TASK1 prediction using suprvd learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author :  BLESSEL SUNNY\n",
    "\n",
    "## Prediction using Supervised learning( TASK # 01)\n",
    "\n",
    "### TSF GRIPMAY21 \n",
    "\n",
    "### question : What will be predicted score if a student studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np                          # Importing all libraries required in this notebook\n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hurray!! 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",
       "  </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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"http://bit.ly/w-data\"\n",
    "s_data = pd.read_csv(url)                               # Reading data from remote link\n",
    "print(\" Hurray!! Data imported successfully\")       \n",
    "s_data.head()                                           # displaying top 5 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "s_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')                         # visualizing the data \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing  the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = s_data.iloc[:, :-1].values          # divide the data into attributes and labels.  \n",
    "y = s_data.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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,   # split the data into training and test sets \n",
    "         test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the algorithem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 25,
   "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": [
    "\n",
    "line = regressor.coef_*X+regressor.intercept_          # Plotting the regression line\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.title('Test Data')\n",
    "plt.plot(X, line);                                    # Plotting for the test data\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
    "                                                    \n",
    "y_pred = regressor.predict(X_test)                     # Predicting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})     # Comparing Actual vs Predicted\n",
    "df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737539\n"
     ]
    }
   ],
   "source": [
    "hours = 9.25\n",
    "own_pred = regressor.predict([[hours]])\n",
    "print(\"No of Hours = {}\".format(hours))                    # test with given data \n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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