GRIP TASK1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  || The Sparks Foundation || GRIPDEC20 TASK 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name : Satyarth Anurup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TASK : Predict the percentage of an student based on the no. of study hours. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing 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",
       "    .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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading data\n",
    "url= 'http://bit.ly/w-data'\n",
    "df= pd.read_csv(url)\n",
    "print(\"data imported successfully\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "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": [
    "#plotting the data\n",
    "df.plot(x='Hours',y='Scores',style='o')\n",
    "plt.title('Hours/scores')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Hours')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dividing the data into dependent and independent variables\n",
    "X= df.iloc[:,:-1]\n",
    "y= df.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours\n",
       "0    2.5\n",
       "1    5.1\n",
       "2    3.2\n",
       "3    8.5\n",
       "4    3.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21\n",
       "1    47\n",
       "2    27\n",
       "3    75\n",
       "4    30\n",
       "Name: Scores, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### importing the train_test_split() from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# dividin the data into train and test\n",
    "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size=0.2, random_state= 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training is complete\n"
     ]
    }
   ],
   "source": [
    "# importing the linear regression module\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg= LinearRegression()\n",
    "reg.fit(X_train,y_train)\n",
    "\n",
    "print(\"training is complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WXZnSUuNHD6As62GYKPt/lFdO3yO8l1eNDR3eEP+yj0jS5TIDvwRYAtT9F1gJzHb3KjOrzLy+osD1SYEUq/9HIXeXqO2rSNNCBbiZHQiMBX4MfD8zfAYwIvPru4G5KMATLcr+H1u27WDQVY8Hxs6p6MdNXzki789U21eRpoWdgd8KXA7ss9tYb3dfC+Dua82swacvzGwcMA6gf//+LShVkiqqPd1q+yrStGYD3Mw+D6x39xozG5HrBdx9MjAZoKKiwnOuUBLrV395jao/vxwY+9sPR9Jrn44F+Xy1fRVpWpgZ+DDgi2Y2BugIdDGze4F1ZtYnM/vuA6yPslBJlmI9Sam2ryKNazbA3X0CMAEgMwP/gbufZ2Y3A+cDVZmvj0RYpySEHoEXSY6W7AOvAqaY2YXASuDswpQkSfT2hx9TccMTgbGbvnw45xyt+xoicckpwN19LrW7TXD3d4CRhS9JkkazbpFk0pOY0qgfT/8Hv376jcCYTscRSQ4FuDQoe9bdrVN7Flx1akzViEhDFOASoOUSkfRQgAsAy9Z9wKj/eiowdu+FxzL80B4Nfr+6BIrETwEuOc+61SVQJBkU4K3Y+b/5G395ZUNg7PVJY2jTpukDFprqEqgAFykeBXgr5O4cNOGxwNgJh/bgnguPDfV+dQkUSQYFeCtTiJuU6hIokgw6E7OVeO7Vt/cI75mXnZjXDpNiHw4hIg3TDLwVKPTWQHUJFEkGBXgJO+v2Z5m/cmNgrFB7utUlUCR+CvAS9PGOnQy4ckZg7DvDDuKqLwyKqSIRiYICvMToSUqR1kMBXiJmLFq7xynw8380iu57d4ipIhGJmgK8BGjWLdI6KcBj0FQfkVx6jHz2qhls3hZ8IjLf4FZvE5H0UYAXWVN9RIBQPUbe37KdI6+bGfjc284dzBmD8wtc9TYRSScFeJE11Uek7tcN/V5dkEaxXKLeJiLppAAvsnz6iKzZuJU7nn6dG6YvCYwX6nQc9TYRSSc9Sl9kjfULOaBrWaO/5xAI70F9urC8amzBjjZrqiYRSS4FeJE11Uekod/LtrxqLI9dckLRahKR5NISSpGF6SNy45+XsG7Tx4H3PTBuKMcevF9sNYlI8pi7F+1iFRUVXl1dXbTrpVEhb1Jqa6BIaTCzGnevyB7XDDwhHqpZxQ8efDEwFuZ0nMZoa6BI6VOAx6yh03GuP/Mwvjn0Uy36XG0NFCl9CvAYDat6co+TbQr1CLy2BoqUPgV4DN58dwsn/GROYGzexJH07tKxYNfQsWcipU8BXmTZNymPPHBfHvne8PrXhbrxOH70gMAaOGhroEipUYAXyX3zVvDDPywKjGUvlxTyxqO2BoqUPgV4Iwo1E965yzlkYvAm5T0XHsMJh/bc43sLfeNRx56JlDYFeAMKNRPO9SalbjyKSC4U4A1o6Uz4lXUfcOp/PRUYW3TtaDrv1fQft248ikgu1AulAS2ZCZdXTg+E9wXDylleNbbZ8Ab1JBGR3GgG3oB8ZsK3zHqF/569LDCW655u3XgUkVwowBuQyxa8j7bvZOCPZgTG/vi9YRxxYNe8rq0bjyISlgK8AWFnwtl7uvdq14alN5xetDpFpHVTgDeiqZlw9fJ3+cqv/hoYe+WG0+nQLtwtBXUJFJFCUIDnKHvWXXn6QL570iGh368ugSJSKArwkK546CUeqH4zMJZP4yl1CRSRQmk2wM2sH/BbYH9gFzDZ3W8zs+7AA0A5sBz4qru/F12puSvEUsX7W7dz5LUzA2NzfjCCg3rsnVdNelhHRAolzAx8B/Cf7j7fzPYBasxsFvBtYLa7V5lZJVAJXBFdqbkpxFLF4OtmsnHL9vrXn+ndmZmXndSiuvSwjogUSrN33dx9rbvPz/z6A2AJ0Bc4A7g78213A2dGVWQ+mlqqaM5LqzZSXjk9EN6vTxrT4vAGPawjIoWT0xq4mZUDQ4B5QG93Xwu1IW9mvRp5zzhgHED//v1bUmtO8lmqaOh0nN+PG8rQAh4mrId1RKRQQge4mXUGHgYudfdNZuHOanT3ycBkqD3UOJ8i85HrUsUvnlzGT2e+Uv+6f/dOPHX5yZHUpod1RKQQQgW4mbWnNrzvc/epmeF1ZtYnM/vuA6yPqsh8hH2acuOWbQy+blZg7MWrT2XfsvZFqVNEJF9hdqEYcCewxN1v2e23/gicD1Rlvj4SSYV5CrNUMXTSbN7a9FH96ytOG8i/jQi/p1tEJE7m3vSqhpkNB54GFlK7jRBgIrXr4FOA/sBK4Gx3f7epz6qoqPDq6uqcCoziqcVnX32bb9wxLzBWqMOERUQKzcxq3L0ie7zZGbi7PwM0tuA9sqWFNaXQTy3u2uUcnHU6zqMXD+ewvvu2vFgRkSJLdD/wlmwFzHbNHxcHwvuY8u4srxqr8BaR1Er0o/SFeGpx3aaPOHbS7MDYkutOo6xD20beISKSDokO8JY+tZh9yELVWYdz7jHF24suIhKlRAd4Lgcr7G7Zug8YtduxZj06d6D6ylGR1SkiEodEB3iuTy3u3OV89X//Ss2KT3pqLfjRKLrt3aEo9YqIFFOiAxzCP7U4/aW1XPS7+fWvb//GUYw5vE+UpYmIxCrxAd6c9zZvY8j1nzxJeXR5N34/7jjatgn3qL+ISFqlOsCv/dNi7np2ef3rJ75/Ip/utU98BYmIFFEqA3zhqvf5wi+eqX99ychDuWzUZ2KsSESk+FIV4Nt37uK0W5/itQ2bAejQtg3zrxpF571S9a8hIlIQqUm+KX9/k8sffqn+9V0XHM3JAxpsQS4i0iqkIsCnVH8S3iMH9uKO8ysI249cRKRUpSLAD+3VmcH9uvLzrw2hX/dOcZcjIpIIqQjwIf27Me2iYXGXISKSKInuRigiIo1TgIuIpJQCXEQkpRTgIiIppQAXEUkpBbiISEopwEVEUkoBLiKSUubuxbuY2QZgRchv7wG8HWE5+VJd4SWxJkhmXUmsCZJZVxJrgmjr+pS798weLGqA58LMqt29Iu46sqmu8JJYEySzriTWBMmsK4k1QTx1aQlFRCSlFOAiIimV5ACfHHcBjVBd4SWxJkhmXUmsCZJZVxJrghjqSuwauIiINC3JM3AREWmCAlxEJKUSF+Bm9hszW29mi+KuZXdm1s/M5pjZEjNbbGaXJKCmjmb2NzN7MVPTtXHXVMfM2prZAjN7NO5a6pjZcjNbaGYvmFl13PXUMbOuZvaQmb2c+ft1XMz1DMj8GdX9s8nMLo2zpjpmdlnm7/oiM7vfzDomoKZLMvUsLvafU+LWwM3sROBD4Lfufljc9dQxsz5AH3efb2b7ADXAme7+jxhrMmBvd//QzNoDzwCXuPvzcdVUx8y+D1QAXdz983HXA7UBDlS4e6IeAjGzu4Gn3f0OM+sAdHL3jXHXBbX/IwZWA8e6e9iH8KKqpS+1f8cHuftWM5sCPObu/xdjTYcBvweOAbYBM4B/c/dlxbh+4mbg7v4U8G7cdWRz97XuPj/z6w+AJUDfmGtyd/8w87J95p/Y/49sZgcCY4E74q4l6cysC3AicCeAu29LSnhnjAReizu8d9MOKDOzdkAnYE3M9fwT8Ly7b3H3HcBfgC8V6+KJC/A0MLNyYAgwL95K6pcqXgDWA7PcPfaagFuBy4FdcReSxYGZZlZjZuPiLibjYGADcFdmyekOM9s77qJ2cy5wf9xFALj7auCnwEpgLfC+u8+MtyoWASea2X5m1gkYA/Qr1sUV4Dkys87Aw8Cl7r4p7nrcfae7DwYOBI7J/EgXGzP7PLDe3WvirKMRw9z9KOB04KLMcl3c2gFHAb909yHAZqAy3pJqZZZzvgg8GHctAGbWDTgDOAg4ANjbzM6LsyZ3XwLcBMyidvnkRWBHsa6vAM9BZp35YeA+d58adz27y/zYPRc4LeZShgFfzKw3/x44xczujbekWu6+JvN1PfAHatct47YKWLXbT04PURvoSXA6MN/d18VdSMbngDfcfYO7bwemAsfHXBPufqe7H+XuJ1K7/FuU9W9QgIeWuWF4J7DE3W+Jux4AM+tpZl0zvy6j9i/4y3HW5O4T3P1Ady+n9sfvJ9091lkSgJntnbn5TGaJ4lRqf/yNlbu/BbxpZgMyQyOB2G6MZ/kaCVk+yVgJDDWzTpn/HkdSey8qVmbWK/O1P3AWRfwza1esC4VlZvcDI4AeZrYKuNrd74y3KqB2ZvlNYGFmzRlgors/FmNNfYC7MzsF2gBT3D0x2/YSpjfwh9r/7mkH/M7dZ8RbUr2LgfsySxavAxfEXA+Z9dxRwL/GXUsdd59nZg8B86ldplhAMh6rf9jM9gO2Axe5+3vFunDithGKiEg4WkIREUkpBbiISEopwEVEUkoBLiKSUgpwEZGUUoCLiKSUAlxEJKX+Hx/kz8yGS6elAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "line= reg.coef_*X+reg.intercept_\n",
    "\n",
    "#plotting for the test data\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,line)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Hours\n",
      "15    8.9\n",
      "24    7.8\n",
      "14    1.1\n",
      "19    7.4\n",
      "13    3.3\n"
     ]
    }
   ],
   "source": [
    "print(X_test)\n",
    "y_pred= reg.predict(X_test)"
   ]
  },
  {
   "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>15</th>\n",
       "      <td>95</td>\n",
       "      <td>88.446633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>86</td>\n",
       "      <td>77.732028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>12.470343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>73.835808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>42</td>\n",
       "      <td>33.899553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "15      95  88.446633\n",
       "24      86  77.732028\n",
       "14      17  12.470343\n",
       "19      69  73.835808\n",
       "13      42  33.899553"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comapring actual vs predicted\n",
    "df_1= pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of Hours = 9.5\n",
      "Predicted Score = 94.29096280584804\n"
     ]
    }
   ],
   "source": [
    "hours= 9.5\n",
    "own_pred= reg.predict((np.array(hours).reshape(1, 1)))\n",
    "print(\"No. of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the model based on r-Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_score: 0.9466449081971943\n"
     ]
    }
   ],
   "source": [
    "print(\"R2_score:\", metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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