1st task .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68195376",
   "metadata": {},
   "source": [
    "# The Sparks Foundation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b33c8a1",
   "metadata": {},
   "source": [
    "# Data Science& Business Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3473cf",
   "metadata": {},
   "source": [
    "Task 1: Prediction using Supervised ML \n",
    "\n",
    "Author Name: Somanath Maruti Shinde\n",
    "\n",
    "Task:Predict the percentage of an student based on the no. of study hours.\n",
    "\n",
    "Dataset: http://bit.ly/w-data\n",
    "\n",
    "Que:What will be predicted score if a student studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c67ea7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4711af02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking the dataset\n",
    "url = \"http://bit.ly/w-data\"\n",
    "dataset = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bdaa84a",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</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\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    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57b12496",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(x='Hours', y='Scores',data=dataset)  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af6915cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assign the values to x and y variable \n",
    "x=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d2d375a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#spliting the values to traing set and testing set\n",
    "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": "code",
   "execution_count": 7,
   "id": "e94bfe30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "l1=LinearRegression()\n",
    "l1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32c14237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the prediction \n",
    "y_pred=l1.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a681c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Draw the predicted line \n",
    "line = l1.coef_*x+l1.intercept_\n",
    "plt.scatter(x, y,c=\"r\")\n",
    "plt.plot(x, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "870b6e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([93.69173249])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What will be predicted score if a student studies for 9.25 hrs/ day?\n",
    "ab=l1.predict([[9.25]])\n",
    "ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fd414e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39.67865467, 20.84840735, 79.32128059, 70.40168976, 12.91988217,\n",
       "       52.56250809, 78.33021494, 34.72332643, 84.27660883, 93.19619966,\n",
       "       62.47316457, 36.70545772, 28.77693254, 56.52677068, 28.77693254,\n",
       "       86.25874013, 26.79480124, 49.58931115, 90.22300272, 46.6161142 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pred=l1.predict(x_train)\n",
    "x_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17ab33ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2155231fc10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#another way for draw the predicted line \n",
    "plt.scatter(x,y,c=\"r\")\n",
    "plt.plot(x_train,x_pred)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a03dce52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}