TASK 1 - Prediction using Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e56ab3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries required in this notebook\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76bfe8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Successfull----\n"
     ]
    },
    {
     "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",
       "  </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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://bit.ly/w-data\"\n",
    "s_data = pd.read_csv(url)\n",
    "print(\"----Successfull----\")\n",
    "s_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1f130b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "s_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7bfb66a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = s_data.iloc[:, :-1].values  \n",
    "y = s_data.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71904454",
   "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, \n",
    "                            test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58013cbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training complete----\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "print(\"----Training complete----\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fa705fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad183dd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test) # Testing data - In Hours\n",
    "y_pred = regressor.predict(X_test) # Predicting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49a033c4",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69d0acdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "568389ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x196363c3d00>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "470462ab",
   "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
}