Breast_Cancer_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast Cancer Detection - Malignant or Benign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Importing the Dataset</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "df=load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         8.758e-02],\n",
       "        ...,\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]]),\n",
       " '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",
       "        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        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]),\n",
       " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
       " '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        largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 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.',\n",
       " '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'),\n",
       " 'filename': 'C:\\\\Users\\\\Abhishek Purohit\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\breast_cancer.csv'}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's see the feature names\n",
    "df['feature_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert our dataset to a <b>Pandas DataFrame</b> -  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(np.c_[df['data'],df['target']],columns=np.append(df['feature_names'],['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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",
       "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 texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890     0.0  \n",
       "1          0.2750                  0.08902     0.0  \n",
       "2          0.3613                  0.08758     0.0  \n",
       "3          0.6638                  0.17300     0.0  \n",
       "4          0.2364                  0.07678     0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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  target                   569 non-null    float64\n",
      "dtypes: float64(31)\n",
      "memory usage: 137.9 KB\n"
     ]
    }
   ],
   "source": [
    "#checking for null values in each feature set\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>target</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      target  \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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the distribution of the <b>'Target Variable'</b> (Malignant tumor represents <b>0</b> and Benign tumor represents <b>1</b>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b9dc24ad48>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the feature set-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features=df.drop('target',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the <b>correlation of each of the features with the target variable</b>, using <b>barplot</b>-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "ax=sns.barplot(df_features.corrwith(df.target).index,df_features.corrwith(df.target))\n",
    "ax.tick_params(labelrotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that features like symmetry error, texture error have very low correlation with the target. Thus we can drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_features.drop(['symmetry error','texture error'],axis=1).values\n",
    "y=df.target.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Splitting</b> the Data into <b>training</b> and <b>testing</b> data-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Feature Scaling</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9766081871345029\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 61,   2],\n",
       "       [  2, 106]], dtype=int64)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression(C=1,solver='liblinear')\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred_lr=lr.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred_lr))\n",
    "confusion_matrix(y_test,y_pred_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9532163742690059\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 57,   6],\n",
       "       [  2, 106]], dtype=int64)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(n_estimators=20,criterion='entropy',random_state=50)\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred_rf=rf.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred_rf))\n",
    "confusion_matrix(y_test,y_pred_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "Ks=20\n",
    "accuracy=[]\n",
    "for k in range(1,Ks):\n",
    "    knn_classifier = KNeighborsClassifier(n_neighbors = k)\n",
    "    knn_classifier.fit(X_train, y_train)\n",
    "    y_pred_knn = knn_classifier.predict(X_test)\n",
    "    accuracy.append(accuracy_score(y_test, y_pred_knn))\n",
    "plt.plot(accuracy)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_classifier = KNeighborsClassifier(n_neighbors = 6)\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "y_pred_knn = knn_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we can see that <b>Logistic Regression</b> is giving best accuracy of 97.66%"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}