Task1 Supervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries required\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data\n",
    "df = pd.read_csv(\"Task1_dataset.csv\")\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLotting the graph of scores\n",
    "df.plot(x='Hours',y='Scores',style='*',color='red')\n",
    "plt.title('Hours vs Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Percentage score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values #all rows and col except last col\n",
    "Y = df.iloc[:, 1].values #all rows and only 1st col i.e scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "# Training the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
    "\n",
    "print(\"Training complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "#Plotting the regression line\n",
    "line = regressor.coef_*X + regressor.intercept_\n",
    "\n",
    "#Plotting for the test data\n",
    "plt.scatter(X,Y,color='red')\n",
    "plt.plot(X,line,color='black');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare actual vs predicted values\n",
    "df = pd.DataFrame( {'Actual':Y_test,'Predicted':Y_pred} )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = [[9.25]]\n",
      "Predicted Score = 93.69173248737539\n"
     ]
    }
   ],
   "source": [
    "# test with your own data\n",
    "Hours = [[9.25]] #predict takes 2D array \n",
    "own_pred = regressor.predict(Hours)\n",
    "print(\"No of Hours = {}\".format(Hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002982\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error:',metrics.mean_absolute_error(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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