Breast cancer-kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kmeans clustering:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook lets see hoew th e k means clustering is done using the breast cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>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",
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       "    <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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.read_csv('C:/Users/18cseg13/Desktop/data.csv')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\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>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>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>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>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>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>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>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",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         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",
       "   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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=a.drop(['id','Unnamed: 32'],axis=1)\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                  0\n",
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "a.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kmeans does not cluster string values, hence we should encode the categorical values:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here i have used the get dummies method from pandas to encode the categorical variable 'diagnosis':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.diagnosis=pd.get_dummies(a.diagnosis)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in k-means clustering is finding the optimal number of clusters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the optimal number of clusters can only be done manually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many techniques are used in finding the optimal number of clusters , here i have used the elbow curve method to find th eoptimal number of clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The elbow cure method uses the inertia or the sum of squared distance and is plotted in the graph ,here the minimum inertia is required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding optimal number of clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss=[]\n",
    "for i in range(1,10):\n",
    "    b=KMeans(algorithm='auto',init='k-means++',n_clusters=i)\n",
    "    b.fit(a)\n",
    "    wcss.append(b.inertia_)\n",
    "        \n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[256677376.96650478,\n",
       " 77943167.51906708,\n",
       " 47264902.354836255,\n",
       " 29226593.690070886,\n",
       " 20539924.34182808,\n",
       " 16566442.374026243,\n",
       " 13247028.17784217,\n",
       " 11315416.096558861,\n",
       " 9447335.24195558]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": [
    "plt.plot(wcss)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the optimal number of clusters is 2.\n",
    "So the k value is 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph we can clearly see the elbow junction point is at 2. so lets fix th enumber of clusters to be 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "clus=KMeans(algorithm='auto',n_clusters=2)\n",
    "clus_fit=clus.fit(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77943167.51906708"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus_fit.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true=clus_fit.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now test if the model can corectly classify the clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=clus_fit.predict(a)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       438\n",
      "           1       1.00      1.00      1.00       131\n",
      "\n",
      "   micro avg       1.00      1.00      1.00       569\n",
      "   macro avg       1.00      1.00      1.00       569\n",
      "weighted avg       1.00      1.00      1.00       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred=test,y_true=y_true))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above classification  report shows a value of 100% both precision and recall. no errors have been made.Also the confusion matrix shown below shows that the model has correctly classified.Hence this is how the kmeans is applied as a part of the eda step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[438   0]\n",
      " [  0 131]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_pred=test,y_true=y_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}