The_Spark_Foundation_Task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science and Business Analytics Internship\n",
    "\n",
    "## GRIP: The Spark Foundation\n",
    "\n",
    "## Author : Ashlesha Datir.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Prediction using Supervised ML\n",
    "            (Level - Beginner)\n",
    "* Predict the percentage of a student based on the no.of study hours.\n",
    "* This is a simple linear Regression task as just 2 variables.\n",
    "* Data can be found on http://bit.ly/w-data\n",
    "\n",
    "\n",
    "* **Q. What will be predicted score if a students studies for 9.25 hr/day ?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Linear Regression with Python Scikit Learn**\n",
    "Here we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.\n",
    "\n",
    "## **Simple Linear Regression**\n",
    "In this regression task we will predict 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,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all required libraries\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,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "import dask\n",
    "# url =\"http://bit.ly/w-data\"\n",
    "w_data = pd.read_csv(\"http://bit.ly/w-data\")\n",
    "print(\"Data imported successfully\")\n",
    "w_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "w_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</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>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</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>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5Qkoer702x1T5/q2AhdFBTc3zqp4pwDMR8QKApJ8D7wSelzQiIlZJGgGszjGDmVnPNMBdwXm28a8AjpY0WJKAycAS4FZgWrrONOCWHDOYmVk7uR3xR8R9km4EFgMbgQeAq4Am4AZJZ5N8OZyeVwYzM9tWrjdwRcRXga+2m72B5OjfzMyqwF02mJk1GBd+M+u9BujXph65rx4z652u+rXZZ5/q5bJu+YjfzHqnQfq1qUcu/GbWOw3Sr81W6qRpy4XfzHqnQfq12aLUtLV8OUS82bRVg8Xfhd/MemfmTBg8eOt5gwcn8+tRHTVtufCbWe9MnQpXXQXNzSAlj1ddVb/dHdRR05av6jGz3muAfm22GDkyad7paH6N8RG/mVkWddS05cJvZpZFHTVtuanHzCyrOmna8hG/mVmDyVT4Jf21pLPS53tJ2j/fWGZmlpduC7+krwIXAhens/oD1+YZyszM8pPliP+DwMnAOoCIeA7YJc9QZmaWnyyF//V00N4AkLRzlg1LOlDSg2XTK5LOl7SHpDslLU0fd9+eD2BmZj2TpfDfIOlKYIikTwHzgB9296aIeDIixkXEOKAFeA24GbgImB8Ro4H56WszM6uQLi/nTAdJvx54O/AKcCDwlYi4s4f7mQz8ISKWSzoFmJDOnw3cRXIOwczMKqDLI/60iec/I+LOiPhCRFzQi6IP8GHguvT58IhYlW5/FTCsF9szs3pTJ10e1wIltb2LFaTvAddExP292oG0E/AccEhEPC9pbUQMKVu+JiK2aeeXNB2YDjB8+PCWuXPnZtpfW1sbTU1NvYmaK+fKroiZoJi5ipgJep5r2Lx5HHjZZfTbsGHLvE0DBvDkBRewesqUqmSqlDxzTZw4cVFEjN9mQUR0OQGPA5uAPwAPA48AD3f3vrL3nwLcUfb6SWBE+nwE8GR322hpaYmsWltbM69bSc6VXREzRRQzVxEzRfQiV3NzRNLL/dZTc3P1MlVInrmAhdFBTc3SZcNJ2/ml8xHebOYBuBWYBlyaPt6ynds3s1pXR10e14Jur+qJiOXAEOAD6TQkndctSYOB44Gfl82+FDhe0tJ02aU9zGxm9abRRvOqsix37s4A5pCchB0GXCvp3Cwbj4jXImLPiHi5bN6LETE5Ikanjy/1NryZpWr9xGgddXlcC7I09ZwNHBUR6wAkfRO4B/hOnsHMLKPSWLClYQFLY8FC7fQkWcp5ySVJ887IkUnRr5X8NSbLDVwiOblbsimdZ2ZFUC9jwU6dCsuWwebNyaOLfm6yHPH/GLhP0s3p61OBq3NLZGY94xOj1kPdFv6I+Jaku4C/JjnSPysiHsg7mJllVEdjwVplZDm5ezSwNCK+HRFXAL+XdFT+0cwsE58YtR7K0sb/faCt7PW6dJ6ZFUEdjQVrlZGljV/pHWAARMRmSR6r16xI6mQsWKuMLEf8T0s6T1L/dJoBPJ13MDMzy0eWwv+/gHcCf0yno0g7TzMzs9qT5aqe1STdKpuZWR3o9Ihf0qckjU6fS9KPJL0s6WFJ76hcRDMz60tdNfXMAJalzz8CHAYcAHwOuCLfWGZmlpeuCv/GiHgjff5+4CdpB2vzgEwDrptZH6r1jtisMLoq/JsljZA0kGTM3HllywblG8vMtlLqiG358mSIkrQjtmHz5nX/XrN2uir8XwEWkjT33BoRjwFIeje+nNOssjrpiO2AWbOqk8dqWqdX9UTEbZKagV0iYk3ZooXAGbknM7M3ddLh2oDVqyscxOpBl9fxR8TGdkWfiFgXEW2dvcfMctBJh2sbhg2rcBCrB1lu4Oo1SUMk3SjpCUlLJB0jaQ9Jd0pamj7unmcGs0Lp7QnaTjpie/qcc/o6oTWAXAs/yWWfv4yIt5NcDroEuAiYHxGjgfnpa7P618kJ2kzFv5OO2FZPmZJ/bqs7WbpllqSPSfpK+nqkpCMzvG9X4DjSQVsi4vWIWAucAsxOV5tNMrCLWf3b3pGyPEKV9RGVdbzZ8QrS94HNwKSIOChtmrkjIo7o5n3jgKuAx0mO9heR3BT2x4gYUrbemojYprlH0nTSPoGGDx/eMnfu3EwfqK2tjaampkzrVpJzZVfETLD9ud49aRLq4P+3kLh7wYKqZMpLEXMVMRPkm2vixImLImL8NgsiossJWJw+PlA276EM7xsPbCQZqB2SZp//Daxtt96a7rbV0tISWbW2tmZet5KcK7siZorog1zNzRFJI8/WU3Nz9TLlpIi5ipgpIt9cwMLooKZmaeN/Q1I/IAAk7UXyC6A7K4GVEXFf+vpG4B3A85JGpNsaAfh6NGsMHinLCiJL4f82cDMwTNJM4DfAP3f3poj4E/CspAPTWZNJmn1uBaal86YBt/Q0tFlN8khZVhBZumWeI2kRSeEWcGpELMm4/XOBOZJ2Irnb9yySL5sbJJ0NrABO71Vys1rkkbKsALot/JL2IGmOua5sXv94swO3TkXEgyRt/e1N7kFGMzPrQ1maehYDLwBPAUvT589IWiypJc9wZmbW97IU/l8C742IoRGxJ3AScAPwaeA/8gxnZmZ9L0vhHx8R/1N6ERF3AMdFxL3AgNySmZlZLrpt4wdeknQhULqD6gxgTXqJZ5bLOs3MrECyHPF/FNgX+E+SSy9HpvP6AR/KLZmZmeUiy+Wcfya5LLMjv+/bOGZmlrcsnbTtJenfJP1C0oLSVIlwZhXhsWytwWRp6pkDPAHsD3ydZCjG+3PMZFY529NVslmNylL494yIq4E3IuLuiPgkcHTOucwqY3u7SjarQVmu6indobtK0vuA50hO9prVvk7Gsu10vlkdyFL4/0nSbsDnge8AuwLn5xnKrGJGjkyadzqab1ansjT1rImIlyPi0YiYGBEtwEt5BzOrCHeVbA0oS+H/TsZ5ZrXHXSVbA+q0qUfSMcA7gb0kfa5s0a4kN2+Z1Qd3lWwNpqs2/p2ApnSdXcrmvwL8bZ6hzMwsP50W/oi4G7hb0jUR0cHZLzMzq0VZruoZIOkqYFT5+hExqbs3SloGvApsAjZGxPh0YJfr0+0tAz4UEWt6GtzMzHonS+H/GfADYBZJAe+piWl/PyUXAfMj4lJJF6WvL+zFds3MrBeyFP6NEfH9PtznKcCE9Pls4C5c+M3MKkYR0fUK0tdIxty9GdhQmh8R3V7LL+kZYA0QwJURcZWktRExpGydNRGxewfvnQ5MBxg+fHjL3Llz26/Soba2NpqamjKtW0nOlV0RM0ExcxUxExQzVxEzQb65Jk6cuCgith33PCK6nIBnOpie7u596Xv3Th+HAQ8BxwFr262zprvttLS0RFatra2Z160k58quiJkiipmriJkiipmriJki8s0FLIwOamqW/vj37+23TUQ8lz6ulnQzcCTwvKQREbFK0giSXxNmZlYhWfrjHyzpH9Mre5A0WtL7M7xvZ0m7lJ4DJwCPArcC09LVppGM6mVmZhWS5eTuj4FFJHfxAqwkudLntm7eNxy4WVJpPz+NiF9Kuh+4QdLZwArg9N4ENzOz3snSV89bIuJfSbtnjoi/AOruTRHxdEQclk6HRMTMdP6LETE5Ikanj+7wzWqHR+uyOpDliP91SYNIrsxB0lsou7rHrFEMmzcPLr/8zYFbSqN1gfv6sZqS5Yj/q8Avgf0kzQHmA1/MNZVZAR0wa5ZH67K6kOWqnjslLSYZblHAjNj6TlyzhjBgdScXoHm0LqsxWa7q+SDJ3bu3R8RtwEZJp+aezKxgNgwb1vECj9ZlNSZTU09EvFx6ERFrSZp/zBrK0+ec49G6rC5kKfwdrZPlpLBZXVk9ZYpH67K6kKWAL5T0LeB7JFf2nEtyXb9Z4/FoXVYHshzxnwu8TtKH/g3AX4DP5BnKzMzy0+URv6R+wC0RMaVCeczMLGddHvFHxCbgNUm7VSiPmZnlLEsb/3rgEUl3AutKMyPivNxSmZlZbrIU/tvTyczM6kC3J3cjYjbJSd17I2J2aco/mhWGOyYzqytZ7tz9APAgSX89SBon6dacc1lRzJmTdES2fDlEvNkxmYu/Wc3Kcjnn10hGzloLEBEPAr0elctqzCWX1GbHZP6VYtapLG38GyPi5XRAlZKuR2i3+tFZB2RF7pis9CvF3SebdSjLEf+jkj4K9EuHXfwO8NusO5DUT9IDkm5LX+8h6U5JS9PH3XuZ3Sqhsw7IitwxWa3+SjGrkKx37h5CMvjKT4GXgfN7sI8ZwJKy1xcB8yNiNEnf/hf1YFtWaTNn1l7HZLX4K8Wsgjot/JIGSjof+FeSsXGPiYgjIuIfI2J9lo1L2hd4HzCrbPYpQOmqoNnAqb3IbZUydWrtdUxWi79SzCqoqyP+2cB44BHgJOCyXmz/30lG69pcNm94RKwCSB876eTcCmPqVFi2DDZvTh6LXPShNn+lmFWQIjo+TyvpkYg4NH2+I/C7iHhH5g1L7wfeGxGfljQBuCAi3i9pbUQMKVtvTURs084vaTowHWD48OEtc+fOzbTftrY2mpqassasGOfKri8yDZs3jwNmzWLA6tVsGDaMp885J+lWucq5+loRM0ExcxUxE+Sba+LEiYsiYvw2CyKiwwlY3NXr7ibgX4CVwDLgT8BrwLXAk8CIdJ0RwJPdbaulpSWyam1tzbxuJTlXdkXMFFHMXEXMFFHMXEXMFJFvLmBhdFBTu2rqOUzSK+n0KjC29FzSK91900TExRGxb0SMAj4MLIiIjwG3AtPS1aYBt3S3LTMz6zudXscfEf1y2uelwA2SziY5aXx6TvsxM7MOVGQIxYi4C7grff4iMLkS+zUzs21luY7fzMzqiAu/mVmDceE3M2swLvxmZg3Ghd8qy90lm1VdRa7qMQPcXbJZQfiI3yrH3SWbFYILv1WOu0s2KwQXfqscd5dsVggu/FY57i7ZrBBc+K1yanFQF7M65Kt6rLKmTnWhN6syH/GbmTUYF34zswbjwm9m1mBc+M3MGowLv5lZg8mt8EsaKOl3kh6S9Jikr6fz95B0p6Sl6ePueWWoO111cFatzs/c6ZpZzcnzcs4NwKSIaJPUH/iNpP8GTgPmR8Slki4CLgIuzDFHfeiqgzOoTudn7nTNrCbldsQfibb0Zf90CuAUYHY6fzZwal4Z6kpXHZxVq/Mzd7pmVpMUEfltXOoHLALeCnwvIi6UtDYihpStsyYitmnukTQdmA4wfPjwlrlz52baZ1tbG01NTX0Rv09tb653T5qEOvhvFRJAp8vuXrAgt1xdZepuv3llylMRcxUxExQzVxEzQb65Jk6cuCgixm+zICJyn4AhQCswBljbbtma7t7f0tISWbW2tmZet5K2O1dzcwRsOzU3d70sz1zbsd/cMuWoiLmKmCmimLmKmCki31zAwuigplbkqp6IWAvcBbwHeF7SCID0cXUlMtS8rjo4q1bnZ+50zawm5XlVz16ShqTPBwFTgCeAW4Fp6WrTgFvyylBXuurgrFqdn7nTNbOalOdVPSOA2Wk7/w7ADRFxm6R7gBsknQ2sAE7PMUN96aqDs2p1fuZO18xqTp5X9TwcEYdHxNiIGBMR30jnvxgRkyNidPr4Ul4ZrJd8bb5ZXXO3zLY1X5tvVvfcZYNtzdfmm9U9F37bmgdEN6t7Lvy2NQ+Iblb3XPjrRV+dkPW1+WZ1z4W/HpROyC5fntw7Wzoh25vi72vzzeqeC39fq8alkH19QnbqVFi2DDZvTh5d9M3qii/n7EtdXQq5zz757dcnZM2sB3zE35eqdSmkT8iaWQ+48Pelah15+4SsmfWAC39fqtaRt0/ImlkPuPD3pWoeefuErJll5MLfl3zkbWY1wFf19DV3U2xmBecj/lri7pLNrA/4iL9WuLtkM+sjeQ69uJ+kVklLJD0maUY6fw9Jd0pamj7unleGXivikbW7SzazPpJnU89G4PMRcRBwNPAZSQcDFwHzI2I0MD99XRx92e9NX/LduWbWR/IcenFVRCxOn78KLAH2AU4BZqerzQZOzStDrxT1yNp355pZH1FE5L8TaRTwK2AMsCIihpQtWxMR2zT3SJoOTAcYPnx4y9y5czPtq62tjaampl5nffekSaiDv0lI3L1gQa+3u725hs2bx4GXXUa/DRu2zNs0YABPXnABq6dMqVquPBQxExQzVxEzQTFzFTET5Jtr4sSJiyJi/DYLIiLXCWgCFgGnpa/Xtlu+prtttLS0RFatra2Z1+1Qc3NE0siz9dTcvF2b3e5cERHXXpvkkJLHa6/d7k32Sa4+VsRMEcXMVcRMEcXMVcRMEfnmAhZGBzU118s5JfUHbgLmRMTP09nPSxqRLh8BrM4zQ48Vud8b351rZn0gz6t6BFwNLImIb5UtuhWYlj6fBtySV4Ze8d23Zlbn8ryO/1jg48Ajkh5M530JuBS4QdLZwArg9Bwz9I7vvjWzOpbnVT2/iQhFxNiIGJdOv4iIFyNickSMTh9fyiVAEa/FNzMrgPq8c9d3uZqZdao+++op6rX4ZmYFUJ+F33e5mpl1qj4Lv+9yNTPrVH0W/iJfi29mVmX1Wfh9Lb6ZWafq86oe8LX4ZmadqM8jfjMz65QLv5lZg3HhNzNrMC78ZmYNxoXfzKzBVGQEru0l6QVgecbVhwJ/zjFObzlXdkXMBMXMVcRMUMxcRcwE+eZqjoi92s+sicLfE5IWRkdDjVWZc2VXxExQzFxFzATFzFXETFCdXG7qMTNrMC78ZmYNph4L/1XVDtAJ58quiJmgmLmKmAmKmauImaAKuequjd/MzLpWj0f8ZmbWBRd+M7MGUzeFX9KPJK2W9Gi1s5STtJ+kVklLJD0maUYBMg2U9DtJD6WZvl7tTCWS+kl6QNJt1c5SImmZpEckPShpYbXzlEgaIulGSU+k/76OqXKeA9O/UWl6RdL51cxUIumz6b/1RyVdJ2lgATLNSPM8Vum/U9208Us6DmgDfhIRY6qdp0TSCGBERCyWtAuwCDg1Ih6vYiYBO0dEm6T+wG+AGRFxb7UylUj6HDAe2DUi3l/tPJAUfmB8RBTq5h9Js4FfR8QsSTsBgyNibZVjAckXOPBH4KiIyHrzZV5Z9iH5N35wRPxF0g3ALyLimipmGgPMBY4EXgd+Cfx9RCytxP7r5og/In4FvFTtHO1FxKqIWJw+fxVYAuxT5UwREW3py/7pVPUjAEn7Au8DZlU7S9FJ2hU4DrgaICJeL0rRT00G/lDtol9mR2CQpB2BwcBzVc5zEHBvRLwWERuBu4EPVmrndVP4a4GkUcDhwH1VjlJqUnkQWA3cGRFVzwT8O/BFYHOVc7QXwB2SFkmaXu0wqQOAF4Afp01jsyTtXO1QZT4MXFftEAAR8UfgMmAFsAp4OSLuqG4qHgWOk7SnpMHAe4H9KrVzF/4KkdQE3AScHxGvVDtPRGyKiHHAvsCR6U/PqpH0fmB1RCyqZo5OHBsR7wBOAj6TNitW247AO4DvR8ThwDrgoupGSqTNTicDP6t2FgBJuwOnAPsDewM7S/pYNTNFxBLgm8CdJM08DwEbK7V/F/4KSNvRbwLmRMTPq52nXNo8cBfwnuom4Vjg5LQ9fS4wSdK11Y2UiIjn0sfVwM0k7bLVthJYWfZL7UaSL4IiOAlYHBHPVztIagrwTES8EBFvAD8H3lnlTETE1RHxjog4jqSZuiLt++DCn7v0ROrVwJKI+Fa18wBI2kvSkPT5IJL/MZ6oZqaIuDgi9o2IUSTNBAsioqpHZQCSdk5PypM2pZxA8jO9qiLiT8Czkg5MZ00GqnbBQDsfoSDNPKkVwNGSBqf/P04mOddWVZKGpY8jgdOo4N+sbgZbl3QdMAEYKmkl8NWIuLq6qYDkSPbjwCNpmzrAlyLiF9WLxAhgdnrlxQ7ADRFRmMsnC2Y4cHNSL9gR+GlE/LK6kbY4F5iTNq08DZxV5Tyk7dXHA39X7SwlEXGfpBuBxSTNKQ9QjO4bbpK0J/AG8JmIWFOpHdfN5ZxmZpaNm3rMzBqMC7+ZWYNx4TczazAu/GZmDcaF38yswbjwW02S1Nbu9ZmSvlvB/R8t6b60F8olkr6Wzp8gqcc3B0m6RtLfps9nSTq4B++dUKTeTK346uY6frO+IKlfRGzKsOps4EMR8VB6P0TpRqoJJL3E/ra3GSLinN6+1ywLH/Fb3ZHULGm+pIfTx5Hp/C1H1enrtvRxQjpmwk9JbrTbWdLt6XgFj0o6o4PdDCPp8KvU79HjaSd8/wv4bPpL4F1d7FOSvivpcUm3p9srrXOXpPHp8xMk3SNpsaSfpX0+Iek9Svrh/w3JXZ9mmbnwW60apLJBP4BvlC37Lsm4DGOBOcC3M2zvSOCSiDiYpN+i5yLisHRsh47u1L0ceFLSzZL+TtLAiFgG/AC4PCLGRcSvu9jfB0l+JRwKfIoO+o6RNBT4R2BK2kncQuBzSgYR+SHwAeBdwF9l+HxmW7jwW636S1pcx6W9jH6lbNkxwE/T5/8X+OsM2/tdRDyTPn8EmCLpm5LeFREvt185Ir5BMmDMHcBH6fjLoSvHAdelvxaeAxZ0sM7RwMHA/0u/3KYBzcDbSTodWxrJrfeF6MzOaocLvzWCUr8kG0n/zaedde1Uts66LStHPAW0kHwB/Iuk8i8Vytb7Q0R8n6TTr8PSflfa62qf3fWXIpKxEkpfcAdHxNkZ32vWKRd+q0e/JenhE2AqybB7AMtICjok/bP37+jNkvYGXouIa0kG8Nimu2NJ70sLOcBoYBOwFngV2KVs1c72+Svgw+mAOCOAiR1EuRc4VtJb030OlvQ2kp5U95f0lnS9j3T0Ocw646t6rB6dB/xI0hdIRqkq9Vr5Q+AWSb8D5lN2lN/OocC/SdpM0nPi33ewzseByyW9RnJUPzUiNkn6L+BGSaeQ9J7Z2T5vBiaR/Kp4imTova1ExAuSzgSukzQgnf2PEfGUkpHAbpf0Z5IvtsKMM23F5945zcwajJt6zMwajAu/mVmDceE3M2swLvxmZg3Ghd/MrMG48JuZNRgXfjOzBvP/AZouZbi9+1wVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the distribution of scores\n",
    "w_data.plot(x='Hours', y='Scores', style='o',c=\"red\",grid=True)\n",
    "plt.title('Hours VS Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph, we can clearly see that there is a **positive linear relation** between the number of **hours studied and percentage of score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# To Check the norrmality\n",
    "import seaborn as sns\n",
    "sns.distplot(w_data['Hours'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "sns.distplot(w_data['Scores'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data \n",
    "The next step is to divide the data into \"Attributes\" (Inputs) & \"lables\"(Output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = w_data.iloc[:, :-1].values\n",
    "y = w_data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test sets\n",
    "# we will do this by using Scikit-Learn's built-in train_test_split()method\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": "markdown",
   "metadata": {},
   "source": [
    "## Training the Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have split our data into training and testing sets, and now is finally the time to train our 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": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions\n",
    "Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can check train and test score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 0.9515510725211552\n",
      "Test Score : 0.9454906892105356\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Score :\",regressor.score(X_train, y_train))\n",
    "print(\"Test Score :\",regressor.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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)\n",
    "plt.title('Hours vs Percentage',size=20)  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.plot(X,line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Evaluating the model**\n",
    "\n",
    "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
   "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": "markdown",
   "metadata": {},
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    "**Small value of Mean absolute error states that the chances of error or wrong forecasting through the model are very less.**\n",
    "You can also predict the marks by giving the input of number of hours studied.\n",
    "* For example: What will be the predicted score of a student if he/she studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "# We can also test with your own data\n",
    "hours = 9.25\n",
    "own_pred = regressor.predict([[hours]])\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "outputs": [],
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