Breast_Cancer_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast Cancer Prediction using Machine Learning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have to predict the Stage of Breast Cancer - M (Malignant) and B (Bengin)\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    " - 1) ID number \n",
    " - 2) Diagnosis (M = malignant, B = benign) \n",
    " \n",
    "-3-32.Ten real-valued features are computed for each cell nucleus:\n",
    "\n",
    " - a) radius (mean of distances from center to points on the perimeter) \n",
    " - b) texture (standard deviation of gray-scale values) \n",
    " - c) perimeter \n",
    " - d) area \n",
    " - e) smoothness (local variation in radius lengths) \n",
    " - f) compactness (perimeter^2 / area - 1.0) \n",
    " - g). concavity (severity of concave portions of the contour) \n",
    " - h). concave points (number of concave portions of the contour) \n",
    " - i). symmetry \n",
    " - j). fractal dimension (\"coastline approximation\" - 1)\n",
    " \n",
    "Attributes (3-32) are divided into three parts each conataining ten features:\n",
    " - Mean (3-13), \n",
    " - Stranded Error(13-23)\n",
    " - Worst(23-32)\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python35\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.gridspec as gridspec \n",
    "import seaborn as sns\n",
    "import time\n",
    "%matplotlib inline \n",
    "\n",
    "#Import models from scikit learn module:\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cross_validation import KFold  \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "      ...       texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0     ...               17.33           184.60      2019.0            0.1622   \n",
       "1     ...               23.41           158.80      1956.0            0.1238   \n",
       "2     ...               25.53           152.50      1709.0            0.1444   \n",
       "3     ...               26.50            98.87       567.7            0.2098   \n",
       "4     ...               16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualisation and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row, Col (569, 31)\n"
     ]
    }
   ],
   "source": [
    "data.drop('id',axis=1,inplace=True) #dropping the 'id' column\n",
    "data.drop('Unnamed: 32',axis=1,inplace=True)\n",
    "print(\"Row, Col\", data.shape)# (row,col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>...</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>1</td>\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>...</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>1</td>\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>...</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>1</td>\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>...</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 × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0          1        17.99         10.38          122.80     1001.0   \n",
       "1          1        20.57         17.77          132.90     1326.0   \n",
       "2          1        19.69         21.25          130.00     1203.0   \n",
       "3          1        11.42         20.38           77.58      386.1   \n",
       "4          1        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\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",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['diagnosis'] = data['diagnosis'].map({'M':1,'B':0})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </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>0.372583</td>\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>...</td>\n",
       "      <td>16.269190</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483918</td>\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>...</td>\n",
       "      <td>4.833242</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\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>...</td>\n",
       "      <td>7.930000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\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>...</td>\n",
       "      <td>13.010000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\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>...</td>\n",
       "      <td>14.970000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\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>...</td>\n",
       "      <td>18.790000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\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>...</td>\n",
       "      <td>36.040000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        diagnosis  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  569.000000   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     0.372583    14.127292     19.289649       91.969033   654.889104   \n",
       "std      0.483918     3.524049      4.301036       24.298981   351.914129   \n",
       "min      0.000000     6.981000      9.710000       43.790000   143.500000   \n",
       "25%      0.000000    11.700000     16.170000       75.170000   420.300000   \n",
       "50%      0.000000    13.370000     18.840000       86.240000   551.100000   \n",
       "75%      1.000000    15.780000     21.800000      104.100000   782.700000   \n",
       "max      1.000000    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\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",
       "       symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "count     569.000000           ...               569.000000     569.000000   \n",
       "mean        0.181162           ...                16.269190      25.677223   \n",
       "std         0.027414           ...                 4.833242       6.146258   \n",
       "min         0.106000           ...                 7.930000      12.020000   \n",
       "25%         0.161900           ...                13.010000      21.080000   \n",
       "50%         0.179200           ...                14.970000      25.410000   \n",
       "75%         0.195700           ...                18.790000      29.720000   \n",
       "max         0.304000           ...                36.040000      49.540000   \n",
       "\n",
       "       perimeter_worst   area_worst  smoothness_worst  compactness_worst  \\\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",
       "       concavity_worst  concave points_worst  symmetry_worst  \\\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",
       "       fractal_dimension_worst  \n",
       "count               569.000000  \n",
       "mean                  0.083946  \n",
       "std                   0.018061  \n",
       "min                   0.055040  \n",
       "25%                   0.071460  \n",
       "50%                   0.080040  \n",
       "75%                   0.092080  \n",
       "max                   0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wFcGK21J7g+rOh7/+E4/93c1s++B1tq8rYHaZDvQGjuvn8a3//gWPNW/mrs+f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\nYsUKdu7cydDQEGVlZQU9tx70pGUSP8VSKhamHX+X5tRBux/qgFgYbBP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HG2+8wc6dO7ni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\niYnJe5D/HwEU8SPOKX5DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c7eef4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='density', subplots=True, layout=(5,7), sharex=False, legend=False, fontsize=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis\n",
      "0    357\n",
      "1    212\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c7f6d3438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(data.groupby('diagnosis').size())\n",
    "sns.countplot(data['diagnosis'],label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c7ef64080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set (398, 31)\n",
      "Test set (171, 31)\n"
     ]
    }
   ],
   "source": [
    "#split our data into train and test\n",
    "traindf, testdf = train_test_split(data, test_size = 0.3)\n",
    "labels = 'Train', 'Test'\n",
    "plt.pie([70, 30], labels=labels, autopct='%1.1f%%', shadow=True)\n",
    "plt.show()\n",
    "print(\"Train set\", traindf.shape)\n",
    "print(\"Test set\", testdf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ePTo3YOeuohM2kDM2c1w+fM1xeSB/HIjAFdjtM8Y0BUYBC4AEoLUxpp1lWVuB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YQQQohaSJI5IYQQQgghhKiFZJilEEIIIYQQolqpyGqWlUF65oQQQgghhBCiFpJkTgghhBBCCCFq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\npGLIr4QAb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+jU3J/Xplh6Fb/4fTfZeQbaBzXCTevI4QtRfLvtIIPatSC4oeWLfWauntmL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\nG65KzpSieXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c7fba37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_mean= list(data.columns[1:11])\n",
    "corr = data[features_mean].corr() # .corr is used for find corelation\n",
    "plt.figure(figsize=(14,14))\n",
    "sns.heatmap(corr, cbar = True,  square = True, annot=True, fmt= '.2f',annot_kws={'size': 15},\n",
    "           xticklabels= features_mean, yticklabels= features_mean,\n",
    "           cmap= 'coolwarm') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    " - the radius, parameter and area are highly correlated as expected from their relation so from these we will use anyone of them\n",
    " - compactness_mean, concavity_mean and concavepoint_mean are highly correlated so we will use compactness_mean from here\n",
    " - so selected Parameter for use is perimeter_mean, texture_mean, compactness_mean, symmetry_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generic function for making a classification model and accessing the performance. \n",
    "Y = data['diagnosis'].values\n",
    "X = data.drop('diagnosis', axis=1).values\n",
    "X_train, X_test, Y_train, Y_test = train_test_split (X, Y, test_size = 0.30, random_state=21)\n",
    "def classification_model(model, data, predictors, outcome):\n",
    "  #Fit the model:\n",
    "  model.fit(data[predictors],data[outcome])\n",
    "  \n",
    "  #Make predictions on training set:\n",
    "  predictions = model.predict(data[predictors])\n",
    "  \n",
    "  #Print accuracy\n",
    "  accuracy = metrics.accuracy_score(predictions,data[outcome])\n",
    "  print(\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))\n",
    "\n",
    "  #Perform k-fold cross-validation with 5 folds\n",
    "  kfold = KFold(data.shape[0], n_folds=5)\n",
    "  error = []\n",
    "  for train, test in kfold:\n",
    "    # Filter training data\n",
    "    train_predictors = (data[predictors].iloc[train,:])\n",
    "    \n",
    "    # The target we're using to train the algorithm.\n",
    "    train_target = data[outcome].iloc[train]\n",
    "    \n",
    "    # Training the algorithm using the predictors and target.\n",
    "    model.fit(train_predictors, train_target)\n",
    "    \n",
    "    #Record error from each cross-validation run\n",
    "    error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "    \n",
    "    print(\"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error)))\n",
    "    \n",
    "  #Fit the model again so that it can be refered outside the function:\n",
    "  model.fit(data[predictors],data[outcome]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 90.201%\n",
      "Cross-Validation Score : 85.000%\n",
      "Cross-Validation Score : 88.125%\n",
      "Cross-Validation Score : 90.417%\n",
      "Cross-Validation Score : 89.015%\n",
      "Cross-Validation Score : 89.440%\n"
     ]
    }
   ],
   "source": [
    "predictor_var = ['texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean']\n",
    "outcome_var='diagnosis'\n",
    "model=LogisticRegression()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 100.000%\n",
      "Cross-Validation Score : 90.000%\n",
      "Cross-Validation Score : 91.875%\n",
      "Cross-Validation Score : 91.250%\n",
      "Cross-Validation Score : 89.956%\n",
      "Cross-Validation Score : 90.446%\n"
     ]
    }
   ],
   "source": [
    "predictor_var = ['texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean']\n",
    "model = DecisionTreeClassifier()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 96.734%\n",
      "Cross-Validation Score : 66.250%\n",
      "Cross-Validation Score : 63.125%\n",
      "Cross-Validation Score : 62.917%\n",
      "Cross-Validation Score : 63.010%\n",
      "Cross-Validation Score : 63.066%\n"
     ]
    }
   ],
   "source": [
    "predictor_var = ['texture_mean']\n",
    "model = DecisionTreeClassifier()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kNN Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 92.965%\n",
      "Cross-Validation Score : 88.750%\n",
      "Cross-Validation Score : 90.000%\n",
      "Cross-Validation Score : 90.417%\n",
      "Cross-Validation Score : 89.331%\n",
      "Cross-Validation Score : 89.693%\n"
     ]
    }
   ],
   "source": [
    "predictor_var = ['texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean']\n",
    "model= KNeighborsClassifier()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 94.724%\n",
      "Cross-Validation Score : 90.000%\n",
      "Cross-Validation Score : 91.250%\n",
      "Cross-Validation Score : 90.000%\n",
      "Cross-Validation Score : 89.335%\n",
      "Cross-Validation Score : 90.203%\n"
     ]
    }
   ],
   "source": [
    "predictor_var = ['texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean']\n",
    "model= SVC()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR:(run time: 0.062139)\n",
      "DT:(run time: 0.077099)\n",
      "SVM:(run time: 0.176490)\n",
      "KNN:(run time: 0.024018)\n"
     ]
    }
   ],
   "source": [
    "Y = data['diagnosis'].values\n",
    "X = data.drop('diagnosis', axis=1).values\n",
    "X_train, X_test, Y_train, Y_test = train_test_split (X, Y, test_size = 0.30, random_state=21)\n",
    "models_list = []\n",
    "models_list.append(('LR', LogisticRegression()))\n",
    "models_list.append(('DT', DecisionTreeClassifier()))\n",
    "models_list.append(('SVM', SVC())) \n",
    "models_list.append(('KNN', KNeighborsClassifier()))\n",
    "num_folds = 10\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models_list:\n",
    "    start = time.time()\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=num_folds, scoring='accuracy')\n",
    "    end = time.time()\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print( \"%s:(run time: %f)\" % (name, end-start))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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