Admission_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graduate Admission Prediction Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graduate Acceptance Dataset \n",
    "Estimate likelihood of candidate acceptance into graduate school. Performed EDA, linear & logistic regressions, along with a input function to predict candidate acceptance when given new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from pandas.util._decorators import Appender\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_v1 =pd.read_csv('Admission_Predict_Ver1.1.csv')\n",
    "df = pd.read_csv('Admission_Predict.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
       "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(df)) #data entries\n",
    "df.columns #ColumnHeads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>316.807500</td>\n",
       "      <td>107.410000</td>\n",
       "      <td>3.087500</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.452500</td>\n",
       "      <td>8.598925</td>\n",
       "      <td>0.547500</td>\n",
       "      <td>0.724350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.614301</td>\n",
       "      <td>11.473646</td>\n",
       "      <td>6.069514</td>\n",
       "      <td>1.143728</td>\n",
       "      <td>1.006869</td>\n",
       "      <td>0.898478</td>\n",
       "      <td>0.596317</td>\n",
       "      <td>0.498362</td>\n",
       "      <td>0.142609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.750000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>300.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.062500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  400.000000  400.000000   400.000000         400.000000  400.000000   \n",
       "mean   200.500000  316.807500   107.410000           3.087500    3.400000   \n",
       "std    115.614301   11.473646     6.069514           1.143728    1.006869   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    100.750000  308.000000   103.000000           2.000000    2.500000   \n",
       "50%    200.500000  317.000000   107.000000           3.000000    3.500000   \n",
       "75%    300.250000  325.000000   112.000000           4.000000    4.000000   \n",
       "max    400.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "             LOR         CGPA    Research  Chance of Admit   \n",
       "count  400.000000  400.000000  400.000000        400.000000  \n",
       "mean     3.452500    8.598925    0.547500          0.724350  \n",
       "std      0.898478    0.596317    0.498362          0.142609  \n",
       "min      1.000000    6.800000    0.000000          0.340000  \n",
       "25%      3.000000    8.170000    0.000000          0.640000  \n",
       "50%      3.500000    8.610000    1.000000          0.730000  \n",
       "75%      4.000000    9.062500    1.000000          0.830000  \n",
       "max      5.000000    9.920000    1.000000          0.970000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "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.isnull().sum() #missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SerialNo.', 'GREScore', 'TOEFLScore', 'UniversityRating', 'SOP', 'LOR',\n",
       "       'CGPA', 'Research', 'ChanceofAdmit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.replace(' ','') #removing space in columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1,3,1)\n",
    "sns.boxplot(x=\"GREScore\",orient='v',data=df,saturation=1)\n",
    "#sns.color_palette(\"light:#5A9\")\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.boxplot(x=\"TOEFLScore\",orient='v',data=df,saturation=1)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.boxplot(x=\"UniversityRating\",orient='v',data=df,saturation=1)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='GREScore')\n",
    "plt.xticks(rotation=45,size=12)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='TOEFLScore')\n",
    "plt.xticks(rotation=45,size=12)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='UniversityRating')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='SOP')\n",
    "plt.xticks(rotation=45,size=12)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='LOR')\n",
    "plt.xticks(rotation=45,size=12)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='CGPA')\n",
    "plt.xticks(rotation=45,size=12)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(data=df,x='Research')\n",
    "plt.xticks(rotation=45,size=12)\n",
    "plt.grid()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "correlation = df.corr()\n",
    "sns.heatmap(correlation, cmap=\"Greens\",linewidths=1,annot=True) \n",
    "plt.title(\"Correlation Between all Variables\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To find multicollinearity\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GREScore</td>\n",
       "      <td>1438.451789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TOEFLScore</td>\n",
       "      <td>1349.746948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UniversityRating</td>\n",
       "      <td>22.143705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SOP</td>\n",
       "      <td>38.050177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LOR</td>\n",
       "      <td>38.411722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CGPA</td>\n",
       "      <td>1080.491179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Research</td>\n",
       "      <td>2.859938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variables          VIF\n",
       "0          GREScore  1438.451789\n",
       "1        TOEFLScore  1349.746948\n",
       "2  UniversityRating    22.143705\n",
       "3               SOP    38.050177\n",
       "4               LOR    38.411722\n",
       "5              CGPA  1080.491179\n",
       "6          Research     2.859938"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_df = df.drop(['SerialNo.','ChanceofAdmit'], axis=1)\n",
    "calc_vif(vif_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA SUMMARY\n",
    "- Data Quality\n",
    "    - No missing data\n",
    "    - 400 Data points\n",
    "    - No Outliers\n",
    "- Analysis \n",
    "   - highly correlating variables (>0.7 reasons to assume multicollinearity) \n",
    "       - GREscore: Toefl score, CGPA\n",
    "       - TOEFL: CGPA, UniversityRating\n",
    "       - UniversityRating: CGPA, SOP\n",
    "       - SOP: CGPA, LOR\n",
    "   - Most features are normally distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 7) (400, 1)\n"
     ]
    }
   ],
   "source": [
    "x= df[['GREScore', 'TOEFLScore', 'UniversityRating', 'SOP', 'LOR', 'CGPA', 'Research']]\n",
    "y= df[['ChanceofAdmit']]\n",
    "\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()\n",
    "train_df = df_copy.sample(frac=0.7, random_state=0)\n",
    "test_df = df_copy.drop(train_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "280 280\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train), len(train_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building\n",
    "### Model 1 : Multiple Linear Regression with all variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = LinearRegression().fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06275055848463834"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = math.sqrt(np.mean(((model1.predict(x_test)-y_test)**2)))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7974225697174359"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.36358482])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00223637,  0.00280676,  0.00188382, -0.00719684,  0.03197831,\n",
       "         0.11323269,  0.02258162]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.00224\n",
      "Feature: 1, Score: 0.00281\n",
      "Feature: 2, Score: 0.00188\n",
      "Feature: 3, Score: -0.00720\n",
      "Feature: 4, Score: 0.03198\n",
      "Feature: 5, Score: 0.11323\n",
      "Feature: 6, Score: 0.02258\n"
     ]
    },
    {
     "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": [
    "importance = model1.coef_[0]\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "    print('Feature: %0d, Score: %.5f' % (i,v))\n",
    "# plot feature importance\n",
    "pyplot.bar([x for x in range(len(importance))], importance)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 1 Summary\n",
    "- Training,Testing: 70/30\n",
    "- Model: ChanceofAdmit = -1.25613433 +0.00173157(GREScore) +0.00309982(TOEFLScore) +0.00464072(UniversityRating)-0.00581009(SOP) +0.02591985(LOR) +0.11651909(CGPA) + 0.02600234(Research)\n",
    "- RMSE = 0.0607\n",
    "- R score = 0.7928"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2: R style, Ordinary Least Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>ChanceofAdmit</td>  <th>  R-squared:         </th> <td>   0.788</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   144.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 07 May 2021</td> <th>  Prob (F-statistic):</th> <td>7.88e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:14:03</td>     <th>  Log-Likelihood:    </th> <td>  378.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   280</td>      <th>  AIC:               </th> <td>  -740.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   272</td>      <th>  BIC:               </th> <td>  -711.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -1.2471</td> <td>    0.153</td> <td>   -8.166</td> <td> 0.000</td> <td>   -1.548</td> <td>   -0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GREScore</th>         <td>    0.0019</td> <td>    0.001</td> <td>    2.559</td> <td> 0.011</td> <td>    0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOEFLScore</th>       <td>    0.0033</td> <td>    0.001</td> <td>    2.421</td> <td> 0.016</td> <td>    0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>UniversityRating</th> <td>    0.0075</td> <td>    0.006</td> <td>    1.309</td> <td> 0.192</td> <td>   -0.004</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SOP</th>              <td>   -0.0027</td> <td>    0.007</td> <td>   -0.400</td> <td> 0.690</td> <td>   -0.016</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LOR</th>              <td>    0.0228</td> <td>    0.007</td> <td>    3.465</td> <td> 0.001</td> <td>    0.010</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CGPA</th>             <td>    0.1075</td> <td>    0.015</td> <td>    7.192</td> <td> 0.000</td> <td>    0.078</td> <td>    0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Research</th>         <td>    0.0249</td> <td>    0.010</td> <td>    2.580</td> <td> 0.010</td> <td>    0.006</td> <td>    0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>73.640</td> <th>  Durbin-Watson:     </th> <td>   2.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 158.854</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.298</td> <th>  Prob(JB):          </th> <td>3.20e-35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.622</td> <th>  Cond. No.          </th> <td>1.35e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.35e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          ChanceofAdmit   R-squared:                       0.788\n",
       "Model:                            OLS   Adj. R-squared:                  0.783\n",
       "Method:                 Least Squares   F-statistic:                     144.6\n",
       "Date:                Fri, 07 May 2021   Prob (F-statistic):           7.88e-88\n",
       "Time:                        10:14:03   Log-Likelihood:                 378.33\n",
       "No. Observations:                 280   AIC:                            -740.7\n",
       "Df Residuals:                     272   BIC:                            -711.6\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -1.2471      0.153     -8.166      0.000      -1.548      -0.946\n",
       "GREScore             0.0019      0.001      2.559      0.011       0.000       0.003\n",
       "TOEFLScore           0.0033      0.001      2.421      0.016       0.001       0.006\n",
       "UniversityRating     0.0075      0.006      1.309      0.192      -0.004       0.019\n",
       "SOP                 -0.0027      0.007     -0.400      0.690      -0.016       0.011\n",
       "LOR                  0.0228      0.007      3.465      0.001       0.010       0.036\n",
       "CGPA                 0.1075      0.015      7.192      0.000       0.078       0.137\n",
       "Research             0.0249      0.010      2.580      0.010       0.006       0.044\n",
       "==============================================================================\n",
       "Omnibus:                       73.640   Durbin-Watson:                   2.042\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              158.854\n",
       "Skew:                          -1.298   Prob(JB):                     3.20e-35\n",
       "Kurtosis:                       5.622   Cond. No.                     1.35e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.35e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = smf.ols('ChanceofAdmit ~ GREScore+ TOEFLScore+ UniversityRating+ SOP+ LOR + CGPA+ Research',\n",
    "                    data=train_df).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "results2 = model2.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06462099907808971"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse2 = np.sqrt(np.mean(((np.array(test_df['ChanceofAdmit'])-results2)**2)))\n",
    "rmse2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model2 Summary\n",
    "- Training,Testing: 70/30\n",
    "- Model: ChanceofAdmit = -1.2471 +0.0019(GREScore) +0.0033(TOEFLScore) +0.0075(UniversityRating) -0.0027(SOP) +0.0228(LOR) +0.1075(CGPA) +0.0249(Research)\n",
    "- RMSE = 0.0646\n",
    "- R score = 0.788\n",
    "- Adjusted R score = 0.783\n",
    "- University Rating and SOP deemed insignificant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model3: OLS Removing non-significant variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>ChanceofAdmit</td>  <th>  R-squared:         </th> <td>   0.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   202.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 07 May 2021</td> <th>  Prob (F-statistic):</th> <td>9.09e-90</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:14:03</td>     <th>  Log-Likelihood:    </th> <td>  377.45</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   280</td>      <th>  AIC:               </th> <td>  -742.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   274</td>      <th>  BIC:               </th> <td>  -721.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>   -1.2987</td> <td>    0.145</td> <td>   -8.968</td> <td> 0.000</td> <td>   -1.584</td> <td>   -1.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GREScore</th>   <td>    0.0019</td> <td>    0.001</td> <td>    2.597</td> <td> 0.010</td> <td>    0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOEFLScore</th> <td>    0.0035</td> <td>    0.001</td> <td>    2.616</td> <td> 0.009</td> <td>    0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LOR</th>        <td>    0.0239</td> <td>    0.006</td> <td>    4.140</td> <td> 0.000</td> <td>    0.013</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CGPA</th>       <td>    0.1110</td> <td>    0.014</td> <td>    7.923</td> <td> 0.000</td> <td>    0.083</td> <td>    0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Research</th>   <td>    0.0255</td> <td>    0.010</td> <td>    2.661</td> <td> 0.008</td> <td>    0.007</td> <td>    0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>73.477</td> <th>  Durbin-Watson:     </th> <td>   2.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 157.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.297</td> <th>  Prob(JB):          </th> <td>4.98e-35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.610</td> <th>  Cond. No.          </th> <td>1.28e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.28e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          ChanceofAdmit   R-squared:                       0.787\n",
       "Model:                            OLS   Adj. R-squared:                  0.783\n",
       "Method:                 Least Squares   F-statistic:                     202.3\n",
       "Date:                Fri, 07 May 2021   Prob (F-statistic):           9.09e-90\n",
       "Time:                        10:14:03   Log-Likelihood:                 377.45\n",
       "No. Observations:                 280   AIC:                            -742.9\n",
       "Df Residuals:                     274   BIC:                            -721.1\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -1.2987      0.145     -8.968      0.000      -1.584      -1.014\n",
       "GREScore       0.0019      0.001      2.597      0.010       0.000       0.003\n",
       "TOEFLScore     0.0035      0.001      2.616      0.009       0.001       0.006\n",
       "LOR            0.0239      0.006      4.140      0.000       0.013       0.035\n",
       "CGPA           0.1110      0.014      7.923      0.000       0.083       0.139\n",
       "Research       0.0255      0.010      2.661      0.008       0.007       0.044\n",
       "==============================================================================\n",
       "Omnibus:                       73.477   Durbin-Watson:                   2.051\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              157.971\n",
       "Skew:                          -1.297   Prob(JB):                     4.98e-35\n",
       "Kurtosis:                       5.610   Cond. No.                     1.28e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.28e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = smf.ols('ChanceofAdmit ~ GREScore+ TOEFLScore+ LOR + CGPA+ Research',\n",
    "                    data=train_df).fit()\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "results3 = model3.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06452052032761145"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse3 = np.sqrt(np.mean(((np.array(test_df['ChanceofAdmit'])-results3)**2)))\n",
    "rmse3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 3 Summary\n",
    "- Training,Testing: 70/30\n",
    "- Model: ChanceofAdmit = -1.2987 +0.0019(GREScore) +0.0035(TOEFLScore)+0.0239(LOR) +0.1110(CGPA) +0.0255(Research)\n",
    "- RMSE = 0.06452\n",
    "- R score = 0.787\n",
    "- Adjusted R score = 0.783"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GREScore', 'TOEFLScore', 'UniversityRating', 'SOP', 'LOR', 'CGPA',\n",
       "       'Research'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap ={'0':\"GREScore\",'1': 'TOEFLScore', '2':'UniversityRating', '3':'SOP', '4':'LOR', '5':'CGPA', '6':'Research'}\n",
    "x_test2 = x_test\n",
    "x_test2.rename(columns=cmap)\n",
    "\n",
    "x_test2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>ChanceofAdmit</th>\n",
       "      <th>Mod1</th>\n",
       "      <th>Mod2</th>\n",
       "      <th>Mod3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.618114</td>\n",
       "      <td>0.621681</td>\n",
       "      <td>0.625718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.682430</td>\n",
       "      <td>0.691658</td>\n",
       "      <td>0.691093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.962464</td>\n",
       "      <td>0.966746</td>\n",
       "      <td>0.965372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.776540</td>\n",
       "      <td>0.781388</td>\n",
       "      <td>0.779977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.587010</td>\n",
       "      <td>0.581348</td>\n",
       "      <td>0.580860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ChanceofAdmit      Mod1      Mod2      Mod3\n",
       "208           0.64  0.618114  0.621681  0.625718\n",
       "114           0.59  0.682430  0.691658  0.691093\n",
       "213           0.96  0.962464  0.966746  0.965372\n",
       "222           0.76  0.776540  0.781388  0.779977\n",
       "168           0.64  0.587010  0.581348  0.580860"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(y_test)\n",
    "df2['Mod1']= model1.predict(x_test2)\n",
    "df2['Mod2']= model2.predict(x_test2)\n",
    "df2['Mod3']= model3.predict(x_test2)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Model1</td>\n",
       "      <td>0.062751</td>\n",
       "      <td>0.044618</td>\n",
       "      <td>7.129046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Model2</td>\n",
       "      <td>0.064621</td>\n",
       "      <td>0.042710</td>\n",
       "      <td>6.921438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Model3</td>\n",
       "      <td>0.064521</td>\n",
       "      <td>0.043246</td>\n",
       "      <td>6.984433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model      RMSE       MAE      MAPE\n",
       "0  Model1  0.062751  0.044618  7.129046\n",
       "1  Model2  0.064621  0.042710  6.921438\n",
       "2  Model3  0.064521  0.043246  6.984433"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_table = pd.DataFrame({'Model':['Model1','Model2','Model3'],\n",
    "                            'RMSE':[rmse,rmse2,rmse3],\n",
    "                            'MAE':[mean_absolute_error(df2['ChanceofAdmit'],df2['Mod1']),mean_absolute_error(df2['ChanceofAdmit'],df2['Mod2']),mean_absolute_error(df2['ChanceofAdmit'],df2['Mod3'])],\n",
    "                            'MAPE':[mean_absolute_percentage_error(df2['ChanceofAdmit'],df2['Mod1']),mean_absolute_percentage_error(df2['ChanceofAdmit'],df2['Mod2']),mean_absolute_percentage_error(df2['ChanceofAdmit'],df2['Mod3'])]})\n",
    "\n",
    "\n",
    "error_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>SerialNo.</th>\n",
       "      <th>GREScore</th>\n",
       "      <th>TOEFLScore</th>\n",
       "      <th>UniversityRating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>ChanceofAdmit</th>\n",
       "      <th>Acceptance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SerialNo.  GREScore  TOEFLScore  UniversityRating  SOP  LOR  CGPA  \\\n",
       "0          1       337         118                 4  4.5  4.5  9.65   \n",
       "1          2       324         107                 4  4.0  4.5  8.87   \n",
       "2          3       316         104                 3  3.0  3.5  8.00   \n",
       "3          4       322         110                 3  3.5  2.5  8.67   \n",
       "4          5       314         103                 2  2.0  3.0  8.21   \n",
       "\n",
       "   Research  ChanceofAdmit Acceptance  \n",
       "0         1           0.92          1  \n",
       "1         1           0.76          1  \n",
       "2         1           0.72          1  \n",
       "3         1           0.80          1  \n",
       "4         0           0.65          1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature engineering binary acceptance column \n",
    "df3 = df\n",
    "\n",
    "def acc_classifier(df3):\n",
    "    if df3['ChanceofAdmit']>= 0.5:\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "\n",
    "df3['Acceptance']= df3.apply(acc_classifier, axis=1)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,5))\n",
    "sns.countplot(data=df3,x='Acceptance')\n",
    "plt.grid()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 7) (280, 1)\n"
     ]
    }
   ],
   "source": [
    "X = df3[['GREScore', 'TOEFLScore', 'UniversityRating', 'SOP', 'LOR', 'CGPA', 'Research']]\n",
    "Y = df3[['Acceptance']]\n",
    "\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y, test_size=0.3)\n",
    "print(Xtrain.shape, Ytrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "model4 = LogisticRegression().fit(Xtrain, Ytrain)\n",
    "#model4.score\n",
    "#rmse = math.sqrt(np.mean(((model1.predict(x_test)-y_test)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.0919846   0.16230521 -0.25420097 -0.24102199  1.08373833  1.49358817\n",
      "   1.06495654]] [-0.4170644]\n"
     ]
    }
   ],
   "source": [
    "print(model4.coef_ ,\n",
    "model4.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: -0.09198\n",
      "Feature: 1, Score: 0.16231\n",
      "Feature: 2, Score: -0.25420\n",
      "Feature: 3, Score: -0.24102\n",
      "Feature: 4, Score: 1.08374\n",
      "Feature: 5, Score: 1.49359\n",
      "Feature: 6, Score: 1.06496\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEdxJREFUeJzt3XuMZnddx/H3x922KF5a2AGavXSrbgxFoMXJqqnBqm3ZWtIl8cJWgUIgmxCKF+JlUdNqkaRqIsRYLmtZWhS7ahVdZbGWS1MMFHYWSsu2tF2Waidb3ZWtaAVptnz9Y07j85vObec5O88Mfb+Sk+ec3+93zvPNZjOf+Z3LnFQVkiQ94VtGXYAkaXkxGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktRYPeoCFmPNmjW1cePGUZchSSvK/v37/6OqxuYbtyKDYePGjUxMTIy6DElaUZL8y0LGeSpJktQwGCRJDYNBktQwGCRJjV6CIcmuJEeSfH6W/guSfCXJnd1y1UDfliT3JTmYZEcf9UiSFq+vGcMNwJZ5xny8qs7tlmsAkqwCrgMuAc4BLk9yTk81SZIWoZdgqKrbgWOL2HUzcLCqDlXVY8BuYGsfNUmSFmcprzH8cJLPJflQkud1bWuBhwbGTHZtkqQRWaoH3D4DnFVVjyb5SeBvgU1AZhg740uok2wHtgNs2LDhZNUp6STauOODoy6h8eC1l466hGVpSWYMVfVfVfVot74XOCXJGqZmCOsHhq4DDs9yjJ1VNV5V42Nj8z7RLUlapCUJhiTPSZJufXP3vV8G9gGbkpyd5FRgG7BnKWqSJM2sl1NJSW4CLgDWJJkErgZOAaiqdwE/Dbw+yXHga8C2qirgeJIrgVuAVcCuqjrQR02SpMXpJRiq6vJ5+v8Y+ONZ+vYCe/uoQ5I0PJ98liQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1egmGJLuSHEny+Vn6fz7JXd3yiSQvHOh7MMndSe5MMtFHPZKkxetrxnADsGWO/i8BP1pVLwDeAuyc1v9jVXVuVY33VI8kaZFW93GQqro9ycY5+j8xsHkHsK6P75Uk9W8U1xheC3xoYLuAf0qyP8n2EdQjSRrQy4xhoZL8GFPB8CMDzedX1eEkzwJuTfKFqrp9hn23A9sBNmzYsCT1StJT0ZLNGJK8ALge2FpVX36ivaoOd59HgA8Am2fav6p2VtV4VY2PjY0tRcmS9JS0JMGQZAPwN8Arq+r+gfanJ/mOJ9aBi4EZ72ySJC2NXk4lJbkJuABYk2QSuBo4BaCq3gVcBTwTeEcSgOPdHUjPBj7Qta0G/ryq/rGPmiRJi9PXXUmXz9P/OuB1M7QfAl745D0kSaPik8+SpIbBIElqGAySpIbBIElqGAySpIbBIElqGAySpIbBIElqGAySpIbBIElqGAySpIbBIElqGAySpIbBIElqLOmrPSX1Z+OOD466hMaD11466hLUE2cMkqSGMwZJmsNympkt1azMGYMkqWEwSJIavQRDkl1JjiT5/Cz9SfJHSQ4muSvJiwb6rkjyQLdc0Uc9kqTF62vGcAOwZY7+S4BN3bIdeCdAkmcAVwM/CGwGrk5yRk81SZIWoZdgqKrbgWNzDNkKvK+m3AGcnuRM4CXArVV1rKoeAW5l7oCRJJ1kS3WNYS3w0MD2ZNc2W7skaUSWKhgyQ1vN0f7kAyTbk0wkmTh69GivxUmS/t9SBcMksH5gex1weI72J6mqnVU1XlXjY2NjJ61QSXqqW6pg2AO8qrs76YeAr1TVw8AtwMVJzuguOl/ctUmSRqSXJ5+T3ARcAKxJMsnUnUanAFTVu4C9wE8CB4GvAq/p+o4leQuwrzvUNVU110VsSdJJ1kswVNXl8/QX8IZZ+nYBu/qoQ5I0PJ98liQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUqOXYEiyJcl9SQ4m2TFD/9uS3Nkt9yf5z4G+xwf69vRRjyRp8VYPe4Akq4DrgIuASWBfkj1Vdc8TY6rqlwfGvxE4b+AQX6uqc4etQ5LUjz5mDJuBg1V1qKoeA3YDW+cYfzlwUw/fK0k6CfoIhrXAQwPbk13bkyQ5Czgb+OhA89OSTCS5I8nLZvuSJNu7cRNHjx7toWxJ0kz6CIbM0FazjN0G3FxVjw+0baiqceDngLcn+Z6ZdqyqnVU1XlXjY2Njw1UsSZpVH8EwCawf2F4HHJ5l7DamnUaqqsPd5yHgNtrrD5KkJdZHMOwDNiU5O8mpTP3wf9LdRUm+DzgD+ORA2xlJTuvW1wDnA/dM31eStHSGviupqo4nuRK4BVgF7KqqA0muASaq6omQuBzYXVWDp5meC7w7yTeYCqlrB+9mkiQtvaGDAaCq9gJ7p7VdNW37t2fY7xPA8/uoQZLUD598liQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1egmGJFuS3JfkYJIdM/S/OsnRJHd2y+sG+q5I8kC3XNFHPZKkxVs97AGSrAKuAy4CJoF9SfZU1T3Thv5FVV05bd9nAFcD40AB+7t9Hxm2LknS4vQxY9gMHKyqQ1X1GLAb2LrAfV8C3FpVx7owuBXY0kNNkqRF6iMY1gIPDWxPdm3T/VSSu5LcnGT9Ce4rSVoifQRDZmiradt/D2ysqhcAHwZuPIF9pwYm25NMJJk4evTooouVJM2tj2CYBNYPbK8DDg8OqKovV9XXu80/AX5gofsOHGNnVY1X1fjY2FgPZUuSZtJHMOwDNiU5O8mpwDZgz+CAJGcObF4G3Nut3wJcnOSMJGcAF3dtkqQRGfqupKo6nuRKpn6grwJ2VdWBJNcAE1W1B/iFJJcBx4FjwKu7fY8leQtT4QJwTVUdG7YmSdLiDR0MAFW1F9g7re2qgfU3A2+eZd9dwK4+6pAkDc8nnyVJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJjV6CIcmWJPclOZhkxwz9b0pyT5K7knwkyVkDfY8nubNb9vRRjyRp8VYPe4Akq4DrgIuASWBfkj1Vdc/AsM8C41X11SSvB34feHnX97WqOnfYOiRJ/ehjxrAZOFhVh6rqMWA3sHVwQFV9rKq+2m3eAazr4XslSSdBH8GwFnhoYHuya5vNa4EPDWw/LclEkjuSvKyHeiRJQxj6VBKQGdpqxoHJK4Bx4EcHmjdU1eEk3w18NMndVfXFGfbdDmwH2LBhw/BVS5Jm1MeMYRJYP7C9Djg8fVCSC4HfBC6rqq8/0V5Vh7vPQ8BtwHkzfUlV7ayq8aoaHxsb66FsSdJM+giGfcCmJGcnORXYBjR3FyU5D3g3U6FwZKD9jCSndetrgPOBwYvWkqQlNvSppKo6nuRK4BZgFbCrqg4kuQaYqKo9wB8A3w78VRKAf62qy4DnAu9O8g2mQuraaXczSZKWWB/XGKiqvcDeaW1XDaxfOMt+nwCe30cNkqR++OSzJKlhMEiSGgaDJKlhMEiSGgaDJKnRy11JOrk27vjgqEtoPHjtpaMuQdJJ5IxBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJDYNBktQwGCRJjV6CIcmWJPclOZhkxwz9pyX5i67/U0k2DvS9uWu/L8lL+qhHkrR4QwdDklXAdcAlwDnA5UnOmTbstcAjVfW9wNuA3+v2PQfYBjwP2AK8ozueJGlE+pgxbAYOVtWhqnoM2A1snTZmK3Bjt34z8BNJ0rXvrqqvV9WXgIPd8SRJI9LHG9zWAg8NbE8CPzjbmKo6nuQrwDO79jum7bt2pi9Jsh3YDrBhw4ZFF7sS34a2Et+Ytpz+nRfy77ec6oVv3v8X1rwy9DFjyAxttcAxC9l3qrFqZ1WNV9X42NjYCZYoSVqoPoJhElg/sL0OODzbmCSrge8Cji1wX0nSEuojGPYBm5KcneRUpi4m75k2Zg9wRbf+08BHq6q69m3dXUtnA5uAT/dQkyRpkYa+xtBdM7gSuAVYBeyqqgNJrgEmqmoP8B7gT5McZGqmsK3b90CSvwTuAY4Db6iqx4etSZK0eH1cfKaq9gJ7p7VdNbD+v8DPzLLvW4G39lGHJGl4PvksSWoYDJKkhsEgSWoYDJKkhsEgSWr0clfSSvJUfLxdkk6EMwZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUsNgkCQ1DAZJUmOoYEjyjCS3Jnmg+zxjhjHnJvlkkgNJ7kry8oG+G5J8Kcmd3XLuMPVIkoY37IxhB/CRqtoEfKTbnu6rwKuq6nnAFuDtSU4f6P/Vqjq3W+4csh5J0pCGfR/DVuCCbv1G4Dbg1wcHVNX9A+uHkxwBxoD/HPK7pd74ng7p/w07Y3h2VT0M0H0+a67BSTYDpwJfHGh+a3eK6W1JTptj3+1JJpJMHD16dMiyJUmzSVXNPSD5MPCcGbp+E7ixqk4fGPtIVT3pOkPXdyZTM4orquqOgbZ/YyosdgJfrKpr5it6fHy8JiYm5hsmSRqQZH9Vjc83bt5TSVV14Rxf8u9Jzqyqh7sf8kdmGfedwAeB33oiFLpjP9ytfj3Je4Ffma8eSdLJNeyppD3AFd36FcDfTR+Q5FTgA8D7quqvpvWd2X0GeBnw+SHrkSQNadhguBa4KMkDwEXdNknGk1zfjflZ4MXAq2e4LfX9Se4G7gbWAL87ZD2SpCHNe41hOfIagySduIVeY/DJZ0lSw2CQJDUMBklSw2CQJDUMBklSY0XelZTkKPAvIy5jDfAfI67hRK20mldavWDNS8WaF+esqhqbb9CKDIblIMnEQm77Wk5WWs0rrV6w5qVizSeXp5IkSQ2DQZLUMBgWb+eoC1iElVbzSqsXrHmpWPNJ5DUGSVLDGYMkqWEwnKAkW5Lcl+Rgkpnecb3sJNmV5EiSFfFnzZOsT/KxJPcmOZDkF0dd03ySPC3Jp5N8rqv5d0Zd00IkWZXks0n+YdS1LESSB5Pc3f2V5hXxlzSTnJ7k5iRf6P5P//Coa5qPp5JOQJJVwP1M/YnxSWAfcHlV3TPSwuaR5MXAo0y9E+P7R13PfLr3dJxZVZ9J8h3AfuBly/nfuXunyNOr6tEkpwD/DPzi4IuplqMkbwLGge+sqpeOup75JHkQGK+qUT8PsGBJbgQ+XlXXd++n+baqWtbvvHfGcGI2Awer6lBVPQbsBraOuKZ5VdXtwLFR17FQVfVwVX2mW/9v4F5g7WirmltNebTbPKVblvVvXUnWAZcC1883VovTvb3yxcB7AKrqseUeCmAwnKi1wEMD25Ms8x9YK12SjcB5wKdGW8n8utMydzL1ittbq2q51/x24NeAb4y6kBNQwD8l2Z9k+6iLWYDvBo4C7+1O2V2f5OmjLmo+BsOJyQxty/q3wpUsybcDfw38UlX916jrmU9VPV5V5wLrgM1Jlu1puyQvBY5U1f5R13KCzq+qFwGXAG/oTpMuZ6uBFwHvrKrzgP8Blv21SYPhxEwC6we21wGHR1TLN7XuPP1fA++vqr8ZdT0nojtVcBuwZcSlzOV84LLunP1u4MeT/NloS5pfVR3uPo8w9S75zaOtaF6TwOTA7PFmpoJiWTMYTsw+YFOSs7uLSNuAPSOu6ZtOdyH3PcC9VfWHo65nIZKMJTm9W/9W4ELgC6OtanZV9eaqWldVG5n6f/zRqnrFiMuaU5Kndzcj0J2OuRhY1nfaVdW/AQ8l+b6u6SeAZXsTxRNWj7qAlaSqjie5ErgFWAXsqqoDIy5rXkluAi4A1iSZBK6uqveMtqo5nQ+8Eri7O2cP8BtVtXeENc3nTODG7s61bwH+sqpWxC2gK8izgQ9M/d7AauDPq+ofR1vSgrwReH/3y+Qh4DUjrmde3q4qSWp4KkmS1DAYJEkNg0GS1DAYJEkNg0GS1DAYJEkNg0GS1DAYJEmN/wPSJzFSZjJZdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance4 = model4.coef_[0]\n",
    "#summarize feature importance\n",
    "for i,v in enumerate(importance4):\n",
    "    print('Feature: %0d, Score: %.5f' % (i,v))\n",
    "#plot feature importance\n",
    "pyplot.bar([x for x in range(len(importance4))], importance4)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1',\n",
       "       '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1',\n",
       "       '1', '1', '1'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results4 = model4.predict(Xtest)\n",
    "results4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(Ytest,results4)\n",
    "df_cm = pd.DataFrame(cm, columns=['Accepted','Rejected'], index =['Accepted','Rejected'])\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "sns.heatmap(df_cm, cmap='Greens' , annot=True, fmt='d')\n",
    "plt.title(\"Confusion Matrix\",fontsize=15)\n",
    "plt.xlabel(\"Predicted \",fontsize=15)\n",
    "plt.ylabel(\"True \",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Ytest,results4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis Summary\n",
    "In all, an interested graduate candidate could be advised that above all their undergraduate college GPA will have the greatest significant effect on their chances of admission into a given grad program. Model 4 was able to correctly the admission status of 91.6% of tested candidates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input Function\n",
    "- Take in the candidate information and return expected admission status\n",
    "- Using Model 4 (Best Performing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graduate_acceptance_estimator():\n",
    "    GRE = int(input(\"Enter GRE Score\"))\n",
    "    TOEFL = int(input(\"Enter TOEFL score\"))\n",
    "    RATING = float(input(\"Please Enter School Rating\"))\n",
    "    SOP = float(input ('Please enter Statement of Purpose and Letter of Recommendation Strength ( out of 5 )'))\n",
    "    LOR= float(input(\"Enter LOR\"))\n",
    "    CGPA= float(input(\"Enter CGPA\"))\n",
    "    RESEARCH = input(\"Have you done research?\")\n",
    "    \n",
    "    Research=0\n",
    "    if RESEARCH in  (\"YES\", \"yes\", \"Yes\"):\n",
    "        Research += 1\n",
    "    elif RESEARCH in (\"no\", \"NO\" , \"No\"):\n",
    "        Research += 0\n",
    "        \n",
    "    feats = np.array([[GRE,TOEFL,RATING,SOP,LOR,CGPA,Research]])\n",
    "    \n",
    "    output= model4.predict(feats)\n",
    "    \n",
    "    #return output[0]\n",
    "    if int(output[0]) >=0.5:\n",
    "        return \"Accepted!\"\n",
    "    else:\n",
    "        return \"I'm sorry, you will most likely not be accepted\"\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter GRE Score337\n",
      "Enter TOEFL score118\n",
      "Please Enter School Rating4\n",
      "Please enter Statement of Purpose and Letter of Recommendation Strength ( out of 5 )4.5\n",
      "Enter LOR4.5\n",
      "Enter CGPA9.65\n",
      "Have you done research?yes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Accepted!'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graduate_acceptance_estimator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}