Prediction Supervised Machine Learning_Hours_Score.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51c03158",
   "metadata": {},
   "source": [
    "## Prediction Using Supervised Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330d73bc",
   "metadata": {},
   "source": [
    "#### We predicted the percentage of marks that a student is expected to score based upon the number of hours they studied                    This is a simple linear regression task as it involves just two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cafd0fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ed7be2",
   "metadata": {},
   "source": [
    "## Step 1 : Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff073288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"student_study.csv\")\n",
    "print(\"Data imported successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc04d424",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
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       "      <th>3</th>\n",
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       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
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       "   Unnamed: 0  Hours  Scores\n",
       "0           1    2.5      21\n",
       "1           2    5.1      47\n",
       "2           3    3.2      27\n",
       "3           4    8.5      75\n",
       "4           5    3.5      30"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "53548b8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.drop(\"Unnamed: 0\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6051f8e3",
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   "outputs": [
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       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
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       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
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       "      <th>13</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
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       "      <th>14</th>\n",
       "      <td>1.5</td>\n",
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       "      <th>17</th>\n",
       "      <td>8.3</td>\n",
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       "      <th>19</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
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       "      <th>20</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
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       "      <th>21</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
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       "      <th>23</th>\n",
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      "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\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.5      30\n",
       "14    1.5      20\n",
       "15    9.2      88\n",
       "16    5.5      60\n",
       "17    8.3      81\n",
       "18    2.7      25\n",
       "19    7.7      85\n",
       "20    5.9      62\n",
       "21    4.5      41\n",
       "22    3.3      42\n",
       "23    1.1      17\n",
       "24    8.9      95\n",
       "25    2.5      30"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ffa88339",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Hours</th>\n",
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       "      <td>2.5</td>\n",
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      "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2ffb7d",
   "metadata": {},
   "source": [
    "## Step 2 - Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8516e453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26 entries, 0 to 25\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   26 non-null     float64\n",
      " 1   Scores  26 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 544.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0c2c48c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "661fe52d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.103846</td>\n",
       "      <td>51.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.644766</td>\n",
       "      <td>26.295598</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>2.825000</td>\n",
       "      <td>27.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>44.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>79.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
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      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  26.000000  26.000000\n",
       "mean    5.103846  51.461538\n",
       "std     2.644766  26.295598\n",
       "min     1.100000  17.000000\n",
       "25%     2.825000  27.750000\n",
       "50%     4.800000  44.500000\n",
       "75%     7.700000  79.500000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0cfb9567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     float64\n",
       "Scores      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b23500d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     16\n",
       "Scores    16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "abf0e57e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35e5154",
   "metadata": {},
   "source": [
    "- There are no missing values in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349862f0",
   "metadata": {},
   "source": [
    "## Step 3 - Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9eae3581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Number of hours')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016f790c",
   "metadata": {},
   "source": [
    "- Observation- Number of hours studied is linearly related to scores of the student"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384525bf",
   "metadata": {},
   "source": [
    "## Checking Outliers using boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90f015a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAI/CAYAAACvVAryAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUAElEQVR4nO3df4zkd13H8dfbXklLKYWmsDlAepJUfshvFgVFs6ZIlBLbBAkYMeWHOTUIaIxyYkKjJnpEYwTRmJMfngIqIEkrR0rL0YkhmEKBQsEDSqBA5aCgsbK1gVY//rEDHufBzt3M3vtu9vFINvPrO995bybfPOf7ndnZGmMEADi5vqd7AADYjgQYABoIMAA0EGAAaCDAANBAgAGgwY6T+WAXXHDB2LVr18l8SBbojjvuyDnnnNM9Bmw7tr3T1wc/+MGvjjHud6zbTmqAd+3alRtuuOFkPiQLNJlMsra21j0GbDu2vdNXVX3uO93mEDQANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA12dA8AsJ1V1ULWM8ZYyHo4eewBAzQaY2z6c+HL3rHpMpx+BBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGswU4Kr6tar6eFV9rKr+tqrOqqrzq+raqrp5enrfrR4WAJbFpgGuqgcmeUmS1THGI5OckeQ5SfYkOTjGuCjJwellAGAGsx6C3pHk7KrakeSeSb6Y5NIk+6e3709y2cKnA4AltWmAxxj/muSPknw+yeEkt48xrkmyMsY4PF3mcJL7b+WgALBMdmy2wPS93UuTfF+S/0jy1qp67qwPUFW7k+xOkpWVlUwmkxMalH7r6+ueP2hi21s+mwY4yVOTfHaM8ZUkqaq3J/nhJF+uqp1jjMNVtTPJbce68xhjX5J9SbK6ujrW1tYWMjgn32QyiecPGlx9wLa3hGYJ8OeTPKmq7pnkziQXJ7khyR1JLk+yd3p65VYNCXC6eszvXJPb77xr7vXs2nPghO973tln5iNXPG3uGVisTQM8xri+qt6W5ENJ7k7y4Wzs0d4ryVuq6oXZiPSztnJQgNPR7XfelVv2XjLXOuY9+jRPvNk6s+wBZ4xxRZIrjrr669nYGwYAjpNvwgKABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0ADAQaABgIMAA0EGAAaCDAANBBgAGggwADQQIABoIEAA0CDmQJcVfepqrdV1Seq6lBVPbmqzq+qa6vq5unpfbd6WABYFrPuAb8qydVjjIcleUySQ0n2JDk4xrgoycHpZQBgBpsGuKruneTHkrwuScYY3xhj/EeSS5Psny62P8llWzMiACyfWfaAH5LkK0neUFUfrqrXVtU5SVbGGIeTZHp6/y2cEwCWyo4Zl3l8khePMa6vqlflOA43V9XuJLuTZGVlJZPJ5ETm5BSwvr7u+YMTMO92s4htz7Z76pklwLcmuXWMcf308tuyEeAvV9XOMcbhqtqZ5LZj3XmMsS/JviRZXV0da2tr809Ni8lkEs8fHKerD8y93cy97S1gBhZv00PQY4wvJflCVT10etXFSf4lyVVJLp9ed3mSK7dkQgBYQrPsASfJi5O8qarukeQzSZ6fjXi/papemOTzSZ61NSMCwPKZKcBjjBuTrB7jposXOg0AbBO+CQsAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaDBju4BAJbZuQ/fk0ft3zP/ivbPM0OSXDL/DCyUAANsoa8d2ptb9s4Xv8lkkrW1tRO+/649B+Z6fLaGQ9AA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0GDmAFfVGVX14ap6x/Ty+VV1bVXdPD2979aNCQDL5Xj2gF+a5NARl/ckOTjGuCjJwellAGAGMwW4qh6U5JIkrz3i6kuT7J+e35/ksoVOBgBLbNY94D9J8ptJ/ueI61bGGIeTZHp6/8WOBgDLa8dmC1TVM5LcNsb4YFWtHe8DVNXuJLuTZGVlJZPJ5HhXwSlifX3d8wcnYN7tZhHbnm331LNpgJP8SJKfrqqnJzkryb2r6o1JvlxVO8cYh6tqZ5LbjnXnMca+JPuSZHV1daytrS1mck66yWQSzx8cp6sPzL3dzL3tLWAGFm/TQ9BjjN8aYzxojLEryXOSvGeM8dwkVyW5fLrY5Umu3LIpAWDJzPN3wHuT/ERV3ZzkJ6aXAYAZzHII+lvGGJMkk+n5f0ty8eJHAoDl55uwAKCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANdnQPALDsdu05MP9Krj7xdZx39pnzPz4LJ8AAW+iWvZfMvY5dew4sZD2cWhyCBoAGAgwADQQYABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGAgwADQQYABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGAgwADQQYABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGAgwADQQYABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGAgwADQQYABpsGuCq+t6quq6qDlXVx6vqpdPrz6+qa6vq5unpfbd+XABYDrPsAd+d5NfHGA9P8qQkL6qqRyTZk+TgGOOiJAenlwGAGWwa4DHG4THGh6bnv5bkUJIHJrk0yf7pYvuTXLZFMwLA0jmu94CraleSxyW5PsnKGONwshHpJPdf+HQAsKR2zLpgVd0ryT8k+dUxxn9W1az3251kd5KsrKxkMpmcwJicCtbX1z1/0MS2t3xmCnBVnZmN+L5pjPH26dVfrqqdY4zDVbUzyW3Huu8YY1+SfUmyuro61tbW5p+aFpPJJJ4/aHD1AdveEprlU9CV5HVJDo0x/viIm65Kcvn0/OVJrlz8eACwnGbZA/6RJD+f5KaqunF63cuT7E3ylqp6YZLPJ3nWlkwIAEto0wCPMd6b5Du94XvxYscBgO3BN2EBQAMBBoAGAgwADQQYABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGM/87QgAWb+Z/7frK7377GGMB03Ay2QMGaDTG2PTnuuuu23QZTj8CDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAgx3dA3BqqKqFrGeMsZD1ACw7e8Ak2QjnZj8Xvuwdmy4DwGwEGAAaCDAANBBgAGggwADQQIABoIEAA0ADfwe8TTzmd67J7XfeNfd6du05cML3Pe/sM/ORK5429wwAy0CAt4nb77wrt+y9ZK51TCaTrK2tnfD954k3wLJxCBoAGggwADQQYABoIMAA0ECAAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0ECAAaCBAANAA/+OcJs49+F78qj9e+Zf0f55ZkiS+f4lIsCyEOBt4muH9vp/wACnEIegAaCBAANAAwEGgAYCDAANBBgAGggwADQQYABoIMAA0MAXcWwjC/kijKtPfB3nnX3m/I8PsCQEeJuY91uwko2AL2I9ADgEDQAtBBgAGggwADQQYABoIMAA0ECAAaCBAANAA38HTJKkqmZb7pXf/fYxxgKmAVh+9oBJshHOzX6uu+66TZcBYDZzBbiqfrKqPllVn66qPYsaCgCW3QkHuKrOSPJnSX4qySOS/GxVPWJRgwHAMptnD/gHk3x6jPGZMcY3kvxdkksXMxYALLd5AvzAJF844vKt0+sAgE3M8ynoY31s9v99CqeqdifZnSQrKyuZTCZzPCSd1tfXPX/QwLa3nOYJ8K1JvveIyw9K8sWjFxpj7EuyL0lWV1fH2traHA9Jp8lkEs8fnHy2veU0zyHoDyS5qKq+r6rukeQ5Sa5azFgAsNxOeA94jHF3Vf1KknclOSPJ68cYH1/YZACwxOb6JqwxxjuTvHNBswDAtuGbsACggQADQAMBBoAGAgwADQQYABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGAgwADQQYABoIMAA0qDHGyXuwqq8k+dxJe0AW7YIkX+0eArYh297p68Ixxv2OdcNJDTCnt6q6YYyx2j0HbDe2veXkEDQANBBgAGggwByPfd0DwDZl21tC3gMGgAb2gAGggQBvQ1W1ftTl51XVa7rmge2gqn67qj5eVR+tqhur6oe6Z6LXju4BWB5VdcYY47+754BTTVU9Ockzkjx+jPH1qrogyT3mWN+OMcbdCxuQFvaA+TZVdWFVHZy+Sj9YVQ+eXv9XVfUzRyy3Pj1dq6rrqurNSW6qqnOq6kBVfaSqPlZVz276VeBUsjPJV8cYX0+SMcZXxxhfrKonVtX7ptvL+6vq3Ko6q6reUFU3VdWHq+rHk28dqXprVf1jkmum29rrq+oD0+UunS73A9N13Tjdji/q+7X5buwBb09nV9WNR1w+P8lV0/OvSfLXY4z9VfWCJK9Octkm6/vBJI8cY3y2qp6Z5ItjjEuSpKrOW+jkcHq6JskrqupTSd6d5O+T/PP09NljjA9U1b2T3JnkpUkyxnhUVT0sG7H9/ul6npzk0WOMf6+q30/ynjHGC6rqPkneX1XvTvJLSV41xnhTVd0jyRkn8ffkONgD3p7uHGM89ps/SV5xxG1PTvLm6fm/SfKUGdb3/jHGZ6fnb0ry1Kp6ZVX96Bjj9oVNDaepMcZ6kick2Z3kK9kI7y8mOTzG+MB0mf+cHlZ+Sja2vYwxPpGNr+/9ZoCvHWP8+/T805Lsmb6YniQ5K8mDsxH2l1fVy7LxNYh3bvkvyAmxB8xmvvl3andn+oKtqirf/v7VHd9aeIxPVdUTkjw9yR9U1TVjjN89WcPCqWr6+YhJkklV3ZTkRfm/7etI9V1Wc8cR5yvJM8cYnzxqmUNVdX2SS5K8q6p+YYzxnhOfnK1iD5ijvS/Jc6bnfy7Je6fnb8nGK/gkuTTJmce6c1U9IMl/jTHemOSPkjx+yyaF00RVPfSo92Ifm+RQkgdU1ROny5xbVTuS/FM2tr1MDz0/OMnRkU2SdyV58fQFcarqcdPThyT5zBjj1dl4a+nRW/JLMTd7wBztJUleX1W/kY1DZc+fXv+XSa6sqvcnOZhvfyV+pEcl+cOq+p8kdyX55S2eF04H90ryp9P3au9O8ulsHI5+w/T6s7Px/u9Tk/x5kr+Y7iXfneR5009OH73O30vyJ0k+Oo3wLdn4pPWzkzy3qu5K8qUkjkCdonwTFgA0cAgaABoIMAA0EGAAaCDAANBAgAGggQADQAMBBoAGAgwADf4X3KQdQixpORkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(figsize=(8,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82231f0a",
   "metadata": {},
   "source": [
    "- No outliers are present in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d077e44",
   "metadata": {},
   "source": [
    "## Step 4 - Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9982c417",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into features and target\n",
    "y = data['Scores']\n",
    "x = data.drop('Scores',axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fba80ca",
   "metadata": {},
   "source": [
    "## Step 5 - Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14a5a62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#Splitting data into training and testing data\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "73377315",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating an instance of the LinearRegression\n",
    "lr = LinearRegression()\n",
    "\n",
    "#Creating the model\n",
    "model = lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "329757e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#defined the necessary variables: x, y, lr.coef_, and lr.intercept_\n",
    "\n",
    "x = np.array(x, dtype=np.float64)  # Convert x to a numeric array\n",
    "\n",
    "# Generate a sequence of x values for plotting the line\n",
    "x_line = np.linspace(min(x), max(x), 100)\n",
    "\n",
    "# Calculate the corresponding y values using the regression line equation\n",
    "y_line = lr.coef_ * x_line + lr.intercept_\n",
    "\n",
    "# Plotting for the test data and regression line\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x_line, y_line, color='red')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Regression Line')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e911aff1",
   "metadata": {},
   "source": [
    "## Step 6 - Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "42d42eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e0607892",
   "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",
       "      <th>predicted_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "      <td>25.474438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "      <td>51.129211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "      <td>32.381492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "      <td>84.677762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "      <td>35.341658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "      <td>15.607217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "      <td>91.584816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "      <td>55.076100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "      <td>82.704318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "      <td>27.447882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "      <td>76.783985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>59.022988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "      <td>45.208879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "      <td>35.341658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "      <td>15.607217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "      <td>91.584816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "      <td>55.076100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "      <td>82.704318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "      <td>27.447882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "      <td>76.783985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>59.022988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "      <td>45.208879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "      <td>33.368214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "      <td>11.660329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "      <td>88.624650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "      <td>25.474438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores  predicted_scores\n",
       "0     2.5      21         25.474438\n",
       "1     5.1      47         51.129211\n",
       "2     3.2      27         32.381492\n",
       "3     8.5      75         84.677762\n",
       "4     3.5      30         35.341658\n",
       "5     1.5      20         15.607217\n",
       "6     9.2      88         91.584816\n",
       "7     5.5      60         55.076100\n",
       "8     8.3      81         82.704318\n",
       "9     2.7      25         27.447882\n",
       "10    7.7      85         76.783985\n",
       "11    5.9      62         59.022988\n",
       "12    4.5      41         45.208879\n",
       "13    3.5      30         35.341658\n",
       "14    1.5      20         15.607217\n",
       "15    9.2      88         91.584816\n",
       "16    5.5      60         55.076100\n",
       "17    8.3      81         82.704318\n",
       "18    2.7      25         27.447882\n",
       "19    7.7      85         76.783985\n",
       "20    5.9      62         59.022988\n",
       "21    4.5      41         45.208879\n",
       "22    3.3      42         33.368214\n",
       "23    1.1      17         11.660329\n",
       "24    8.9      95         88.624650\n",
       "25    2.5      30         25.474438"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "data[\"predicted_scores\"] = model.predict(data[['Hours']])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fa1199b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score = 92.07817721976338\n"
     ]
    }
   ],
   "source": [
    "# Testing the model with our own data\n",
    "own_pred = model.predict([[9.25]])\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9f1758",
   "metadata": {},
   "source": [
    "## Step 7 - Evaluating the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee2f512",
   "metadata": {},
   "source": [
    "- The final step is to evaluate the performance of algorithm. \n",
    "- This step is particularly important to compare how well different algorithms perform on a particular dataset. \n",
    "- Here different errors have been calculated to compare the model performance and predict the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "24cb92bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.6375666154818904\n",
      "Mean Squared Error: 14.697240279437494\n",
      "Root Mean Squared Error: 3.8336979901183525\n",
      "R-2: 0.9744198099951774\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:',metrics.mean_absolute_error(y_test, y_pred)) \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('R-2:', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0037e0",
   "metadata": {},
   "source": [
    "**R-2 gives the score of model fit and in this case we have R-2 = 0.9744198099951774 which is actually a great score for this model.**"
   ]
  }
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