SVM_cancer_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.\n",
    "\n",
    "SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"load_dataset\">Load the Cancer data</h2>\n",
    "The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepository.html]. The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n",
    "\n",
    "|Field name|Description|\n",
    "|--- |--- |\n",
    "|ID|Clump thickness|\n",
    "|Clump|Clump thickness|\n",
    "|UnifSize|Uniformity of cell size|\n",
    "|UnifShape|Uniformity of cell shape|\n",
    "|MargAdh|Marginal adhesion|\n",
    "|SingEpiSize|Single epithelial cell size|\n",
    "|BareNuc|Bare nuclei|\n",
    "|BlandChrom|Bland chromatin|\n",
    "|NormNucl|Normal nucleoli|\n",
    "|Mit|Mitoses|\n",
    "|Class|Benign or malignant|\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "We will use a smaller feature set in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-05-07 19:18:54--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 20675 (20K) [text/csv]\n",
      "Saving to: ‘cell_samples.csv’\n",
      "\n",
      "cell_samples.csv    100%[===================>]  20.19K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2020-05-07 19:18:54 (1014 KB/s) - ‘cell_samples.csv’ saved [20675/20675]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Click here and press Shift+Enter\n",
    "!wget -O cell_samples.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data From CSV File  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Clump</th>\n",
       "      <th>UnifSize</th>\n",
       "      <th>UnifShape</th>\n",
       "      <th>MargAdh</th>\n",
       "      <th>SingEpiSize</th>\n",
       "      <th>BareNuc</th>\n",
       "      <th>BlandChrom</th>\n",
       "      <th>NormNucl</th>\n",
       "      <th>Mit</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>10</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "0  1000025      5         1          1        1            2       1   \n",
       "1  1002945      5         4          4        5            7      10   \n",
       "2  1015425      3         1          1        1            2       2   \n",
       "3  1016277      6         8          8        1            3       4   \n",
       "4  1017023      4         1          1        3            2       1   \n",
       "\n",
       "   BlandChrom  NormNucl  Mit  Class  \n",
       "0           3         1    1      2  \n",
       "1           3         2    1      2  \n",
       "2           3         1    1      2  \n",
       "3           3         7    1      2  \n",
       "4           3         1    1      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
    "cell_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ID field contains the patient identifiers. The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign.\n",
    "\n",
    "The Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4).\n",
    "\n",
    "Lets look at the distribution of the classes based on Clump thickness and Uniformity of cell size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\n",
    "cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing and selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first look at columns data types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              int64\n",
       "Clump           int64\n",
       "UnifSize        int64\n",
       "UnifShape       int64\n",
       "MargAdh         int64\n",
       "SingEpiSize     int64\n",
       "BareNuc        object\n",
       "BlandChrom      int64\n",
       "NormNucl        int64\n",
       "Mit             int64\n",
       "Class           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the __BareNuc__ column includes some values that are not numerical. We can drop those rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             int64\n",
       "Clump          int64\n",
       "UnifSize       int64\n",
       "UnifShape      int64\n",
       "MargAdh        int64\n",
       "SingEpiSize    int64\n",
       "BareNuc        int64\n",
       "BlandChrom     int64\n",
       "NormNucl       int64\n",
       "Mit            int64\n",
       "Class          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\n",
    "cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\n",
    "cell_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  1,  1,  1,  2,  1,  3,  1,  1],\n",
       "       [ 5,  4,  4,  5,  7, 10,  3,  2,  1],\n",
       "       [ 3,  1,  1,  1,  2,  2,  3,  1,  1],\n",
       "       [ 6,  8,  8,  1,  3,  4,  3,  7,  1],\n",
       "       [ 4,  1,  1,  3,  2,  1,  3,  1,  1]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
    "X = np.asarray(feature_df)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want the model to predict the value of Class (that is, benign (=2) or malignant (=4)). As this field can have one of only two possible values, we need to change its measurement level to reflect this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df['Class'] = cell_df['Class'].astype('int')\n",
    "y = np.asarray(cell_df['Class'])\n",
    "y [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, we split our dataset into train and test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (546, 9) (546,)\n",
      "Test set: (137, 9) (137,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SVM algorithm offers a choice of kernel functions for performing its processing. Basically, mapping data into a higher dimensional space is called kernelling. The mathematical function used for the transformation is known as the kernel function, and can be of different types, such as:\n",
    "\n",
    "    1.Linear\n",
    "    2.Polynomial\n",
    "    3.Radial basis function (RBF)\n",
    "    4.Sigmoid\n",
    "Each of these functions has its characteristics, its pros and cons, and its equation, but as there's no easy way of knowing which function performs best with any given dataset, we usually choose different functions in turn and compare the results. Let's just use the default, RBF (Radial Basis Function) for this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After being fitted, the model can then be used to predict new values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2, 4, 2])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf.predict(X_test)\n",
    "yhat [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"evaluation\">Evaluation</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       1.00      0.94      0.97        90\n",
      "           4       0.90      1.00      0.95        47\n",
      "\n",
      "   micro avg       0.96      0.96      0.96       137\n",
      "   macro avg       0.95      0.97      0.96       137\n",
      "weighted avg       0.97      0.96      0.96       137\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[85  5]\n",
      " [ 0 47]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also easily use the __f1_score__ from sklearn library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9639038982104676"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, yhat, average='weighted') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try jaccard index for accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9635036496350365"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "jaccard_similarity_score(y_test, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Model 2\">Model 2</h2>\n",
    "Let's rebuild the model, but this time with a __linear__ kernel using __kernel='linear'__ option. Let us observe how the accuracy changes with the new kernel function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2 = svm.SVC(kernel='linear')\n",
    "clf2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2, 4, 2])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf2.predict(X_test)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[85  5]\n",
      " [ 0 47]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix2 = confusion_matrix(y_test,yhat,labels=[2,4])\n",
    "#print(cnf_matrix2)\n",
    "\n",
    "plot_confusion_matrix(cnf_matrix2, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classification Report,Jaccard index and F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       1.00      0.94      0.97        90\n",
      "           4       0.90      1.00      0.95        47\n",
      "\n",
      "   micro avg       0.96      0.96      0.96       137\n",
      "   macro avg       0.95      0.97      0.96       137\n",
      "weighted avg       0.97      0.96      0.96       137\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaccard index :  0.9635036496350365\n",
      "F1 score :  0.9639038982104676\n"
     ]
    }
   ],
   "source": [
    "print('Jaccard index : ',jaccard_similarity_score(y_test,yhat))\n",
    "print('F1 score : ',f1_score(y_test,yhat,average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this we can conclude that we do not see any difference between Linear and RBF kernels for this particular dataset."
   ]
  }
 ],
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