Cancer_data_Classification_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout,Flatten\n",
    "from keras.layers import Conv2D,MaxPooling2D\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import time\n",
    "import seaborn as sns\n",
    "from IPython.display import clear_output\n",
    "from keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('C:\\\\Users\\\\suneelnair1\\\\Documents\\\\Sunil\\\\Class_notes\\\\Kaggle_datasets\\\\Kaggle_datasets\\\\Cancer_sample_datasets\\\\Breast_cancer_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "    </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",
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       "    <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",
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       "  </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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('id',axis=1,inplace=True)\n",
    "data.drop('Unnamed: 32',axis=1,inplace=True)\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row,Col (569, 31)\n"
     ]
    }
   ],
   "source": [
    "print('Row,Col',data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['diagnosis']=data['diagnosis'].map({'M':1,'B':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>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  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>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  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 35 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 62,
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the Dataset into Train and Test sets\n",
    "\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traindf: (398, 31)\n",
      "Testdf: (171, 31)\n"
     ]
    }
   ],
   "source": [
    "# Shapes of Train and Test data\n",
    "\n",
    "print('Traindf:',traindf.shape)\n",
    "print('Testdf:',testdf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_mean=list(data.columns[1:11])\n",
    "corr = data[features_mean].corr()\n",
    "plt.figure(figsize=(14,14))\n",
    "sns.heatmap(corr,cbar=True,square=True,annot=True,fmt='.2f',annot_kws={'size':15},xticklabels=features_mean,yticklabels=features_mean,cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generic function for creating a classification model and accessing the performance\n",
    "\n",
    "Y = data['diagnosis'].values\n",
    "X = data.drop('diagnosis',axis=1).values\n",
    "\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3,random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\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 data\n",
    "    predictions = model.predict(data[predictors])\n",
    "    \n",
    "    # Print accuracy\n",
    "    accuracy = accuracy_score(predictions,data[outcome])\n",
    "    print('Accuracy: %s' % \"{0:0.3%}\".format(accuracy))\n",
    "    \n",
    "    # Perform k-fold cross-validation with 5 folds\n",
    "    kf = KFold(n_splits=5)\n",
    "    kfold = kf.get_n_splits(X)\n",
    "    #kfold = KFold(data.shape[0],n_splits=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])\n",
    "        \n",
    "        \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 88.945%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-7aa631e62e81>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-71-7e52d4751218>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;31m#kfold = KFold(data.shape[0],n_splits=5)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkfold\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m         \u001b[1;31m# Filter training data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[0mtrain_predictors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "predictor_var = ['texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean']\n",
    "outcome_var='diagnosis'\n",
    "\n",
    "model = LogisticRegression()\n",
    "classification_model(model,traindf,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.000%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'k'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-da25485a5998>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'texture_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'perimeter_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'smoothness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'compactness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'symmetry_mean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-35-6195b8b4d49f>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;31m# Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkfold\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'k'"
     ]
    }
   ],
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 91.960%\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'k'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-313a3c7d954a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'texture_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'perimeter_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'smoothness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'compactness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'symmetry_mean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-35-6195b8b4d49f>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;31m# Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkfold\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'k'"
     ]
    }
   ],
   "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": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.472%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'k'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-29870c261986>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpredictor_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'texture_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'perimeter_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'smoothness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'compactness_mean'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'symmetry_mean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassification_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictor_var\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutcome_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-35-6195b8b4d49f>\u001b[0m in \u001b[0;36mclassification_model\u001b[1;34m(model, data, predictors, outcome)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;31m# Perform k-fold cross-validation with 5 folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkfold\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'k'"
     ]
    }
   ],
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR:(run time: 0.072033)\n",
      "DT:(run time: 0.055324)\n",
      "SVM:(run time: 0.197286)\n",
      "KNN:(run time: 0.031241)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\suneelnair1\\AppData\\Local\\conda\\conda\\envs\\neuralnets\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\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))"
   ]
  }
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