breast_cancer_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb98fa56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74cdfc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b66dc493",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "915aa8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "536211cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5800145e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3591553d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feat = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a6d3a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.8</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.6</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>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.9</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.8</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38           122.8     1001.0          0.11840   \n",
       "1        20.57         17.77           132.9     1326.0          0.08474   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33            184.6   \n",
       "1                 0.05667  ...         24.99          23.41            158.8   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4c19596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "858a1f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b01f81bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat\n",
    "y = cancer['target']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0baf4343",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa83351d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f3e0568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41dfd5df",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "66a2af66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3b34598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 56  10]\n",
      " [  3 102]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.85      0.90        66\n",
      "           1       0.91      0.97      0.94       105\n",
      "\n",
      "    accuracy                           0.92       171\n",
      "   macro avg       0.93      0.91      0.92       171\n",
      "weighted avg       0.93      0.92      0.92       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02996813",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "656ff5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C':[0.1,1,10,100,1000],'gamma':[1,0.1,0.01,0.001,0.0001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4d6befb",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(),param_grid,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d65868bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV 1/5] END ....................C=0.1, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ....................C=0.1, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ....................C=0.1, gamma=1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ....................C=0.1, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ....................C=0.1, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ..................C=0.1, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ..................C=0.1, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ..................C=0.1, gamma=0.1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ..................C=0.1, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ..................C=0.1, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END .................C=0.1, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END .................C=0.1, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END .................C=0.1, gamma=0.01;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END .................C=0.1, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END .................C=0.1, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ................C=0.1, gamma=0.001;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ................C=0.1, gamma=0.001;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ................C=0.1, gamma=0.001;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ................C=0.1, gamma=0.001;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ................C=0.1, gamma=0.001;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ...............C=0.1, gamma=0.0001;, score=0.887 total time=   0.0s\n",
      "[CV 2/5] END ...............C=0.1, gamma=0.0001;, score=0.938 total time=   0.0s\n",
      "[CV 3/5] END ...............C=0.1, gamma=0.0001;, score=0.963 total time=   0.0s\n",
      "[CV 4/5] END ...............C=0.1, gamma=0.0001;, score=0.962 total time=   0.0s\n",
      "[CV 5/5] END ...............C=0.1, gamma=0.0001;, score=0.886 total time=   0.0s\n",
      "[CV 1/5] END ......................C=1, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ......................C=1, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ......................C=1, gamma=1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ......................C=1, gamma=1;, score=0.633 total time=   0.2s\n",
      "[CV 5/5] END ......................C=1, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ....................C=1, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ....................C=1, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ....................C=1, gamma=0.1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ....................C=1, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ....................C=1, gamma=0.1;, score=0.633 total time=   0.1s\n",
      "[CV 1/5] END ...................C=1, gamma=0.01;, score=0.637 total time=   0.1s\n",
      "[CV 2/5] END ...................C=1, gamma=0.01;, score=0.637 total time=   0.1s\n",
      "[CV 3/5] END ...................C=1, gamma=0.01;, score=0.625 total time=   0.1s\n",
      "[CV 4/5] END ...................C=1, gamma=0.01;, score=0.633 total time=   0.5s\n",
      "[CV 5/5] END ...................C=1, gamma=0.01;, score=0.633 total time=   0.1s\n",
      "[CV 1/5] END ..................C=1, gamma=0.001;, score=0.900 total time=   0.0s\n",
      "[CV 2/5] END ..................C=1, gamma=0.001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END ..................C=1, gamma=0.001;, score=0.925 total time=   0.0s\n",
      "[CV 4/5] END ..................C=1, gamma=0.001;, score=0.962 total time=   0.0s\n",
      "[CV 5/5] END ..................C=1, gamma=0.001;, score=0.937 total time=   0.0s\n",
      "[CV 1/5] END .................C=1, gamma=0.0001;, score=0.912 total time=   0.0s\n",
      "[CV 2/5] END .................C=1, gamma=0.0001;, score=0.950 total time=   0.0s\n",
      "[CV 3/5] END .................C=1, gamma=0.0001;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END .................C=1, gamma=0.0001;, score=0.962 total time=   0.0s\n",
      "[CV 5/5] END .................C=1, gamma=0.0001;, score=0.937 total time=   0.0s\n",
      "[CV 1/5] END .....................C=10, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END .....................C=10, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END .....................C=10, gamma=1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END .....................C=10, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END .....................C=10, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ...................C=10, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ...................C=10, gamma=0.1;, score=0.637 total time=   0.1s\n",
      "[CV 3/5] END ...................C=10, gamma=0.1;, score=0.625 total time=   0.5s\n",
      "[CV 4/5] END ...................C=10, gamma=0.1;, score=0.633 total time=   0.2s\n",
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      "[CV 1/5] END ..................C=10, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ..................C=10, gamma=0.01;, score=0.637 total time=   0.1s\n",
      "[CV 3/5] END ..................C=10, gamma=0.01;, score=0.613 total time=   0.1s\n",
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      "[CV 5/5] END ..................C=10, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END .................C=10, gamma=0.001;, score=0.887 total time=   0.0s\n",
      "[CV 2/5] END .................C=10, gamma=0.001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END .................C=10, gamma=0.001;, score=0.900 total time=   0.2s\n",
      "[CV 4/5] END .................C=10, gamma=0.001;, score=0.937 total time=   0.1s\n",
      "[CV 5/5] END .................C=10, gamma=0.001;, score=0.924 total time=   0.1s\n",
      "[CV 1/5] END ................C=10, gamma=0.0001;, score=0.950 total time=   0.1s\n",
      "[CV 2/5] END ................C=10, gamma=0.0001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END ................C=10, gamma=0.0001;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END ................C=10, gamma=0.0001;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END ................C=10, gamma=0.0001;, score=0.949 total time=   0.0s\n",
      "[CV 1/5] END ....................C=100, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ....................C=100, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ....................C=100, gamma=1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ....................C=100, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ....................C=100, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ..................C=100, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ..................C=100, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ..................C=100, gamma=0.1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END ..................C=100, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ..................C=100, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END .................C=100, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END .................C=100, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END .................C=100, gamma=0.01;, score=0.613 total time=   0.0s\n",
      "[CV 4/5] END .................C=100, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END .................C=100, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ................C=100, gamma=0.001;, score=0.887 total time=   0.0s\n",
      "[CV 2/5] END ................C=100, gamma=0.001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END ................C=100, gamma=0.001;, score=0.900 total time=   0.0s\n",
      "[CV 4/5] END ................C=100, gamma=0.001;, score=0.937 total time=   0.0s\n",
      "[CV 5/5] END ................C=100, gamma=0.001;, score=0.924 total time=   0.0s\n",
      "[CV 1/5] END ...............C=100, gamma=0.0001;, score=0.925 total time=   0.0s\n",
      "[CV 2/5] END ...............C=100, gamma=0.0001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END ...............C=100, gamma=0.0001;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END ...............C=100, gamma=0.0001;, score=0.937 total time=   0.0s\n",
      "[CV 5/5] END ...............C=100, gamma=0.0001;, score=0.949 total time=   0.0s\n",
      "[CV 1/5] END ...................C=1000, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ...................C=1000, gamma=1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ...................C=1000, gamma=1;, score=0.625 total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 4/5] END ...................C=1000, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ...................C=1000, gamma=1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END .................C=1000, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END .................C=1000, gamma=0.1;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END .................C=1000, gamma=0.1;, score=0.625 total time=   0.0s\n",
      "[CV 4/5] END .................C=1000, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END .................C=1000, gamma=0.1;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ................C=1000, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 2/5] END ................C=1000, gamma=0.01;, score=0.637 total time=   0.0s\n",
      "[CV 3/5] END ................C=1000, gamma=0.01;, score=0.613 total time=   0.0s\n",
      "[CV 4/5] END ................C=1000, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 5/5] END ................C=1000, gamma=0.01;, score=0.633 total time=   0.0s\n",
      "[CV 1/5] END ...............C=1000, gamma=0.001;, score=0.887 total time=   0.0s\n",
      "[CV 2/5] END ...............C=1000, gamma=0.001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END ...............C=1000, gamma=0.001;, score=0.900 total time=   0.0s\n",
      "[CV 4/5] END ...............C=1000, gamma=0.001;, score=0.937 total time=   0.0s\n",
      "[CV 5/5] END ...............C=1000, gamma=0.001;, score=0.924 total time=   0.0s\n",
      "[CV 1/5] END ..............C=1000, gamma=0.0001;, score=0.938 total time=   0.0s\n",
      "[CV 2/5] END ..............C=1000, gamma=0.0001;, score=0.912 total time=   0.0s\n",
      "[CV 3/5] END ..............C=1000, gamma=0.0001;, score=0.963 total time=   0.0s\n",
      "[CV 4/5] END ..............C=1000, gamma=0.0001;, score=0.924 total time=   0.0s\n",
      "[CV 5/5] END ..............C=1000, gamma=0.0001;, score=0.962 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001]},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c8cb2902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.0001}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bace623a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.0001)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be6e1b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "606bae9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 56  10]\n",
      " [  3 102]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.85      0.90        66\n",
      "           1       0.91      0.97      0.94       105\n",
      "\n",
      "    accuracy                           0.92       171\n",
      "   macro avg       0.93      0.91      0.92       171\n",
      "weighted avg       0.93      0.92      0.92       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b159d649",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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