model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         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]\n",
      "(569, 33)\n"
     ]
    }
   ],
   "source": [
    "# Loading dataset\n",
    "df = pd.read_csv(\"./dataset/data.csv\" , index_col=False)\n",
    "print(df.head(5))\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</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>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>count</th>\n",
       "      <td>5.690000e+02</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</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>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</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>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</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>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</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>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</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>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</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>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</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>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    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  ...  texture_worst  perimeter_worst   area_worst  \\\n",
       "count     569.000000  ...     569.000000       569.000000   569.000000   \n",
       "mean        0.181162  ...      25.677223       107.261213   880.583128   \n",
       "std         0.027414  ...       6.146258        33.602542   569.356993   \n",
       "min         0.106000  ...      12.020000        50.410000   185.200000   \n",
       "25%         0.161900  ...      21.080000        84.110000   515.300000   \n",
       "50%         0.179200  ...      25.410000        97.660000   686.500000   \n",
       "75%         0.195700  ...      29.720000       125.400000  1084.000000   \n",
       "max         0.304000  ...      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            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",
       "       Unnamed: 32  \n",
       "count          0.0  \n",
       "mean           NaN  \n",
       "std            NaN  \n",
       "min            NaN  \n",
       "25%            NaN  \n",
       "50%            NaN  \n",
       "75%            NaN  \n",
       "max            NaN  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 31)\n"
     ]
    }
   ],
   "source": [
    "# data preprocessing \n",
    "df['diagnosis'] = df['diagnosis'].apply(lambda x : '1' if x == 'M' else '0')\n",
    "df = df.set_index('id')\n",
    "del df['Unnamed: 32']\n",
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis\n",
      "0    357\n",
      "1    212\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('diagnosis').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data visualization\n",
    "df.plot(kind='density', subplots=True, layout=(5,6), sharex=False, legend=False, fontsize=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\nIt is good to check the correlations between the attributes. From the output graph below,\\nThe red around the diagonal suggests that attributes are correlated with each other. The \\nyellow and green patches suggest some moderate correlation and the blue boxes show \\nnegative correlations.\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import cm as cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "cmap = cm.get_cmap('jet', 30)\n",
    "cax = ax1.imshow(df.corr(), interpolation=\"none\", cmap=cmap)\n",
    "ax1.grid(True)\n",
    "plt.title('Breast Cancer Attributes Correlation')\n",
    "# Add colorbar, make sure to specify tick locations to match desired ticklabels\n",
    "fig.colorbar(cax, ticks=[.75,.8,.85,.90,.95,1])\n",
    "plt.show()\n",
    "\n",
    "'''\n",
    "It is good to check the correlations between the attributes. From the output graph below,\n",
    "The red around the diagonal suggests that attributes are correlated with each other. The \n",
    "yellow and green patches suggest some moderate correlation and the blue boxes show \n",
    "negative correlations.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(569,)\n",
      "(569, 30)\n"
     ]
    }
   ],
   "source": [
    "# splitting into training and test sets\n",
    "Y = df['diagnosis'].values\n",
    "print(type(Y))\n",
    "print(Y.shape)\n",
    "X = df.drop(['diagnosis'] , axis = 1).values\n",
    "print(X.shape)\n",
    "\n",
    "X_train , X_test , Y_train , Y_test  = train_test_split(X , Y , test_size = 0.2 , random_state = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_list = []\n",
    "models_list.append(('CART', DecisionTreeClassifier()))\n",
    "models_list.append(('SVM' , SVC()))\n",
    "models_list.append(('NB' , GaussianNB()))\n",
    "models_list.append(('KNN' , KNeighborsClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CART: 0.929662 (0.041559) (run time: 0.054887)\n",
      "SVM: 0.925266 (0.027974) (run time: 0.052757)\n",
      "NB: 0.951594 (0.023672) (run time: 0.024678)\n",
      "KNN: 0.936184 (0.018513) (run time: 0.044813)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KIIT\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "C:\\Users\\KIIT\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "C:\\Users\\KIIT\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "C:\\Users\\KIIT\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    }
   ],
   "source": [
    "num_folds = 10\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name , model in models_list:\n",
    "    kfold = KFold(n_splits = num_folds , random_state = 120)\n",
    "    start = time.time()\n",
    "    cv_results = cross_val_score(model , X_train , Y_train , cv = kfold , scoring = 'accuracy')\n",
    "    end = time.time()\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print(\"%s: %f (%f) (run time: %f)\" %(name, cv_results.mean(), cv_results.std(), end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "fig = plt.figure()\n",
    "fig.suptitle('Performance Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScaledCART: 0.927391 (0.046136) (run time: 0.062049)\n",
      "ScaledSVM: 0.969275 (0.022249) (run time: 0.033332)\n",
      "ScaledNB: 0.949372 (0.024407) (run time: 0.031245)\n",
      "ScaledKNN: 0.969324 (0.026251) (run time: 0.062261)\n"
     ]
    }
   ],
   "source": [
    "#Evaluation of algorithm on Standardised dataset\n",
    "\n",
    "import warnings \n",
    "\n",
    "pipelines = []\n",
    "\n",
    "pipelines.append(('ScaledCART', Pipeline([('Scaler', StandardScaler()),('CART',\n",
    "                                                                        DecisionTreeClassifier())])))\n",
    "pipelines.append(('ScaledSVM', Pipeline([('Scaler', StandardScaler()),('SVM', SVC( ))])))\n",
    "pipelines.append(('ScaledNB', Pipeline([('Scaler', StandardScaler()),('NB',\n",
    "                                                                      GaussianNB())])))\n",
    "pipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()),('KNN',\n",
    "                                                                       KNeighborsClassifier())])))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    kfold = KFold(n_splits = num_folds , random_state = 123)\n",
    "    for name , model in pipelines:\n",
    "        start = time.time()\n",
    "        cv_results = cross_val_score(model , X_train , Y_train , cv = kfold , scoring = 'accuracy')\n",
    "        end = time.time()\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        print(\"%s: %f (%f) (run time: %f)\" % (name , cv_results.mean() , cv_results.std() , end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "fig = plt.figure()\n",
    "fig.suptitle('Performance Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KIIT\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best : 0.982367 using {'C': 0.1, 'kernel': 'linear'}\n",
      "0.982367 (0.016592) with: {'C': 0.1, 'kernel': 'linear'}\n",
      "0.833140 (0.048830) with: {'C': 0.1, 'kernel': 'poly'}\n",
      "0.949517 (0.021903) with: {'C': 0.1, 'kernel': 'rbf'}\n",
      "0.953865 (0.030138) with: {'C': 0.1, 'kernel': 'sigmoid'}\n",
      "0.982319 (0.013302) with: {'C': 0.3, 'kernel': 'linear'}\n",
      "0.879324 (0.040308) with: {'C': 0.3, 'kernel': 'poly'}\n",
      "0.960531 (0.021324) with: {'C': 0.3, 'kernel': 'rbf'}\n",
      "0.967053 (0.014624) with: {'C': 0.3, 'kernel': 'sigmoid'}\n",
      "0.973575 (0.013257) with: {'C': 0.5, 'kernel': 'linear'}\n",
      "0.894589 (0.032249) with: {'C': 0.5, 'kernel': 'poly'}\n",
      "0.971449 (0.016994) with: {'C': 0.5, 'kernel': 'rbf'}\n",
      "0.960483 (0.019046) with: {'C': 0.5, 'kernel': 'sigmoid'}\n",
      "0.977971 (0.017091) with: {'C': 0.7, 'kernel': 'linear'}\n",
      "0.901208 (0.026200) with: {'C': 0.7, 'kernel': 'poly'}\n",
      "0.973671 (0.019010) with: {'C': 0.7, 'kernel': 'rbf'}\n",
      "0.956039 (0.024173) with: {'C': 0.7, 'kernel': 'sigmoid'}\n",
      "0.975797 (0.018259) with: {'C': 0.9, 'kernel': 'linear'}\n",
      "0.907826 (0.025221) with: {'C': 0.9, 'kernel': 'poly'}\n",
      "0.969275 (0.022249) with: {'C': 0.9, 'kernel': 'rbf'}\n",
      "0.960435 (0.023724) with: {'C': 0.9, 'kernel': 'sigmoid'}\n",
      "0.978019 (0.019663) with: {'C': 1.0, 'kernel': 'linear'}\n",
      "0.910048 (0.024421) with: {'C': 1.0, 'kernel': 'poly'}\n",
      "0.969275 (0.022249) with: {'C': 1.0, 'kernel': 'rbf'}\n",
      "0.960386 (0.019290) with: {'C': 1.0, 'kernel': 'sigmoid'}\n",
      "0.975797 (0.020686) with: {'C': 1.3, 'kernel': 'linear'}\n",
      "0.918792 (0.019411) with: {'C': 1.3, 'kernel': 'poly'}\n",
      "0.971498 (0.021874) with: {'C': 1.3, 'kernel': 'rbf'}\n",
      "0.962560 (0.026265) with: {'C': 1.3, 'kernel': 'sigmoid'}\n",
      "0.975797 (0.020686) with: {'C': 1.5, 'kernel': 'linear'}\n",
      "0.923188 (0.022220) with: {'C': 1.5, 'kernel': 'poly'}\n",
      "0.978019 (0.019554) with: {'C': 1.5, 'kernel': 'rbf'}\n",
      "0.955942 (0.032770) with: {'C': 1.5, 'kernel': 'sigmoid'}\n",
      "0.978019 (0.019554) with: {'C': 1.7, 'kernel': 'linear'}\n",
      "0.923188 (0.022220) with: {'C': 1.7, 'kernel': 'poly'}\n",
      "0.977971 (0.017091) with: {'C': 1.7, 'kernel': 'rbf'}\n",
      "0.962512 (0.034383) with: {'C': 1.7, 'kernel': 'sigmoid'}\n",
      "0.980242 (0.020634) with: {'C': 2.0, 'kernel': 'linear'}\n",
      "0.927536 (0.021935) with: {'C': 2.0, 'kernel': 'poly'}\n",
      "0.980193 (0.018322) with: {'C': 2.0, 'kernel': 'rbf'}\n",
      "0.958213 (0.033445) with: {'C': 2.0, 'kernel': 'sigmoid'}\n"
     ]
    }
   ],
   "source": [
    "# tuning SVM\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "rescaledX = scaler.transform(X_train)\n",
    "c_values = [0.1 , 0.3 , 0.5 , 0.7 , 0.9 , 1.0 , 1.3 , 1.5 , 1.7 , 2.0]\n",
    "kernel_values = ['linear' , 'poly' , 'rbf' , 'sigmoid']\n",
    "param_grid = dict(C = c_values , kernel = kernel_values)\n",
    "model = SVC()\n",
    "kfold = KFold(n_splits = num_folds , random_state = 21)\n",
    "grid = GridSearchCV(estimator = model , param_grid = param_grid , scoring = 'accuracy' , cv = kfold)\n",
    "grid_result = grid.fit(rescaledX , Y_train)\n",
    "print(\"Best : %f using %s\" % (grid_result.best_score_ , grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run Time: 0.002993\n"
     ]
    }
   ],
   "source": [
    "# prepare the model\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "model = SVC(C=2.0, kernel='rbf')\n",
    "start = time.time()\n",
    "model.fit(X_train_scaled, Y_train)\n",
    "end = time.time()\n",
    "print( \"Run Time: %f\" % (end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# estimate accuracy on test dataset\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "predictions = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score 0.956140\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97        75\n",
      "           1       0.97      0.90      0.93        39\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.94      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score %f\" % accuracy_score(Y_test, predictions))\n",
    "print(classification_report(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[74  1]\n",
      " [ 4 35]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test, predictions))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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