SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   Unnamed: 9  \n",
       "0         1              0.92         NaN  \n",
       "1         1              0.76         NaN  \n",
       "2         1              0.72         NaN  \n",
       "3         1              0.80         NaN  \n",
       "4         0              0.65         NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating  class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = []\n",
    "for row in df['Chance of Admit ']:\n",
    "    if row>0.75:\n",
    "        classes.append(1)\n",
    "    else:\n",
    "        classes.append(0)\n",
    "print(len(classes))\n",
    "df['Result']=classes\n",
    "len(df[df['Result']==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Unnamed: 9  Result  \n",
       "0         1         NaN       1  \n",
       "1         1         NaN       1  \n",
       "2         1         NaN       0  \n",
       "3         1         NaN       1  \n",
       "4         0         NaN       0  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(['Chance of Admit '] , axis = 1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Serial No.         500 non-null    int64  \n",
      " 1   GRE Score          500 non-null    int64  \n",
      " 2   TOEFL Score        500 non-null    int64  \n",
      " 3   University Rating  500 non-null    int64  \n",
      " 4   SOP                500 non-null    float64\n",
      " 5   LOR                500 non-null    float64\n",
      " 6   CGPA               500 non-null    float64\n",
      " 7   Research           500 non-null    int64  \n",
      " 8   Unnamed: 9         0 non-null      float64\n",
      " 9   Result             500 non-null    int64  \n",
      "dtypes: float64(4), int64(6)\n",
      "memory usage: 39.2 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of      Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0             1        337          118                  4  4.5   4.5  9.65   \n",
       "1             2        324          107                  4  4.0   4.5  8.87   \n",
       "2             3        316          104                  3  3.0   3.5  8.00   \n",
       "3             4        322          110                  3  3.5   2.5  8.67   \n",
       "4             5        314          103                  2  2.0   3.0  8.21   \n",
       "..          ...        ...          ...                ...  ...   ...   ...   \n",
       "495         496        332          108                  5  4.5   4.0  9.02   \n",
       "496         497        337          117                  5  5.0   5.0  9.87   \n",
       "497         498        330          120                  5  4.5   5.0  9.56   \n",
       "498         499        312          103                  4  4.0   5.0  8.43   \n",
       "499         500        327          113                  4  4.5   4.5  9.04   \n",
       "\n",
       "     Research  Unnamed: 9  Result  \n",
       "0           1         NaN       1  \n",
       "1           1         NaN       1  \n",
       "2           1         NaN       0  \n",
       "3           1         NaN       1  \n",
       "4           0         NaN       0  \n",
       "..        ...         ...     ...  \n",
       "495         1         NaN       1  \n",
       "496         1         NaN       1  \n",
       "497         1         NaN       1  \n",
       "498         0         NaN       0  \n",
       "499         0         NaN       1  \n",
       "\n",
       "[500 rows x 10 columns]>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot between CGPA and University Rating for the two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2487ff60e88>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_yes = df1[df1['Result'] ==1]\n",
    "df_no = df1[df1['Result'] ==0]\n",
    "\n",
    "plt.scatter(df_yes['CGPA'], df_yes['University Rating'], color=\"green\")\n",
    "plt.scatter(df_no['CGPA'], df_no['University Rating'], color = \"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feature = df[['GRE Score', 'TOEFL Score', 'CGPA', 'University Rating']]\n",
    "X = np.asarray(df_feature)\n",
    "y = np.asarray(df['Result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[337.  , 118.  ,   9.65,   4.  ],\n",
       "       [324.  , 107.  ,   8.87,   4.  ],\n",
       "       [316.  , 104.  ,   8.  ,   3.  ],\n",
       "       [322.  , 110.  ,   8.67,   3.  ],\n",
       "       [314.  , 103.  ,   8.21,   2.  ]])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=2, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "classifier = svm.SVC(kernel='rbf',gamma='auto',C=2)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.86      0.84        37\n",
      "           1       0.86      0.82      0.84        38\n",
      "\n",
      "    accuracy                           0.84        75\n",
      "   macro avg       0.84      0.84      0.84        75\n",
      "weighted avg       0.84      0.84      0.84        75\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid={'C':[0.1,1,10,100,1000],'gamma':[1,0.1,0.01,0.001,0.0001],'kernel':['rbf','poly']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(SVC(),param_grid,refit=True,verbose=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.600, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.600, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.600, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.588, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.588, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=poly .....................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......... C=0.1, gamma=1, kernel=poly, score=0.941, total=   1.4s\n",
      "[CV] C=0.1, gamma=1, kernel=poly .....................................\n"
     ]
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_estimator_"
   ]
  }
 ],
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