Task1- supervisedlearning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c3399e14-ee02-4b96-adc7-c0dc51669750",
   "metadata": {},
   "source": [
    "# Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939324ba-2d31-462c-86e2-66c1162621bf",
   "metadata": {},
   "source": [
    "### predicting the percentage of a student based on the number of study hours "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28c7c17-b4dc-476e-a1bb-8fd2350eb451",
   "metadata": {},
   "source": [
    "#### Using **Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c7b63fb-82d0-4bba-8792-715f01c6ba54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing used libraries\n",
    "\n",
    "#linear algebra operations, manipulation, visualization...\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "368dac56-a289-4ea0-be38-e00d938e8cb3",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# laoding dataset\n",
    "\n",
    "path =\"student_scores.csv\"\n",
    "data=pd.read_csv(path) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3fea19-c1df-4f4a-9abf-b646fe9dae1d",
   "metadata": {},
   "source": [
    "## Data Discovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "275d59dc-a949-469f-bfe4-d678c5d82e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98766173-b245-4b6a-bfe1-1c35d75b16f3",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78af201d-e623-4c83-bb07-a9d6e3bef450",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61f07980-7470-4eec-b694-4b3087f8c365",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wIiKiKm0lfkknUUyodgcg4BBJS21/p7LIIjoka9lG07Rb6vnfwMts3wYg6VnARcALqgosohOylm00Ubvj+Ke3kj6A7Z+ym2mZI3pB1rKNJmq3xT8o6XzgS+X2G4F11YQU0TmZKjmaqN0W/7uAWyimajiLYrK1d1YVVESnZKrkaKJ2E/8ewCdtL7H9GuBTwLTdHBPR9TJVcjRRu4l/LTC8CTQD+D9TH05EZ2Wq5Giidmv8e9ne0dqwvUPSzIpiiuioTJUcTdNui/9BSce2NiQNAOn9iojoQe22+M8GviZpM8UCLPOB11cVVEREVGfcFr+kF0r6Q9s/BJ4NfBV4jGLt3dt3d3JJd0i6SdKNkgbLfbMlrZG0sXzcfwreR0REtGl3pZ7PAI+Uz48HzgP+EbgPWNnmNf6j7aNtt1biWgastX04RafxsomFHBERT8XuEv802/eWz18PrLR9qe2/AZ45yWueTjHvD+Xj4kmeJyIiJmG3iV9Sqx9gEXDNsN+10z9g4GpJ6ySdWe47yPYWgPJx7mgHSjpT0qCkwaGhoTYuFRER7dhd8r4I+LakX1OM4vkugKRnUqy7uzsn2N4saS6wRtKt7QZmeyVlOWlgYMDtHhcREeMbN/Hb/pCktcA84GrbrQT8NIoF2Mdle3P5uE3S5cBxwFZJ82xvkTQP2PaU3kFERExIO2vuXmf7ctvDl1z8qe0bxjtO0t6S9m09B14G3AysBpaWL1sKXDnZ4CMiYuLaHcc/GQcBl0tqXedC21dJ+iFwsaS3A3cCr6swhoiIGKGyxG/7F8DzR9l/D0VHcUTPyWpd0Q+qbPFH9JWs1hX9ot25eiIaL6t1Rb9I4o9oU1brin6RxB/RpqzWFf0iiT+iTVmtK/pFOncj2tTqwM2onuh1SfwRE5DVuqIfpNQTEdEwSfwREQ2TxB8R0TBJ/BERDZPEHxHRMBnVE7uVicki+ksSf4wrE5NF9J8k/hjXeBOTdXPiz7eUiLEl8ce4enFisnxLiRhf5Z27kqZJWi/p6+X2bElrJG0sH/evOoaYvF6cmCzTJ0eMrxOjes4CNgzbXgastX04sLbcji7VixOT9eK3lIhOqjTxSzoYeCXw2WG7TwdWlc9XAYurjCGemsXHLGD5kqNYMGsGAhbMmsHyJUd1dcmkF7+lRHRS1TX+TwDvB/Ydtu8g21sAbG+RNLfiGOIp6rWJyc459YhdavzQ/d9SIjqpsha/pNOAbbbXTfL4MyUNShocGhqa4uiin/Xit5SITpLtak4sLQfeDDwG7AXsB1wGvBA4qWztzwOutT1uU2xgYMCDg4OVxBkR0a8krbM9MHJ/ZS1+2+faPtj2QuAM4BrbbwJWA0vLly0FrqwqhoiIeLI65upZAZwiaSNwSrkdEREd0pEbuGxfC1xbPr8HWNSJ60ZExJNlds6IiIZJ4o+IaJgk/oiIhknij4homMzOGR2V6ZIj6pfEHx2T6ZIjukNKPdExmS45ojsk8UfHZLrkiO6QxB8dk+mSI7pDEn90TC8u6hLRj9K5Gx3T6sDNqJ6IeiXxR0f12qIuEf0opZ6IiIZJ4o+IaJgk/oiIhknij4homCT+iIiGqWxUj6S9gO8Ae5bXucT2ByXNBr4KLATuAP7C9n1VxdFPxpvgrK7JzzLpWkTvqXI458PAS23vkDQd+J6kbwJLgLW2V0haBiwDPlBhHH1hvAnOgFomP8ukaxG9qbJSjws7ys3p5Y+B04FV5f5VwOKqYugn401wVtfkZ5l0LaI3VVrjlzRN0o3ANmCN7euBg2xvASgf545x7JmSBiUNDg0NVRlmTxhvgrO6Jj/LpGsRvanSxG/7cdtHAwcDx0k6cgLHrrQ9YHtgzpw5lcXYK8ab4Kyuyc8y6VpEb+rIqB7b24FrgZcDWyXNAygft3Uihl433gRndU1+lknXInpTlaN65gCP2t4uaQZwMvBhYDWwFFhRPl5ZVQz9pJ0Jzjo9uiaTrkX0Jtmu5sTS8yg6b6dRfLO42PbfSzoAuBg4FLgTeJ3te8c718DAgAcHByuJMyKiX0laZ3tg5P7KWvy2fwwcM8r+e4BFVV03nrqMzY/ob5mWOXaRsfkR/S9TNsQuMjY/ov8l8ccuMjY/ov8l8ccuMjY/ov8l8feJK9Zv4oQV13DYsm9wwopruGL9pkmdJ2PzI/pfOnf7wFR2yGZsfkT/S+KfYnUMhRyvQ3Yy186C6BH9LYl/CtU1FDIdshExEanxT6G6hkKmQzYiJiKJfwrV1fJOh2xETEQS/xSqq+W9+JgFLF9yFAtmzUDAglkzWL7kqNTpI2JUqfFPoXNOPWKXGj90ruWdDtmIaFcS/xTKUMiI6AVJ/FMsLe+I6HZJ/D0k0yVHxFRI4u8RmS45IqZKZaN6JB0i6d8lbZB0i6Szyv2zJa2RtLF83L+qGCZrqua9mUqZLjkipkqVwzkfA/7a9nOAFwPvlvRcYBmw1vbhwNpyu2u0Wtabtu/EPNGyrjv55+7ciJgqlSV+21ts31A+/w2wAVgAnE6xFi/l4+KqYpiMbm1Z5+7ciJgqHbmBS9JCivV3rwcOsr0Fig8HYO4Yx5wpaVDS4NDQUCfCBLq3ZZ27cyNiqlSe+CXtA1wKnG37gXaPs73S9oDtgTlz5lQX4Ajd2rLO3bkRMVUqHdUjaTpF0r/A9mXl7q2S5tneImkesK3KGCaqzrtvdyf3CETEVKhyVI+A84ENtj827FergaXl86XAlVXFMBlpWUdEv5Ptak4svQT4LnAT8Lty93kUdf6LgUOBO4HX2b53vHMNDAx4cHCwkjgjIvqVpHW2B0bur6zUY/t7gMb49aKqrtuSu1wjIkbXl3fu5i7XiIix9eV8/N06Fj8iohv0ZeLv1rH4ERHdoC8Tf7eOxY+I6AZ9mfhzl2tExNj6snM3K2FFRIytLxM/5C7XiIix9GWpJyIixpbEHxHRMEn8ERENk8QfEdEwSfwREQ1T2eycU0nSEPDLNl9+IPDrCsOZrMTVvm6MCbozrm6MCbozrm6MCaqN6xm2n7SSVU8k/omQNDjaNKR1S1zt68aYoDvj6saYoDvj6saYoJ64UuqJiGiYJP6IiIbpx8S/su4AxpC42teNMUF3xtWNMUF3xtWNMUENcfVdjT8iIsbXjy3+iIgYRxJ/RETD9E3il/Q5Sdsk3Vx3LMNJOkTSv0vaIOkWSWd1QUx7SfqBpB+VMf1d3TG1SJomab2kr9cdS4ukOyTdJOlGSYN1x9MiaZakSyTdWv77Or7meI4o/0atnwcknV1nTC2S3lf+W79Z0kWS9uqCmM4q47ml03+nvqnxSzoR2AF80faRdcfTImkeMM/2DZL2BdYBi23/pMaYBOxte4ek6cD3gLNsX1dXTC2S/jswAOxn+7S644Ei8QMDtrvq5h9Jq4Dv2v6spKcDM21vrzksoPgABzYBL7Ld7s2XVcWygOLf+HNt75R0MfBvtr9QY0xHAl8BjgMeAa4C3mV7Yyeu3zctftvfAe6tO46RbG+xfUP5/DfABqDWhQJc2FFuTi9/am8BSDoYeCXw2bpj6XaS9gNOBM4HsP1ItyT90iLg53Un/WH2AGZI2gOYCWyuOZ7nANfZfsj2Y8C3gdd06uJ9k/h7gaSFwDHA9TWH0iqp3AhsA9bYrj0m4BPA+4Hf1RzHSAaulrRO0pl1B1P6I2AI+HxZGvuspL3rDmqYM4CL6g4CwPYm4KPAncAW4H7bV9cbFTcDJ0o6QNJM4M+BQzp18ST+DpG0D3ApcLbtB+qOx/bjto8GDgaOK7961kbSacA22+vqjGMMJ9g+FngF8O6yrFi3PYBjgX+2fQzwILCs3pAKZdnp1cDX6o4FQNL+wOnAYcB8YG9Jb6ozJtsbgA8DayjKPD8CHuvU9ZP4O6Cso18KXGD7srrjGa4sD1wLvLzeSDgBeHVZT/8K8FJJX643pILtzeXjNuByirps3e4G7h72Te0Sig+CbvAK4AbbW+sOpHQycLvtIduPApcBf1pzTNg+3/axtk+kKFN3pL4PSfyVKztSzwc22P5Y3fEASJojaVb5fAbFf4xb64zJ9rm2D7a9kKJMcI3tWltlAJL2LjvlKUspL6P4ml4r278C7pJ0RLlrEVDbgIER3kCXlHlKdwIvljSz/P+4iKKvrVaS5paPhwJL6ODfrG8WW5d0EXAScKCku4EP2j6/3qiAoiX7ZuCmsqYOcJ7tf6svJOYBq8qRF08DLrbdNcMnu8xBwOVFvmAP4ELbV9Ub0u+9B7igLK38AnhbzfFQ1qtPAf6q7lhabF8v6RLgBopyynq6Y/qGSyUdADwKvNv2fZ26cN8M54yIiPak1BMR0TBJ/BERDZPEHxHRMEn8ERENk8QfEdEwSfwRJUk7Rmy/VdKn64onoipJ/BEVK++XiOgaSfwRbZD0DElrJf24fDy03P8FSf9p2Ot2lI8nleswXEhx897ekr5RroFws6TX1/RWIvrnzt2IKTBj2N3VALOB1eXzT1Os9bBK0n8GPgUs3s35jgOOtH27pNcCm22/EkDSH0xp5BETkBZ/xBN22j669QP8z2G/Ox64sHz+JeAlbZzvB7ZvL5/fBJws6cOS/sz2/VMWdcQEJfFHTE5rrpPHKP8flROAPX3Yax78/YvtnwIvoPgAWC5p+IdKREcl8Ue05/sUs4YCvJFiKT+AOygSOhRzvk8f7WBJ84GHbH+ZYlGQbplCORooNf6I9rwX+JykcyhWvmrNhPkvwJWSfgCsZVgrf4SjgI9I+h3FbIzvqjjeiDFlds6IiIZJqSciomGS+CMiGiaJPyKiYZL4IyIaJok/IqJhkvgjIhomiT8iomH+P+eew9dJHpK9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=data.Hours,y=data.Scores)\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.title(\"Hours Vs Scores \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db353f9-b700-4d25-adb9-dc444a266af3",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c476388-cd5c-4690-bacb-df73d852a659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Cleaning \n",
    "\n",
    "data.isnull().sum()   #no null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a71decb-4989-407e-abca-37441f1ebdca",
   "metadata": {},
   "source": [
    "### Spliting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc0c2ad3-e341-4368-a153-9625dee98c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of x (25, 1)\n",
      "shape of y (25,)\n"
     ]
    }
   ],
   "source": [
    "# split dataset for training\n",
    "\n",
    "\n",
    "x = data.iloc[:, :-1].values  \n",
    "y = data.iloc[:, 1].values  \n",
    "print(\"shape of x\",x.shape)\n",
    "print(\"shape of y\", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce835c60-f0cb-441e-a9c3-7a13e1ef72e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sklearn to train the dataset\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(x,y,test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80d2ebc8-f7e9-4afb-a202-42161109c778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X Train (20, 1)\n",
      "shape of Y Train (20,)\n",
      "shape of X Test (5, 1)\n",
      "shape of Y Test (5,)\n"
     ]
    }
   ],
   "source": [
    "print(\"shape of X Train\",X_train.shape)\n",
    "print(\"shape of Y Train\",Y_train.shape)\n",
    "print(\"shape of X Test\", X_test.shape)\n",
    "print(\"shape of Y Test\",Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28213846-176b-45b3-a07e-4eb0c128ee77",
   "metadata": {},
   "source": [
    "## Linear regression modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd994d08-bc50-4c66-a635-21ec416e5e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6183017e-37e3-42db-820b-43d86b5ef7b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21883062-7ffa-4d29-aa92-7f0b54f9b35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.91065648])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e1b3a01-e94a-4c8f-81af-b5ab7cd7803a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.018160041434683"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e23aa13-8ae1-4833-86f9-73f214d03e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5fed670-b5a2-4f6d-bc3e-cf8f2c190b39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[93.69173249]\n"
     ]
    }
   ],
   "source": [
    "#early prediction for given number of hours\n",
    "\n",
    "hours= [[9.25]]\n",
    "pred=lr.predict(hours)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea2f6f2f-bf21-40ba-af4c-4441975af9d4",
   "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>study_hours</th>\n",
       "      <th>student_marks_original</th>\n",
       "      <th>student_marks_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69.0</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   study_hours  student_marks_original  student_marks_predicted\n",
       "0          1.5                    20.0                16.884145\n",
       "1          3.2                    27.0                33.732261\n",
       "2          7.4                    69.0                75.357018\n",
       "3          2.5                    30.0                26.794801\n",
       "4          5.9                    62.0                60.491033"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.c_[X_test,Y_test,y_pred], \n",
    "             columns=[\"study_hours\",\"student_marks_original\",\"student_marks_predicted\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75cf56c-7e6a-49f1-8adb-cdbaca9c4721",
   "metadata": {},
   "source": [
    "## Fine tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55b941da-0226-4eaf-80e4-89088add81af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9454906892105356"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,Y_test) # accuracy= 94.54%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f477af5-f748-4d99-aec9-e2f1aebfa46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x3ecf5b36d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16c5d085-ba87-4232-b9be-7ba9fa5dba87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3ecf613a00>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,Y_test)\n",
    "plt.plot(X_train,lr.predict(X_train),color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243b5569-a068-4c97-ac20-58c1de65bec2",
   "metadata": {},
   "source": [
    "## *PREDICT*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b345d5f-ab67-4211-a8b1-75f006c4e963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Student_Marks_Predictor.pkl']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joblib \n",
    "\n",
    "#save and load data  -> predict\n",
    "\n",
    "import joblib\n",
    "joblib.dump(lr,\"Student_Marks_Predictor.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "838d0d90-2fd9-4d39-be49-bcc3ef987596",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=joblib.load(\"Student_Marks_Predictor.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39847961-a93f-40d9-8a0b-de68abc5dbd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([93.69173249])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[9.25]])"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}