Admission (rev).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
    "import joblib\n",
    "from tkinter import *\n",
    "from tkinter import messagebox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing CSV To Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Serial No          400 non-null    int64  \n",
      " 1   GRE Score          397 non-null    float64\n",
      " 2   TOEFL Score        383 non-null    float64\n",
      " 3   University Rating  400 non-null    int64  \n",
      " 4    SOP               379 non-null    float64\n",
      " 5   LOR                400 non-null    float64\n",
      " 6   CGPA               400 non-null    float64\n",
      " 7   Research           400 non-null    int64  \n",
      " 8   Chance of Admit    400 non-null    float64\n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 28.3 KB\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337.0</td>\n",
       "      <td>118.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324.0</td>\n",
       "      <td>107.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316.0</td>\n",
       "      <td>104.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322.0</td>\n",
       "      <td>110.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</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      337.0        118.0                  4   4.5   4.5  9.65   \n",
       "1          2      324.0        107.0                  4   4.0   4.5  8.87   \n",
       "2          3      316.0        104.0                  3   3.0   3.5  8.00   \n",
       "3          4      322.0        110.0                  3   3.5   2.5  8.67   \n",
       "4          5      314.0        103.0                  2   NaN   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Admission Chance.csv')\n",
    "df.info()\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check For Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No             0\n",
       "GRE Score             3\n",
       "TOEFL Score          17\n",
       "University Rating     0\n",
       " SOP                 21\n",
       "LOR                   0\n",
       "CGPA                  0\n",
       "Research              0\n",
       "Chance of Admit       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replace Missing Value with Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shera\\AppData\\Local\\Temp\\ipykernel_8224\\3672466597.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['GRE Score'].fillna(df['GRE Score'].mean(), inplace=True)\n",
      "C:\\Users\\shera\\AppData\\Local\\Temp\\ipykernel_8224\\3672466597.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['TOEFL Score'].fillna(df['TOEFL Score'].mean(), inplace=True)\n",
      "C:\\Users\\shera\\AppData\\Local\\Temp\\ipykernel_8224\\3672466597.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[' SOP'].fillna(df[' SOP'].mean(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['GRE Score'].fillna(df['GRE Score'].mean(), inplace=True)\n",
    "df['TOEFL Score'].fillna(df['TOEFL Score'].mean(), inplace=True)\n",
    "df[' SOP'].fillna(df[' SOP'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No            0\n",
       "GRE Score            0\n",
       "TOEFL Score          0\n",
       "University Rating    0\n",
       " SOP                 0\n",
       "LOR                  0\n",
       "CGPA                 0\n",
       "Research             0\n",
       "Chance of Admit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop Data With No Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Serial No'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standarisasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   GRE Score  TOEFL Score  University Rating       SOP      LOR       CGPA  \\\n",
      "0   1.761834     1.788633           0.798829  1.122847  1.167321  1.764818   \n",
      "1   0.627075    -0.093363           0.798829  0.598759  1.167321  0.455151   \n",
      "2  -0.071239    -0.606634          -0.076600 -0.449415  0.052933 -1.005631   \n",
      "3   0.452497     0.419909          -0.076600  0.074672 -1.061454  0.119339   \n",
      "4  -0.245817    -0.777725          -0.952029  0.000000 -0.504260 -0.653029   \n",
      "\n",
      "   Research  \n",
      "0  0.909112  \n",
      "1  0.909112  \n",
      "2  0.909112  \n",
      "3  0.909112  \n",
      "4 -1.099975  \n"
     ]
    }
   ],
   "source": [
    "X = df.drop(['Chance of Admit '], axis=1)\n",
    "y = df['Chance of Admit ']\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "print(scaled_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training (Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "\n",
    "svm = SVR()\n",
    "svm.fit(X_train, y_train)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "\n",
    "gr = GradientBoostingRegressor()\n",
    "gr.fit(X_train, y_train)\n",
    "y_pred_gr = gr.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model (Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Score_lr = metrics.r2_score(y_test,y_pred_lr)\n",
    "Score_svr = metrics.r2_score(y_test,y_pred_svm)\n",
    "Score_rf = metrics.r2_score(y_test,y_pred_rf)\n",
    "Score_gr = metrics.r2_score(y_test,y_pred_gr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Models</th>\n",
       "      <th>R2_SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.819739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVR</td>\n",
       "      <td>0.627900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.802256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GR</td>\n",
       "      <td>0.800075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models  R2_SCORE\n",
       "0     LR  0.819739\n",
       "1    SVR  0.627900\n",
       "2     RF  0.802256\n",
       "3     GR  0.800075"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = pd.DataFrame({'Models': ['LR', 'SVR', 'RF', 'GR'], 'R2_SCORE': [Score_lr, Score_svr, Score_rf, Score_gr]})\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Models', ylabel='R2_SCORE'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "custom_palette = sns.color_palette(\"Set2\", 4)\n",
    "\n",
    "sns.barplot(x='Models', y='R2_SCORE', data=final_data, hue='Models', palette=custom_palette, legend=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Can See That Linear Regression is the best model to use "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training (Clasification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = [1 if value>0.7 else 0 for value in y_train]\n",
    "y_test = [1 if value>0.7 else 0 for value in y_test]\n",
    "\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred_1= lr.predict(X_test)\n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "y_pred_2 = svm.predict(X_test)\n",
    "\n",
    "knn=KNeighborsClassifier()\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred_3 = knn.predict(X_test)\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred_4 = rf.predict(X_test)\n",
    "\n",
    "gr = GradientBoostingClassifier()\n",
    "gr.fit(X_train,y_train)\n",
    "y_pred_5 = gr.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model (Clasification)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Models</th>\n",
       "      <th>ACC_SCORE</th>\n",
       "      <th>RECALL_SCORE</th>\n",
       "      <th>PRECISION_SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.803922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.701493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.854167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.872340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GBC</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>0.840000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models  ACC_SCORE  RECALL_SCORE  PRECISION_SCORE\n",
       "0     LR     0.8000      0.872340         0.803922\n",
       "1    SVC     0.7500      1.000000         0.701493\n",
       "2    KNN     0.8375      0.872340         0.854167\n",
       "3     RF     0.8500      0.872340         0.872340\n",
       "4    GBC     0.8375      0.893617         0.840000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = pd.DataFrame({\n",
    "    'Models': ['LR', 'SVC', 'KNN', 'RF', 'GBC'],\n",
    "    'ACC_SCORE': [accuracy_score(y_test, y_pred_1), accuracy_score(y_test, y_pred_2),\n",
    "                  accuracy_score(y_test, y_pred_3), accuracy_score(y_test, y_pred_4),\n",
    "                  accuracy_score(y_test, y_pred_5)],\n",
    "    'RECALL_SCORE': [recall_score(y_test, y_pred_1), recall_score(y_test, y_pred_2),\n",
    "                     recall_score(y_test, y_pred_3), recall_score(y_test, y_pred_4),\n",
    "                     recall_score(y_test, y_pred_5)],\n",
    "    'PRECISION_SCORE': [precision_score(y_test, y_pred_1), precision_score(y_test, y_pred_2),\n",
    "                        precision_score(y_test, y_pred_3), precision_score(y_test, y_pred_4),\n",
    "                        precision_score(y_test, y_pred_5)]\n",
    "})\n",
    "\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Models', ylabel='ACC_SCORE'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "custom_palette = sns.color_palette(\"Set2\", 5)\n",
    "\n",
    "sns.barplot(x='Models', y='ACC_SCORE', data=final_data, hue='Models', palette=custom_palette, legend=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest (RF) is the best model to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Chance of Admit ',axis=1)\n",
    "y = df['Chance of Admit ']\n",
    "y  = [1 if value>0.7 else 0 for value in y]\n",
    "y = np.array(y)\n",
    "y\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "gr = GradientBoostingClassifier()\n",
    "gr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Chance of Admit ',axis=1)\n",
    "y = df['Chance of Admit ']\n",
    "y  = [1 if value>0.7 else 0 for value in y]\n",
    "y = np.array(y)\n",
    "y\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Chance of Admit ',axis=1)\n",
    "y = df['Chance of Admit ']\n",
    "y  = [1 if value>0.7 else 0 for value in y]\n",
    "y = np.array(y)\n",
    "y\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Chance of Admit ',axis=1)\n",
    "y = df['Chance of Admit ']\n",
    "y  = [1 if value>0.7 else 0 for value in y]\n",
    "y = np.array(y)\n",
    "y\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "svm = SVC()\n",
    "svm.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('Chance of Admit ',axis=1)\n",
    "y = df['Chance of Admit ']\n",
    "y  = [1 if value>0.7 else 0 for value in y]\n",
    "y = np.array(y)\n",
    "y\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GRE Score', 'TOEFL Score', 'University Rating', ' SOP', 'LOR ', 'CGPA',\n",
       "       'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(svm,'admission_model')\n",
    "model = joblib.load('admission_model')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(lr,'model_lr')\n",
    "model_lr = joblib.load('model_lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(svm,'model_svm')\n",
    "model_svm = joblib.load('model_svm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(knn,'model_knn')\n",
    "model_knn = joblib.load('model_knn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(rf,'model_rf')\n",
    "model_rf = joblib.load('model_rf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(gr,'model_gr')\n",
    "model_gr = joblib.load('model_gr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasil Lr :  [1]\n",
      "Hasil Svm :  [1]\n",
      "Hasil KNN :  [1]\n",
      "Hasil RF :  [1]\n",
      " Hasil gr :  [1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\shera\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print(f'Hasil Lr : ', model_lr.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]])))\n",
    "print(f'Hasil Svm : ', model_svm.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]])))\n",
    "print(f'Hasil KNN : ', model_knn.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]])))\n",
    "print(f'Hasil RF : ', model_rf.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]])))\n",
    "print(f' Hasil gr : ', model_gr.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as tk\n",
    "\n",
    "# Load the models\n",
    "model_dict = {\n",
    "    \"Logistic Regression\": joblib.load('model_lr'),\n",
    "    \"SVM\": joblib.load('model_svm'),\n",
    "    \"KNN\": joblib.load('model_knn'),\n",
    "    \"Random Forest\": joblib.load('model_rf'),\n",
    "    \"Gradient Boosting\": joblib.load('model_gr')\n",
    "}\n",
    "\n",
    "# Function to validate input\n",
    "def validate_input(event):\n",
    "    entry_ranges = {\n",
    "        e1: (260, 340), \n",
    "        e2: (0, 120),     \n",
    "        e3: (1, 5),     \n",
    "        e4: (1, 5),       \n",
    "        e5: (1, 5),       \n",
    "        e6: (0, 10),     \n",
    "        e7: (0, 1)       \n",
    "    }\n",
    "    \n",
    "    try:\n",
    "        min_value, max_value = entry_ranges[event.widget]\n",
    "        value = float(event.widget.get())\n",
    "        \n",
    "        if not (min_value <= value <= max_value):\n",
    "            raise ValueError\n",
    "        \n",
    "    except ValueError:\n",
    "        messagebox.showerror(\"Error\", f\"Please enter a valid number between {min_value} and {max_value}.\")\n",
    "        event.widget.delete(0, tk.END)\n",
    "\n",
    "# Function to show entry\n",
    "def show_entry():\n",
    "    try:\n",
    "        p1 = float(e1.get())\n",
    "        p2 = float(e2.get())\n",
    "        p3 = float(e3.get())\n",
    "        p4 = float(e4.get())\n",
    "        p5 = float(e5.get())\n",
    "        p6 = float(e6.get())\n",
    "        p7 = float(e7.get())\n",
    "\n",
    "        selected_model = model_var.get()\n",
    "        \n",
    "        model = model_dict[selected_model]\n",
    "        \n",
    "        result = model.predict(sc.transform([[p1, p2, p3, p4, p5, p6, p7]]))\n",
    "        \n",
    "        if result == 1:\n",
    "            result_label.config(text=\"High Chance of getting admission !!\", fg=\"green\")\n",
    "        else:\n",
    "            result_label.config(text=\"Low Chance of Admission !!\", fg=\"red\")\n",
    "    except ValueError:\n",
    "        messagebox.showerror(\"Error\", \"Please enter valid numeric values.\")\n",
    "    except Exception as e:\n",
    "        messagebox.showerror(\"Error\", str(e))\n",
    "\n",
    "# Create main window\n",
    "master = tk.Tk()\n",
    "master.title(\"Graduate Admission Analysis and Prediction\")\n",
    "\n",
    "master.configure(bg=\"#ffffcc\")\n",
    "master.geometry(\"400x350\")\n",
    "\n",
    "header_label = tk.Label(master, text=\"Graduate Admission Prediction\", font=(\"Arial\", 16), bg=\"#ffffcc\", fg=\"#333\")\n",
    "header_label.grid(row=0, columnspan=2, pady=10)\n",
    "\n",
    "input_frame = tk.Frame(master, bg=\"#ffffcc\")\n",
    "input_frame.grid(row=1, columnspan=2, pady=10)\n",
    "\n",
    "tk.Label(input_frame, text=\"GRE Score (260-340):\", bg=\"#ffffcc\").grid(row=0, column=0, padx=5, sticky=\"e\")\n",
    "tk.Label(input_frame, text=\"TOEFL Score (0-120):\", bg=\"#ffffcc\").grid(row=1, column=0, padx=5, sticky=\"e\")\n",
    "tk.Label(input_frame, text=\"University Rating (1-5):\", bg=\"#ffffcc\").grid(row=2, column=0, padx=5, sticky=\"e\")\n",
    "tk.Label(input_frame, text=\"SOP (1-5):\", bg=\"#ffffcc\").grid(row=3, column=0, padx=5, sticky=\"e\")\n",
    "tk.Label(input_frame, text=\"LOR (1-5):\", bg=\"#ffffcc\").grid(row=4, column=0, padx=5, sticky=\"e\")\n",
    "tk.Label(input_frame, text=\"CGPA (0-10):\", bg=\"#ffffcc\").grid(row=5, column=0, padx=5, sticky=\"e\")\n",
    "tk.Label(input_frame, text=\"Research (0/1):\", bg=\"#ffffcc\").grid(row=6, column=0, padx=5, sticky=\"e\")\n",
    "\n",
    "e1 = tk.Entry(input_frame)\n",
    "e2 = tk.Entry(input_frame)\n",
    "e3 = tk.Entry(input_frame)\n",
    "e4 = tk.Entry(input_frame)\n",
    "e5 = tk.Entry(input_frame)\n",
    "e6 = tk.Entry(input_frame)\n",
    "e7 = tk.Entry(input_frame)\n",
    "\n",
    "e1.grid(row=0, column=1, padx=5)\n",
    "e2.grid(row=1, column=1, padx=5)\n",
    "e3.grid(row=2, column=1, padx=5)\n",
    "e4.grid(row=3, column=1, padx=5)\n",
    "e5.grid(row=4, column=1, padx=5)\n",
    "e6.grid(row=5, column=1, padx=5)\n",
    "e7.grid(row=6, column=1, padx=5)\n",
    "\n",
    "for entry in (e1, e2, e3, e4, e5, e6, e7):\n",
    "    entry.bind(\"<FocusOut>\", validate_input)\n",
    "\n",
    "model_var = tk.StringVar(master)\n",
    "model_var.set(\"Logistic Regression\") \n",
    "\n",
    "model_options = [\"Logistic Regression\", \"SVM\", \"KNN\", \"Random Forest\", \"Gradient Boosting\"]\n",
    "model_menu = tk.OptionMenu(master, model_var, *model_options)\n",
    "model_menu.grid(row=7, columnspan=2, pady=10)\n",
    "\n",
    "predict_button = tk.Button(master, text=\"Predict\", command=show_entry, bg=\"#4CAF50\", fg=\"white\", font=(\"Arial\", 12))\n",
    "predict_button.grid(row=8, column=0, columnspan=2, pady=10)\n",
    "\n",
    "result_label = tk.Label(master, text=\"\", font=(\"Arial\", 14), bg=\"#ffffcc\")\n",
    "result_label.grid(row=9, columnspan=2)\n",
    "\n",
    "master.mainloop()\n"
   ]
  }
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