SVM_WEEK9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c8900e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef9a803f",
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>...</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>...</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>...</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>...</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>satisfactory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  reason  \\\n",
       "0   F   18       U     GT3       A     4     4  at_home   teacher  course   \n",
       "1   F   17       U     GT3       T     1     1  at_home     other  course   \n",
       "2   F   15       U     LE3       T     1     1  at_home     other   other   \n",
       "3   F   15       U     GT3       T     4     2   health  services    home   \n",
       "4   F   16       U     GT3       T     3     3    other     other    home   \n",
       "\n",
       "   ... internet  romantic  famrel  freetime goout Dalc Walc health absences  \\\n",
       "0  ...       no        no       4         3     4    1    1      3        6   \n",
       "1  ...      yes        no       5         3     3    1    1      3        4   \n",
       "2  ...      yes        no       4         3     2    2    3      3       10   \n",
       "3  ...      yes       yes       3         2     2    1    1      5        2   \n",
       "4  ...       no        no       4         3     2    1    2      5        4   \n",
       "\n",
       "         grades  \n",
       "0          poor  \n",
       "1          poor  \n",
       "2  satisfactory  \n",
       "3     very good  \n",
       "4  satisfactory  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code to read data\n",
    "data=pd.read_csv('student_performances.csv')\n",
    "\n",
    "# Your code to print first few samples of the dataset.\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "279da00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the columns ['Fjob', 'Mjob', 'reason', 'guardian'] into one hot vectors.\n",
    "\n",
    "\n",
    "\n",
    "data = pd.get_dummies(data, columns = ['Fjob', 'Mjob', 'reason', 'guardian'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5521a8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting other text values in the dataframe to numbers using label encoding.\n",
    "#Is their an efficient way to do this? Could you use loops. Try to convert it into an efficient code and replace with the above code?\n",
    "#Next - Print few samples of the data to ensure everything is number except 'grades' column.\n",
    "\n",
    "\n",
    "from sklearn import preprocessing\n",
    "labelencoder = preprocessing.LabelEncoder()\n",
    "## Your code to convert transform sex to the numbers\n",
    "labelencoder.fit(data['sex'])\n",
    "data ['sex'] = labelencoder.transform(data['sex'])\n",
    "\n",
    "## Your code to convert transform address to the numbers\n",
    "labelencoder.fit(data['address'])\n",
    "# #\n",
    "data['address']=labelencoder.transform(data['address'])\n",
    "\n",
    "## Your code to convert transform famsize to the numbers\n",
    "\n",
    "labelencoder.fit(data['famsize'])\n",
    "\n",
    "#\n",
    "data['famsize']=labelencoder.transform(data['famsize'])\n",
    "\n",
    "\n",
    "#\n",
    "## Your code to convert transform Pstatus to the numbers\n",
    "labelencoder.fit(data['Pstatus'])\n",
    "#\n",
    "data['Pstatus']=labelencoder.transform(data['Pstatus'])\n",
    "#\n",
    "## Your code to convert transform schoolsup to the numbers\n",
    "labelencoder.fit(data['schoolsup'])\n",
    "#\n",
    "data['schoolsup']=labelencoder.transform(data['schoolsup'])\n",
    "#\n",
    "## Your code to convert transform famsup to the numbers\n",
    "labelencoder.fit(data['famsup'])\n",
    "#\n",
    "data['famsup']=labelencoder.transform(data['famsup'])\n",
    "## Your code to convert transform paid to the numbers\n",
    "labelencoder.fit(data['paid'])\n",
    "data['paid']=labelencoder.transform(data['paid'])\n",
    "## Your code to convert transform activities to the numbers\n",
    "labelencoder.fit(data['activities'])\n",
    "#\n",
    "data['activities']=labelencoder.transform(data['activities'])\n",
    "## Your code to convert transform nursery to the numbers\n",
    "labelencoder.fit(data['nursery'])\n",
    "data['nursery']=labelencoder.transform(data['nursery'])\n",
    "## Your code to convert transform higher to the numbers\n",
    "labelencoder.fit(data['higher'])\n",
    "data['higher']=labelencoder.transform(data['higher'])\n",
    "## Your code to convert transform internet to the numbers\n",
    "labelencoder.fit(data['internet'])\n",
    "data['internet']=labelencoder.transform(data['internet'])\n",
    "## Your code to convert transform romantic to the numbers\n",
    "labelencoder.fit(data['romantic'])\n",
    "data['romantic']=labelencoder.transform(data['romantic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "727beafe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 395 entries, 0 to 394\n",
      "Data columns (total 43 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   sex                395 non-null    int64 \n",
      " 1   age                395 non-null    int64 \n",
      " 2   address            395 non-null    int64 \n",
      " 3   famsize            395 non-null    int64 \n",
      " 4   Pstatus            395 non-null    int64 \n",
      " 5   Medu               395 non-null    int64 \n",
      " 6   Fedu               395 non-null    int64 \n",
      " 7   traveltime         395 non-null    int64 \n",
      " 8   studytime          395 non-null    int64 \n",
      " 9   failures           395 non-null    int64 \n",
      " 10  schoolsup          395 non-null    int64 \n",
      " 11  famsup             395 non-null    int64 \n",
      " 12  paid               395 non-null    int64 \n",
      " 13  activities         395 non-null    int64 \n",
      " 14  nursery            395 non-null    int64 \n",
      " 15  higher             395 non-null    int64 \n",
      " 16  internet           395 non-null    int64 \n",
      " 17  romantic           395 non-null    int64 \n",
      " 18  famrel             395 non-null    int64 \n",
      " 19  freetime           395 non-null    int64 \n",
      " 20  goout              395 non-null    int64 \n",
      " 21  Dalc               395 non-null    int64 \n",
      " 22  Walc               395 non-null    int64 \n",
      " 23  health             395 non-null    int64 \n",
      " 24  absences           395 non-null    int64 \n",
      " 25  grades             395 non-null    object\n",
      " 26  Fjob_at_home       395 non-null    bool  \n",
      " 27  Fjob_health        395 non-null    bool  \n",
      " 28  Fjob_other         395 non-null    bool  \n",
      " 29  Fjob_services      395 non-null    bool  \n",
      " 30  Fjob_teacher       395 non-null    bool  \n",
      " 31  Mjob_at_home       395 non-null    bool  \n",
      " 32  Mjob_health        395 non-null    bool  \n",
      " 33  Mjob_other         395 non-null    bool  \n",
      " 34  Mjob_services      395 non-null    bool  \n",
      " 35  Mjob_teacher       395 non-null    bool  \n",
      " 36  reason_course      395 non-null    bool  \n",
      " 37  reason_home        395 non-null    bool  \n",
      " 38  reason_other       395 non-null    bool  \n",
      " 39  reason_reputation  395 non-null    bool  \n",
      " 40  guardian_father    395 non-null    bool  \n",
      " 41  guardian_mother    395 non-null    bool  \n",
      " 42  guardian_other     395 non-null    bool  \n",
      "dtypes: bool(17), int64(25), object(1)\n",
      "memory usage: 86.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c543cfd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separate input X and output y.\n",
    "#X is the all columns except 'grades'.\n",
    "#y is 'grades' column.\n",
    "\n",
    "# Your code to get X from data\n",
    "X=data[['sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
    "       'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid',\n",
    "       'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel',\n",
    "       'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences',\n",
    "     'Fjob_at_home', 'Fjob_health', 'Fjob_other', 'Fjob_services',\n",
    "       'Fjob_teacher', 'Mjob_at_home', 'Mjob_health', 'Mjob_other',\n",
    "       'Mjob_services', 'Mjob_teacher', 'reason_course', 'reason_home',\n",
    "       'reason_other', 'reason_reputation', 'guardian_father',\n",
    "       'guardian_mother', 'guardian_other']]\n",
    "# Your code to get y from data\n",
    "y=data['grades']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1857df34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316, 42)\n",
      "(79, 42)\n",
      "(316,)\n",
      "(79,)\n"
     ]
    }
   ],
   "source": [
    "# Your code to get y from data\n",
    "y=data['grades']\n",
    "#Next - Split data into train and test set.\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "#\n",
    "#\n",
    "# Print shapes of train and test data to make sure everyhing is in order.\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "916907bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Next - Develop a SVM classifier and fit into the train dataset.\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Encode the categorical variables in the training set\n",
    "X_train_encoded = X_train.copy()\n",
    "for column in X_train_encoded.columns:\n",
    "    X_train_encoded[column] = label_encoder.fit_transform(X_train_encoded[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d1dbdc7",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an SVM classifier with the RBF kernel\n",
    "classifier = SVC(kernel='rbf')\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "classifier.fit(X_train_encoded, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8576d36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy test set: 0.34177215189873417\n",
      "Precision test set: 0.34177215189873417\n",
      "Recall test set: 0.34177215189873417\n",
      "F-score test set: 0.34177215189873417\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   excellent       0.00      0.00      0.00        10\n",
      "        good       0.00      0.00      0.00        10\n",
      "        poor       0.35      1.00      0.51        27\n",
      "satisfactory       0.00      0.00      0.00        16\n",
      "   very good       0.00      0.00      0.00        16\n",
      "\n",
      "    accuracy                           0.34        79\n",
      "   macro avg       0.07      0.20      0.10        79\n",
      "weighted avg       0.12      0.34      0.18        79\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mak/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/mak/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/mak/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#Next - predict the values on the input test set.\n",
    "#Find accuracy, precision, recall, f-score.\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Create a LabelEncoder object\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Encode the categorical variables in the training set\n",
    "X_train_encoded = X_train.copy()\n",
    "for column in X_train_encoded.columns:\n",
    "    X_train_encoded[column] = label_encoder.fit_transform(X_train_encoded[column])\n",
    "\n",
    "# Encode the categorical variables in the test set\n",
    "X_test_encoded = X_test.copy()\n",
    "for column in X_test_encoded.columns:\n",
    "    X_test_encoded[column] = label_encoder.transform(X_test_encoded[column])\n",
    "\n",
    "# Create an SVM classifier with the RBF kernel\n",
    "classifier = SVC(kernel='rbf')\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "classifier.fit(X_train_encoded, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = classifier.predict(X_test_encoded)\n",
    "\n",
    "# Compute evaluation metrics\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='micro')\n",
    "recall = recall_score(y_test, y_pred, average='micro')\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"Accuracy test set:\", accuracy)\n",
    "print(\"Precision test set:\", precision)\n",
    "print(\"Recall test set:\", recall)\n",
    "print(\"F-score test set:\", f1)\n",
    "\n",
    "# Print the classification report\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dfb4578e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy train set:  0.3259493670886076\n",
      "None\n",
      "Precision train set:  0.3259493670886076\n",
      "None\n",
      "Recall train set:  0.3259493670886076\n",
      "None\n",
      "F-score train set:  0.3259493670886076\n",
      "None\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   excellent       0.00      0.00      0.00        30\n",
      "        good       0.00      0.00      0.00        52\n",
      "        poor       0.33      1.00      0.49       103\n",
      "satisfactory       0.00      0.00      0.00        87\n",
      "   very good       0.00      0.00      0.00        44\n",
      "\n",
      "    accuracy                           0.33       316\n",
      "   macro avg       0.07      0.20      0.10       316\n",
      "weighted avg       0.11      0.33      0.16       316\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mak/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/mak/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/mak/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#Find accuracy, precision, recall, and f-score on training dataset.\n",
    "\n",
    "train_data_predictions = classifier.predict(X_train)\n",
    "\n",
    "print (print(\"Accuracy train set: \", accuracy_score(y_train, train_data_predictions)))\n",
    "print (print(\"Precision train set: \", precision_score(y_train, train_data_predictions, average = 'micro' )))\n",
    "print (print(\"Recall train set: \", recall_score(y_train, train_data_predictions, average = 'micro' )))\n",
    "print (print(\"F-score train set: \", f1_score(y_train, train_data_predictions, average = 'micro' )))\n",
    "\n",
    "print (classification_report(y_train, train_data_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efb41c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display confusion matrix between actual and predicted values on the test data.\n",
    "#\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "#  Your code to display confusion matrix between predicted output and actual output\n",
    "cm = confusion_matrix(y_test, y_pred) \n",
    "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = classifier.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b8c0a386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with linear kernel: 0.35443037974683544\n",
      "Accuracy with poly kernel: 0.3037974683544304\n",
      "Accuracy with rbf kernel: 0.25316455696202533\n",
      "Accuracy with sigmoid kernel: 0.3291139240506329\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Earlier, we used rfb Kernel. Now, we will test other kernels too.\n",
    "#We will test on ['linear', 'poly', 'rbf', 'sigmoid' ] kernels.¶\n",
    "# Define the list of kernels to test\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# List of kernels to test\n",
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "\n",
    "# List to store accuracy for each kernel\n",
    "accuracies = []\n",
    "\n",
    "# Loop through each kernel type\n",
    "for kernel in kernels:\n",
    "    # Initialize the SVM classifier with the current kernel\n",
    "    classifier = SVC(kernel=kernel)\n",
    "    \n",
    "    # Train the classifier\n",
    "    classifier.fit(X_train, y_train)\n",
    "    \n",
    "    # Make predictions on the test set\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    \n",
    "    # Calculate the accuracy and append to the accuracies list\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracies.append(accuracy)\n",
    "    print(f\"Accuracy with {kernel} kernel: {accuracy}\")\n",
    "\n",
    "#\n",
    "# Your code to plot accuracy vs kernels\n",
    "#\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(kernels, accuracies, marker='o', linestyle='-', color='b')\n",
    "plt.title('Accuracy vs Kernel Type')\n",
    "plt.xlabel('Kernel')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9ed42418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.20253164556962025,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0e9eed74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.20253164556962025,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c0b79453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.20253164556962025,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b459fdcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.20253164556962025,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417,\n",
       " 0.34177215189873417]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e69f7d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the evaluation metrics\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(kernels, accuracy_scores, marker='o', label='Accuracy')\n",
    "plt.plot(kernels, precision_scores, marker='o', label='Precision')\n",
    "plt.plot(kernels, recall_scores, marker='o', label='Recall')\n",
    "#plt.plot(kernels, f1_scores, marker='o', label='F1-score')\n",
    "plt.xlabel('Kernel')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Performance of SVM Classifier with Different Kernels')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b158af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}