Deep_Neural_Networks_Image_Processing_Mini_Project.ipynb

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  "metadata": {
    "colab": {
      "name": "DNN_Image_Processing_Mini_Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Stark-developer01/Image-Processing-for-Classification-of-Cancerous-Tumours/blob/main/Deep_Neural_Networks_Image_Processing_Mini_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WL-Wmpq9_nFR"
      },
      "source": [
        "#This program detects breast cancer based on data samples \n",
        "#Deep Neural Networks have been used in this model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQ82XqMzfpGB",
        "outputId": "2f2238bb-ba7f-4938-888b-adfdd84cf0b5"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RKyZTT9CBnq"
      },
      "source": [
        "#importing the essential libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBtKysGAC8iU",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
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              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c5da8294-e11f-4a4b-df33-83b237c5a997"
      },
      "source": [
        "#loading the data\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "df = pd.read_csv('data.csv')\n",
        "df.head(50)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "     <output id=\"result-b714c74b-89e0-4335-a345-748f33a56577\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Saving data.csv to data.csv\n"
          ],
          "name": "stdout"
        },
        {
          "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>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</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>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
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              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.990</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.147100</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.400</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.049040</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.015870</td>\n",
              "      <td>0.030030</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.71190</td>\n",
              "      <td>0.26540</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.570</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.070170</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.080</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.013400</td>\n",
              "      <td>0.013890</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.24160</td>\n",
              "      <td>0.18600</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.690</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.127900</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.030</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.040060</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.020580</td>\n",
              "      <td>0.022500</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.45040</td>\n",
              "      <td>0.24300</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.420</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.105200</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.230</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.074580</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.018670</td>\n",
              "      <td>0.059630</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.68690</td>\n",
              "      <td>0.25750</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.290</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.104300</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.440</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.024610</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.018850</td>\n",
              "      <td>0.017560</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.40000</td>\n",
              "      <td>0.16250</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>843786</td>\n",
              "      <td>M</td>\n",
              "      <td>12.450</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0.17000</td>\n",
              "      <td>0.15780</td>\n",
              "      <td>0.080890</td>\n",
              "      <td>0.2087</td>\n",
              "      <td>0.07613</td>\n",
              "      <td>0.3345</td>\n",
              "      <td>0.8902</td>\n",
              "      <td>2.217</td>\n",
              "      <td>27.190</td>\n",
              "      <td>0.007510</td>\n",
              "      <td>0.033450</td>\n",
              "      <td>0.03672</td>\n",
              "      <td>0.011370</td>\n",
              "      <td>0.021650</td>\n",
              "      <td>0.005082</td>\n",
              "      <td>15.470</td>\n",
              "      <td>23.75</td>\n",
              "      <td>103.40</td>\n",
              "      <td>741.6</td>\n",
              "      <td>0.17910</td>\n",
              "      <td>0.52490</td>\n",
              "      <td>0.53550</td>\n",
              "      <td>0.17410</td>\n",
              "      <td>0.3985</td>\n",
              "      <td>0.12440</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>844359</td>\n",
              "      <td>M</td>\n",
              "      <td>18.250</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0.10900</td>\n",
              "      <td>0.11270</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.1794</td>\n",
              "      <td>0.05742</td>\n",
              "      <td>0.4467</td>\n",
              "      <td>0.7732</td>\n",
              "      <td>3.180</td>\n",
              "      <td>53.910</td>\n",
              "      <td>0.004314</td>\n",
              "      <td>0.013820</td>\n",
              "      <td>0.02254</td>\n",
              "      <td>0.010390</td>\n",
              "      <td>0.013690</td>\n",
              "      <td>0.002179</td>\n",
              "      <td>22.880</td>\n",
              "      <td>27.66</td>\n",
              "      <td>153.20</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>0.14420</td>\n",
              "      <td>0.25760</td>\n",
              "      <td>0.37840</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>84458202</td>\n",
              "      <td>M</td>\n",
              "      <td>13.710</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>0.059850</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>0.07451</td>\n",
              "      <td>0.5835</td>\n",
              "      <td>1.3770</td>\n",
              "      <td>3.856</td>\n",
              "      <td>50.960</td>\n",
              "      <td>0.008805</td>\n",
              "      <td>0.030290</td>\n",
              "      <td>0.02488</td>\n",
              "      <td>0.014480</td>\n",
              "      <td>0.014860</td>\n",
              "      <td>0.005412</td>\n",
              "      <td>17.060</td>\n",
              "      <td>28.14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.16540</td>\n",
              "      <td>0.36820</td>\n",
              "      <td>0.26780</td>\n",
              "      <td>0.15560</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>844981</td>\n",
              "      <td>M</td>\n",
              "      <td>13.000</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.18590</td>\n",
              "      <td>0.093530</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>0.07389</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>1.0020</td>\n",
              "      <td>2.406</td>\n",
              "      <td>24.320</td>\n",
              "      <td>0.005731</td>\n",
              "      <td>0.035020</td>\n",
              "      <td>0.03553</td>\n",
              "      <td>0.012260</td>\n",
              "      <td>0.021430</td>\n",
              "      <td>0.003749</td>\n",
              "      <td>15.490</td>\n",
              "      <td>30.73</td>\n",
              "      <td>106.20</td>\n",
              "      <td>739.3</td>\n",
              "      <td>0.17030</td>\n",
              "      <td>0.54010</td>\n",
              "      <td>0.53900</td>\n",
              "      <td>0.20600</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>0.10720</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>84501001</td>\n",
              "      <td>M</td>\n",
              "      <td>12.460</td>\n",
              "      <td>24.04</td>\n",
              "      <td>83.97</td>\n",
              "      <td>475.9</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0.23960</td>\n",
              "      <td>0.22730</td>\n",
              "      <td>0.085430</td>\n",
              "      <td>0.2030</td>\n",
              "      <td>0.08243</td>\n",
              "      <td>0.2976</td>\n",
              "      <td>1.5990</td>\n",
              "      <td>2.039</td>\n",
              "      <td>23.940</td>\n",
              "      <td>0.007149</td>\n",
              "      <td>0.072170</td>\n",
              "      <td>0.07743</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.017890</td>\n",
              "      <td>0.010080</td>\n",
              "      <td>15.090</td>\n",
              "      <td>40.68</td>\n",
              "      <td>97.65</td>\n",
              "      <td>711.4</td>\n",
              "      <td>0.18530</td>\n",
              "      <td>1.05800</td>\n",
              "      <td>1.10500</td>\n",
              "      <td>0.22100</td>\n",
              "      <td>0.4366</td>\n",
              "      <td>0.20750</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>845636</td>\n",
              "      <td>M</td>\n",
              "      <td>16.020</td>\n",
              "      <td>23.24</td>\n",
              "      <td>102.70</td>\n",
              "      <td>797.8</td>\n",
              "      <td>0.08206</td>\n",
              "      <td>0.06669</td>\n",
              "      <td>0.03299</td>\n",
              "      <td>0.033230</td>\n",
              "      <td>0.1528</td>\n",
              "      <td>0.05697</td>\n",
              "      <td>0.3795</td>\n",
              "      <td>1.1870</td>\n",
              "      <td>2.466</td>\n",
              "      <td>40.510</td>\n",
              "      <td>0.004029</td>\n",
              "      <td>0.009269</td>\n",
              "      <td>0.01101</td>\n",
              "      <td>0.007591</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.003042</td>\n",
              "      <td>19.190</td>\n",
              "      <td>33.88</td>\n",
              "      <td>123.80</td>\n",
              "      <td>1150.0</td>\n",
              "      <td>0.11810</td>\n",
              "      <td>0.15510</td>\n",
              "      <td>0.14590</td>\n",
              "      <td>0.09975</td>\n",
              "      <td>0.2948</td>\n",
              "      <td>0.08452</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>84610002</td>\n",
              "      <td>M</td>\n",
              "      <td>15.780</td>\n",
              "      <td>17.89</td>\n",
              "      <td>103.60</td>\n",
              "      <td>781.0</td>\n",
              "      <td>0.09710</td>\n",
              "      <td>0.12920</td>\n",
              "      <td>0.09954</td>\n",
              "      <td>0.066060</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.06082</td>\n",
              "      <td>0.5058</td>\n",
              "      <td>0.9849</td>\n",
              "      <td>3.564</td>\n",
              "      <td>54.160</td>\n",
              "      <td>0.005771</td>\n",
              "      <td>0.040610</td>\n",
              "      <td>0.02791</td>\n",
              "      <td>0.012820</td>\n",
              "      <td>0.020080</td>\n",
              "      <td>0.004144</td>\n",
              "      <td>20.420</td>\n",
              "      <td>27.28</td>\n",
              "      <td>136.50</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>0.13960</td>\n",
              "      <td>0.56090</td>\n",
              "      <td>0.39650</td>\n",
              "      <td>0.18100</td>\n",
              "      <td>0.3792</td>\n",
              "      <td>0.10480</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>846226</td>\n",
              "      <td>M</td>\n",
              "      <td>19.170</td>\n",
              "      <td>24.80</td>\n",
              "      <td>132.40</td>\n",
              "      <td>1123.0</td>\n",
              "      <td>0.09740</td>\n",
              "      <td>0.24580</td>\n",
              "      <td>0.20650</td>\n",
              "      <td>0.111800</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07800</td>\n",
              "      <td>0.9555</td>\n",
              "      <td>3.5680</td>\n",
              "      <td>11.070</td>\n",
              "      <td>116.200</td>\n",
              "      <td>0.003139</td>\n",
              "      <td>0.082970</td>\n",
              "      <td>0.08890</td>\n",
              "      <td>0.040900</td>\n",
              "      <td>0.044840</td>\n",
              "      <td>0.012840</td>\n",
              "      <td>20.960</td>\n",
              "      <td>29.94</td>\n",
              "      <td>151.70</td>\n",
              "      <td>1332.0</td>\n",
              "      <td>0.10370</td>\n",
              "      <td>0.39030</td>\n",
              "      <td>0.36390</td>\n",
              "      <td>0.17670</td>\n",
              "      <td>0.3176</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>846381</td>\n",
              "      <td>M</td>\n",
              "      <td>15.850</td>\n",
              "      <td>23.95</td>\n",
              "      <td>103.70</td>\n",
              "      <td>782.7</td>\n",
              "      <td>0.08401</td>\n",
              "      <td>0.10020</td>\n",
              "      <td>0.09938</td>\n",
              "      <td>0.053640</td>\n",
              "      <td>0.1847</td>\n",
              "      <td>0.05338</td>\n",
              "      <td>0.4033</td>\n",
              "      <td>1.0780</td>\n",
              "      <td>2.903</td>\n",
              "      <td>36.580</td>\n",
              "      <td>0.009769</td>\n",
              "      <td>0.031260</td>\n",
              "      <td>0.05051</td>\n",
              "      <td>0.019920</td>\n",
              "      <td>0.029810</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>16.840</td>\n",
              "      <td>27.66</td>\n",
              "      <td>112.00</td>\n",
              "      <td>876.5</td>\n",
              "      <td>0.11310</td>\n",
              "      <td>0.19240</td>\n",
              "      <td>0.23220</td>\n",
              "      <td>0.11190</td>\n",
              "      <td>0.2809</td>\n",
              "      <td>0.06287</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>84667401</td>\n",
              "      <td>M</td>\n",
              "      <td>13.730</td>\n",
              "      <td>22.61</td>\n",
              "      <td>93.60</td>\n",
              "      <td>578.3</td>\n",
              "      <td>0.11310</td>\n",
              "      <td>0.22930</td>\n",
              "      <td>0.21280</td>\n",
              "      <td>0.080250</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.07682</td>\n",
              "      <td>0.2121</td>\n",
              "      <td>1.1690</td>\n",
              "      <td>2.061</td>\n",
              "      <td>19.210</td>\n",
              "      <td>0.006429</td>\n",
              "      <td>0.059360</td>\n",
              "      <td>0.05501</td>\n",
              "      <td>0.016280</td>\n",
              "      <td>0.019610</td>\n",
              "      <td>0.008093</td>\n",
              "      <td>15.030</td>\n",
              "      <td>32.01</td>\n",
              "      <td>108.80</td>\n",
              "      <td>697.7</td>\n",
              "      <td>0.16510</td>\n",
              "      <td>0.77250</td>\n",
              "      <td>0.69430</td>\n",
              "      <td>0.22080</td>\n",
              "      <td>0.3596</td>\n",
              "      <td>0.14310</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>84799002</td>\n",
              "      <td>M</td>\n",
              "      <td>14.540</td>\n",
              "      <td>27.54</td>\n",
              "      <td>96.73</td>\n",
              "      <td>658.8</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.15950</td>\n",
              "      <td>0.16390</td>\n",
              "      <td>0.073640</td>\n",
              "      <td>0.2303</td>\n",
              "      <td>0.07077</td>\n",
              "      <td>0.3700</td>\n",
              "      <td>1.0330</td>\n",
              "      <td>2.879</td>\n",
              "      <td>32.550</td>\n",
              "      <td>0.005607</td>\n",
              "      <td>0.042400</td>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.010900</td>\n",
              "      <td>0.018570</td>\n",
              "      <td>0.005466</td>\n",
              "      <td>17.460</td>\n",
              "      <td>37.13</td>\n",
              "      <td>124.10</td>\n",
              "      <td>943.2</td>\n",
              "      <td>0.16780</td>\n",
              "      <td>0.65770</td>\n",
              "      <td>0.70260</td>\n",
              "      <td>0.17120</td>\n",
              "      <td>0.4218</td>\n",
              "      <td>0.13410</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>848406</td>\n",
              "      <td>M</td>\n",
              "      <td>14.680</td>\n",
              "      <td>20.13</td>\n",
              "      <td>94.74</td>\n",
              "      <td>684.5</td>\n",
              "      <td>0.09867</td>\n",
              "      <td>0.07200</td>\n",
              "      <td>0.07395</td>\n",
              "      <td>0.052590</td>\n",
              "      <td>0.1586</td>\n",
              "      <td>0.05922</td>\n",
              "      <td>0.4727</td>\n",
              "      <td>1.2400</td>\n",
              "      <td>3.195</td>\n",
              "      <td>45.400</td>\n",
              "      <td>0.005718</td>\n",
              "      <td>0.011620</td>\n",
              "      <td>0.01998</td>\n",
              "      <td>0.011090</td>\n",
              "      <td>0.014100</td>\n",
              "      <td>0.002085</td>\n",
              "      <td>19.070</td>\n",
              "      <td>30.88</td>\n",
              "      <td>123.40</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>0.14640</td>\n",
              "      <td>0.18710</td>\n",
              "      <td>0.29140</td>\n",
              "      <td>0.16090</td>\n",
              "      <td>0.3029</td>\n",
              "      <td>0.08216</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>84862001</td>\n",
              "      <td>M</td>\n",
              "      <td>16.130</td>\n",
              "      <td>20.68</td>\n",
              "      <td>108.10</td>\n",
              "      <td>798.8</td>\n",
              "      <td>0.11700</td>\n",
              "      <td>0.20220</td>\n",
              "      <td>0.17220</td>\n",
              "      <td>0.102800</td>\n",
              "      <td>0.2164</td>\n",
              "      <td>0.07356</td>\n",
              "      <td>0.5692</td>\n",
              "      <td>1.0730</td>\n",
              "      <td>3.854</td>\n",
              "      <td>54.180</td>\n",
              "      <td>0.007026</td>\n",
              "      <td>0.025010</td>\n",
              "      <td>0.03188</td>\n",
              "      <td>0.012970</td>\n",
              "      <td>0.016890</td>\n",
              "      <td>0.004142</td>\n",
              "      <td>20.960</td>\n",
              "      <td>31.48</td>\n",
              "      <td>136.80</td>\n",
              "      <td>1315.0</td>\n",
              "      <td>0.17890</td>\n",
              "      <td>0.42330</td>\n",
              "      <td>0.47840</td>\n",
              "      <td>0.20730</td>\n",
              "      <td>0.3706</td>\n",
              "      <td>0.11420</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>849014</td>\n",
              "      <td>M</td>\n",
              "      <td>19.810</td>\n",
              "      <td>22.15</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>0.09831</td>\n",
              "      <td>0.10270</td>\n",
              "      <td>0.14790</td>\n",
              "      <td>0.094980</td>\n",
              "      <td>0.1582</td>\n",
              "      <td>0.05395</td>\n",
              "      <td>0.7582</td>\n",
              "      <td>1.0170</td>\n",
              "      <td>5.865</td>\n",
              "      <td>112.400</td>\n",
              "      <td>0.006494</td>\n",
              "      <td>0.018930</td>\n",
              "      <td>0.03391</td>\n",
              "      <td>0.015210</td>\n",
              "      <td>0.013560</td>\n",
              "      <td>0.001997</td>\n",
              "      <td>27.320</td>\n",
              "      <td>30.88</td>\n",
              "      <td>186.80</td>\n",
              "      <td>2398.0</td>\n",
              "      <td>0.15120</td>\n",
              "      <td>0.31500</td>\n",
              "      <td>0.53720</td>\n",
              "      <td>0.23880</td>\n",
              "      <td>0.2768</td>\n",
              "      <td>0.07615</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8510426</td>\n",
              "      <td>B</td>\n",
              "      <td>13.540</td>\n",
              "      <td>14.36</td>\n",
              "      <td>87.46</td>\n",
              "      <td>566.3</td>\n",
              "      <td>0.09779</td>\n",
              "      <td>0.08129</td>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.047810</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.05766</td>\n",
              "      <td>0.2699</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>2.058</td>\n",
              "      <td>23.560</td>\n",
              "      <td>0.008462</td>\n",
              "      <td>0.014600</td>\n",
              "      <td>0.02387</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.019800</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>15.110</td>\n",
              "      <td>19.26</td>\n",
              "      <td>99.70</td>\n",
              "      <td>711.2</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.17730</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>0.12880</td>\n",
              "      <td>0.2977</td>\n",
              "      <td>0.07259</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>8510653</td>\n",
              "      <td>B</td>\n",
              "      <td>13.080</td>\n",
              "      <td>15.71</td>\n",
              "      <td>85.63</td>\n",
              "      <td>520.0</td>\n",
              "      <td>0.10750</td>\n",
              "      <td>0.12700</td>\n",
              "      <td>0.04568</td>\n",
              "      <td>0.031100</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>0.06811</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.383</td>\n",
              "      <td>14.670</td>\n",
              "      <td>0.004097</td>\n",
              "      <td>0.018980</td>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.006490</td>\n",
              "      <td>0.016780</td>\n",
              "      <td>0.002425</td>\n",
              "      <td>14.500</td>\n",
              "      <td>20.49</td>\n",
              "      <td>96.09</td>\n",
              "      <td>630.5</td>\n",
              "      <td>0.13120</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.18900</td>\n",
              "      <td>0.07283</td>\n",
              "      <td>0.3184</td>\n",
              "      <td>0.08183</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8510824</td>\n",
              "      <td>B</td>\n",
              "      <td>9.504</td>\n",
              "      <td>12.44</td>\n",
              "      <td>60.34</td>\n",
              "      <td>273.9</td>\n",
              "      <td>0.10240</td>\n",
              "      <td>0.06492</td>\n",
              "      <td>0.02956</td>\n",
              "      <td>0.020760</td>\n",
              "      <td>0.1815</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.9768</td>\n",
              "      <td>1.909</td>\n",
              "      <td>15.700</td>\n",
              "      <td>0.009606</td>\n",
              "      <td>0.014320</td>\n",
              "      <td>0.01985</td>\n",
              "      <td>0.014210</td>\n",
              "      <td>0.020270</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>10.230</td>\n",
              "      <td>15.66</td>\n",
              "      <td>65.13</td>\n",
              "      <td>314.9</td>\n",
              "      <td>0.13240</td>\n",
              "      <td>0.11480</td>\n",
              "      <td>0.08867</td>\n",
              "      <td>0.06227</td>\n",
              "      <td>0.2450</td>\n",
              "      <td>0.07773</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>8511133</td>\n",
              "      <td>M</td>\n",
              "      <td>15.340</td>\n",
              "      <td>14.26</td>\n",
              "      <td>102.50</td>\n",
              "      <td>704.4</td>\n",
              "      <td>0.10730</td>\n",
              "      <td>0.21350</td>\n",
              "      <td>0.20770</td>\n",
              "      <td>0.097560</td>\n",
              "      <td>0.2521</td>\n",
              "      <td>0.07032</td>\n",
              "      <td>0.4388</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>3.384</td>\n",
              "      <td>44.910</td>\n",
              "      <td>0.006789</td>\n",
              "      <td>0.053280</td>\n",
              "      <td>0.06446</td>\n",
              "      <td>0.022520</td>\n",
              "      <td>0.036720</td>\n",
              "      <td>0.004394</td>\n",
              "      <td>18.070</td>\n",
              "      <td>19.08</td>\n",
              "      <td>125.10</td>\n",
              "      <td>980.9</td>\n",
              "      <td>0.13900</td>\n",
              "      <td>0.59540</td>\n",
              "      <td>0.63050</td>\n",
              "      <td>0.23930</td>\n",
              "      <td>0.4667</td>\n",
              "      <td>0.09946</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>851509</td>\n",
              "      <td>M</td>\n",
              "      <td>21.160</td>\n",
              "      <td>23.04</td>\n",
              "      <td>137.20</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>0.09428</td>\n",
              "      <td>0.10220</td>\n",
              "      <td>0.10970</td>\n",
              "      <td>0.086320</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.05278</td>\n",
              "      <td>0.6917</td>\n",
              "      <td>1.1270</td>\n",
              "      <td>4.303</td>\n",
              "      <td>93.990</td>\n",
              "      <td>0.004728</td>\n",
              "      <td>0.012590</td>\n",
              "      <td>0.01715</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.010830</td>\n",
              "      <td>0.001987</td>\n",
              "      <td>29.170</td>\n",
              "      <td>35.59</td>\n",
              "      <td>188.00</td>\n",
              "      <td>2615.0</td>\n",
              "      <td>0.14010</td>\n",
              "      <td>0.26000</td>\n",
              "      <td>0.31550</td>\n",
              "      <td>0.20090</td>\n",
              "      <td>0.2822</td>\n",
              "      <td>0.07526</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>852552</td>\n",
              "      <td>M</td>\n",
              "      <td>16.650</td>\n",
              "      <td>21.38</td>\n",
              "      <td>110.00</td>\n",
              "      <td>904.6</td>\n",
              "      <td>0.11210</td>\n",
              "      <td>0.14570</td>\n",
              "      <td>0.15250</td>\n",
              "      <td>0.091700</td>\n",
              "      <td>0.1995</td>\n",
              "      <td>0.06330</td>\n",
              "      <td>0.8068</td>\n",
              "      <td>0.9017</td>\n",
              "      <td>5.455</td>\n",
              "      <td>102.600</td>\n",
              "      <td>0.006048</td>\n",
              "      <td>0.018820</td>\n",
              "      <td>0.02741</td>\n",
              "      <td>0.011300</td>\n",
              "      <td>0.014680</td>\n",
              "      <td>0.002801</td>\n",
              "      <td>26.460</td>\n",
              "      <td>31.56</td>\n",
              "      <td>177.00</td>\n",
              "      <td>2215.0</td>\n",
              "      <td>0.18050</td>\n",
              "      <td>0.35780</td>\n",
              "      <td>0.46950</td>\n",
              "      <td>0.20950</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.09564</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>852631</td>\n",
              "      <td>M</td>\n",
              "      <td>17.140</td>\n",
              "      <td>16.40</td>\n",
              "      <td>116.00</td>\n",
              "      <td>912.7</td>\n",
              "      <td>0.11860</td>\n",
              "      <td>0.22760</td>\n",
              "      <td>0.22290</td>\n",
              "      <td>0.140100</td>\n",
              "      <td>0.3040</td>\n",
              "      <td>0.07413</td>\n",
              "      <td>1.0460</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>7.276</td>\n",
              "      <td>111.400</td>\n",
              "      <td>0.008029</td>\n",
              "      <td>0.037990</td>\n",
              "      <td>0.03732</td>\n",
              "      <td>0.023970</td>\n",
              "      <td>0.023080</td>\n",
              "      <td>0.007444</td>\n",
              "      <td>22.250</td>\n",
              "      <td>21.40</td>\n",
              "      <td>152.40</td>\n",
              "      <td>1461.0</td>\n",
              "      <td>0.15450</td>\n",
              "      <td>0.39490</td>\n",
              "      <td>0.38530</td>\n",
              "      <td>0.25500</td>\n",
              "      <td>0.4066</td>\n",
              "      <td>0.10590</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>852763</td>\n",
              "      <td>M</td>\n",
              "      <td>14.580</td>\n",
              "      <td>21.53</td>\n",
              "      <td>97.41</td>\n",
              "      <td>644.8</td>\n",
              "      <td>0.10540</td>\n",
              "      <td>0.18680</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.087830</td>\n",
              "      <td>0.2252</td>\n",
              "      <td>0.06924</td>\n",
              "      <td>0.2545</td>\n",
              "      <td>0.9832</td>\n",
              "      <td>2.110</td>\n",
              "      <td>21.050</td>\n",
              "      <td>0.004452</td>\n",
              "      <td>0.030550</td>\n",
              "      <td>0.02681</td>\n",
              "      <td>0.013520</td>\n",
              "      <td>0.014540</td>\n",
              "      <td>0.003711</td>\n",
              "      <td>17.620</td>\n",
              "      <td>33.21</td>\n",
              "      <td>122.40</td>\n",
              "      <td>896.9</td>\n",
              "      <td>0.15250</td>\n",
              "      <td>0.66430</td>\n",
              "      <td>0.55390</td>\n",
              "      <td>0.27010</td>\n",
              "      <td>0.4264</td>\n",
              "      <td>0.12750</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>852781</td>\n",
              "      <td>M</td>\n",
              "      <td>18.610</td>\n",
              "      <td>20.25</td>\n",
              "      <td>122.10</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>0.09440</td>\n",
              "      <td>0.10660</td>\n",
              "      <td>0.14900</td>\n",
              "      <td>0.077310</td>\n",
              "      <td>0.1697</td>\n",
              "      <td>0.05699</td>\n",
              "      <td>0.8529</td>\n",
              "      <td>1.8490</td>\n",
              "      <td>5.632</td>\n",
              "      <td>93.540</td>\n",
              "      <td>0.010750</td>\n",
              "      <td>0.027220</td>\n",
              "      <td>0.05081</td>\n",
              "      <td>0.019110</td>\n",
              "      <td>0.022930</td>\n",
              "      <td>0.004217</td>\n",
              "      <td>21.310</td>\n",
              "      <td>27.26</td>\n",
              "      <td>139.90</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>0.13380</td>\n",
              "      <td>0.21170</td>\n",
              "      <td>0.34460</td>\n",
              "      <td>0.14900</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.07421</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>852973</td>\n",
              "      <td>M</td>\n",
              "      <td>15.300</td>\n",
              "      <td>25.27</td>\n",
              "      <td>102.40</td>\n",
              "      <td>732.4</td>\n",
              "      <td>0.10820</td>\n",
              "      <td>0.16970</td>\n",
              "      <td>0.16830</td>\n",
              "      <td>0.087510</td>\n",
              "      <td>0.1926</td>\n",
              "      <td>0.06540</td>\n",
              "      <td>0.4390</td>\n",
              "      <td>1.0120</td>\n",
              "      <td>3.498</td>\n",
              "      <td>43.500</td>\n",
              "      <td>0.005233</td>\n",
              "      <td>0.030570</td>\n",
              "      <td>0.03576</td>\n",
              "      <td>0.010830</td>\n",
              "      <td>0.017680</td>\n",
              "      <td>0.002967</td>\n",
              "      <td>20.270</td>\n",
              "      <td>36.71</td>\n",
              "      <td>149.30</td>\n",
              "      <td>1269.0</td>\n",
              "      <td>0.16410</td>\n",
              "      <td>0.61100</td>\n",
              "      <td>0.63350</td>\n",
              "      <td>0.20240</td>\n",
              "      <td>0.4027</td>\n",
              "      <td>0.09876</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>853201</td>\n",
              "      <td>M</td>\n",
              "      <td>17.570</td>\n",
              "      <td>15.05</td>\n",
              "      <td>115.00</td>\n",
              "      <td>955.1</td>\n",
              "      <td>0.09847</td>\n",
              "      <td>0.11570</td>\n",
              "      <td>0.09875</td>\n",
              "      <td>0.079530</td>\n",
              "      <td>0.1739</td>\n",
              "      <td>0.06149</td>\n",
              "      <td>0.6003</td>\n",
              "      <td>0.8225</td>\n",
              "      <td>4.655</td>\n",
              "      <td>61.100</td>\n",
              "      <td>0.005627</td>\n",
              "      <td>0.030330</td>\n",
              "      <td>0.03407</td>\n",
              "      <td>0.013540</td>\n",
              "      <td>0.019250</td>\n",
              "      <td>0.003742</td>\n",
              "      <td>20.010</td>\n",
              "      <td>19.52</td>\n",
              "      <td>134.90</td>\n",
              "      <td>1227.0</td>\n",
              "      <td>0.12550</td>\n",
              "      <td>0.28120</td>\n",
              "      <td>0.24890</td>\n",
              "      <td>0.14560</td>\n",
              "      <td>0.2756</td>\n",
              "      <td>0.07919</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>853401</td>\n",
              "      <td>M</td>\n",
              "      <td>18.630</td>\n",
              "      <td>25.11</td>\n",
              "      <td>124.80</td>\n",
              "      <td>1088.0</td>\n",
              "      <td>0.10640</td>\n",
              "      <td>0.18870</td>\n",
              "      <td>0.23190</td>\n",
              "      <td>0.124400</td>\n",
              "      <td>0.2183</td>\n",
              "      <td>0.06197</td>\n",
              "      <td>0.8307</td>\n",
              "      <td>1.4660</td>\n",
              "      <td>5.574</td>\n",
              "      <td>105.000</td>\n",
              "      <td>0.006248</td>\n",
              "      <td>0.033740</td>\n",
              "      <td>0.05196</td>\n",
              "      <td>0.011580</td>\n",
              "      <td>0.020070</td>\n",
              "      <td>0.004560</td>\n",
              "      <td>23.150</td>\n",
              "      <td>34.01</td>\n",
              "      <td>160.50</td>\n",
              "      <td>1670.0</td>\n",
              "      <td>0.14910</td>\n",
              "      <td>0.42570</td>\n",
              "      <td>0.61330</td>\n",
              "      <td>0.18480</td>\n",
              "      <td>0.3444</td>\n",
              "      <td>0.09782</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>853612</td>\n",
              "      <td>M</td>\n",
              "      <td>11.840</td>\n",
              "      <td>18.70</td>\n",
              "      <td>77.93</td>\n",
              "      <td>440.6</td>\n",
              "      <td>0.11090</td>\n",
              "      <td>0.15160</td>\n",
              "      <td>0.12180</td>\n",
              "      <td>0.051820</td>\n",
              "      <td>0.2301</td>\n",
              "      <td>0.07799</td>\n",
              "      <td>0.4825</td>\n",
              "      <td>1.0300</td>\n",
              "      <td>3.475</td>\n",
              "      <td>41.000</td>\n",
              "      <td>0.005551</td>\n",
              "      <td>0.034140</td>\n",
              "      <td>0.04205</td>\n",
              "      <td>0.010440</td>\n",
              "      <td>0.022730</td>\n",
              "      <td>0.005667</td>\n",
              "      <td>16.820</td>\n",
              "      <td>28.12</td>\n",
              "      <td>119.40</td>\n",
              "      <td>888.7</td>\n",
              "      <td>0.16370</td>\n",
              "      <td>0.57750</td>\n",
              "      <td>0.69560</td>\n",
              "      <td>0.15460</td>\n",
              "      <td>0.4761</td>\n",
              "      <td>0.14020</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>85382601</td>\n",
              "      <td>M</td>\n",
              "      <td>17.020</td>\n",
              "      <td>23.98</td>\n",
              "      <td>112.80</td>\n",
              "      <td>899.3</td>\n",
              "      <td>0.11970</td>\n",
              "      <td>0.14960</td>\n",
              "      <td>0.24170</td>\n",
              "      <td>0.120300</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.06382</td>\n",
              "      <td>0.6009</td>\n",
              "      <td>1.3980</td>\n",
              "      <td>3.999</td>\n",
              "      <td>67.780</td>\n",
              "      <td>0.008268</td>\n",
              "      <td>0.030820</td>\n",
              "      <td>0.05042</td>\n",
              "      <td>0.011120</td>\n",
              "      <td>0.021020</td>\n",
              "      <td>0.003854</td>\n",
              "      <td>20.880</td>\n",
              "      <td>32.09</td>\n",
              "      <td>136.10</td>\n",
              "      <td>1344.0</td>\n",
              "      <td>0.16340</td>\n",
              "      <td>0.35590</td>\n",
              "      <td>0.55880</td>\n",
              "      <td>0.18470</td>\n",
              "      <td>0.3530</td>\n",
              "      <td>0.08482</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>854002</td>\n",
              "      <td>M</td>\n",
              "      <td>19.270</td>\n",
              "      <td>26.47</td>\n",
              "      <td>127.90</td>\n",
              "      <td>1162.0</td>\n",
              "      <td>0.09401</td>\n",
              "      <td>0.17190</td>\n",
              "      <td>0.16570</td>\n",
              "      <td>0.075930</td>\n",
              "      <td>0.1853</td>\n",
              "      <td>0.06261</td>\n",
              "      <td>0.5558</td>\n",
              "      <td>0.6062</td>\n",
              "      <td>3.528</td>\n",
              "      <td>68.170</td>\n",
              "      <td>0.005015</td>\n",
              "      <td>0.033180</td>\n",
              "      <td>0.03497</td>\n",
              "      <td>0.009643</td>\n",
              "      <td>0.015430</td>\n",
              "      <td>0.003896</td>\n",
              "      <td>24.150</td>\n",
              "      <td>30.90</td>\n",
              "      <td>161.40</td>\n",
              "      <td>1813.0</td>\n",
              "      <td>0.15090</td>\n",
              "      <td>0.65900</td>\n",
              "      <td>0.60910</td>\n",
              "      <td>0.17850</td>\n",
              "      <td>0.3672</td>\n",
              "      <td>0.11230</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>854039</td>\n",
              "      <td>M</td>\n",
              "      <td>16.130</td>\n",
              "      <td>17.88</td>\n",
              "      <td>107.00</td>\n",
              "      <td>807.2</td>\n",
              "      <td>0.10400</td>\n",
              "      <td>0.15590</td>\n",
              "      <td>0.13540</td>\n",
              "      <td>0.077520</td>\n",
              "      <td>0.1998</td>\n",
              "      <td>0.06515</td>\n",
              "      <td>0.3340</td>\n",
              "      <td>0.6857</td>\n",
              "      <td>2.183</td>\n",
              "      <td>35.030</td>\n",
              "      <td>0.004185</td>\n",
              "      <td>0.028680</td>\n",
              "      <td>0.02664</td>\n",
              "      <td>0.009067</td>\n",
              "      <td>0.017030</td>\n",
              "      <td>0.003817</td>\n",
              "      <td>20.210</td>\n",
              "      <td>27.26</td>\n",
              "      <td>132.70</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.14460</td>\n",
              "      <td>0.58040</td>\n",
              "      <td>0.52740</td>\n",
              "      <td>0.18640</td>\n",
              "      <td>0.4270</td>\n",
              "      <td>0.12330</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>854253</td>\n",
              "      <td>M</td>\n",
              "      <td>16.740</td>\n",
              "      <td>21.59</td>\n",
              "      <td>110.10</td>\n",
              "      <td>869.5</td>\n",
              "      <td>0.09610</td>\n",
              "      <td>0.13360</td>\n",
              "      <td>0.13480</td>\n",
              "      <td>0.060180</td>\n",
              "      <td>0.1896</td>\n",
              "      <td>0.05656</td>\n",
              "      <td>0.4615</td>\n",
              "      <td>0.9197</td>\n",
              "      <td>3.008</td>\n",
              "      <td>45.190</td>\n",
              "      <td>0.005776</td>\n",
              "      <td>0.024990</td>\n",
              "      <td>0.03695</td>\n",
              "      <td>0.011950</td>\n",
              "      <td>0.027890</td>\n",
              "      <td>0.002665</td>\n",
              "      <td>20.010</td>\n",
              "      <td>29.02</td>\n",
              "      <td>133.50</td>\n",
              "      <td>1229.0</td>\n",
              "      <td>0.15630</td>\n",
              "      <td>0.38350</td>\n",
              "      <td>0.54090</td>\n",
              "      <td>0.18130</td>\n",
              "      <td>0.4863</td>\n",
              "      <td>0.08633</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>854268</td>\n",
              "      <td>M</td>\n",
              "      <td>14.250</td>\n",
              "      <td>21.72</td>\n",
              "      <td>93.63</td>\n",
              "      <td>633.0</td>\n",
              "      <td>0.09823</td>\n",
              "      <td>0.10980</td>\n",
              "      <td>0.13190</td>\n",
              "      <td>0.055980</td>\n",
              "      <td>0.1885</td>\n",
              "      <td>0.06125</td>\n",
              "      <td>0.2860</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>2.657</td>\n",
              "      <td>24.910</td>\n",
              "      <td>0.005878</td>\n",
              "      <td>0.029950</td>\n",
              "      <td>0.04815</td>\n",
              "      <td>0.011610</td>\n",
              "      <td>0.020280</td>\n",
              "      <td>0.004022</td>\n",
              "      <td>15.890</td>\n",
              "      <td>30.36</td>\n",
              "      <td>116.20</td>\n",
              "      <td>799.6</td>\n",
              "      <td>0.14460</td>\n",
              "      <td>0.42380</td>\n",
              "      <td>0.51860</td>\n",
              "      <td>0.14470</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>0.10140</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>854941</td>\n",
              "      <td>B</td>\n",
              "      <td>13.030</td>\n",
              "      <td>18.42</td>\n",
              "      <td>82.61</td>\n",
              "      <td>523.8</td>\n",
              "      <td>0.08983</td>\n",
              "      <td>0.03766</td>\n",
              "      <td>0.02562</td>\n",
              "      <td>0.029230</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.05863</td>\n",
              "      <td>0.1839</td>\n",
              "      <td>2.3420</td>\n",
              "      <td>1.170</td>\n",
              "      <td>14.160</td>\n",
              "      <td>0.004352</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.01343</td>\n",
              "      <td>0.011640</td>\n",
              "      <td>0.026710</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>13.300</td>\n",
              "      <td>22.81</td>\n",
              "      <td>84.46</td>\n",
              "      <td>545.9</td>\n",
              "      <td>0.09701</td>\n",
              "      <td>0.04619</td>\n",
              "      <td>0.04833</td>\n",
              "      <td>0.05013</td>\n",
              "      <td>0.1987</td>\n",
              "      <td>0.06169</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>855133</td>\n",
              "      <td>M</td>\n",
              "      <td>14.990</td>\n",
              "      <td>25.20</td>\n",
              "      <td>95.54</td>\n",
              "      <td>698.8</td>\n",
              "      <td>0.09387</td>\n",
              "      <td>0.05131</td>\n",
              "      <td>0.02398</td>\n",
              "      <td>0.028990</td>\n",
              "      <td>0.1565</td>\n",
              "      <td>0.05504</td>\n",
              "      <td>1.2140</td>\n",
              "      <td>2.1880</td>\n",
              "      <td>8.077</td>\n",
              "      <td>106.000</td>\n",
              "      <td>0.006883</td>\n",
              "      <td>0.010940</td>\n",
              "      <td>0.01818</td>\n",
              "      <td>0.019170</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.001754</td>\n",
              "      <td>14.990</td>\n",
              "      <td>25.20</td>\n",
              "      <td>95.54</td>\n",
              "      <td>698.8</td>\n",
              "      <td>0.09387</td>\n",
              "      <td>0.05131</td>\n",
              "      <td>0.02398</td>\n",
              "      <td>0.02899</td>\n",
              "      <td>0.1565</td>\n",
              "      <td>0.05504</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>855138</td>\n",
              "      <td>M</td>\n",
              "      <td>13.480</td>\n",
              "      <td>20.82</td>\n",
              "      <td>88.40</td>\n",
              "      <td>559.2</td>\n",
              "      <td>0.10160</td>\n",
              "      <td>0.12550</td>\n",
              "      <td>0.10630</td>\n",
              "      <td>0.054390</td>\n",
              "      <td>0.1720</td>\n",
              "      <td>0.06419</td>\n",
              "      <td>0.2130</td>\n",
              "      <td>0.5914</td>\n",
              "      <td>1.545</td>\n",
              "      <td>18.520</td>\n",
              "      <td>0.005367</td>\n",
              "      <td>0.022390</td>\n",
              "      <td>0.03049</td>\n",
              "      <td>0.012620</td>\n",
              "      <td>0.013770</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>15.530</td>\n",
              "      <td>26.02</td>\n",
              "      <td>107.30</td>\n",
              "      <td>740.4</td>\n",
              "      <td>0.16100</td>\n",
              "      <td>0.42250</td>\n",
              "      <td>0.50300</td>\n",
              "      <td>0.22580</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.10710</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>855167</td>\n",
              "      <td>M</td>\n",
              "      <td>13.440</td>\n",
              "      <td>21.58</td>\n",
              "      <td>86.18</td>\n",
              "      <td>563.0</td>\n",
              "      <td>0.08162</td>\n",
              "      <td>0.06031</td>\n",
              "      <td>0.03110</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.1784</td>\n",
              "      <td>0.05587</td>\n",
              "      <td>0.2385</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>1.572</td>\n",
              "      <td>20.530</td>\n",
              "      <td>0.003280</td>\n",
              "      <td>0.011020</td>\n",
              "      <td>0.01390</td>\n",
              "      <td>0.006881</td>\n",
              "      <td>0.013800</td>\n",
              "      <td>0.001286</td>\n",
              "      <td>15.930</td>\n",
              "      <td>30.25</td>\n",
              "      <td>102.50</td>\n",
              "      <td>787.9</td>\n",
              "      <td>0.10940</td>\n",
              "      <td>0.20430</td>\n",
              "      <td>0.20850</td>\n",
              "      <td>0.11120</td>\n",
              "      <td>0.2994</td>\n",
              "      <td>0.07146</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>855563</td>\n",
              "      <td>M</td>\n",
              "      <td>10.950</td>\n",
              "      <td>21.35</td>\n",
              "      <td>71.90</td>\n",
              "      <td>371.1</td>\n",
              "      <td>0.12270</td>\n",
              "      <td>0.12180</td>\n",
              "      <td>0.10440</td>\n",
              "      <td>0.056690</td>\n",
              "      <td>0.1895</td>\n",
              "      <td>0.06870</td>\n",
              "      <td>0.2366</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>1.822</td>\n",
              "      <td>16.970</td>\n",
              "      <td>0.008064</td>\n",
              "      <td>0.017640</td>\n",
              "      <td>0.02595</td>\n",
              "      <td>0.010370</td>\n",
              "      <td>0.013570</td>\n",
              "      <td>0.003040</td>\n",
              "      <td>12.840</td>\n",
              "      <td>35.34</td>\n",
              "      <td>87.22</td>\n",
              "      <td>514.0</td>\n",
              "      <td>0.19090</td>\n",
              "      <td>0.26980</td>\n",
              "      <td>0.40230</td>\n",
              "      <td>0.14240</td>\n",
              "      <td>0.2964</td>\n",
              "      <td>0.09606</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>855625</td>\n",
              "      <td>M</td>\n",
              "      <td>19.070</td>\n",
              "      <td>24.81</td>\n",
              "      <td>128.30</td>\n",
              "      <td>1104.0</td>\n",
              "      <td>0.09081</td>\n",
              "      <td>0.21900</td>\n",
              "      <td>0.21070</td>\n",
              "      <td>0.099610</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>0.06343</td>\n",
              "      <td>0.9811</td>\n",
              "      <td>1.6660</td>\n",
              "      <td>8.830</td>\n",
              "      <td>104.900</td>\n",
              "      <td>0.006548</td>\n",
              "      <td>0.100600</td>\n",
              "      <td>0.09723</td>\n",
              "      <td>0.026380</td>\n",
              "      <td>0.053330</td>\n",
              "      <td>0.007646</td>\n",
              "      <td>24.090</td>\n",
              "      <td>33.17</td>\n",
              "      <td>177.40</td>\n",
              "      <td>1651.0</td>\n",
              "      <td>0.12470</td>\n",
              "      <td>0.74440</td>\n",
              "      <td>0.72420</td>\n",
              "      <td>0.24930</td>\n",
              "      <td>0.4670</td>\n",
              "      <td>0.10380</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>856106</td>\n",
              "      <td>M</td>\n",
              "      <td>13.280</td>\n",
              "      <td>20.28</td>\n",
              "      <td>87.32</td>\n",
              "      <td>545.2</td>\n",
              "      <td>0.10410</td>\n",
              "      <td>0.14360</td>\n",
              "      <td>0.09847</td>\n",
              "      <td>0.061580</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.06782</td>\n",
              "      <td>0.3704</td>\n",
              "      <td>0.8249</td>\n",
              "      <td>2.427</td>\n",
              "      <td>31.330</td>\n",
              "      <td>0.005072</td>\n",
              "      <td>0.021470</td>\n",
              "      <td>0.02185</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.017190</td>\n",
              "      <td>0.003317</td>\n",
              "      <td>17.380</td>\n",
              "      <td>28.00</td>\n",
              "      <td>113.10</td>\n",
              "      <td>907.2</td>\n",
              "      <td>0.15300</td>\n",
              "      <td>0.37240</td>\n",
              "      <td>0.36640</td>\n",
              "      <td>0.14920</td>\n",
              "      <td>0.3739</td>\n",
              "      <td>0.10270</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>85638502</td>\n",
              "      <td>M</td>\n",
              "      <td>13.170</td>\n",
              "      <td>21.81</td>\n",
              "      <td>85.42</td>\n",
              "      <td>531.5</td>\n",
              "      <td>0.09714</td>\n",
              "      <td>0.10470</td>\n",
              "      <td>0.08259</td>\n",
              "      <td>0.052520</td>\n",
              "      <td>0.1746</td>\n",
              "      <td>0.06177</td>\n",
              "      <td>0.1938</td>\n",
              "      <td>0.6123</td>\n",
              "      <td>1.334</td>\n",
              "      <td>14.490</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>0.013840</td>\n",
              "      <td>0.01452</td>\n",
              "      <td>0.006853</td>\n",
              "      <td>0.011130</td>\n",
              "      <td>0.001720</td>\n",
              "      <td>16.230</td>\n",
              "      <td>29.89</td>\n",
              "      <td>105.50</td>\n",
              "      <td>740.7</td>\n",
              "      <td>0.15030</td>\n",
              "      <td>0.39040</td>\n",
              "      <td>0.37280</td>\n",
              "      <td>0.16070</td>\n",
              "      <td>0.3693</td>\n",
              "      <td>0.09618</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>857010</td>\n",
              "      <td>M</td>\n",
              "      <td>18.650</td>\n",
              "      <td>17.60</td>\n",
              "      <td>123.70</td>\n",
              "      <td>1076.0</td>\n",
              "      <td>0.10990</td>\n",
              "      <td>0.16860</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.100900</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.06049</td>\n",
              "      <td>0.6289</td>\n",
              "      <td>0.6633</td>\n",
              "      <td>4.293</td>\n",
              "      <td>71.560</td>\n",
              "      <td>0.006294</td>\n",
              "      <td>0.039940</td>\n",
              "      <td>0.05554</td>\n",
              "      <td>0.016950</td>\n",
              "      <td>0.024280</td>\n",
              "      <td>0.003535</td>\n",
              "      <td>22.820</td>\n",
              "      <td>21.32</td>\n",
              "      <td>150.60</td>\n",
              "      <td>1567.0</td>\n",
              "      <td>0.16790</td>\n",
              "      <td>0.50900</td>\n",
              "      <td>0.73450</td>\n",
              "      <td>0.23780</td>\n",
              "      <td>0.3799</td>\n",
              "      <td>0.09185</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>85713702</td>\n",
              "      <td>B</td>\n",
              "      <td>8.196</td>\n",
              "      <td>16.84</td>\n",
              "      <td>51.71</td>\n",
              "      <td>201.9</td>\n",
              "      <td>0.08600</td>\n",
              "      <td>0.05943</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.1769</td>\n",
              "      <td>0.06503</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.9567</td>\n",
              "      <td>1.094</td>\n",
              "      <td>8.205</td>\n",
              "      <td>0.008968</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.005917</td>\n",
              "      <td>0.025740</td>\n",
              "      <td>0.002582</td>\n",
              "      <td>8.964</td>\n",
              "      <td>21.96</td>\n",
              "      <td>57.26</td>\n",
              "      <td>242.2</td>\n",
              "      <td>0.12970</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.06880</td>\n",
              "      <td>0.02564</td>\n",
              "      <td>0.3105</td>\n",
              "      <td>0.07409</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>85715</td>\n",
              "      <td>M</td>\n",
              "      <td>13.170</td>\n",
              "      <td>18.66</td>\n",
              "      <td>85.98</td>\n",
              "      <td>534.6</td>\n",
              "      <td>0.11580</td>\n",
              "      <td>0.12310</td>\n",
              "      <td>0.12260</td>\n",
              "      <td>0.073400</td>\n",
              "      <td>0.2128</td>\n",
              "      <td>0.06777</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.8937</td>\n",
              "      <td>1.897</td>\n",
              "      <td>24.250</td>\n",
              "      <td>0.006532</td>\n",
              "      <td>0.023360</td>\n",
              "      <td>0.02905</td>\n",
              "      <td>0.012150</td>\n",
              "      <td>0.017430</td>\n",
              "      <td>0.003643</td>\n",
              "      <td>15.670</td>\n",
              "      <td>27.95</td>\n",
              "      <td>102.80</td>\n",
              "      <td>759.4</td>\n",
              "      <td>0.17860</td>\n",
              "      <td>0.41660</td>\n",
              "      <td>0.50060</td>\n",
              "      <td>0.20880</td>\n",
              "      <td>0.3900</td>\n",
              "      <td>0.11790</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>857155</td>\n",
              "      <td>B</td>\n",
              "      <td>12.050</td>\n",
              "      <td>14.63</td>\n",
              "      <td>78.04</td>\n",
              "      <td>449.3</td>\n",
              "      <td>0.10310</td>\n",
              "      <td>0.09092</td>\n",
              "      <td>0.06592</td>\n",
              "      <td>0.027490</td>\n",
              "      <td>0.1675</td>\n",
              "      <td>0.06043</td>\n",
              "      <td>0.2636</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>1.848</td>\n",
              "      <td>19.870</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>0.014270</td>\n",
              "      <td>0.02322</td>\n",
              "      <td>0.005660</td>\n",
              "      <td>0.014280</td>\n",
              "      <td>0.002422</td>\n",
              "      <td>13.760</td>\n",
              "      <td>20.70</td>\n",
              "      <td>89.88</td>\n",
              "      <td>582.6</td>\n",
              "      <td>0.14940</td>\n",
              "      <td>0.21560</td>\n",
              "      <td>0.30500</td>\n",
              "      <td>0.06548</td>\n",
              "      <td>0.2747</td>\n",
              "      <td>0.08301</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>857156</td>\n",
              "      <td>B</td>\n",
              "      <td>13.490</td>\n",
              "      <td>22.30</td>\n",
              "      <td>86.91</td>\n",
              "      <td>561.0</td>\n",
              "      <td>0.08752</td>\n",
              "      <td>0.07698</td>\n",
              "      <td>0.04751</td>\n",
              "      <td>0.033840</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05718</td>\n",
              "      <td>0.2338</td>\n",
              "      <td>1.3530</td>\n",
              "      <td>1.735</td>\n",
              "      <td>20.200</td>\n",
              "      <td>0.004455</td>\n",
              "      <td>0.013820</td>\n",
              "      <td>0.02095</td>\n",
              "      <td>0.011840</td>\n",
              "      <td>0.016410</td>\n",
              "      <td>0.001956</td>\n",
              "      <td>15.150</td>\n",
              "      <td>31.82</td>\n",
              "      <td>99.00</td>\n",
              "      <td>698.8</td>\n",
              "      <td>0.11620</td>\n",
              "      <td>0.17110</td>\n",
              "      <td>0.22820</td>\n",
              "      <td>0.12820</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.06917</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0     842302         M  ...                  0.11890          NaN\n",
              "1     842517         M  ...                  0.08902          NaN\n",
              "2   84300903         M  ...                  0.08758          NaN\n",
              "3   84348301         M  ...                  0.17300          NaN\n",
              "4   84358402         M  ...                  0.07678          NaN\n",
              "5     843786         M  ...                  0.12440          NaN\n",
              "6     844359         M  ...                  0.08368          NaN\n",
              "7   84458202         M  ...                  0.11510          NaN\n",
              "8     844981         M  ...                  0.10720          NaN\n",
              "9   84501001         M  ...                  0.20750          NaN\n",
              "10    845636         M  ...                  0.08452          NaN\n",
              "11  84610002         M  ...                  0.10480          NaN\n",
              "12    846226         M  ...                  0.10230          NaN\n",
              "13    846381         M  ...                  0.06287          NaN\n",
              "14  84667401         M  ...                  0.14310          NaN\n",
              "15  84799002         M  ...                  0.13410          NaN\n",
              "16    848406         M  ...                  0.08216          NaN\n",
              "17  84862001         M  ...                  0.11420          NaN\n",
              "18    849014         M  ...                  0.07615          NaN\n",
              "19   8510426         B  ...                  0.07259          NaN\n",
              "20   8510653         B  ...                  0.08183          NaN\n",
              "21   8510824         B  ...                  0.07773          NaN\n",
              "22   8511133         M  ...                  0.09946          NaN\n",
              "23    851509         M  ...                  0.07526          NaN\n",
              "24    852552         M  ...                  0.09564          NaN\n",
              "25    852631         M  ...                  0.10590          NaN\n",
              "26    852763         M  ...                  0.12750          NaN\n",
              "27    852781         M  ...                  0.07421          NaN\n",
              "28    852973         M  ...                  0.09876          NaN\n",
              "29    853201         M  ...                  0.07919          NaN\n",
              "30    853401         M  ...                  0.09782          NaN\n",
              "31    853612         M  ...                  0.14020          NaN\n",
              "32  85382601         M  ...                  0.08482          NaN\n",
              "33    854002         M  ...                  0.11230          NaN\n",
              "34    854039         M  ...                  0.12330          NaN\n",
              "35    854253         M  ...                  0.08633          NaN\n",
              "36    854268         M  ...                  0.10140          NaN\n",
              "37    854941         B  ...                  0.06169          NaN\n",
              "38    855133         M  ...                  0.05504          NaN\n",
              "39    855138         M  ...                  0.10710          NaN\n",
              "40    855167         M  ...                  0.07146          NaN\n",
              "41    855563         M  ...                  0.09606          NaN\n",
              "42    855625         M  ...                  0.10380          NaN\n",
              "43    856106         M  ...                  0.10270          NaN\n",
              "44  85638502         M  ...                  0.09618          NaN\n",
              "45    857010         M  ...                  0.09185          NaN\n",
              "46  85713702         B  ...                  0.07409          NaN\n",
              "47     85715         M  ...                  0.11790          NaN\n",
              "48    857155         B  ...                  0.08301          NaN\n",
              "49    857156         B  ...                  0.06917          NaN\n",
              "\n",
              "[50 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ub8oH7ixF9wV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95fc4b55-5847-4703-b473-d281628f5553"
      },
      "source": [
        "#Number of empty or invalid values (NaN, NAN, na) in each of the column\n",
        "df.isna().sum()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           0\n",
              "diagnosis                    0\n",
              "radius_mean                  0\n",
              "texture_mean                 0\n",
              "perimeter_mean               0\n",
              "area_mean                    0\n",
              "smoothness_mean              0\n",
              "compactness_mean             0\n",
              "concavity_mean               0\n",
              "concave points_mean          0\n",
              "symmetry_mean                0\n",
              "fractal_dimension_mean       0\n",
              "radius_se                    0\n",
              "texture_se                   0\n",
              "perimeter_se                 0\n",
              "area_se                      0\n",
              "smoothness_se                0\n",
              "compactness_se               0\n",
              "concavity_se                 0\n",
              "concave points_se            0\n",
              "symmetry_se                  0\n",
              "fractal_dimension_se         0\n",
              "radius_worst                 0\n",
              "texture_worst                0\n",
              "perimeter_worst              0\n",
              "area_worst                   0\n",
              "smoothness_worst             0\n",
              "compactness_worst            0\n",
              "concavity_worst              0\n",
              "concave points_worst         0\n",
              "symmetry_worst               0\n",
              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDsuRPmDN7_J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac04f00f-7752-45c1-d3e3-5b5a18c24d92"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNBXJpuoHdlU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18d2d1a3-a0bc-4afb-fbcc-f11a58a28c34"
      },
      "source": [
        "#Dropping the column with no values that is \"Unnamed: 32\"\n",
        "df = df.dropna(axis=1)\n",
        "#Getting the new count\n",
        "df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyC1A-nfH4PJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96c35b54-b94f-460d-c0a9-5a1eac6b8549"
      },
      "source": [
        "#Getting a count of the Malignant(M) or Benign(B) cells\n",
        "df['diagnosis'].value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k67WxOQJIrsX"
      },
      "source": [
        "#Visualizing the Count\n",
        "sns.countplot(df['diagnosis'], label='count')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-n_b31N_KN5M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea74465c-5654-40c3-ede3-205e67996f19"
      },
      "source": [
        "#Discovering the data types of all the columns to see which columns need to be encoded\n",
        "df.dtypes"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           int64\n",
              "diagnosis                   object\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JX-IrTJcNz0e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC1ZmrRJz92C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2d79393-7f75-44b9-aa6a-a80399ee3d21"
      },
      "source": [
        "#Define the dependent variable that needs to be predicted\n",
        "\n",
        "Y = df.iloc[:,1].values\n",
        "print(\"Labels before encoding are: \", np.unique(Y))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels before encoding are:  ['B' 'M']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4VMItWq8G5K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "143c0782-a393-4625-afd1-9c47b290c0f1"
      },
      "source": [
        "#Encoding the categorical data values\n",
        "from sklearn import preprocessing\n",
        "labelencoder_Y = preprocessing.LabelEncoder()\n",
        "Y = labelencoder_Y.fit_transform(Y)\n",
        "print(\"Labels after encoding are : \", np.unique(Y))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels after encoding are :  [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQiacazVFpQg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae95fb63-f689-49c8-83db-5ac717687db7"
      },
      "source": [
        "#Defining X values\n",
        "X = df.iloc[:,2:31].values\n",
        "print(X)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 17.99    10.38   122.8    ...   0.7119   0.2654   0.4601]\n",
            " [ 20.57    17.77   132.9    ...   0.2416   0.186    0.275 ]\n",
            " [ 19.69    21.25   130.     ...   0.4504   0.243    0.3613]\n",
            " ...\n",
            " [ 16.6     28.08   108.3    ...   0.3403   0.1418   0.2218]\n",
            " [ 20.6     29.33   140.1    ...   0.9387   0.265    0.4087]\n",
            " [  7.76    24.54    47.92   ...   0.       0.       0.2871]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYvPTwZlKG_J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e79178a-b3f0-4a36-e16a-9131cc8155ef"
      },
      "source": [
        "#Scaling / Normalizing the values to bring them in the same range\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X)\n",
        "X = scaler.transform(X)\n",
        "print(X) #Scaled Values"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.52103744 0.0226581  0.54598853 ... 0.56861022 0.91202749 0.59846245]\n",
            " [0.64314449 0.27257355 0.61578329 ... 0.19297125 0.63917526 0.23358959]\n",
            " [0.60149557 0.3902604  0.59574321 ... 0.35974441 0.83505155 0.40370589]\n",
            " ...\n",
            " [0.45525108 0.62123774 0.44578813 ... 0.27180511 0.48728522 0.12872068]\n",
            " [0.64456434 0.66351031 0.66553797 ... 0.74976038 0.91065292 0.49714173]\n",
            " [0.03686876 0.50152181 0.02853984 ... 0.         0.         0.25744136]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjkdtM9lH4rN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d73b4f8-e56f-4832-8453-417270699cb2"
      },
      "source": [
        "#Splitting the dataset into 75% for training purpose and 25% for testing purpose\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=0)\n",
        "print(\"Shape of the training data : \", X_train.shape)\n",
        "print(\"Shape of the testing data : \", X_test.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the training data :  (426, 29)\n",
            "Shape of the testing data :  (143, 29)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pg71m3A8KlsW"
      },
      "source": [
        "#Scaling the data (Feature Scaling)\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "#sc = StandardScaler()\n",
        "#X_train = sc.fit_transform(X_train)\n",
        "#X_test = sc.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7W_eOJ55u9G"
      },
      "source": [
        "#Importing the Keras Models for Deep Learning\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKReNODl6BM5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "811d01b1-4d39-4bcd-e380-fad0fe040268"
      },
      "source": [
        "#Defining the Deep Learning Model\n",
        "model = Sequential()\n",
        "model.add(Dense(16, input_dim=29, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                480       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 497\n",
            "Trainable params: 497\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpYTRCjE_qj9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a1cc81b-a194-434a-a18f-c3f5fde4a7f9"
      },
      "source": [
        "#Fitting with no early stopping or other callbacks\n",
        "history = model.fit(X_train, Y_train, verbose=1, epochs=100, batch_size=64, validation_data=(X_test, Y_test))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.2006 - accuracy: 0.9178 - val_loss: 0.1804 - val_accuracy: 0.9441\n",
            "Epoch 2/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1962 - accuracy: 0.9249 - val_loss: 0.1794 - val_accuracy: 0.9441\n",
            "Epoch 3/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1941 - accuracy: 0.9225 - val_loss: 0.1785 - val_accuracy: 0.9371\n",
            "Epoch 4/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1844 - accuracy: 0.9343 - val_loss: 0.1776 - val_accuracy: 0.9371\n",
            "Epoch 5/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1867 - accuracy: 0.9343 - val_loss: 0.1764 - val_accuracy: 0.9371\n",
            "Epoch 6/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1871 - accuracy: 0.9343 - val_loss: 0.1750 - val_accuracy: 0.9371\n",
            "Epoch 7/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1843 - accuracy: 0.9460 - val_loss: 0.1737 - val_accuracy: 0.9371\n",
            "Epoch 8/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1835 - accuracy: 0.9272 - val_loss: 0.1729 - val_accuracy: 0.9441\n",
            "Epoch 9/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1858 - accuracy: 0.9366 - val_loss: 0.1717 - val_accuracy: 0.9371\n",
            "Epoch 10/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1774 - accuracy: 0.9437 - val_loss: 0.1706 - val_accuracy: 0.9371\n",
            "Epoch 11/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1921 - accuracy: 0.9131 - val_loss: 0.1696 - val_accuracy: 0.9371\n",
            "Epoch 12/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1848 - accuracy: 0.9343 - val_loss: 0.1678 - val_accuracy: 0.9441\n",
            "Epoch 13/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1742 - accuracy: 0.9366 - val_loss: 0.1670 - val_accuracy: 0.9510\n",
            "Epoch 14/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1748 - accuracy: 0.9319 - val_loss: 0.1661 - val_accuracy: 0.9441\n",
            "Epoch 15/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1738 - accuracy: 0.9460 - val_loss: 0.1652 - val_accuracy: 0.9441\n",
            "Epoch 16/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1667 - accuracy: 0.9507 - val_loss: 0.1639 - val_accuracy: 0.9441\n",
            "Epoch 17/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1718 - accuracy: 0.9390 - val_loss: 0.1628 - val_accuracy: 0.9441\n",
            "Epoch 18/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1825 - accuracy: 0.9343 - val_loss: 0.1621 - val_accuracy: 0.9510\n",
            "Epoch 19/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.1724 - accuracy: 0.9366 - val_loss: 0.1611 - val_accuracy: 0.9510\n",
            "Epoch 20/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1583 - accuracy: 0.9413 - val_loss: 0.1604 - val_accuracy: 0.9510\n",
            "Epoch 21/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1614 - accuracy: 0.9460 - val_loss: 0.1597 - val_accuracy: 0.9510\n",
            "Epoch 22/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1591 - accuracy: 0.9437 - val_loss: 0.1589 - val_accuracy: 0.9441\n",
            "Epoch 23/100\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.1617 - accuracy: 0.9437 - val_loss: 0.1577 - val_accuracy: 0.9510\n",
            "Epoch 24/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1543 - accuracy: 0.9554 - val_loss: 0.1571 - val_accuracy: 0.9510\n",
            "Epoch 25/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.1472 - accuracy: 0.9554 - val_loss: 0.1562 - val_accuracy: 0.9510\n",
            "Epoch 26/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1818 - accuracy: 0.9225 - val_loss: 0.1555 - val_accuracy: 0.9510\n",
            "Epoch 27/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1591 - accuracy: 0.9460 - val_loss: 0.1551 - val_accuracy: 0.9580\n",
            "Epoch 28/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1554 - accuracy: 0.9484 - val_loss: 0.1541 - val_accuracy: 0.9510\n",
            "Epoch 29/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1597 - accuracy: 0.9390 - val_loss: 0.1536 - val_accuracy: 0.9510\n",
            "Epoch 30/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1604 - accuracy: 0.9484 - val_loss: 0.1530 - val_accuracy: 0.9510\n",
            "Epoch 31/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1587 - accuracy: 0.9484 - val_loss: 0.1525 - val_accuracy: 0.9580\n",
            "Epoch 32/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1547 - accuracy: 0.9343 - val_loss: 0.1520 - val_accuracy: 0.9580\n",
            "Epoch 33/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1463 - accuracy: 0.9460 - val_loss: 0.1511 - val_accuracy: 0.9580\n",
            "Epoch 34/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1571 - accuracy: 0.9507 - val_loss: 0.1505 - val_accuracy: 0.9580\n",
            "Epoch 35/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.1462 - accuracy: 0.9460 - val_loss: 0.1507 - val_accuracy: 0.9441\n",
            "Epoch 36/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1503 - accuracy: 0.9554 - val_loss: 0.1502 - val_accuracy: 0.9441\n",
            "Epoch 37/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1524 - accuracy: 0.9507 - val_loss: 0.1491 - val_accuracy: 0.9510\n",
            "Epoch 38/100\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.1523 - accuracy: 0.9413 - val_loss: 0.1480 - val_accuracy: 0.9580\n",
            "Epoch 39/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1494 - accuracy: 0.9601 - val_loss: 0.1476 - val_accuracy: 0.9580\n",
            "Epoch 40/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1409 - accuracy: 0.9577 - val_loss: 0.1468 - val_accuracy: 0.9580\n",
            "Epoch 41/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1541 - accuracy: 0.9413 - val_loss: 0.1466 - val_accuracy: 0.9510\n",
            "Epoch 42/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1494 - accuracy: 0.9437 - val_loss: 0.1456 - val_accuracy: 0.9580\n",
            "Epoch 43/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1420 - accuracy: 0.9601 - val_loss: 0.1452 - val_accuracy: 0.9580\n",
            "Epoch 44/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1455 - accuracy: 0.9507 - val_loss: 0.1446 - val_accuracy: 0.9580\n",
            "Epoch 45/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.1449 - accuracy: 0.9437 - val_loss: 0.1441 - val_accuracy: 0.9510\n",
            "Epoch 46/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1369 - accuracy: 0.9554 - val_loss: 0.1434 - val_accuracy: 0.9580\n",
            "Epoch 47/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1323 - accuracy: 0.9577 - val_loss: 0.1431 - val_accuracy: 0.9510\n",
            "Epoch 48/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1418 - accuracy: 0.9554 - val_loss: 0.1423 - val_accuracy: 0.9510\n",
            "Epoch 49/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1449 - accuracy: 0.9601 - val_loss: 0.1417 - val_accuracy: 0.9510\n",
            "Epoch 50/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1282 - accuracy: 0.9601 - val_loss: 0.1406 - val_accuracy: 0.9720\n",
            "Epoch 51/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1401 - accuracy: 0.9460 - val_loss: 0.1403 - val_accuracy: 0.9650\n",
            "Epoch 52/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1458 - accuracy: 0.9507 - val_loss: 0.1401 - val_accuracy: 0.9650\n",
            "Epoch 53/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1355 - accuracy: 0.9601 - val_loss: 0.1398 - val_accuracy: 0.9650\n",
            "Epoch 54/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1330 - accuracy: 0.9624 - val_loss: 0.1399 - val_accuracy: 0.9510\n",
            "Epoch 55/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1335 - accuracy: 0.9484 - val_loss: 0.1391 - val_accuracy: 0.9580\n",
            "Epoch 56/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1365 - accuracy: 0.9531 - val_loss: 0.1387 - val_accuracy: 0.9580\n",
            "Epoch 57/100\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.1375 - accuracy: 0.9507 - val_loss: 0.1382 - val_accuracy: 0.9650\n",
            "Epoch 58/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1283 - accuracy: 0.9531 - val_loss: 0.1376 - val_accuracy: 0.9720\n",
            "Epoch 59/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1415 - accuracy: 0.9507 - val_loss: 0.1375 - val_accuracy: 0.9720\n",
            "Epoch 60/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1239 - accuracy: 0.9765 - val_loss: 0.1371 - val_accuracy: 0.9650\n",
            "Epoch 61/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1437 - accuracy: 0.9507 - val_loss: 0.1371 - val_accuracy: 0.9580\n",
            "Epoch 62/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1347 - accuracy: 0.9507 - val_loss: 0.1376 - val_accuracy: 0.9580\n",
            "Epoch 63/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1359 - accuracy: 0.9507 - val_loss: 0.1369 - val_accuracy: 0.9580\n",
            "Epoch 64/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1392 - accuracy: 0.9460 - val_loss: 0.1360 - val_accuracy: 0.9720\n",
            "Epoch 65/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1216 - accuracy: 0.9695 - val_loss: 0.1355 - val_accuracy: 0.9790\n",
            "Epoch 66/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1292 - accuracy: 0.9648 - val_loss: 0.1353 - val_accuracy: 0.9790\n",
            "Epoch 67/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1243 - accuracy: 0.9601 - val_loss: 0.1349 - val_accuracy: 0.9720\n",
            "Epoch 68/100\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.1305 - accuracy: 0.9624 - val_loss: 0.1345 - val_accuracy: 0.9790\n",
            "Epoch 69/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1206 - accuracy: 0.9577 - val_loss: 0.1342 - val_accuracy: 0.9720\n",
            "Epoch 70/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1243 - accuracy: 0.9601 - val_loss: 0.1339 - val_accuracy: 0.9790\n",
            "Epoch 71/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1315 - accuracy: 0.9554 - val_loss: 0.1337 - val_accuracy: 0.9720\n",
            "Epoch 72/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1360 - accuracy: 0.9554 - val_loss: 0.1337 - val_accuracy: 0.9720\n",
            "Epoch 73/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1258 - accuracy: 0.9577 - val_loss: 0.1339 - val_accuracy: 0.9720\n",
            "Epoch 74/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.1286 - accuracy: 0.9554 - val_loss: 0.1330 - val_accuracy: 0.9720\n",
            "Epoch 75/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1179 - accuracy: 0.9577 - val_loss: 0.1326 - val_accuracy: 0.9720\n",
            "Epoch 76/100\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.1249 - accuracy: 0.9648 - val_loss: 0.1318 - val_accuracy: 0.9790\n",
            "Epoch 77/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1184 - accuracy: 0.9671 - val_loss: 0.1313 - val_accuracy: 0.9790\n",
            "Epoch 78/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1216 - accuracy: 0.9648 - val_loss: 0.1309 - val_accuracy: 0.9720\n",
            "Epoch 79/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.1206 - accuracy: 0.9648 - val_loss: 0.1306 - val_accuracy: 0.9720\n",
            "Epoch 80/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1226 - accuracy: 0.9507 - val_loss: 0.1306 - val_accuracy: 0.9720\n",
            "Epoch 81/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1311 - accuracy: 0.9577 - val_loss: 0.1305 - val_accuracy: 0.9720\n",
            "Epoch 82/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1254 - accuracy: 0.9484 - val_loss: 0.1302 - val_accuracy: 0.9720\n",
            "Epoch 83/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1193 - accuracy: 0.9648 - val_loss: 0.1299 - val_accuracy: 0.9720\n",
            "Epoch 84/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1172 - accuracy: 0.9718 - val_loss: 0.1292 - val_accuracy: 0.9720\n",
            "Epoch 85/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1130 - accuracy: 0.9718 - val_loss: 0.1285 - val_accuracy: 0.9790\n",
            "Epoch 86/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1198 - accuracy: 0.9648 - val_loss: 0.1283 - val_accuracy: 0.9720\n",
            "Epoch 87/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1106 - accuracy: 0.9695 - val_loss: 0.1284 - val_accuracy: 0.9790\n",
            "Epoch 88/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1234 - accuracy: 0.9507 - val_loss: 0.1280 - val_accuracy: 0.9790\n",
            "Epoch 89/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1152 - accuracy: 0.9695 - val_loss: 0.1278 - val_accuracy: 0.9720\n",
            "Epoch 90/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1073 - accuracy: 0.9718 - val_loss: 0.1275 - val_accuracy: 0.9790\n",
            "Epoch 91/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1138 - accuracy: 0.9624 - val_loss: 0.1276 - val_accuracy: 0.9790\n",
            "Epoch 92/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1151 - accuracy: 0.9671 - val_loss: 0.1271 - val_accuracy: 0.9790\n",
            "Epoch 93/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1108 - accuracy: 0.9742 - val_loss: 0.1266 - val_accuracy: 0.9790\n",
            "Epoch 94/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1107 - accuracy: 0.9695 - val_loss: 0.1260 - val_accuracy: 0.9790\n",
            "Epoch 95/100\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1080 - accuracy: 0.9671 - val_loss: 0.1258 - val_accuracy: 0.9790\n",
            "Epoch 96/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.1089 - accuracy: 0.9601 - val_loss: 0.1256 - val_accuracy: 0.9790\n",
            "Epoch 97/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1126 - accuracy: 0.9554 - val_loss: 0.1256 - val_accuracy: 0.9790\n",
            "Epoch 98/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1064 - accuracy: 0.9718 - val_loss: 0.1258 - val_accuracy: 0.9790\n",
            "Epoch 99/100\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.1102 - accuracy: 0.9695 - val_loss: 0.1252 - val_accuracy: 0.9790\n",
            "Epoch 100/100\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.1195 - accuracy: 0.9601 - val_loss: 0.1246 - val_accuracy: 0.9790\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ee3TkvB-RrAk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "3ba6025e-38f6-466a-cbd8-3352fc41b875"
      },
      "source": [
        "#Plot the training and validation accuracy and loss at each epoch\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'y', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "acc=history.history['accuracy']\n",
        "val_acc=history.history['val_accuracy']\n",
        "plt.plot(epochs, acc, 'y', label='Training Acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation Acc')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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": {
        "id": "uXrQoVfbdAy9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0062a74-a237-4b7b-bdf2-14fb274cf412"
      },
      "source": [
        "#Predicting the test set results\n",
        "y_pred = model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[9.49783206e-01]\n",
            " [5.85199073e-02]\n",
            " [1.65783931e-02]\n",
            " [9.00372714e-02]\n",
            " [3.95830534e-03]\n",
            " [1.16533078e-02]\n",
            " [1.96723752e-02]\n",
            " [5.00672963e-03]\n",
            " [1.63596263e-03]\n",
            " [2.66480172e-04]\n",
            " [2.41682217e-01]\n",
            " [1.17105804e-01]\n",
            " [3.46311717e-03]\n",
            " [4.37480778e-01]\n",
            " [4.24508512e-01]\n",
            " [9.61897492e-01]\n",
            " [1.80159733e-02]\n",
            " [9.93691444e-01]\n",
            " [9.68935192e-01]\n",
            " [9.98822749e-01]\n",
            " [9.19925988e-01]\n",
            " [9.57914114e-01]\n",
            " [6.43942207e-02]\n",
            " [1.32989604e-02]\n",
            " [9.83906746e-01]\n",
            " [1.36863748e-02]\n",
            " [5.92772756e-03]\n",
            " [9.29244518e-01]\n",
            " [1.78335570e-02]\n",
            " [9.90560830e-01]\n",
            " [2.01648078e-03]\n",
            " [9.82007325e-01]\n",
            " [4.64398891e-01]\n",
            " [9.47865009e-01]\n",
            " [8.28507647e-04]\n",
            " [9.39313114e-01]\n",
            " [6.85209334e-02]\n",
            " [9.75282311e-01]\n",
            " [4.43506949e-02]\n",
            " [9.45925355e-01]\n",
            " [5.29151499e-01]\n",
            " [5.16937394e-03]\n",
            " [6.59242809e-01]\n",
            " [1.74939225e-03]\n",
            " [3.46584231e-01]\n",
            " [9.98896480e-01]\n",
            " [6.04543136e-04]\n",
            " [2.25744769e-02]\n",
            " [3.42594716e-03]\n",
            " [9.75411713e-01]\n",
            " [9.86242414e-01]\n",
            " [8.93534660e-01]\n",
            " [9.83734488e-01]\n",
            " [1.89104360e-02]\n",
            " [2.35986467e-02]\n",
            " [4.74259490e-03]\n",
            " [1.48938838e-02]\n",
            " [1.02161542e-02]\n",
            " [2.75197532e-02]\n",
            " [9.99138594e-01]\n",
            " [8.36834133e-01]\n",
            " [9.59116101e-01]\n",
            " [5.07186120e-03]\n",
            " [1.36891101e-02]\n",
            " [9.85848665e-01]\n",
            " [8.46865773e-02]\n",
            " [9.99924064e-01]\n",
            " [9.93273973e-01]\n",
            " [9.94826972e-01]\n",
            " [2.62733712e-03]\n",
            " [4.11945343e-01]\n",
            " [9.86469626e-01]\n",
            " [1.37169315e-02]\n",
            " [6.42244875e-01]\n",
            " [9.94151652e-01]\n",
            " [4.35372405e-02]\n",
            " [2.35145260e-03]\n",
            " [5.55337518e-02]\n",
            " [9.08646733e-03]\n",
            " [5.26278920e-04]\n",
            " [9.20136333e-01]\n",
            " [9.99052703e-01]\n",
            " [9.85650241e-01]\n",
            " [3.70765966e-03]\n",
            " [9.42894578e-01]\n",
            " [7.77748553e-03]\n",
            " [6.10326044e-03]\n",
            " [1.58223428e-03]\n",
            " [9.79876161e-01]\n",
            " [9.99598444e-01]\n",
            " [1.20558974e-03]\n",
            " [5.91795862e-01]\n",
            " [5.18928051e-01]\n",
            " [9.92487669e-01]\n",
            " [1.92315644e-03]\n",
            " [1.49411466e-02]\n",
            " [9.96477067e-01]\n",
            " [3.65699232e-01]\n",
            " [1.74482800e-02]\n",
            " [4.28354554e-03]\n",
            " [6.55832337e-05]\n",
            " [3.44628543e-02]\n",
            " [3.54106240e-02]\n",
            " [1.41259611e-01]\n",
            " [9.86035347e-01]\n",
            " [6.14639430e-04]\n",
            " [9.94271517e-01]\n",
            " [1.20061986e-01]\n",
            " [4.18763131e-01]\n",
            " [5.68351336e-03]\n",
            " [1.98166128e-02]\n",
            " [9.90002990e-01]\n",
            " [9.83566761e-01]\n",
            " [2.26919368e-01]\n",
            " [4.55222046e-03]\n",
            " [2.73175873e-02]\n",
            " [2.11243510e-01]\n",
            " [3.88962240e-03]\n",
            " [4.56695184e-02]\n",
            " [5.42129688e-02]\n",
            " [1.58224758e-02]\n",
            " [8.11359100e-03]\n",
            " [5.53791583e-01]\n",
            " [3.89959887e-02]\n",
            " [9.96528924e-01]\n",
            " [5.37761413e-02]\n",
            " [3.15724820e-01]\n",
            " [5.43608516e-03]\n",
            " [2.39197677e-03]\n",
            " [2.98113329e-03]\n",
            " [9.75649714e-01]\n",
            " [2.03560037e-03]\n",
            " [4.06779163e-03]\n",
            " [2.58204907e-01]\n",
            " [6.98128343e-02]\n",
            " [2.69922428e-03]\n",
            " [2.56743550e-01]\n",
            " [6.25297427e-01]\n",
            " [9.99745309e-01]\n",
            " [9.57685756e-04]\n",
            " [2.92002521e-02]\n",
            " [2.75083957e-03]\n",
            " [9.98676002e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2w2y1zx9M__Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce233ce5-22b8-40df-80d0-8b9dff6a8e52"
      },
      "source": [
        "#Printing the prediction of Deep Neural Networks\n",
        "print('Values predicted by the Deep Neural Networks Model - \\n', y_pred)\n",
        "print()\n",
        "print('Actual Values \\n', Y_test)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Values predicted by the Deep Neural Networks Model - \n",
            " [[9.49783206e-01]\n",
            " [5.85199073e-02]\n",
            " [1.65783931e-02]\n",
            " [9.00372714e-02]\n",
            " [3.95830534e-03]\n",
            " [1.16533078e-02]\n",
            " [1.96723752e-02]\n",
            " [5.00672963e-03]\n",
            " [1.63596263e-03]\n",
            " [2.66480172e-04]\n",
            " [2.41682217e-01]\n",
            " [1.17105804e-01]\n",
            " [3.46311717e-03]\n",
            " [4.37480778e-01]\n",
            " [4.24508512e-01]\n",
            " [9.61897492e-01]\n",
            " [1.80159733e-02]\n",
            " [9.93691444e-01]\n",
            " [9.68935192e-01]\n",
            " [9.98822749e-01]\n",
            " [9.19925988e-01]\n",
            " [9.57914114e-01]\n",
            " [6.43942207e-02]\n",
            " [1.32989604e-02]\n",
            " [9.83906746e-01]\n",
            " [1.36863748e-02]\n",
            " [5.92772756e-03]\n",
            " [9.29244518e-01]\n",
            " [1.78335570e-02]\n",
            " [9.90560830e-01]\n",
            " [2.01648078e-03]\n",
            " [9.82007325e-01]\n",
            " [4.64398891e-01]\n",
            " [9.47865009e-01]\n",
            " [8.28507647e-04]\n",
            " [9.39313114e-01]\n",
            " [6.85209334e-02]\n",
            " [9.75282311e-01]\n",
            " [4.43506949e-02]\n",
            " [9.45925355e-01]\n",
            " [5.29151499e-01]\n",
            " [5.16937394e-03]\n",
            " [6.59242809e-01]\n",
            " [1.74939225e-03]\n",
            " [3.46584231e-01]\n",
            " [9.98896480e-01]\n",
            " [6.04543136e-04]\n",
            " [2.25744769e-02]\n",
            " [3.42594716e-03]\n",
            " [9.75411713e-01]\n",
            " [9.86242414e-01]\n",
            " [8.93534660e-01]\n",
            " [9.83734488e-01]\n",
            " [1.89104360e-02]\n",
            " [2.35986467e-02]\n",
            " [4.74259490e-03]\n",
            " [1.48938838e-02]\n",
            " [1.02161542e-02]\n",
            " [2.75197532e-02]\n",
            " [9.99138594e-01]\n",
            " [8.36834133e-01]\n",
            " [9.59116101e-01]\n",
            " [5.07186120e-03]\n",
            " [1.36891101e-02]\n",
            " [9.85848665e-01]\n",
            " [8.46865773e-02]\n",
            " [9.99924064e-01]\n",
            " [9.93273973e-01]\n",
            " [9.94826972e-01]\n",
            " [2.62733712e-03]\n",
            " [4.11945343e-01]\n",
            " [9.86469626e-01]\n",
            " [1.37169315e-02]\n",
            " [6.42244875e-01]\n",
            " [9.94151652e-01]\n",
            " [4.35372405e-02]\n",
            " [2.35145260e-03]\n",
            " [5.55337518e-02]\n",
            " [9.08646733e-03]\n",
            " [5.26278920e-04]\n",
            " [9.20136333e-01]\n",
            " [9.99052703e-01]\n",
            " [9.85650241e-01]\n",
            " [3.70765966e-03]\n",
            " [9.42894578e-01]\n",
            " [7.77748553e-03]\n",
            " [6.10326044e-03]\n",
            " [1.58223428e-03]\n",
            " [9.79876161e-01]\n",
            " [9.99598444e-01]\n",
            " [1.20558974e-03]\n",
            " [5.91795862e-01]\n",
            " [5.18928051e-01]\n",
            " [9.92487669e-01]\n",
            " [1.92315644e-03]\n",
            " [1.49411466e-02]\n",
            " [9.96477067e-01]\n",
            " [3.65699232e-01]\n",
            " [1.74482800e-02]\n",
            " [4.28354554e-03]\n",
            " [6.55832337e-05]\n",
            " [3.44628543e-02]\n",
            " [3.54106240e-02]\n",
            " [1.41259611e-01]\n",
            " [9.86035347e-01]\n",
            " [6.14639430e-04]\n",
            " [9.94271517e-01]\n",
            " [1.20061986e-01]\n",
            " [4.18763131e-01]\n",
            " [5.68351336e-03]\n",
            " [1.98166128e-02]\n",
            " [9.90002990e-01]\n",
            " [9.83566761e-01]\n",
            " [2.26919368e-01]\n",
            " [4.55222046e-03]\n",
            " [2.73175873e-02]\n",
            " [2.11243510e-01]\n",
            " [3.88962240e-03]\n",
            " [4.56695184e-02]\n",
            " [5.42129688e-02]\n",
            " [1.58224758e-02]\n",
            " [8.11359100e-03]\n",
            " [5.53791583e-01]\n",
            " [3.89959887e-02]\n",
            " [9.96528924e-01]\n",
            " [5.37761413e-02]\n",
            " [3.15724820e-01]\n",
            " [5.43608516e-03]\n",
            " [2.39197677e-03]\n",
            " [2.98113329e-03]\n",
            " [9.75649714e-01]\n",
            " [2.03560037e-03]\n",
            " [4.06779163e-03]\n",
            " [2.58204907e-01]\n",
            " [6.98128343e-02]\n",
            " [2.69922428e-03]\n",
            " [2.56743550e-01]\n",
            " [6.25297427e-01]\n",
            " [9.99745309e-01]\n",
            " [9.57685756e-04]\n",
            " [2.92002521e-02]\n",
            " [2.75083957e-03]\n",
            " [9.98676002e-01]]\n",
            "\n",
            "Actual Values \n",
            " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1\n",
            " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
            " 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lL-XD_r6g1rJ"
      },
      "source": [
        "y_pred = (y_pred > 0.5)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lzeh5lHzdjVb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "6ec18557-8276-407b-b77d-73b6ce88cf6e"
      },
      "source": [
        "#Creating the confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(Y_test, y_pred)\n",
        "\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61afc6ec90>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKY0yq5JlwMF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "679bf812-bdf3-4f75-b897-30843559d64e"
      },
      "source": [
        "#Easier way to get the metrics of the Models\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "print('Model - ')\n",
        "print(classification_report(Y_test, y_pred))\n",
        "print('Testing accuracy of the Model using Deep Neural Networks is -', accuracy_score(Y_test, y_pred))\n",
        "print()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model - \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98        90\n",
            "           1       0.98      0.96      0.97        53\n",
            "\n",
            "    accuracy                           0.98       143\n",
            "   macro avg       0.98      0.98      0.98       143\n",
            "weighted avg       0.98      0.98      0.98       143\n",
            "\n",
            "Testing accuracy of the Model using Deep Neural Networks is - 0.9790209790209791\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}