596685</td>\n",
              "      <td>0.100514</td>\n",
              "      <td>0.246793</td>\n",
              "      <td>0.831240</td>\n",
              "      <td>0.270695</td>\n",
              "      <td>0.856072</td>\n",
              "      <td>0.809646</td>\n",
              "      <td>0.458687</td>\n",
              "      <td>0.667358</td>\n",
              "      <td>0.749064</td>\n",
              "      <td>0.906347</td>\n",
              "      <td>0.370343</td>\n",
              "      <td>0.365750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Symmetry</th>\n",
              "      <td>0.324597</td>\n",
              "      <td>0.159431</td>\n",
              "      <td>0.054891</td>\n",
              "      <td>0.192677</td>\n",
              "      <td>0.162558</td>\n",
              "      <td>0.543816</td>\n",
              "      <td>0.585863</td>\n",
              "      <td>0.496806</td>\n",
              "      <td>0.457422</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.458964</td>\n",
              "      <td>0.277106</td>\n",
              "      <td>0.114372</td>\n",
              "      <td>0.286699</td>\n",
              "      <td>0.207923</td>\n",
              "      <td>0.164448</td>\n",
              "      <td>0.407379</td>\n",
              "      <td>0.336963</td>\n",
              "      <td>0.366068</td>\n",
              "      <td>0.458266</td>\n",
              "      <td>0.310421</td>\n",
              "      <td>0.190892</td>\n",
              "      <td>0.081532</td>\n",
              "      <td>0.222095</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>0.415406</td>\n",
              "      <td>0.472140</td>\n",
              "      <td>0.439229</td>\n",
              "      <td>0.426124</td>\n",
              "      <td>0.700508</td>\n",
              "      <td>0.427166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean_Fractal_Dimension</th>\n",
              "      <td>-0.023644</td>\n",
              "      <td>-0.299837</td>\n",
              "      <td>-0.092349</td>\n",
              "      <td>-0.250755</td>\n",
              "      <td>-0.272146</td>\n",
              "      <td>0.582842</td>\n",
              "      <td>0.562328</td>\n",
              "      <td>0.333480</td>\n",
              "      <td>0.166575</td>\n",
              "      <td>0.458964</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017073</td>\n",
              "      <td>0.157139</td>\n",
              "      <td>0.020418</td>\n",
              "      <td>-0.099559</td>\n",
              "      <td>0.383091</td>\n",
              "      <td>0.558162</td>\n",
              "      <td>0.442705</td>\n",
              "      <td>0.332145</td>\n",
              "      <td>0.348023</td>\n",
              "      <td>0.695081</td>\n",
              "      <td>-0.247106</td>\n",
              "      <td>-0.062924</td>\n",
              "      <td>-0.200464</td>\n",
              "      <td>-0.226155</td>\n",
              "      <td>0.493655</td>\n",
              "      <td>0.463808</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.172914</td>\n",
              "      <td>0.321228</td>\n",
              "      <td>0.776946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radius_SE</th>\n",
              "      <td>0.562344</td>\n",
              "      <td>0.683750</td>\n",
              "      <td>0.262373</td>\n",
              "      <td>0.693927</td>\n",
              "      <td>0.739731</td>\n",
              "      <td>0.296862</td>\n",
              "      <td>0.472103</td>\n",
              "      <td>0.617278</td>\n",
              "      <td>0.685003</td>\n",
              "      <td>0.277106</td>\n",
              "      <td>-0.017073</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.198579</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.953203</td>\n",
              "      <td>0.161605</td>\n",
              "      <td>0.339197</td>\n",
              "      <td>0.318366</td>\n",
              "      <td>0.492332</td>\n",
              "      <td>0.238315</td>\n",
              "      <td>0.214353</td>\n",
              "      <td>0.717105</td>\n",
              "      <td>0.179111</td>\n",
              "      <td>0.719858</td>\n",
              "      <td>0.755857</td>\n",
              "      <td>0.138863</td>\n",
              "      <td>0.270523</td>\n",
              "      <td>0.366695</td>\n",
              "      <td>0.515648</td>\n",
              "      <td>0.075343</td>\n",
              "      <td>0.034701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Texture_SE</th>\n",
              "      <td>-0.021672</td>\n",
              "      <td>-0.114350</td>\n",
              "      <td>0.386603</td>\n",
              "      <td>-0.104625</td>\n",
              "      <td>-0.081250</td>\n",
              "      <td>0.057337</td>\n",
              "      <td>0.017192</td>\n",
              "      <td>0.057446</td>\n",
              "      <td>0.000983</td>\n",
              "      <td>0.114372</td>\n",
              "      <td>0.157139</td>\n",
              "      <td>0.198579</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.205918</td>\n",
              "      <td>0.098625</td>\n",
              "      <td>0.386096</td>\n",
              "      <td>0.216309</td>\n",
              "      <td>0.177332</td>\n",
              "      <td>0.199304</td>\n",
              "      <td>0.411635</td>\n",
              "      <td>0.268378</td>\n",
              "      <td>-0.128365</td>\n",
              "      <td>0.408348</td>\n",
              "      <td>-0.119920</td>\n",
              "      <td>-0.096448</td>\n",
              "      <td>-0.088646</td>\n",
              "      <td>-0.110653</td>\n",
              "      <td>-0.086205</td>\n",
              "      <td>-0.145696</td>\n",
              "      <td>-0.137802</td>\n",
              "      <td>-0.055185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <td>0.551723</td>\n",
              "      <td>0.681281</td>\n",
              "      <td>0.266864</td>\n",
              "      <td>0.697611</td>\n",
              "      <td>0.736365</td>\n",
              "      <td>0.290060</td>\n",
              "      <td>0.523927</td>\n",
              "      <td>0.644674</td>\n",
              "      <td>0.697917</td>\n",
              "      <td>0.286699</td>\n",
              "      <td>0.020418</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.205918</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.941315</td>\n",
              "      <td>0.144100</td>\n",
              "      <td>0.401096</td>\n",
              "      <td>0.345571</td>\n",
              "      <td>0.531447</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>0.229798</td>\n",
              "      <td>0.701595</td>\n",
              "      <td>0.184740</td>\n",
              "      <td>0.723266</td>\n",
              "      <td>0.737454</td>\n",
              "      <td>0.125946</td>\n",
              "      <td>0.329070</td>\n",
              "      <td>0.405875</td>\n",
              "      <td>0.540290</td>\n",
              "      <td>0.093832</td>\n",
              "      <td>0.072072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area_SE</th>\n",
              "      <td>0.541411</td>\n",
              "      <td>0.736570</td>\n",
              "      <td>0.247254</td>\n",
              "      <td>0.744135</td>\n",
              "      <td>0.802887</td>\n",
              "      <td>0.242471</td>\n",
              "      <td>0.439633</td>\n",
              "      <td>0.607270</td>\n",
              "      <td>0.680514</td>\n",
              "      <td>0.207923</td>\n",
              "      <td>-0.099559</td>\n",
              "      <td>0.953203</td>\n",
              "      <td>0.098625</td>\n",
              "      <td>0.941315</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.076448</td>\n",
              "      <td>0.272369</td>\n",
              "      <td>0.258992</td>\n",
              "      <td>0.398951</td>\n",
              "      <td>0.133497</td>\n",
              "      <td>0.115554</td>\n",
              "      <td>0.755887</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>0.759038</td>\n",
              "      <td>0.811598</td>\n",
              "      <td>0.123809</td>\n",
              "      <td>0.271281</td>\n",
              "      <td>0.373973</td>\n",
              "      <td>0.526130</td>\n",
              "      <td>0.059577</td>\n",
              "      <td>0.006182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <td>-0.072968</td>\n",
              "      <td>-0.209729</td>\n",
              "      <td>-0.006484</td>\n",
              "      <td>-0.191024</td>\n",
              "      <td>-0.154053</td>\n",
              "      <td>0.335643</td>\n",
              "      <td>0.121548</td>\n",
              "      <td>0.093549</td>\n",
              "      <td>0.023643</td>\n",
              "      <td>0.164448</td>\n",
              "      <td>0.383091</td>\n",
              "      <td>0.161605</td>\n",
              "      <td>0.386096</td>\n",
              "      <td>0.144100</td>\n",
              "      <td>0.076448</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.331255</td>\n",
              "      <td>0.258884</td>\n",
              "      <td>0.316369</td>\n",
              "      <td>0.405105</td>\n",
              "      <td>0.422608</td>\n",
              "      <td>-0.222252</td>\n",
              "      <td>-0.086065</td>\n",
              "      <td>-0.210476</td>\n",
              "      <td>-0.173489</td>\n",
              "      <td>0.311070</td>\n",
              "      <td>-0.063834</td>\n",
              "      <td>-0.063996</td>\n",
              "      <td>-0.110423</td>\n",
              "      <td>-0.123336</td>\n",
              "      <td>0.093343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Compactness_SE</th>\n",
              "      <td>0.272885</td>\n",
              "      <td>0.205723</td>\n",
              "      <td>0.163232</td>\n",
              "      <td>0.249648</td>\n",
              "      <td>0.210969</td>\n",
              "      <td>0.309318</td>\n",
              "      <td>0.731025</td>\n",
              "      <td>0.663497</td>\n",
              "      <td>0.481100</td>\n",
              "      <td>0.407379</td>\n",
              "      <td>0.558162</td>\n",
              "      <td>0.339197</td>\n",
              "      <td>0.216309</td>\n",
              "      <td>0.401096</td>\n",
              "      <td>0.272369</td>\n",
              "      <td>0.331255</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.796424</td>\n",
              "      <td>0.747876</td>\n",
              "      <td>0.410036</td>\n",
              "      <td>0.805266</td>\n",
              "      <td>0.196434</td>\n",
              "      <td>0.114272</td>\n",
              "      <td>0.252884</td>\n",
              "      <td>0.190706</td>\n",
              "      <td>0.206712</td>\n",
              "      <td>0.670046</td>\n",
              "      <td>0.631038</td>\n",
              "      <td>0.472684</td>\n",
              "      <td>0.268518</td>\n",
              "      <td>0.585553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concavity_SE</th>\n",
              "      <td>0.235707</td>\n",
              "      <td>0.190821</td>\n",
              "      <td>0.116490</td>\n",
              "      <td>0.223668</td>\n",
              "      <td>0.202597</td>\n",
              "      <td>0.244146</td>\n",
              "      <td>0.559763</td>\n",
              "      <td>0.685358</td>\n",
              "      <td>0.427087</td>\n",
              "      <td>0.336963</td>\n",
              "      <td>0.442705</td>\n",
              "      <td>0.318366</td>\n",
              "      <td>0.177332</td>\n",
              "      <td>0.345571</td>\n",
              "      <td>0.258992</td>\n",
              "      <td>0.258884</td>\n",
              "      <td>0.796424</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.776207</td>\n",
              "      <td>0.313454</td>\n",
              "      <td>0.727315</td>\n",
              "      <td>0.179927</td>\n",
              "      <td>0.076003</td>\n",
              "      <td>0.218579</td>\n",
              "      <td>0.180329</td>\n",
              "      <td>0.157563</td>\n",
              "      <td>0.479425</td>\n",
              "      <td>0.660230</td>\n",
              "      <td>0.431751</td>\n",
              "      <td>0.193919</td>\n",
              "      <td>0.438233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <td>0.390464</td>\n",
              "      <td>0.373024</td>\n",
              "      <td>0.136297</td>\n",
              "      <td>0.402142</td>\n",
              "      <td>0.367392</td>\n",
              "      <td>0.369149</td>\n",
              "      <td>0.624128</td>\n",
              "      <td>0.672002</td>\n",
              "      <td>0.596685</td>\n",
              "      <td>0.366068</td>\n",
              "      <td>0.332145</td>\n",
              "      <td>0.492332</td>\n",
              "      <td>0.199304</td>\n",
              "      <td>0.531447</td>\n",
              "      <td>0.398951</td>\n",
              "      <td>0.316369</td>\n",
              "      <td>0.747876</td>\n",
              "      <td>0.776207</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.310494</td>\n",
              "      <td>0.616870</td>\n",
              "      <td>0.352040</td>\n",
              "      <td>0.061980</td>\n",
              "      <td>0.386775</td>\n",
              "      <td>0.335802</td>\n",
              "      <td>0.208040</td>\n",
              "      <td>0.451124</td>\n",
              "      <td>0.549797</td>\n",
              "      <td>0.589912</td>\n",
              "      <td>0.133997</td>\n",
              "      <td>0.314660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <td>0.000782</td>\n",
              "      <td>-0.097291</td>\n",
              "      <td>0.010619</td>\n",
              "      <td>-0.073806</td>\n",
              "      <td>-0.065685</td>\n",
              "      <td>0.200667</td>\n",
              "      <td>0.240811</td>\n",
              "      <td>0.190646</td>\n",
              "      <td>0.100514</td>\n",
              "      <td>0.458266</td>\n",
              "      <td>0.348023</td>\n",
              "      <td>0.238315</td>\n",
              "      <td>0.411635</td>\n",
              "      <td>0.265745</td>\n",
              "      <td>0.133497</td>\n",
              "      <td>0.405105</td>\n",
              "      <td>0.410036</td>\n",
              "      <td>0.313454</td>\n",
              "      <td>0.310494</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.370941</td>\n",
              "      <td>-0.121271</td>\n",
              "      <td>-0.068224</td>\n",
              "      <td>-0.095874</td>\n",
              "      <td>-0.104143</td>\n",
              "      <td>-0.005014</td>\n",
              "      <td>0.082315</td>\n",
              "      <td>0.056315</td>\n",
              "      <td>-0.020998</td>\n",
              "      <td>0.399618</td>\n",
              "      <td>0.093332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fractal_Dimension_SE</th>\n",
              "      <td>0.061546</td>\n",
              "      <td>-0.047544</td>\n",
              "      <td>0.037794</td>\n",
              "      <td>-0.011012</td>\n",
              "      <td>-0.024577</td>\n",
              "      <td>0.273901</td>\n",
              "      <td>0.499654</td>\n",
              "      <td>0.442651</td>\n",
              "      <td>0.246793</td>\n",
              "      <td>0.310421</td>\n",
              "      <td>0.695081</td>\n",
              "      <td>0.214353</td>\n",
              "      <td>0.268378</td>\n",
              "      <td>0.229798</td>\n",
              "      <td>0.115554</td>\n",
              "      <td>0.422608</td>\n",
              "      <td>0.805266</td>\n",
              "      <td>0.727315</td>\n",
              "      <td>0.616870</td>\n",
              "      <td>0.370941</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.046821</td>\n",
              "      <td>-0.021988</td>\n",
              "      <td>-0.011145</td>\n",
              "      <td>-0.031178</td>\n",
              "      <td>0.153533</td>\n",
              "      <td>0.381267</td>\n",
              "      <td>0.373183</td>\n",
              "      <td>0.203247</td>\n",
              "      <td>0.092214</td>\n",
              "      <td>0.590613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Radius</th>\n",
              "      <td>0.776062</td>\n",
              "      <td>0.968712</td>\n",
              "      <td>0.336573</td>\n",
              "      <td>0.968077</td>\n",
              "      <td>0.961810</td>\n",
              "      <td>0.215811</td>\n",
              "      <td>0.537986</td>\n",
              "      <td>0.688221</td>\n",
              "      <td>0.831240</td>\n",
              "      <td>0.190892</td>\n",
              "      <td>-0.247106</td>\n",
              "      <td>0.717105</td>\n",
              "      <td>-0.128365</td>\n",
              "      <td>0.701595</td>\n",
              "      <td>0.755887</td>\n",
              "      <td>-0.222252</td>\n",
              "      <td>0.196434</td>\n",
              "      <td>0.179927</td>\n",
              "      <td>0.352040</td>\n",
              "      <td>-0.121271</td>\n",
              "      <td>-0.046821</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340978</td>\n",
              "      <td>0.993863</td>\n",
              "      <td>0.983745</td>\n",
              "      <td>0.226289</td>\n",
              "      <td>0.470428</td>\n",
              "      <td>0.568768</td>\n",
              "      <td>0.788533</td>\n",
              "      <td>0.243152</td>\n",
              "      <td>0.087078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Texture</th>\n",
              "      <td>0.437429</td>\n",
              "      <td>0.278169</td>\n",
              "      <td>0.910412</td>\n",
              "      <td>0.282986</td>\n",
              "      <td>0.268279</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>0.220847</td>\n",
              "      <td>0.275716</td>\n",
              "      <td>0.270695</td>\n",
              "      <td>0.081532</td>\n",
              "      <td>-0.062924</td>\n",
              "      <td>0.179111</td>\n",
              "      <td>0.408348</td>\n",
              "      <td>0.184740</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>-0.086065</td>\n",
              "      <td>0.114272</td>\n",
              "      <td>0.076003</td>\n",
              "      <td>0.061980</td>\n",
              "      <td>-0.068224</td>\n",
              "      <td>-0.021988</td>\n",
              "      <td>0.340978</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344989</td>\n",
              "      <td>0.327447</td>\n",
              "      <td>0.200238</td>\n",
              "      <td>0.333210</td>\n",
              "      <td>0.338788</td>\n",
              "      <td>0.338052</td>\n",
              "      <td>0.227270</td>\n",
              "      <td>0.192847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Perimeter</th>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.964859</td>\n",
              "      <td>0.340065</td>\n",
              "      <td>0.969352</td>\n",
              "      <td>0.958844</td>\n",
              "      <td>0.239724</td>\n",
              "      <td>0.591737</td>\n",
              "      <td>0.728383</td>\n",
              "      <td>0.856072</td>\n",
              "      <td>0.222095</td>\n",
              "      <td>-0.200464</td>\n",
              "      <td>0.719858</td>\n",
              "      <td>-0.119920</td>\n",
              "      <td>0.723266</td>\n",
              "      <td>0.759038</td>\n",
              "      <td>-0.210476</td>\n",
              "      <td>0.252884</td>\n",
              "      <td>0.218579</td>\n",
              "      <td>0.386775</td>\n",
              "      <td>-0.095874</td>\n",
              "      <td>-0.011145</td>\n",
              "      <td>0.993863</td>\n",
              "      <td>0.344989</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977577</td>\n",
              "      <td>0.244560</td>\n",
              "      <td>0.524669</td>\n",
              "      <td>0.612958</td>\n",
              "      <td>0.816340</td>\n",
              "      <td>0.268631</td>\n",
              "      <td>0.131858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Area</th>\n",
              "      <td>0.731475</td>\n",
              "      <td>0.939486</td>\n",
              "      <td>0.327659</td>\n",
              "      <td>0.939479</td>\n",
              "      <td>0.957940</td>\n",
              "      <td>0.208972</td>\n",
              "      <td>0.510953</td>\n",
              "      <td>0.674761</td>\n",
              "      <td>0.809646</td>\n",
              "      <td>0.181974</td>\n",
              "      <td>-0.226155</td>\n",
              "      <td>0.755857</td>\n",
              "      <td>-0.096448</td>\n",
              "      <td>0.737454</td>\n",
              "      <td>0.811598</td>\n",
              "      <td>-0.173489</td>\n",
              "      <td>0.190706</td>\n",
              "      <td>0.180329</td>\n",
              "      <td>0.335802</td>\n",
              "      <td>-0.104143</td>\n",
              "      <td>-0.031178</td>\n",
              "      <td>0.983745</td>\n",
              "      <td>0.327447</td>\n",
              "      <td>0.977577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216841</td>\n",
              "      <td>0.431484</td>\n",
              "      <td>0.536393</td>\n",
              "      <td>0.746961</td>\n",
              "      <td>0.206582</td>\n",
              "      <td>0.072245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Smoothness</th>\n",
              "      <td>0.414404</td>\n",
              "      <td>0.133208</td>\n",
              "      <td>0.046780</td>\n",
              "      <td>0.162731</td>\n",
              "      <td>0.135704</td>\n",
              "      <td>0.806685</td>\n",
              "      <td>0.563269</td>\n",
              "      <td>0.449117</td>\n",
              "      <td>0.458687</td>\n",
              "      <td>0.415406</td>\n",
              "      <td>0.493655</td>\n",
              "      <td>0.138863</td>\n",
              "      <td>-0.088646</td>\n",
              "      <td>0.125946</td>\n",
              "      <td>0.123809</td>\n",
              "      <td>0.311070</td>\n",
              "      <td>0.206712</td>\n",
              "      <td>0.157563</td>\n",
              "      <td>0.208040</td>\n",
              "      <td>-0.005014</td>\n",
              "      <td>0.153533</td>\n",
              "      <td>0.226289</td>\n",
              "      <td>0.200238</td>\n",
              "      <td>0.244560</td>\n",
              "      <td>0.216841</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.557487</td>\n",
              "      <td>0.512539</td>\n",
              "      <td>0.550918</td>\n",
              "      <td>0.480191</td>\n",
              "      <td>0.598972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Compactness</th>\n",
              "      <td>0.573433</td>\n",
              "      <td>0.414634</td>\n",
              "      <td>0.248266</td>\n",
              "      <td>0.456249</td>\n",
              "      <td>0.390384</td>\n",
              "      <td>0.463619</td>\n",
              "      <td>0.870988</td>\n",
              "      <td>0.755013</td>\n",
              "      <td>0.667358</td>\n",
              "      <td>0.472140</td>\n",
              "      <td>0.463808</td>\n",
              "      <td>0.270523</td>\n",
              "      <td>-0.110653</td>\n",
              "      <td>0.329070</td>\n",
              "      <td>0.271281</td>\n",
              "      <td>-0.063834</td>\n",
              "      <td>0.670046</td>\n",
              "      <td>0.479425</td>\n",
              "      <td>0.451124</td>\n",
              "      <td>0.082315</td>\n",
              "      <td>0.381267</td>\n",
              "      <td>0.470428</td>\n",
              "      <td>0.333210</td>\n",
              "      <td>0.524669</td>\n",
              "      <td>0.431484</td>\n",
              "      <td>0.557487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.891236</td>\n",
              "      <td>0.803026</td>\n",
              "      <td>0.616957</td>\n",
              "      <td>0.805732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concavity</th>\n",
              "      <td>0.640670</td>\n",
              "      <td>0.526622</td>\n",
              "      <td>0.268615</td>\n",
              "      <td>0.562656</td>\n",
              "      <td>0.510847</td>\n",
              "      <td>0.430050</td>\n",
              "      <td>0.817473</td>\n",
              "      <td>0.883090</td>\n",
              "      <td>0.749064</td>\n",
              "      <td>0.439229</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.366695</td>\n",
              "      <td>-0.086205</td>\n",
              "      <td>0.405875</td>\n",
              "      <td>0.373973</td>\n",
              "      <td>-0.063996</td>\n",
              "      <td>0.631038</td>\n",
              "      <td>0.660230</td>\n",
              "      <td>0.549797</td>\n",
              "      <td>0.056315</td>\n",
              "      <td>0.373183</td>\n",
              "      <td>0.568768</td>\n",
              "      <td>0.338788</td>\n",
              "      <td>0.612958</td>\n",
              "      <td>0.536393</td>\n",
              "      <td>0.512539</td>\n",
              "      <td>0.891236</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855267</td>\n",
              "      <td>0.536054</td>\n",
              "      <td>0.681879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Concave_Points</th>\n",
              "      <td>0.783377</td>\n",
              "      <td>0.748107</td>\n",
              "      <td>0.269750</td>\n",
              "      <td>0.773543</td>\n",
              "      <td>0.724547</td>\n",
              "      <td>0.496579</td>\n",
              "      <td>0.813259</td>\n",
              "      <td>0.858034</td>\n",
              "      <td>0.906347</td>\n",
              "      <td>0.426124</td>\n",
              "      <td>0.172914</td>\n",
              "      <td>0.515648</td>\n",
              "      <td>-0.145696</td>\n",
              "      <td>0.540290</td>\n",
              "      <td>0.526130</td>\n",
              "      <td>-0.110423</td>\n",
              "      <td>0.472684</td>\n",
              "      <td>0.431751</td>\n",
              "      <td>0.589912</td>\n",
              "      <td>-0.020998</td>\n",
              "      <td>0.203247</td>\n",
              "      <td>0.788533</td>\n",
              "      <td>0.338052</td>\n",
              "      <td>0.816340</td>\n",
              "      <td>0.746961</td>\n",
              "      <td>0.550918</td>\n",
              "      <td>0.803026</td>\n",
              "      <td>0.855267</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.506077</td>\n",
              "      <td>0.509532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Symmetry</th>\n",
              "      <td>0.410486</td>\n",
              "      <td>0.166336</td>\n",
              "      <td>0.092974</td>\n",
              "      <td>0.190466</td>\n",
              "      <td>0.144212</td>\n",
              "      <td>0.371951</td>\n",
              "      <td>0.505076</td>\n",
              "      <td>0.406493</td>\n",
              "      <td>0.370343</td>\n",
              "      <td>0.700508</td>\n",
              "      <td>0.321228</td>\n",
              "      <td>0.075343</td>\n",
              "      <td>-0.137802</td>\n",
              "      <td>0.093832</td>\n",
              "      <td>0.059577</td>\n",
              "      <td>-0.123336</td>\n",
              "      <td>0.268518</td>\n",
              "      <td>0.193919</td>\n",
              "      <td>0.133997</td>\n",
              "      <td>0.399618</td>\n",
              "      <td>0.092214</td>\n",
              "      <td>0.243152</td>\n",
              "      <td>0.227270</td>\n",
              "      <td>0.268631</td>\n",
              "      <td>0.206582</td>\n",
              "      <td>0.480191</td>\n",
              "      <td>0.616957</td>\n",
              "      <td>0.536054</td>\n",
              "      <td>0.506077</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.526349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worst_Fractal_Dimension</th>\n",
              "      <td>0.304205</td>\n",
              "      <td>0.006463</td>\n",
              "      <td>0.091530</td>\n",
              "      <td>0.049737</td>\n",
              "      <td>0.002342</td>\n",
              "      <td>0.487634</td>\n",
              "      <td>0.688946</td>\n",
              "      <td>0.511359</td>\n",
              "      <td>0.365750</td>\n",
              "      <td>0.427166</td>\n",
              "      <td>0.776946</td>\n",
              "      <td>0.034701</td>\n",
              "      <td>-0.055185</td>\n",
              "      <td>0.072072</td>\n",
              "      <td>0.006182</td>\n",
              "      <td>0.093343</td>\n",
              "      <td>0.585553</td>\n",
              "      <td>0.438233</td>\n",
              "      <td>0.314660</td>\n",
              "      <td>0.093332</td>\n",
              "      <td>0.590613</td>\n",
              "      <td>0.087078</td>\n",
              "      <td>0.192847</td>\n",
              "      <td>0.131858</td>\n",
              "      <td>0.072245</td>\n",
              "      <td>0.598972</td>\n",
              "      <td>0.805732</td>\n",
              "      <td>0.681879</td>\n",
              "      <td>0.509532</td>\n",
              "      <td>0.526349</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Diagnosis  ...  Worst_Fractal_Dimension\n",
              "Diagnosis                 1.000000  ...                 0.304205\n",
              "Mean_Radius               0.732356  ...                 0.006463\n",
              "Mean_Texture              0.394542  ...                 0.091530\n",
              "Mean_Perimeter            0.742983  ...                 0.049737\n",
              "Mean_Area                 0.709895  ...                 0.002342\n",
              "Mean_Smoothness           0.348769  ...                 0.487634\n",
              "Mean_Compactness          0.584313  ...                 0.688946\n",
              "Mean_Concavity            0.684041  ...                 0.511359\n",
              "Mean_Concave_Points       0.768244  ...                 0.365750\n",
              "Mean_Symmetry             0.324597  ...                 0.427166\n",
              "Mean_Fractal_Dimension   -0.023644  ...                 0.776946\n",
              "Radius_SE                 0.562344  ...                 0.034701\n",
              "Texture_SE               -0.021672  ...                -0.055185\n",
              "Perimeter_SE              0.551723  ...                 0.072072\n",
              "Area_SE                   0.541411  ...                 0.006182\n",
              "Smoothness_SE            -0.072968  ...                 0.093343\n",
              "Compactness_SE            0.272885  ...                 0.585553\n",
              "Concavity_SE              0.235707  ...                 0.438233\n",
              "Concave_Points_SE         0.390464  ...                 0.314660\n",
              "Symmetry_SE               0.000782  ...                 0.093332\n",
              "Fractal_Dimension_SE      0.061546  ...                 0.590613\n",
              "Worst_Radius              0.776062  ...                 0.087078\n",
              "Worst_Texture             0.437429  ...                 0.192847\n",
              "Worst_Perimeter           0.781000  ...                 0.131858\n",
              "Worst_Area                0.731475  ...                 0.072245\n",
              "Worst_Smoothness          0.414404  ...                 0.598972\n",
              "Worst_Compactness         0.573433  ...                 0.805732\n",
              "Worst_Concavity           0.640670  ...                 0.681879\n",
              "Worst_Concave_Points      0.783377  ...                 0.509532\n",
              "Worst_Symmetry            0.410486  ...                 0.526349\n",
              "Worst_Fractal_Dimension   0.304205  ...                 1.000000\n",
              "\n",
              "[31 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJzjhwOkmmzq"
      },
      "source": [
        "# Training the linear regression model\n",
        "\n",
        "Here we are using linear regression to compare features in our model to see the correlation between them.  We are training this model to show us how dependent the variables are on one another."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BC07qA925yK9"
      },
      "source": [
        "# Creating linear regression model for Area Severity and Smoothness Severity\n",
        "Here I have trained a model to show us the negative correlation between two features in our dataset, we can clearly see from this model that the Mean_Radius has a negative impact on the Mean_Fractal_Dimension.  When the smoothness severity value increases then on average the area severity value decreases and vice versa."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "3Kz8R71_g0zy",
        "outputId": "545520cf-1538-4f63-8cc5-ed65403f6b03"
      },
      "source": [
        "# Here we train a model to perform linear regression data set to predict the diagnosis\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from math import sqrt\n",
        "from scipy import stats\n",
        "# create the linear regression model by fitting training set to the test set\n",
        "y_testArray = np.array(y_test['Mean_Fractal_Dimension']).reshape(-1,1)\n",
        "y_trainArray = np.array(y_train['Mean_Fractal_Dimension']).reshape(-1,1)\n",
        "X_trainArray = np.array(X_train['Mean_Radius']).reshape(-1,1)\n",
        "X_testArray = np.array(X_test['Mean_Radius']).reshape(-1,1)\n",
        "regression_model = LinearRegression().fit(X_trainArray, y_trainArray)\n",
        "# Score the model and retrive r^2 value\n",
        "y_pred = regression_model.predict(y_testArray)\n",
        "rSquared = regression_model.score(y_testArray, y_pred)\n",
        "print('y test r^2 value: ', rSquared)\n",
        "print('mean_squared_error value: ', mean_squared_error(y_testArray, y_pred))\n",
        "print('Coefficient: ',regression_model.coef_)\n",
        "print('Intercept: ',regression_model.intercept_)\n",
        "\n",
        "plt.xlabel(\"Mean_Radius\")\n",
        "plt.ylabel(\"Mean_Fractal_Dimension\")\n",
        "plt.title('Mean_Fractal_Dimension and Mean_Radius')\n",
        "plt.scatter(y_testArray,y_pred)\n",
        "plt.plot(y_testArray,y_pred,c='r')\n",
        "print('predict ',regression_model.predict([[153.4]]))\n",
        "y_test.shape\n"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y test r^2 value:  1.0\n",
            "mean_squared_error value:  0.00022366751239025245\n",
            "Coefficient:  [[-0.00085343]]\n",
            "Intercept:  [0.07591936]\n",
            "predict  [[-0.05499741]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(513, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFls_Z_FdLOf"
      },
      "source": [
        "I will demonstrate with some predictions below that the values are negatively skewed.  We will see that whenever we pass in a high fractal dimension the value will be lower and vice versa."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-YeLvDgWm1Z",
        "outputId": "6808e2de-bca5-48a0-a2ad-bc2d8feffcd1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# As we can see not too much of a difference between the values here\n",
        "print('this value will be low: ', regression_model.predict([[0.5]]))\n",
        "print('this value will be high: ', regression_model.predict([[0.1]]))\n",
        "\n",
        "# just to make our example clearer let's use ridiculously inflated values  \n",
        "print('let\\'s make our model predict some grossly inflated / deflated values to show that these features do indeed have a negative impact on one another')\n",
        "print('this value will be low: ', regression_model.predict([[100]]))\n",
        "# I use a negative value here to demonstrate the radius the higher the mean fractal dimension\n",
        "print('this value will be high: ', regression_model.predict([[-100]]))\n"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "this value will be low:  [[0.11939575]]\n",
            "this value will be high:  [[0.02813939]]\n",
            "let's make our model predict some grossly inflated / deflated values to show that these features do indeed have a negative impact on one another\n",
            "this value will be low:  [[22.81941643]]\n",
            "this value will be high:  [[-22.80876583]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6iBpsTa-er-E"
      },
      "source": [
        "As we can see above that whenever I make the value of the mean fractal dimension higher then the value of our dependent variable will decrease, whenever I make it lower it will increase.  We can also see that the values don't have too much of an effect on each other this is because the negative correlation is around ~ -0.29"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7End3_e5JQP"
      },
      "source": [
        "# Positive correlation\n",
        "Here we compare the concave points severity with the concavity severity, we see that there is a positive linear correlation between the two variables, we also see that the R^2 value is one which indicates a possible overfitting of our model.  We can see clearly from this graph that the two variables have a high correlation and that if the value increases in one of the variables then we can predict that the other variable also increases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "NUm5oqU4YyxT",
        "outputId": "a80c6f4c-ea3d-4835-da33-b799fe9a632d"
      },
      "source": [
        "# create the linear regression model by fitting training set to the test set\n",
        "y_testArray = np.array(y_test['Concave_Points_SE']).reshape(-1,1)\n",
        "y_trainArray = np.array(y_train['Concave_Points_SE']).reshape(-1,1)\n",
        "X_trainArray = np.array(X_train['Concavity_SE']).reshape(-1,1)\n",
        "X_testArray = np.array(X_test['Concavity_SE']).reshape(-1,1)\n",
        "# here we need to reshape the data using numpy so that the training data matches the testdata\n",
        "regression_model = LinearRegression().fit(X_trainArray, y_trainArray)\n",
        "# Score the model and retrive r^2 value\n",
        "y_pred = regression_model.predict(y_testArray)\n",
        "rSquared = regression_model.score(y_testArray, y_pred)\n",
        "print('y test r^2 value: ', rSquared)\n",
        "print('mean_squared_error value: ', mean_squared_error(y_testArray, y_pred))\n",
        "print('Coefficient: ',regression_model.coef_)\n",
        "print('Intercept: ',regression_model.intercept_)\n",
        "plt.xlabel(\"Concavity_SE\")\n",
        "plt.ylabel(\"Concave_Points_SE\")\n",
        "plt.title('Linear regression using Convatiy Severity and Concave Points Severity')\n",
        "plt.scatter(y_testArray,y_pred)\n",
        "plt.plot(y_testArray,y_pred,c='r')"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y test r^2 value:  1.0\n",
            "mean_squared_error value:  3.5963144457288783e-05\n",
            "Coefficient:  [[0.22814091]]\n",
            "Intercept:  [0.0053253]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6db8a7ca90>]"
            ]
          },
          "metadata": {},
          "execution_count": 181
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZ9SNcrxiE0I"
      },
      "source": [
        "Below we see the second models features have on one another, we can clearly see that the data has a positive correlation from our graph but let's show some concrete examples and perform some predictions using it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofv2lg_kajVZ",
        "outputId": "e7df8105-3c47-40aa-b8d2-b1ebfb978782",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('this value will be high: ', regression_model.predict([[0.5]]))\n",
        "print('this value will be low: ', regression_model.predict([[0.1]]))\n",
        "\n",
        "# just to show how high the impact will be with grossly inflated / deflated values values\n",
        "\n",
        "print('let\\'s make our model predict some grossly inflated / deflated values to show severity of impact')\n",
        "print('this value will be high: ', regression_model.predict([[100]]))\n",
        "print('this value will be low: ', regression_model.predict([[0.0000000001]]))\n"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "this value will be high:  [[0.11939575]]\n",
            "this value will be low:  [[0.02813939]]\n",
            "let's make our model predict some grossly inflated / deflated values\n",
            "this value will be high:  [[22.81941643]]\n",
            "this value will be low:  [[0.0053253]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znQ9wWrKfenz"
      },
      "source": [
        "Since the above values are highly correlated we see a greater rise and decrease in the predicted values when predicting than we did in the first linear regression model.  This is due to the fact that these two variables have a positive correlation of 0.776207, the features we used in our first model have a lower negative correlation which is why we don't see as much of an impact on them.  If they had a negative correlation of 0.776207 we would see the same rise and decrease in the first linear regression models predicted valules as well"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGr0Ctt4eZ-b"
      },
      "source": [
        "# Examples of low and high correlation in our dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-czUs2SDbPeP"
      },
      "source": [
        "Additionally we can see here the Mean Perimeter has a positive correlation with our Mean Radius feature.  Below this image we can see an example of low negative correlation, these variables don't really impact each other much"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Zsy1970XoOZO",
        "outputId": "1e7b1e56-ca62-4315-8303-0fd6fd699984"
      },
      "source": [
        "# Example of positive Linear correlation between radius and perimeter\n",
        "# since these correlate highly we can remove one of them from set\n",
        "\n",
        "plt.scatter(X_train['Mean_Perimeter'],X_train['Mean_Radius'])\n"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6db9bd9d90>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "hhwzfmxSoTtU",
        "outputId": "e8bbb7c3-79c1-4dad-e5d6-311d98204b9c"
      },
      "source": [
        "# example of low negative correlation\n",
        "plt.scatter(X_train['Mean_Fractal_Dimension'],X_train['Mean_Radius'])\n"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6db9b41590>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWb0lEQVR4nO3df7BcdXnH8ffHJJQb/HGhiQy5ck3oQDpKKilXyhgdFSyhrZpM6qiIlk6ZZkY7rWWc2FDb0nbsJBWmjh3/YJiBglMa8UcMjLSNlKi0DmBvSJBESUEFmks0AQxaiUjg6R/3XNlsdu/dH2f3nO85n9fMneyePXf3uYfl2e8+5/l+jyICMzNLz0uKDsDMzHrjBG5mligncDOzRDmBm5klygnczCxR84f5YosWLYqlS5cO8yXNzJK3c+fOJyJicfP2oSbwpUuXMjk5OcyXNDNLnqRHW213CcXMLFFO4GZmiXICNzNLlBO4mVminMDNzBI11C6Uqti2a4qrt+/j8cNHWDI6wobVy1m7cqzosMysZpzAu7Rt1xRXbn2AI889D8DU4SNcufUBACdxMxsql1C6dPX2fb9I3jOOPPc8V2/fV1BEZlZXHoF36fHDR7rabsVwmcvqwCPwLi0ZHelquw3fTJlr6vARghfLXNt2TRUdmlmunMC7tGH1ckYWzDtm28iCeWxYvbygiKyZy1xWFy6hdGnma7i/npeXy1xWF07gPVi7cswJu8SWjI4w1SJZu8xlVeMSilWOy1xWFx6Bz8KdDGlymcvqwgm8DU/YSZvLXFYHLqG04U4GMys7J/A23MlgZmXnBN5Gu46Fl0gs23g7qzbv8MQQMyuUE3gbrToZAJ6PKHR237ZdU6zavMMfImbmBN7O2pVjbFq3grHREQTMk47bZ9g1cU8RN7NGcyZwSadL+qqkb0vaK+nD2fZTJN0h6aHs35MHH+5wrV05xjc2XsD3N/8OL0S03GeYNXGfWDWzRp2MwI8CH4mI1wDnA38k6TXARuDOiDgTuDO7X1llWMTKJ1bNrNGcCTwiDkTEfdntnwDfAcaANcBN2W43AWsHFWQZlGF2Xxk+RMysPLqqgUtaCqwE7gVOjYgD2UM/AE5t8zvrJU1Kmjx06FAfoRaruSY+NjrCpnUrhjpZpAwfImZWHoo2td3jdpReCnwd+LuI2CrpcESMNjz+o4iYtQ4+MTERk5OTfQVcd57eb1Y/knZGxETz9o6m0ktaAHwRuDkitmabfyjptIg4IOk04GB+4Vqz5sT9yfec48RtVnOddKEIuB74TkT8Q8NDtwGXZbcvA27NPzwDtw+aWWudjMBXAR8AHpC0O9v258Bm4HOSLgceBd49mBCrrZOSyGztgx6Fm9XXnAk8Iv4LOH4Wy7QL8w2nXjpd8dDtg2bWimdiFqjTiTluHzSzVpzAC9TpyNrtg2bWihN4gTodWZehB93MysdX5CnQhtXLj6mBQ/uRta8wY2bNnMApbnJMUddu9GQgs2qofQIv+tqXwx5ZF/33mll+al8Dr9sSrXX7e82qrPYJvG491nX7e82qrPYJvG491nX7e82qrPYJvG491nX7e82qrPYnMYvqBClK3f5esyrreD3wPHg9cDOz7rVbD7z2JRQzs1Q5gZuZJcoJ3MwsUU7gZmaJcgI3M0uUE7iZWaKcwM3MEuUEbmaWKCdwM7NEzTmVXtINwNuBgxFxdrbtHOBa4ETgKPChiPjmIAO19nyBBrN66mQEfiNwcdO2TwB/ExHnAH+V3bcCzFygYerwEYIXL9CwbddU0aGZ2YDNmcAj4i7gqebNwMuz268AHs85rl/YtmuKVZt3sGzj7azavMOJqYkv0GBWX72uRvinwHZJ1zD9IfCGdjtKWg+sBxgfH+/qRXz5r7n5Ag1m9dXrScwPAldExOnAFcD17XaMiOsiYiIiJhYvXtzVi9RtdNnLtw1foMGsvnpN4JcBW7PbnwfOyyecY9VpdNlrLdsXaDCrr14T+OPAm7PbFwAP5RPOseo0uuz128balWNsWreCsdERBIyNjrBp3QqXmMxqoJM2wi3AW4BFkvYDVwF/CHxK0nzgZ2Q17rxtWL38mBo4VHd02c+3jbUrx5ywzWpozgQeEZe0eejcnGM5Tp0u/7VkdISpFsm6it82zCwfpb8mZl1Gl3X6tmFm+Sh9Aq+LOn3bMLN8OIGXSF2+bZhZPpzALVdel8VseJzALTeeOZsPfwhap7ycrOWmbjNnB8GLk1k3nMAtN3WaOTso/hC0bjiBW27qNHN2UPwhaN1wArfceF2W/vlD0LrhBG658bos/fOHoHXDXSiWK/ey98cTuqwbTuBmJeMPQeuUSyhmZolyAjczS5QTuJlZopzAzcwS5QRuZpYoJ3Azs0Q5gZuZJcoJ3MwsUXMmcEk3SDooaU/T9j+W9KCkvZI+MbgQzcyslU5G4DcCFzdukPRWYA3wuoh4LXBN/qGZmdls5kzgEXEX8FTT5g8CmyPi2WyfgwOIzczMZtFrDfws4E2S7pX0dUmvb7ejpPWSJiVNHjp0qMeXMzOzZr0m8PnAKcD5wAbgc5LUaseIuC4iJiJiYvHixT2+nJmZNet1NcL9wNaICOCbkl4AFgEeYptZV3wR5971OgLfBrwVQNJZwAnAE3kFZWb14Is496eTNsItwN3Ackn7JV0O3ACckbUWfha4LBuNm5l1zBdx7s+cJZSIuKTNQ+/PORYzqxlfxLk/nolpZoXxRZz74wReEdt2TbFq8w6WbbydVZt3uIZoSfBFnPvja2JWwMyJoJla4syJIMBn863UfBHn/jiBV8BsJ4L8P4KVnS/i3DuXUCrAJ4LM6skj8ApYMjrCVItk3e2JIE+oMEuLR+AVkMeJIE+oMEuPE3gFrF05xqZ1KxgbHUHA2OgIm9at6Gr07AkVZulxCaUi+j0RlFId3aUes2kegRuQzoQKl3rMXuQEbkA6Eypc6jF7kUsoBqQzoSKlUo/ZoDmB2y+kMKEir5ZJsypwCcWSMuxSj9eYsTLzCNySMsxSj9eYsbJzArfkDKvU4zVmrOxcQjFrwydMrewqNwLPa5KHJ4tYnidM/X6yQahUAs+rZlnW2qeTwHBtWL38mPcB9HbCtKzvJ0tfpUooeU3yKONkEc9AHL481piBcr6frBoqNQLPq2ZZxtqnT6gVI48TpmV8P1k1zDkCl3SDpIOS9rR47COSQtKiwYTXnbzW8yjjuiBOAukq4/vJqqGTEsqNwMXNGyWdDlwEPJZzTD3La5JHGdcFcRJIVxnfT1YNc5ZQIuIuSUtbPPRJ4KPArTnH1LO8JnkMe7JIJ6+T1wm1uiryBHAq68xYehQRc+80ncC/HBFnZ/fXABdExIclPQJMRMQTbX53PbAeYHx8/NxHH300n8groLk7AaaTcrsTZe5C6U23x9msbCTtjIiJ47Z3m8AlLQS+ClwUEU/PlcAbTUxMxOTkZLexV9aqzTta9hmPjY7wjY0XFBBRNfk4W+raJfBeulB+BVgG3C8J4FXAfZLOi4gf9BdmvaRwYrIKo/4UjrNZL7pO4BHxAPDKmfvdjMDLqMgENbpwAT965rnjtpflxGRVJqB4CVqrqk7aCLcAdwPLJe2XdPngwxqOIifHbNs1xf/97Ohx2xfMU2lOTFZlAoq7QKyqOulCuWSOx5fmFs2QFTk55urt+3juhePPP5x0wvzSjG5nKz2kVFpxF4hVVaVmYnZr2LXRxqTX7tTx00eOL6kUpV3pYXThguRKKylcbcisW5VaC6Vbw5wc01yu6TamIrQrPURQidKKWepqncCHWRttVa5pVra6bLvFnNp9S3BXh9lw1bqEMsza6GzJTVDaumyr0sPV2/e5q8OsBGqdwGF4tdF29eQUJ5N4Wr9ZOdS6hDLMK45XqZUtr3Wyzaw/tR2BD3uSStVa2dzVYVa82ibwInrAnfTMLE+1TeBeH6MzKU3YMaub2ibwQa2PUdaE10tcVVkLxayqansScxAnFct64eFe46rKWihmVVXbBD6IToqyJrxe43KZyazcaltCgfxPKpY14fUal5dhNSu32o7AB6GsFx7uNa4q9a6bVZETeI4GnfB6nXjUa1yesJOuYU5Ss+LUuoSSt0FO1umnI6SfuKrUu17WDqG8uXuoPjq6qHFefFHjzjUnm58+e5TDLVYBTHEtlSLU6cr0vohz9bS7qLFLKCXUqu2vVfKG4k+QpqKsHUKDUNaT6ZY/J/AS6mTt8BmvGFkw4GiqoU5Jrawn0y1/TuAl1E1SkQYYSIXUKam5e6g+Orkq/Q2SDkra07DtakkPSvqWpC9JGh1smPXSTVI5/Ex5rqFZZnVKau4eqo9OulBuBD4NfKZh2x3AlRFxVNLfA1cCf5Z/ePXU6oIJgpbX0kxxBFlEN0jVlvOdS5W6h6y9ORN4RNwlaWnTtq803L0HeFe+YdVbq2Tz1l9dzBd3TiV/FZwiW9yc1Kxq8ugD/wPglnYPSloPrAcYHx/P4eXqoVWymXj1KcmPIItYh92sqvpK4JI+BhwFbm63T0RcB1wH033g/bxe3ZV1BNlNSaRO3SBmg9ZzApf0+8DbgQtjmLOBSqwuM/0atSqJXHHLbiYffYqPr11x3P5eIGt2dXwPWe96aiOUdDHwUeCdEfFMviGlqaxrgQ9aq5JIADff81jLv71O3SDdqut7yHrXSRvhFuBuYLmk/ZIuZ7or5WXAHZJ2S7p2wHGWXp1m+jVqV/oIaPm3u8Wtvbq+h6x3nXShXNJi8/UDiCVpda3ttiuJQPu/vay1/KLV9T1kvfNMzJzUaaZfow2rl9NuMmjV//a81fU9ZL1zAs9JXWu7a1eOcen548cl8Tr87Xmr63vIeuf1wHNSt5l+jT6+dkUletSLVuf3kPXG64GbmZVcu/XAPQKvMPcUm1WbE3hFzbbmCNTra7o/yKyqnMArql1P8V/ftpdnj75Qm+sl+vqQVmXuQqmodr3Dh488V6vJIp4cY1XmBF5R3fYOV3WyiCfHWJU5gVdUu57ikxe2voZmVSeLeHKMVZkTeEW1W3Pkqne8tlaTRTw5xqrMJzErbLY1R+rSleHJMVZlnshjZlZynshjhXIvtln+nMBt4NyLbTYYPolpA+debLPBcAK3gXMvttlgOIHbwLkX22wwnMBt4Iruxd62a4pVm3ewbOPtrNq8wxcJtsrwSUwbuCJ7sX0C1arMCdyGoqgLGc92AtUJ3FI3ZwlF0g2SDkra07DtFEl3SHoo+/fkwYZp1hufQLUq66QGfiNwcdO2jcCdEXEmcGd236x0fALVqmzOBB4RdwFPNW1eA9yU3b4JWJtzXGa5KPoEqtkg9VoDPzUiDmS3fwCc2m5HSeuB9QDj4+M9vpxZb7yYlVVZR4tZSVoKfDkizs7uH46I0YbHfxQRc9bBvZiVmVn38l7M6oeSTouIA5JOAw72F56lxotTmRWv14k8twGXZbcvA27NJxxLwUxv9dThIwQv9lZ7gozZcHXSRrgFuBtYLmm/pMuBzcBvSnoIeFt232oitcWpPBPTqmrOEkpEXNLmoQtzjsUSkVJv9V9se4Cb73mMmTM9nolpVeK1UKxrqfRWb9s1dUzynlHmbwtm3XACt66l0lt99fZ9xyXvGWX8tmDWLa+FYl1Lpbd6tiRdtm8LZr1wAreeFLU4VTeWjI4w1SKJC0r3bcGsFy6hWGW1KvUIuPT88dJ/+Jh1wiNwq6xUSj1WbYOc9OYEbpWWQqnHqmvQFxRxArdCeUq+VdmgLyjiBG6F8eXOrOoGPenNJzGtMKlNyTfr1qAnvTmBW2FSmpJv1otBT3pzCcUK065PO7VJNq7jWzuD7oRyArfCbFi9/JgaOJRzSv5sXMe3uQyyE8olFCvM2pVjbFq3grHREQSMjY6wad2KpBKf6/hWJI/ArVCp92m7jm9F8gjcrA+pLK1r1eQEbtaHVJbWtWpyCcWsD15vxYrkBG7Wp9Tr+JYul1DMzBLVVwKXdIWkvZL2SNoi6cS8AjMzs9n1nMAljQF/AkxExNnAPOC9eQVmZmaz67eEMh8YkTQfWAg83n9IZmbWiZ4TeERMAdcAjwEHgKcj4ivN+0laL2lS0uShQ4d6j9TMzI7RTwnlZGANsAxYApwk6f3N+0XEdRExERETixcv7j1SMzM7Rj9thG8Dvh8RhwAkbQXeAPxzHoGZ2fB4RcU09ZPAHwPOl7QQOAJcCEzmEpWZDY1XVExXPzXwe4EvAPcBD2TPdV1OcZnZkHhFxXT1NRMzIq4CrsopFjMrgFdUTJdnYprVnFdUTJcTuFnNeUXFdHkxK7Oa84qK6XICNzOvqJgol1DMzBLlBG5mligncDOzRDmBm5klygnczCxRiojhvZh0CHh0aC/YuUXAE0UH0SHHOjgpxZtSrJBWvGWM9dURcdxyrkNN4GUlaTIiJoqOoxOOdXBSijelWCGteFOK1SUUM7NEOYGbmSXKCXxaSsvgOtbBSSnelGKFtOJNJlbXwM3MEuURuJlZopzAzcwSVbkELuliSfskPSxpY4vHf0nSLdnj90pamm1fKumIpN3Zz7UNv/O17DlnHntlkbFmj/2apLsl7ZX0gKQTs+3nZvcflvSPkpRHrAOMt1THVtKlDbHslvSCpHOyx0p3bOeIt2zHdoGkm7Jj+B1JV3b6nCWM95Fs+25JxV0LOCIq8wPMA74LnAGcANwPvKZpnw8B12a33wvckt1eCuxp87xfAyZKFOt84FvA67L7vwzMy25/EzgfEPBvwG+VPN5SHdumfVYA3224X7pjO0e8pTq2wPuAz2a3FwKPZP/fzfmcZYo3u/8IsCjPY9vLT9VG4OcBD0fE9yLi58BngTVN+6wBbspufwG4MM+RVBf6ifUi4FsRcT9ARDwZEc9LOg14eUTcE9Pvss8Aa8sab05x5R1ro0uy36XEx7ZlvAPUT6wBnCRpPjAC/Bz4cYfPWaZ4S6NqCXwM+N+G+/uzbS33iYijwNNMjwgBlknaJenrkt7U9Hv/lH1d+sucEn4/sZ4FhKTtku6T9NGG/ffP8ZxlindGmY5to/cAWxr2L+OxbRfvjDId2y8APwUOAI8B10TEUx0+Z5nihenk/hVJOyWtzynWrvmKPC86AIxHxJOSzgW2SXptRPwYuDQipiS9DPgi8AGmR2BFmQ+8EXg98Axwp6SdTL/xyqhlvBFxJ+U7tgBI+g3gmYjYU3QsnWgTb9mO7XnA88AS4GTgPyX9R4HxzKVlvBHxPeCN2bF9JXCHpAcj4q5hB1i1EfgUcHrD/Vdl21ruk301egXwZEQ8GxFPAkTETqbrZmdl96eyf38C/AvT/2ELi5XpUcRdEfFERDwD/Cvw69n+r5rjOcsUbxmP7Yz3cuxotqzHtl28ZTy27wP+PSKei4iDwDeAiQ6fs0zxNh7bg8CXyOfYdq1qCfy/gTMlLZN0AtNv6tua9rkNuCy7/S5gR0SEpMWS5gFIOgM4E/iepPmSFmXbFwBvB/IYlfUcK7AdWCFpYfaGezPw7Yg4APxY0vnZ1+XfA27NIdaBxFvSY4uklwDvpqGeXOJj2zLekh7bx4ALsphOYvqE8IMdPmdp4pV0UvatZmb7ReRzbLtX9FnUvH+A3wb+h+kR9MeybX8LvDO7fSLweeBhprsKzsi2/y6wF9gN3Ae8I9t+ErCT6S6KvcCnyDooioo1e+z9WTx7gE80bJ/Itn0X+DTZbNsyxlviY/sW4J4Wz1nWY3tcvGU8tsBLs+17gW8DG2Z7zqKPbbt4me5ouT/72Zt3vN38eCq9mVmiqlZCMTOrDSdwM7NEOYGbmSXKCdzMLFFO4GZmiXICNzNLlBO4mVmi/h9MGYIuzTUQ6QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEhh-MN3TxmC"
      },
      "source": [
        "# KNN Predictions\n",
        "\n",
        "Here we are using KNN to make predictions based on a number of features fed into the model.  As we can see it's score is 0.96 which isn't perfect but it's better to avoid overfitting the model so that it can adapt to new data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ndm4DL8G1lG-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e836d376-6eed-4f21-bf36-c3fbf8d4561d"
      },
      "source": [
        "# K Nearest Neighbour\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X_train,y_train[['Diagnosis']])\n",
        "print('Performance on X_test: ',knn.score(X_train,y_train['Diagnosis']))\n"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performance on X_test:  0.9464285714285714\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \"\"\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuntf9ZpWj4M"
      },
      "source": [
        "# Make prediction on testSet\n",
        "X_predictions = knn.predict(X_test)\n",
        "y_predictions = knn.predict(y_test)\n",
        "X_trainPredictions = knn.predict(X_train)\n",
        "y_trainPredictions = knn.predict(y_train)\n"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2rlHNip4WmK",
        "outputId": "62a7e5e4-e213-4b39-ab84-02521a5c65ad"
      },
      "source": [
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(X_test['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', X_predictions[count])\n",
        "  if X_predictions[count] == actualValues[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n",
        "\n",
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(X_train['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', X_trainPredictions[count])\n",
        "  if X_trainPredictions[count] == actualValues[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
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            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  513  correctly predicted values:  474\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  56  correctly predicted values:  53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bRoTc-p46aD",
        "outputId": "71d8d946-30d0-482e-86eb-a7cc24b04248"
      },
      "source": [
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(y_test['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', y_predictions[count])\n",
        "  if y_predictions[count] == actualValues[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n",
        "\n",
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(y_train['Diagnosis']).reshape(-1,1)\n",
        "\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', y_trainPredictions[count])\n",
        "  if y_trainPredictions[count] == actualValues[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n",
        "\n"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Total values:  513  correctly predicted values:  474\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  56  correctly predicted values:  53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZglfe8VT1ZP"
      },
      "source": [
        "# Naive Bayes prediction of diagnosis\n",
        "\n",
        "Here we use naive bayes on both sets to perform  the diagnosis based on a few features from the dataset, I chose to input a large number of features into this model as generally this algorithm performs better when fitted with more features, in Naive Bayes the weights of the features have no bearing on the output.  The model below is performing accurately but perhaps it is overfitting which is why I included two Naive Bayes Models in this notebook, the only difference being the number of features.  \n",
        "\n",
        "## References\n",
        "\n",
        "Sklearn documentation: https://scikit-learn.org/stable/modules/naive_bayes.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkv0-QPOmtPV"
      },
      "source": [
        "You may also notice we are using a particular kind of Naive Bayes called Gaussian Naive Bayes for this model.  We chose to include this version of Naive Bayes due to it's ability to handle continuous data. The algorithm also has the advantage of speed and is frequently used in email spam classifiers.  The algorithm works by assuming that the continuous values across the features are normally distributed in a Gaussian fashion(think of a bell curve!)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt8iXCKZNIvw"
      },
      "source": [
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "naive_bayes = GaussianNB()\n",
        "naive_bayes.fit(X_train[['Mean_Radius','Mean_Texture','Mean_Perimeter','Mean_Area','Mean_Smoothness','Mean_Compactness','Mean_Concavity','Mean_Concave_Points','Mean_Symmetry','Mean_Fractal_Dimension','Radius_SE','Texture_SE','Perimeter_SE','Area_SE','Smoothness_SE','Compactness_SE','Concavity_SE','Concave_Points_SE']],y_train['Diagnosis'])\n",
        "XDiagnosisPrediction = naive_bayes.predict(X_test[['Diagnosis']])\n",
        "yDiagnosisPrediction = naive_bayes.predict(y_test[['Diagnosis']])\n",
        "XTrainDiagnosisPrediction = naive_bayes.predict(X_train[['Diagnosis']])\n",
        "YTrainDiagnosisPrediction = naive_bayes.predict(y_train[['Diagnosis']])\n"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j01Qkq9V77GH",
        "outputId": "655ce9f3-8706-4052-c49e-428ebf7a198e"
      },
      "source": [
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(X_test['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', XDiagnosisPrediction[count])\n",
        "  if actualValues[count] == XDiagnosisPrediction[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n",
        "\n",
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(X_train['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', XTrainDiagnosisPrediction[count])\n",
        "  if actualValues[count] == XTrainDiagnosisPrediction[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  513  correctly predicted values:  513\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  56  correctly predicted values:  56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LlAxnEfQ8fC",
        "outputId": "787083bc-5c18-481f-d066-adabf9a6efa2"
      },
      "source": [
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(y_test['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', yDiagnosisPrediction[count])\n",
        "  if actualValues[count] == yDiagnosisPrediction[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n",
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(y_train['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', YTrainDiagnosisPrediction[count])\n",
        "  if actualValues[count] == YTrainDiagnosisPrediction[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  513  correctly predicted values:  513\n",
            "Actual:  [1]  Predicted:  1\n",
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            "Actual:  [0]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [1]  Predicted:  1\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  56  correctly predicted values:  56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OITs6W0S9KoV"
      },
      "source": [
        "# Second Naive Bayes Model\n",
        "As we can see the model below has worse performance than the model above, you may be asking why we've included this model, the reason why is that the above model seems to be trained a little too well on our training data and may not be able to predict values outside of our dataset too well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNPxb66o8LmE",
        "outputId": "0c901e30-27fc-4f22-a9b8-c594961a43f5"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "naive_bayes = GaussianNB()\n",
        "naive_bayes.fit(X_train[['Radius_SE','Texture_SE','Perimeter_SE','Area_SE','Smoothness_SE','Compactness_SE','Concavity_SE','Concave_Points_SE','Mean_Radius','Mean_Texture','Mean_Perimeter','Mean_Area']],y_train[['Diagnosis']])\n",
        "XDiagnosisPrediction = naive_bayes.predict(X_test[['Texture_SE']])\n",
        "yDiagnosisPrediction = naive_bayes.predict(y_test[['Texture_SE']])"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/naive_bayes.py:206: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzT98qno8Pt5",
        "outputId": "21afb4aa-5c3e-4ca7-d92e-bd1b039edf51"
      },
      "source": [
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(y_test['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', yDiagnosisPrediction[count])\n",
        "  if actualValues[count] == yDiagnosisPrediction[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
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            "Total values:  513  correctly predicted values:  325\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuaMtGOV8W2c",
        "outputId": "256b91bc-f825-41d2-be55-4dff67c1177a"
      },
      "source": [
        "correct = 0;\n",
        "count = 0;\n",
        "actualValues = np.array(X_test['Diagnosis']).reshape(-1,1)\n",
        "for i in actualValues:\n",
        "  print('Actual: ', actualValues[count], ' Predicted: ', XDiagnosisPrediction[count])\n",
        "  if XDiagnosisPrediction[count] == actualValues[count]:\n",
        "    correct+=1\n",
        "  count+=1\n",
        "print('Total values: ',count, ' correctly predicted values: ',correct)\n"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Actual:  [1]  Predicted:  0\n",
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            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [1]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Actual:  [0]  Predicted:  0\n",
            "Total values:  513  correctly predicted values:  325\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RM5qdiPE7i1r"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}