20-DL-Breast-Cancer-Classification/Breast_Cancer_Classification_with_NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7dbb63c8",
   "metadata": {},
   "source": [
    "# Breast Cancer Classification with NN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cda65cb",
   "metadata": {},
   "source": [
    "### Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67fab22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.datasets\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a224747",
   "metadata": {},
   "source": [
    "### Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5cc06e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_cancer_dataset = sklearn.datasets.load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "185c187a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
      "        1.189e-01],\n",
      "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
      "        8.902e-02],\n",
      "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
      "        8.758e-02],\n",
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      "        7.820e-02],\n",
      "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
      "        1.240e-01],\n",
      "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
      "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
      "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
      "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
      "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
      "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
      "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
      "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
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      "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
      "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
      "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
      "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
      "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
      "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
      "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
      "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
      "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
      "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
      "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
      "       'smoothness error', 'compactness error', 'concavity error',\n",
      "       'concave points error', 'symmetry error',\n",
      "       'fractal dimension error', 'worst radius', 'worst texture',\n",
      "       'worst perimeter', 'worst area', 'worst smoothness',\n",
      "       'worst compactness', 'worst concavity', 'worst concave points',\n",
      "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
     ]
    }
   ],
   "source": [
    "print(breast_cancer_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "254e372c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading data to dataframe \n",
    "data_frame = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfd6a727",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
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       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9995d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding target column to dataframe\n",
    "data_frame[\"label\"] = breast_cancer_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "548a7537",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
       "568                 0.05884  ...          30.37            59.16       268.6   \n",
       "\n",
       "     worst smoothness  worst compactness  worst concavity  \\\n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  label  \n",
       "564                0.2216          0.2060                  0.07115      0  \n",
       "565                0.1628          0.2572                  0.06637      0  \n",
       "566                0.1418          0.2218                  0.07820      0  \n",
       "567                0.2650          0.4087                  0.12400      0  \n",
       "568                0.0000          0.2871                  0.07039      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abd815ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb0b4f10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      " 30  label                    569 non-null    int32  \n",
      "dtypes: float64(30), int32(1)\n",
      "memory usage: 135.7 KB\n"
     ]
    }
   ],
   "source": [
    "# getting information of dataset\n",
    "data_frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7310971a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "label                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values\n",
    "data_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cbe26fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
       "count     569.000000              569.000000  ...     569.000000   \n",
       "mean        0.181162                0.062798  ...      25.677223   \n",
       "std         0.027414                0.007060  ...       6.146258   \n",
       "min         0.106000                0.049960  ...      12.020000   \n",
       "25%         0.161900                0.057700  ...      21.080000   \n",
       "50%         0.179200                0.061540  ...      25.410000   \n",
       "75%         0.195700                0.066120  ...      29.720000   \n",
       "max         0.304000                0.097440  ...      49.540000   \n",
       "\n",
       "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
       "count       569.000000   569.000000        569.000000         569.000000   \n",
       "mean        107.261213   880.583128          0.132369           0.254265   \n",
       "std          33.602542   569.356993          0.022832           0.157336   \n",
       "min          50.410000   185.200000          0.071170           0.027290   \n",
       "25%          84.110000   515.300000          0.116600           0.147200   \n",
       "50%          97.660000   686.500000          0.131300           0.211900   \n",
       "75%         125.400000  1084.000000          0.146000           0.339100   \n",
       "max         251.200000  4254.000000          0.222600           1.058000   \n",
       "\n",
       "       worst concavity  worst concave points  worst symmetry  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.272188              0.114606        0.290076   \n",
       "std           0.208624              0.065732        0.061867   \n",
       "min           0.000000              0.000000        0.156500   \n",
       "25%           0.114500              0.064930        0.250400   \n",
       "50%           0.226700              0.099930        0.282200   \n",
       "75%           0.382900              0.161400        0.317900   \n",
       "max           1.252000              0.291000        0.663800   \n",
       "\n",
       "       worst fractal dimension       label  \n",
       "count               569.000000  569.000000  \n",
       "mean                  0.083946    0.627417  \n",
       "std                   0.018061    0.483918  \n",
       "min                   0.055040    0.000000  \n",
       "25%                   0.071460    0.000000  \n",
       "50%                   0.080040    1.000000  \n",
       "75%                   0.092080    1.000000  \n",
       "max                   0.207500    1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measures of dataset\n",
    "data_frame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b7177f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of data\n",
    "data_frame[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19911c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.462830</td>\n",
       "      <td>21.604906</td>\n",
       "      <td>115.365377</td>\n",
       "      <td>978.376415</td>\n",
       "      <td>0.102898</td>\n",
       "      <td>0.145188</td>\n",
       "      <td>0.160775</td>\n",
       "      <td>0.087990</td>\n",
       "      <td>0.192909</td>\n",
       "      <td>0.062680</td>\n",
       "      <td>...</td>\n",
       "      <td>21.134811</td>\n",
       "      <td>29.318208</td>\n",
       "      <td>141.370330</td>\n",
       "      <td>1422.286321</td>\n",
       "      <td>0.144845</td>\n",
       "      <td>0.374824</td>\n",
       "      <td>0.450606</td>\n",
       "      <td>0.182237</td>\n",
       "      <td>0.323468</td>\n",
       "      <td>0.091530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.146524</td>\n",
       "      <td>17.914762</td>\n",
       "      <td>78.075406</td>\n",
       "      <td>462.790196</td>\n",
       "      <td>0.092478</td>\n",
       "      <td>0.080085</td>\n",
       "      <td>0.046058</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>0.174186</td>\n",
       "      <td>0.062867</td>\n",
       "      <td>...</td>\n",
       "      <td>13.379801</td>\n",
       "      <td>23.515070</td>\n",
       "      <td>87.005938</td>\n",
       "      <td>558.899440</td>\n",
       "      <td>0.124959</td>\n",
       "      <td>0.182673</td>\n",
       "      <td>0.166238</td>\n",
       "      <td>0.074444</td>\n",
       "      <td>0.270246</td>\n",
       "      <td>0.079442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
       "label                                                                           \n",
       "0        17.462830     21.604906      115.365377  978.376415         0.102898   \n",
       "1        12.146524     17.914762       78.075406  462.790196         0.092478   \n",
       "\n",
       "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "label                                                                         \n",
       "0              0.145188        0.160775             0.087990       0.192909   \n",
       "1              0.080085        0.046058             0.025717       0.174186   \n",
       "\n",
       "       mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "label                          ...                                \n",
       "0                    0.062680  ...     21.134811      29.318208   \n",
       "1                    0.062867  ...     13.379801      23.515070   \n",
       "\n",
       "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
       "label                                                                      \n",
       "0           141.370330  1422.286321          0.144845           0.374824   \n",
       "1            87.005938   558.899440          0.124959           0.182673   \n",
       "\n",
       "       worst concavity  worst concave points  worst symmetry  \\\n",
       "label                                                          \n",
       "0             0.450606              0.182237        0.323468   \n",
       "1             0.166238              0.074444        0.270246   \n",
       "\n",
       "       worst fractal dimension  \n",
       "label                           \n",
       "0                     0.091530  \n",
       "1                     0.079442  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean values\n",
    "data_frame.groupby(\"label\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52e48efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating features and target\n",
    "X = data_frame.drop(columns = \"label\", axis = 1)\n",
    "Y = data_frame[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3b94af9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0          17.99         10.38          122.80     1001.0          0.11840   \n",
      "1          20.57         17.77          132.90     1326.0          0.08474   \n",
      "2          19.69         21.25          130.00     1203.0          0.10960   \n",
      "3          11.42         20.38           77.58      386.1          0.14250   \n",
      "4          20.29         14.34          135.10     1297.0          0.10030   \n",
      "..           ...           ...             ...        ...              ...   \n",
      "564        21.56         22.39          142.00     1479.0          0.11100   \n",
      "565        20.13         28.25          131.20     1261.0          0.09780   \n",
      "566        16.60         28.08          108.30      858.1          0.08455   \n",
      "567        20.60         29.33          140.10     1265.0          0.11780   \n",
      "568         7.76         24.54           47.92      181.0          0.05263   \n",
      "\n",
      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0             0.27760         0.30010              0.14710         0.2419   \n",
      "1             0.07864         0.08690              0.07017         0.1812   \n",
      "2             0.15990         0.19740              0.12790         0.2069   \n",
      "3             0.28390         0.24140              0.10520         0.2597   \n",
      "4             0.13280         0.19800              0.10430         0.1809   \n",
      "..                ...             ...                  ...            ...   \n",
      "564           0.11590         0.24390              0.13890         0.1726   \n",
      "565           0.10340         0.14400              0.09791         0.1752   \n",
      "566           0.10230         0.09251              0.05302         0.1590   \n",
      "567           0.27700         0.35140              0.15200         0.2397   \n",
      "568           0.04362         0.00000              0.00000         0.1587   \n",
      "\n",
      "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
      "0                   0.07871  ...        25.380          17.33   \n",
      "1                   0.05667  ...        24.990          23.41   \n",
      "2                   0.05999  ...        23.570          25.53   \n",
      "3                   0.09744  ...        14.910          26.50   \n",
      "4                   0.05883  ...        22.540          16.67   \n",
      "..                      ...  ...           ...            ...   \n",
      "564                 0.05623  ...        25.450          26.40   \n",
      "565                 0.05533  ...        23.690          38.25   \n",
      "566                 0.05648  ...        18.980          34.12   \n",
      "567                 0.07016  ...        25.740          39.42   \n",
      "568                 0.05884  ...         9.456          30.37   \n",
      "\n",
      "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
      "0             184.60      2019.0           0.16220            0.66560   \n",
      "1             158.80      1956.0           0.12380            0.18660   \n",
      "2             152.50      1709.0           0.14440            0.42450   \n",
      "3              98.87       567.7           0.20980            0.86630   \n",
      "4             152.20      1575.0           0.13740            0.20500   \n",
      "..               ...         ...               ...                ...   \n",
      "564           166.10      2027.0           0.14100            0.21130   \n",
      "565           155.00      1731.0           0.11660            0.19220   \n",
      "566           126.70      1124.0           0.11390            0.30940   \n",
      "567           184.60      1821.0           0.16500            0.86810   \n",
      "568            59.16       268.6           0.08996            0.06444   \n",
      "\n",
      "     worst concavity  worst concave points  worst symmetry  \\\n",
      "0             0.7119                0.2654          0.4601   \n",
      "1             0.2416                0.1860          0.2750   \n",
      "2             0.4504                0.2430          0.3613   \n",
      "3             0.6869                0.2575          0.6638   \n",
      "4             0.4000                0.1625          0.2364   \n",
      "..               ...                   ...             ...   \n",
      "564           0.4107                0.2216          0.2060   \n",
      "565           0.3215                0.1628          0.2572   \n",
      "566           0.3403                0.1418          0.2218   \n",
      "567           0.9387                0.2650          0.4087   \n",
      "568           0.0000                0.0000          0.2871   \n",
      "\n",
      "     worst fractal dimension  \n",
      "0                    0.11890  \n",
      "1                    0.08902  \n",
      "2                    0.08758  \n",
      "3                    0.17300  \n",
      "4                    0.07678  \n",
      "..                       ...  \n",
      "564                  0.07115  \n",
      "565                  0.06637  \n",
      "566                  0.07820  \n",
      "567                  0.12400  \n",
      "568                  0.07039  \n",
      "\n",
      "[569 rows x 30 columns]\n",
      "0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n",
      "      ..\n",
      "564    0\n",
      "565    0\n",
      "566    0\n",
      "567    0\n",
      "568    1\n",
      "Name: label, Length: 569, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b882a92b",
   "metadata": {},
   "source": [
    "#### Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a40be69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e2f152e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "83a72223",
   "metadata": {},
   "outputs": [],
   "source": [
    "standardized_data = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a0720ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.09706398 -2.07333501  1.26993369 ...  2.29607613  2.75062224\n",
      "   1.93701461]\n",
      " [ 1.82982061 -0.35363241  1.68595471 ...  1.0870843  -0.24388967\n",
      "   0.28118999]\n",
      " [ 1.57988811  0.45618695  1.56650313 ...  1.95500035  1.152255\n",
      "   0.20139121]\n",
      " ...\n",
      " [ 0.70228425  2.0455738   0.67267578 ...  0.41406869 -1.10454895\n",
      "  -0.31840916]\n",
      " [ 1.83834103  2.33645719  1.98252415 ...  2.28998549  1.91908301\n",
      "   2.21963528]\n",
      " [-1.80840125  1.22179204 -1.81438851 ... -1.74506282 -0.04813821\n",
      "  -0.75120669]]\n"
     ]
    }
   ],
   "source": [
    "print(standardized_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87980ccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.09706398 -2.07333501  1.26993369 ...  2.29607613  2.75062224\n",
      "   1.93701461]\n",
      " [ 1.82982061 -0.35363241  1.68595471 ...  1.0870843  -0.24388967\n",
      "   0.28118999]\n",
      " [ 1.57988811  0.45618695  1.56650313 ...  1.95500035  1.152255\n",
      "   0.20139121]\n",
      " ...\n",
      " [ 0.70228425  2.0455738   0.67267578 ...  0.41406869 -1.10454895\n",
      "  -0.31840916]\n",
      " [ 1.83834103  2.33645719  1.98252415 ...  2.28998549  1.91908301\n",
      "   2.21963528]\n",
      " [-1.80840125  1.22179204 -1.81438851 ... -1.74506282 -0.04813821\n",
      "  -0.75120669]]\n"
     ]
    }
   ],
   "source": [
    "# updating features with standardized data\n",
    "X = standardized_data\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "096d41d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n",
      "      ..\n",
      "564    0\n",
      "565    0\n",
      "566    0\n",
      "567    0\n",
      "568    1\n",
      "Name: label, Length: 569, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79bf7648",
   "metadata": {},
   "source": [
    "### Split Train and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4b5e395",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba92150d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data:  (569, 30)\n",
      "Train data:  (455, 30)\n",
      "Test data:  (114, 30)\n"
     ]
    }
   ],
   "source": [
    "print(\"Total data: \", X.shape)\n",
    "print(\"Train data: \", X_train.shape)\n",
    "print(\"Test data: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81c4e85",
   "metadata": {},
   "source": [
    "### Building Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012dd59e",
   "metadata": {},
   "source": [
    "#### Importing Tensorflow and Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "17fdc50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.random.set_seed(3)\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "292c2393",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up the layers of Neural Network\n",
    "\n",
    "model = keras.Sequential([\n",
    "    \n",
    "    keras.layers.Flatten(input_shape = (30,)),\n",
    "    keras.layers.Dense(20, activation = 'relu'),\n",
    "    keras.layers.Dense(2, activation = \"sigmoid\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "03b1f4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling the Neural Network\n",
    "\n",
    "model.compile(optimizer = \"adam\",\n",
    "             loss = \"sparse_categorical_crossentropy\",\n",
    "             metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ba0a717f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "13/13 [==============================] - 0s 10ms/step - loss: 0.7720 - accuracy: 0.4523 - val_loss: 0.6600 - val_accuracy: 0.5870\n",
      "Epoch 2/10\n",
      "13/13 [==============================] - 0s 3ms/step - loss: 0.5253 - accuracy: 0.7922 - val_loss: 0.4667 - val_accuracy: 0.8478\n",
      "Epoch 3/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3880 - accuracy: 0.8875 - val_loss: 0.3581 - val_accuracy: 0.9130\n",
      "Epoch 4/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.3069 - accuracy: 0.9144 - val_loss: 0.2917 - val_accuracy: 0.9348\n",
      "Epoch 5/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.2517 - accuracy: 0.9315 - val_loss: 0.2496 - val_accuracy: 0.9348\n",
      "Epoch 6/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.2144 - accuracy: 0.9389 - val_loss: 0.2199 - val_accuracy: 0.9348\n",
      "Epoch 7/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.1877 - accuracy: 0.9462 - val_loss: 0.1978 - val_accuracy: 0.9348\n",
      "Epoch 8/10\n",
      "13/13 [==============================] - 0s 4ms/step - loss: 0.1671 - accuracy: 0.9462 - val_loss: 0.1803 - val_accuracy: 0.9348\n",
      "Epoch 9/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.1514 - accuracy: 0.9462 - val_loss: 0.1657 - val_accuracy: 0.9348\n",
      "Epoch 10/10\n",
      "13/13 [==============================] - 0s 2ms/step - loss: 0.1376 - accuracy: 0.9560 - val_loss: 0.1543 - val_accuracy: 0.9565\n"
     ]
    }
   ],
   "source": [
    "# training the Neural Network\n",
    "\n",
    "history = model.fit(X_train, Y_train, validation_split = 0.1, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30a2c0fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19e8b44ba00>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize train validation accuracy\n",
    "\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.xlabel(\"epoch\")\n",
    "\n",
    "plt.legend([\"training\", \"validation\"], loc = \"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f33a8681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19e8b2f0af0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize train validation loss\n",
    "\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "\n",
    "plt.legend([\"training\", \"validation\"], loc = \"upper right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3da5f45",
   "metadata": {},
   "source": [
    "### Accuracy on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "718e3596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1402 - accuracy: 0.9649\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7d48070a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9649122953414917\n"
     ]
    }
   ],
   "source": [
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "446d9e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(114, 30)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3d6f5662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.05319351 -1.42408329 -0.06833592 -0.17260604  2.02392516 -0.1286992\n",
      "  0.15317924  0.44445235  0.60014708  0.25125475  0.50863855  2.57300558\n",
      "  0.60638318  0.09687179  0.99971423  0.3907719   0.52176832  2.71439431\n",
      " -0.41073446  0.65554594 -0.3415184  -1.67683887 -0.3795077  -0.3995439\n",
      "  0.30822795 -0.74978636 -0.55789673 -0.19956318 -1.19838023 -0.63261684]\n"
     ]
    }
   ],
   "source": [
    "print(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6516d2d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6608d010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(114, 2)\n"
     ]
    }
   ],
   "source": [
    "print(Y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4a8d8292",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.80580282e-01 3.25303674e-01]\n",
      " [4.00586158e-01 5.64572453e-01]\n",
      " [6.19726144e-02 5.79110444e-01]\n",
      " [9.41306710e-01 6.86075306e-04]\n",
      " [1.68490976e-01 4.10280824e-01]\n",
      " [9.01930094e-01 1.44960303e-02]\n",
      " [2.20659822e-01 5.39898217e-01]\n",
      " [1.84332635e-02 7.08341122e-01]\n",
      " [8.40566605e-02 6.30798757e-01]\n",
      " [9.57605615e-02 7.16690063e-01]\n",
      " [4.59069788e-01 5.63535154e-01]\n",
      " [2.30619729e-01 4.18735981e-01]\n",
      " [4.11641061e-01 5.36629915e-01]\n",
      " [3.38374466e-01 6.69319749e-01]\n",
      " [8.86892453e-02 6.04105711e-01]\n",
      " [5.49484789e-01 1.51873782e-01]\n",
      " [1.24987289e-01 6.41290069e-01]\n",
      " [9.08953175e-02 5.09522319e-01]\n",
      " [6.10249490e-02 4.89589632e-01]\n",
      " [8.38953197e-01 8.91702157e-03]\n",
      " [3.35946912e-04 2.48149671e-02]\n",
      " [3.45254317e-02 6.04846776e-01]\n",
      " [1.22320369e-01 6.81798637e-01]\n",
      " [3.36710177e-02 6.93576634e-01]\n",
      " [9.28178653e-02 5.79511881e-01]\n",
      " [7.41548419e-01 5.25906943e-02]\n",
      " [1.13643177e-01 6.32771552e-01]\n",
      " [1.10372379e-01 5.29826641e-01]\n",
      " [5.56508362e-01 1.85755029e-01]\n",
      " [7.58250356e-01 1.14575617e-01]\n",
      " [7.64246807e-02 5.49830735e-01]\n",
      " [1.13285542e-01 6.15059435e-01]\n",
      " [2.27489136e-02 4.76285458e-01]\n",
      " [9.23382461e-01 1.48010645e-02]\n",
      " [7.96139002e-01 4.50374186e-02]\n",
      " [1.60566449e-01 5.30571580e-01]\n",
      " [5.77757470e-02 8.24828923e-01]\n",
      " [1.88226983e-01 7.08311856e-01]\n",
      " [7.21279532e-02 7.24554360e-01]\n",
      " [1.55912802e-01 5.92655540e-01]\n",
      " [9.57738638e-01 1.16276264e-03]\n",
      " [5.54833293e-01 3.50607336e-01]\n",
      " [2.13276252e-01 9.20545995e-01]\n",
      " [3.90536450e-02 4.76125807e-01]\n",
      " [5.19536972e-01 1.52727619e-01]\n",
      " [5.14298901e-02 5.60706258e-01]\n",
      " [9.65917204e-03 4.51839358e-01]\n",
      " [1.76396757e-01 7.21280813e-01]\n",
      " [7.81166434e-01 7.52157271e-02]\n",
      " [7.30869889e-01 9.94213820e-02]\n",
      " [5.55938259e-02 6.73402965e-01]\n",
      " [4.30722773e-01 2.65711039e-01]\n",
      " [3.78956407e-01 6.11069143e-01]\n",
      " [7.18913674e-02 6.62696481e-01]\n",
      " [3.49075720e-02 6.05360210e-01]\n",
      " [2.85496801e-01 2.59709179e-01]\n",
      " [2.35385716e-01 6.94373608e-01]\n",
      " [2.07911190e-02 5.30921221e-01]\n",
      " [9.55710784e-02 1.07712522e-02]\n",
      " [3.61065045e-02 3.88472795e-01]\n",
      " [2.25749448e-01 4.79985386e-01]\n",
      " [5.28910100e-01 3.18630971e-02]\n",
      " [4.80409935e-02 7.21798480e-01]\n",
      " [7.91918635e-01 5.19000180e-02]\n",
      " [4.98691261e-01 2.71469623e-01]\n",
      " [2.80347671e-02 1.45101041e-01]\n",
      " [8.27616811e-01 2.52969917e-02]\n",
      " [6.72371805e-01 1.26031891e-01]\n",
      " [1.92288235e-01 6.54888928e-01]\n",
      " [2.15822905e-01 2.24455699e-01]\n",
      " [5.35945535e-01 1.19255506e-01]\n",
      " [6.75195634e-01 2.52579190e-02]\n",
      " [9.35380384e-02 5.91325700e-01]\n",
      " [6.36132717e-01 1.62417501e-01]\n",
      " [2.02159826e-02 6.71649992e-01]\n",
      " [6.77214265e-01 1.75438479e-01]\n",
      " [1.21988885e-01 7.28516936e-01]\n",
      " [2.45469604e-02 5.26511133e-01]\n",
      " [2.12290779e-01 4.50889975e-01]\n",
      " [5.53406477e-01 2.08686411e-01]\n",
      " [7.70385325e-01 3.61663550e-02]\n",
      " [6.86095595e-01 1.66715428e-01]\n",
      " [8.50891411e-01 2.74228845e-02]\n",
      " [1.58213526e-01 5.27098060e-01]\n",
      " [2.23039418e-01 6.33844852e-01]\n",
      " [3.96386027e-01 5.78070164e-01]\n",
      " [7.69431842e-03 3.95019352e-01]\n",
      " [3.33974175e-02 4.86973792e-01]\n",
      " [6.41181469e-02 2.79889673e-01]\n",
      " [8.26448083e-01 4.15967330e-02]\n",
      " [2.83282716e-02 5.45532167e-01]\n",
      " [1.49629772e-01 7.27436781e-01]\n",
      " [1.01870149e-02 2.52338976e-01]\n",
      " [6.90371513e-01 9.95781571e-02]\n",
      " [6.29738212e-01 2.17616633e-01]\n",
      " [3.75147648e-02 5.43697119e-01]\n",
      " [7.79797971e-01 2.86676846e-02]\n",
      " [7.10010529e-01 4.98198941e-02]\n",
      " [2.43353173e-01 7.77844369e-01]\n",
      " [2.85720751e-02 6.62522793e-01]\n",
      " [1.57105438e-02 6.36715531e-01]\n",
      " [4.78853673e-01 2.22064272e-01]\n",
      " [8.50502133e-01 1.56207411e-02]\n",
      " [9.01700020e-01 4.88460856e-03]\n",
      " [1.34612456e-01 6.90749466e-01]\n",
      " [3.48112062e-02 5.00106275e-01]\n",
      " [1.70416590e-02 3.76978278e-01]\n",
      " [2.30908077e-02 5.70067883e-01]\n",
      " [3.25023048e-02 7.93150604e-01]\n",
      " [3.74827772e-01 7.78844476e-01]\n",
      " [7.72616923e-01 4.94829752e-02]\n",
      " [8.58565569e-01 2.32815985e-02]\n",
      " [3.91242266e-01 5.54798543e-01]\n",
      " [7.11177230e-01 6.61827102e-02]]\n"
     ]
    }
   ],
   "source": [
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "38e856d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "# converting the prediction probability to class labels\n",
    "Y_pred_labels = [np.argmax(i) for i in Y_pred]\n",
    "print(Y_pred_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ac4191",
   "metadata": {},
   "source": [
    "### Predictive System"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273cd4ea",
   "metadata": {},
   "source": [
    "#### taking data directly from dataset so we need to do some preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a9bfcd06",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = data_frame.drop(\"label\", axis = 1).iloc[0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6bcbab6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01\n",
      " 1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02\n",
      " 6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01\n",
      " 1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01\n",
      " 4.601e-01 1.189e-01]\n",
      "[[ 1.09706398 -2.07333501  1.26993369  0.9843749   1.56846633  3.28351467\n",
      "   2.65287398  2.53247522  2.21751501  2.25574689  2.48973393 -0.56526506\n",
      "   2.83303087  2.48757756 -0.21400165  1.31686157  0.72402616  0.66081994\n",
      "   1.14875667  0.90708308  1.88668963 -1.35929347  2.30360062  2.00123749\n",
      "   1.30768627  2.61666502  2.10952635  2.29607613  2.75062224  1.93701461]]\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "[[0.7400751  0.00730012]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TanveerKader\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data = (sample)\n",
    "print(input_data)\n",
    "\n",
    "# reshape the array as we are predicting for one instance\n",
    "input_data_reshaped = input_data.reshape(1, -1)\n",
    "\n",
    "# standardize the input data as model is trained as such\n",
    "std_data = scaler.transform(input_data_reshaped)\n",
    "print(std_data)\n",
    "\n",
    "prediction = model.predict(std_data)\n",
    "print(prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3cf4d2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "The tumor is Malignant.\n"
     ]
    }
   ],
   "source": [
    "predictin_label = [np.argmax(prediction)]\n",
    "print(predictin_label)\n",
    "\n",
    "if(predictin_label[0] == 0):\n",
    "    print(\"The tumor is Malignant.\")\n",
    "else:\n",
    "    print(\"The tumor is Benign.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac060dd2",
   "metadata": {},
   "source": [
    "##### data was standardised before split so we can use it directly after reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "903ca0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.05319351 -1.42408329 -0.06833592 -0.17260604  2.02392516 -0.1286992\n",
      "  0.15317924  0.44445235  0.60014708  0.25125475  0.50863855  2.57300558\n",
      "  0.60638318  0.09687179  0.99971423  0.3907719   0.52176832  2.71439431\n",
      " -0.41073446  0.65554594 -0.3415184  -1.67683887 -0.3795077  -0.3995439\n",
      "  0.30822795 -0.74978636 -0.55789673 -0.19956318 -1.19838023 -0.63261684]\n",
      "[[-0.05319351 -1.42408329 -0.06833592 -0.17260604  2.02392516 -0.1286992\n",
      "   0.15317924  0.44445235  0.60014708  0.25125475  0.50863855  2.57300558\n",
      "   0.60638318  0.09687179  0.99971423  0.3907719   0.52176832  2.71439431\n",
      "  -0.41073446  0.65554594 -0.3415184  -1.67683887 -0.3795077  -0.3995439\n",
      "   0.30822795 -0.74978636 -0.55789673 -0.19956318 -1.19838023 -0.63261684]]\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "[[0.28058025 0.3253037 ]]\n"
     ]
    }
   ],
   "source": [
    "input_data = X_test[0]\n",
    "print(input_data)\n",
    "\n",
    "# reshape the array as we are predicting for one instance\n",
    "input_data_reshaped = input_data.reshape(1, -1)\n",
    "\n",
    "print(input_data_reshaped)\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "02232014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "The tumor is Benign.\n"
     ]
    }
   ],
   "source": [
    "predictin_label = [np.argmax(prediction)]\n",
    "print(predictin_label)\n",
    "\n",
    "if(predictin_label[0] == 0):\n",
    "    print(\"The tumor is Malignant.\")\n",
    "else:\n",
    "    print(\"The tumor is Benign.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "965857ca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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