Admission prediction-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REGRESSION ALGORITHMS FOR PREDICTING GRADUATE ADMISSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('df2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Enroll</th>\n",
       "      <th>InStateCost</th>\n",
       "      <th>OutStateCost</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>9397</td>\n",
       "      <td>49940.0</td>\n",
       "      <td>49940.0</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1566</td>\n",
       "      <td>44046.0</td>\n",
       "      <td>44046.0</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>490</td>\n",
       "      <td>34752.0</td>\n",
       "      <td>15700.0</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>844</td>\n",
       "      <td>34752.0</td>\n",
       "      <td>15700.0</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2617</td>\n",
       "      <td>40946.0</td>\n",
       "      <td>40946.0</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2344</td>\n",
       "      <td>48020.0</td>\n",
       "      <td>48020.0</td>\n",
       "      <td>330</td>\n",
       "      <td>115</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>138</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>321</td>\n",
       "      <td>109</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>982</td>\n",
       "      <td>34752.0</td>\n",
       "      <td>15700.0</td>\n",
       "      <td>308</td>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2574</td>\n",
       "      <td>64712.0</td>\n",
       "      <td>64712.0</td>\n",
       "      <td>302</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Enroll  InStateCost  OutStateCost  Gre_score  TOEFL Score  \\\n",
       "0    9397      49940.0       49940.0        337          118   \n",
       "1    1566      44046.0       44046.0        324          107   \n",
       "2     490      34752.0       15700.0        316          104   \n",
       "3     844      34752.0       15700.0        322          110   \n",
       "4    2617      40946.0       40946.0        314          103   \n",
       "5    2344      48020.0       48020.0        330          115   \n",
       "6     138      14600.0       14600.0        321          109   \n",
       "7     982      34752.0       15700.0        308          101   \n",
       "8    2574      64712.0       64712.0        302          102   \n",
       "\n",
       "   University Rating  SOP  LOR  CGPA  Research  Chance of Admit  \n",
       "0                  4  4.5  4.5  9.65         1             0.92  \n",
       "1                  4  4.0  4.5  8.87         1             0.76  \n",
       "2                  3  3.0  3.5  8.00         1             0.72  \n",
       "3                  3  3.5  2.5  8.67         1             0.80  \n",
       "4                  2  2.0  3.0  8.21         0             0.65  \n",
       "5                  5  4.5  3.0  9.34         1             0.90  \n",
       "6                  3  3.0  4.0  8.20         1             0.75  \n",
       "7                  2  3.0  4.0  7.90         0             0.68  \n",
       "8                  1  2.0  1.5  8.00         0             0.50  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['Enroll','InStateCost','OutStateCost','Gre_score','TOEFL Score','University Rating','SOP','LOR','CGPA','Research','Chance of Admit']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = cdf.iloc[:, :-1].values\n",
    "y = cdf.iloc[:, 10].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test,y_train, y_test = train_test_split(x,y,test_size = 0.20,random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "y_pred = lr.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real value of y_test[0]:= 0.66\tpredicted value = 0.7708759887513175\n",
      "real value of y_test[3]:= 0.85\tpredicted value = 0.8259715299650394\n",
      "real value of y_test[20]:= 0.66\tpredicted value = 0.623715230482536\n"
     ]
    }
   ],
   "source": [
    "print(\"real value of y_test[0]:= \" + str(y_test[0]) + \"\\t\" \"predicted value = \" +str(y_pred[0]))\n",
    "print(\"real value of y_test[3]:= \" + str(y_test[3]) + \"\\t\" \"predicted value = \" +str(y_pred[3]))\n",
    "print(\"real value of y_test[20]:= \" + str(y_test[20]) + \"\\t\" \"predicted value = \" +str(y_pred[20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score:  0.6820558528080667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(\"r_square score: \", r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score (train dataset):  0.8169588420788972\n"
     ]
    }
   ],
   "source": [
    "y_head_lr_train = lr.predict(x_train)\n",
    "print(\"r_square score (train dataset): \", r2_score(y_train,y_head_lr_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor(n_estimators = 100, random_state = 42)\n",
    "rfr.fit(x_train,y_train)\n",
    "y_pred_rfr = rfr.predict(x_test) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real value of y_test[0]:= 0.66\tpredicted value = 0.8120000000000002\n",
      "real value of y_test[3]:= 0.85\tpredicted value = 0.8986999999999996\n",
      "real value of y_test[20]:= 0.66\tpredicted value = 0.6489999999999999\n"
     ]
    }
   ],
   "source": [
    "print(\"real value of y_test[0]:= \" + str(y_test[0]) + \"\\t\" \"predicted value = \" +str(y_pred_rfr[0]))\n",
    "print(\"real value of y_test[3]:= \" + str(y_test[3]) + \"\\t\" \"predicted value = \" +str(y_pred_rfr[3]))\n",
    "print(\"real value of y_test[20]:= \" + str(y_test[20]) + \"\\t\" \"predicted value = \" +str(y_pred_rfr[20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score:  0.6234650979981138\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(\"r_square score: \", r2_score(y_test,y_pred_rfr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score (train dataset):  0.9667044560889697\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf_train = rfr.predict(x_train)\n",
    "print(\"r_square score (train dataset): \", r2_score(y_train,y_pred_rf_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Regression\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtr = DecisionTreeRegressor(random_state = 42)\n",
    "dtr.fit(x_train,y_train)\n",
    "y_pred_dtr = dtr.predict(x_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "real value of y_test[0]:= 0.66\tpredicted value = 0.79\n",
      "real value of y_test[3]:= 0.85\tpredicted value = 0.9\n",
      "real value of y_test[20]:= 0.66\tpredicted value = 0.72\n"
     ]
    }
   ],
   "source": [
    "print(\"real value of y_test[0]:= \" + str(y_test[0]) + \"\\t\" \"predicted value = \" +str(y_pred_dtr[0]))\n",
    "print(\"real value of y_test[3]:= \" + str(y_test[3]) + \"\\t\" \"predicted value = \" +str(y_pred_dtr[3]))\n",
    "print(\"real value of y_test[20]:= \" + str(y_test[20]) + \"\\t\" \"predicted value = \" +str(y_pred_dtr[20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score:  0.3786814799287286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(\"r_square score: \", r2_score(y_test,y_pred_dtr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score (train dataset):  1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred_dtr_train = dtr.predict(x_train)\n",
    "print(\"r_square score (train dataset): \", r2_score(y_train,y_pred_dtr_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of Regression Algorithms\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y = np.array([r2_score(y_test,y_head_lr),r2_score(y_test,y_pred_rfr),r2_score(y_test,y_pred_dtr)])\n",
    "x = [\"LinearRegression\",\"RandomForestReg.\",\"DecisionTreeReg.\"]\n",
    "plt.bar(x,y)\n",
    "plt.title(\"Comparison of Regression Algorithms\")\n",
    "plt.xlabel(\"Regressor\")\n",
    "plt.ylabel(\"r2_score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression and random forest regression algorithms were better than decision tree regression algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}