Predicting Admissions Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting Admissions Problem\n",
    "- Predict Chance of admit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading data\n",
    "data = pd.read_csv('admission_predict.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Serial No.         500 non-null    int64  \n",
      " 1   GRE Score          500 non-null    int64  \n",
      " 2   TOEFL Score        500 non-null    int64  \n",
      " 3   University Rating  500 non-null    int64  \n",
      " 4   SOP                500 non-null    float64\n",
      " 5   LOR                500 non-null    float64\n",
      " 6   CGPA               500 non-null    float64\n",
      " 7   Research           500 non-null    int64  \n",
      " 8   Chance of Admit    500 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 35.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Serial No.</th>\n",
       "      <td>500.0</td>\n",
       "      <td>250.50000</td>\n",
       "      <td>144.481833</td>\n",
       "      <td>1.00</td>\n",
       "      <td>125.7500</td>\n",
       "      <td>250.50</td>\n",
       "      <td>375.25</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRE Score</th>\n",
       "      <td>500.0</td>\n",
       "      <td>316.47200</td>\n",
       "      <td>11.295148</td>\n",
       "      <td>290.00</td>\n",
       "      <td>308.0000</td>\n",
       "      <td>317.00</td>\n",
       "      <td>325.00</td>\n",
       "      <td>340.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOEFL Score</th>\n",
       "      <td>500.0</td>\n",
       "      <td>107.19200</td>\n",
       "      <td>6.081868</td>\n",
       "      <td>92.00</td>\n",
       "      <td>103.0000</td>\n",
       "      <td>107.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University Rating</th>\n",
       "      <td>500.0</td>\n",
       "      <td>3.11400</td>\n",
       "      <td>1.143512</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOP</th>\n",
       "      <td>500.0</td>\n",
       "      <td>3.37400</td>\n",
       "      <td>0.991004</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOR</th>\n",
       "      <td>500.0</td>\n",
       "      <td>3.48400</td>\n",
       "      <td>0.925450</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGPA</th>\n",
       "      <td>500.0</td>\n",
       "      <td>8.57644</td>\n",
       "      <td>0.604813</td>\n",
       "      <td>6.80</td>\n",
       "      <td>8.1275</td>\n",
       "      <td>8.56</td>\n",
       "      <td>9.04</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.56000</td>\n",
       "      <td>0.496884</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chance of Admit</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.72174</td>\n",
       "      <td>0.141140</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.6300</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count       mean         std     min       25%     50%  \\\n",
       "Serial No.         500.0  250.50000  144.481833    1.00  125.7500  250.50   \n",
       "GRE Score          500.0  316.47200   11.295148  290.00  308.0000  317.00   \n",
       "TOEFL Score        500.0  107.19200    6.081868   92.00  103.0000  107.00   \n",
       "University Rating  500.0    3.11400    1.143512    1.00    2.0000    3.00   \n",
       "SOP                500.0    3.37400    0.991004    1.00    2.5000    3.50   \n",
       "LOR                500.0    3.48400    0.925450    1.00    3.0000    3.50   \n",
       "CGPA               500.0    8.57644    0.604813    6.80    8.1275    8.56   \n",
       "Research           500.0    0.56000    0.496884    0.00    0.0000    1.00   \n",
       "Chance of Admit    500.0    0.72174    0.141140    0.34    0.6300    0.72   \n",
       "\n",
       "                      75%     max  \n",
       "Serial No.         375.25  500.00  \n",
       "GRE Score          325.00  340.00  \n",
       "TOEFL Score        112.00  120.00  \n",
       "University Rating    4.00    5.00  \n",
       "SOP                  4.00    5.00  \n",
       "LOR                  4.00    5.00  \n",
       "CGPA                 9.04    9.92  \n",
       "Research             1.00    1.00  \n",
       "Chance of Admit      0.82    0.97  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exclude serial number\n",
    "data.iloc[:, 1:].hist(figsize= (12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Research'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 2, 5, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['University Rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.5, 4. , 3. , 3.5, 2. , 5. , 1.5, 1. , 2.5])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SOP'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.5, 3.5, 2.5, 3. , 4. , 1.5, 2. , 5. , 1. ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['LOR '].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = list(data.columns)[:-1]\n",
    "b = a[1:]\n",
    "b\n",
    "\n",
    "for i in b:\n",
    "    sns.relplot(x = i, y = 'Chance of Admit ', data = data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- All most input attributes fits in a linear fashion so we can go for **linear regression** but lets check with other models as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "      <th>University Rating_1</th>\n",
       "      <th>University Rating_2</th>\n",
       "      <th>University Rating_3</th>\n",
       "      <th>University Rating_4</th>\n",
       "      <th>University Rating_5</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>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  SOP  LOR   CGPA  Research  \\\n",
       "0           1        337          118  4.5   4.5  9.65         1   \n",
       "1           2        324          107  4.0   4.5  8.87         1   \n",
       "2           3        316          104  3.0   3.5  8.00         1   \n",
       "3           4        322          110  3.5   2.5  8.67         1   \n",
       "4           5        314          103  2.0   3.0  8.21         0   \n",
       "\n",
       "   Chance of Admit   University Rating_1  University Rating_2  \\\n",
       "0              0.92                    0                    0   \n",
       "1              0.76                    0                    0   \n",
       "2              0.72                    0                    0   \n",
       "3              0.80                    0                    0   \n",
       "4              0.65                    0                    1   \n",
       "\n",
       "   University Rating_3  University Rating_4  University Rating_5  \n",
       "0                    0                    1                    0  \n",
       "1                    0                    1                    0  \n",
       "2                    1                    0                    0  \n",
       "3                    1                    0                    0  \n",
       "4                    0                    0                    0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data, columns= ['University Rating'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'SOP', 'LOR ', 'CGPA',\n",
       "       'Research', 'Chance of Admit ', 'University Rating_1',\n",
       "       'University Rating_2', 'University Rating_3', 'University Rating_4',\n",
       "       'University Rating_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "      <th>University Rating_1</th>\n",
       "      <th>University Rating_2</th>\n",
       "      <th>University Rating_3</th>\n",
       "      <th>University Rating_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324</td>\n",
       "      <td>107</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRE Score  TOEFL Score  SOP  LOR   CGPA  Research  Chance of Admit   \\\n",
       "0        337          118  4.5   4.5  9.65         1              0.92   \n",
       "1        324          107  4.0   4.5  8.87         1              0.76   \n",
       "2        316          104  3.0   3.5  8.00         1              0.72   \n",
       "3        322          110  3.5   2.5  8.67         1              0.80   \n",
       "4        314          103  2.0   3.0  8.21         0              0.65   \n",
       "\n",
       "   University Rating_1  University Rating_2  University Rating_3  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    1   \n",
       "3                    0                    0                    1   \n",
       "4                    0                    1                    0   \n",
       "\n",
       "   University Rating_4  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns= ['University Rating_5', 'Serial No.'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(columns= 'Chance of Admit ').values\n",
    "y = data['Chance of Admit '].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>model</th>\n",
       "      <th>best_parameters</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>{'normalize': True}</td>\n",
       "      <td>0.822027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>{'alpha': 1, 'selection': 'random'}</td>\n",
       "      <td>-0.004323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>svr</td>\n",
       "      <td>{'gamma': 'scale'}</td>\n",
       "      <td>0.707430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'splitter': 'ran...</td>\n",
       "      <td>0.626886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>{'n_estimators': 15}</td>\n",
       "      <td>0.772187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>knn</td>\n",
       "      <td>{'n_neighbors': 10}</td>\n",
       "      <td>0.750483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model                                    best_parameters  \\\n",
       "0  linear_regression                                {'normalize': True}   \n",
       "1              lasso                {'alpha': 1, 'selection': 'random'}   \n",
       "2                svr                                 {'gamma': 'scale'}   \n",
       "3      decision_tree  {'criterion': 'friedman_mse', 'splitter': 'ran...   \n",
       "4      random_forest                               {'n_estimators': 15}   \n",
       "5                knn                                {'n_neighbors': 10}   \n",
       "\n",
       "      score  \n",
       "0  0.822027  \n",
       "1 -0.004323  \n",
       "2  0.707430  \n",
       "3  0.626886  \n",
       "4  0.772187  \n",
       "5  0.750483  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using GridSearchCV to find the best algorithm for this problem\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Creating a function to calculate best model for this problem\n",
    "def find_best_model(X, y):\n",
    "    models = {\n",
    "        'linear_regression': {\n",
    "            'model': LinearRegression(),\n",
    "            'parameters': {\n",
    "                'normalize': [True,False]\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'parameters': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        'svr': {\n",
    "            'model': SVR(),\n",
    "            'parameters': {\n",
    "                'gamma': ['auto','scale']\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'parameters': {\n",
    "                'criterion': ['mse', 'friedman_mse'],\n",
    "                'splitter': ['best', 'random']\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        'random_forest': {\n",
    "            'model': RandomForestRegressor(criterion='mse'),\n",
    "            'parameters': {\n",
    "                'n_estimators': [5,10,15,20]\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        'knn': {\n",
    "            'model': KNeighborsRegressor(algorithm='auto'),\n",
    "            'parameters': {\n",
    "                'n_neighbors': [2,5,10,20]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    scores = []\n",
    "    for model_name, model_params in models.items():\n",
    "        gs = GridSearchCV(model_params['model'], model_params['parameters'], cv=5, return_train_score=False)\n",
    "        gs.fit(X, y)\n",
    "        scores.append({\n",
    "            'model': model_name,\n",
    "            'best_parameters': gs.best_params_,\n",
    "            'score': gs.best_score_\n",
    "        })\n",
    "        \n",
    "    return pd.DataFrame(scores, columns=['model','best_parameters','score'])\n",
    "        \n",
    "find_best_model(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest Accuracy : 81.0%\n"
     ]
    }
   ],
   "source": [
    "# Using cross_val_score for gaining highest accuracy\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(LinearRegression(normalize=True), X, y, cv=5)\n",
    "print('Highest Accuracy : {}%'.format(round(sum(scores)*100/len(scores)), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = linear_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "traing score: 0.8332135424266612, test score: 0.767456840332593\n"
     ]
    }
   ],
   "source": [
    "print('traing score: {}, test score: {}'.format(linear_reg.score(X_train, y_train), linear_reg.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06372839357618663"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sizes = [1, 50, 150, 200, 250, 300, 350, 400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def learning_curves_fun(X,y, model):\n",
    "    train_sizes = [1, 50, 150, 200, 250, 300, 350, 400]\n",
    "    train_sizes, train_scores, validation_scores = learning_curve(\n",
    "    estimator = model,\n",
    "    X = X,\n",
    "    y = y, train_sizes = train_sizes, cv = 5,\n",
    "    scoring = 'neg_mean_squared_error')\n",
    "    train_scores_mean = -train_scores.mean(axis = 1)\n",
    "    validation_scores_mean = -validation_scores.mean(axis = 1)\n",
    "    plt.style.use('seaborn')\n",
    "    plt.plot(train_sizes, train_scores_mean, label = 'Training error')\n",
    "    plt.plot(train_sizes, validation_scores_mean, label = 'Validation error')\n",
    "    plt.ylabel('MSE', fontsize = 14)\n",
    "    plt.xlabel('Training set size', fontsize = 14)\n",
    "    plt.title('Learning curves for a linear regression model', fontsize = 18, y = 1.03)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_curves_fun(X, y, LinearRegression())"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}