Student Score Prediction wrt Hours .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V9QN2ZxC38pB"
   },
   "source": [
    "# Predicting Student Scores\n",
    "In this We will Predict Student Scores wrt Hours they Read.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imported libraries\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2534,
     "status": "ok",
     "timestamp": 1544113345787,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "LtU4YMEhqm9m",
    "outputId": "5b4b36af-1545-497e-a6dc-7658bab71dbc"
   },
   "outputs": [],
   "source": [
    "#importing dataset from url \n",
    "path = \"http://bit.ly/w-data\"\n",
    "data = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()#printing top 5 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Hours', 'Scores'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns in dataset\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "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 dataset\n",
    "plt.scatter('Hours','Scores',data=data)\n",
    "plt.title(\"Hours Vs Scores\")\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape#checking dataset shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training testing and spliting dataset\n",
    "train,test = train_test_split(data,test_size=0.25,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting all dataset(train,test) in same dimension array\n",
    "train_x=train.drop(\"Scores\",axis=1)\n",
    "train_y=train[\"Scores\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using Linear_Regression model for prediction\n",
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(train_x,train_y) #fitting train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.61718609])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Linear_regression coefficient\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.448617866650636"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear_regression interception\n",
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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 # formula for line is y=m*x + c\n",
    "line = lr.coef_*train_x+lr.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(train_x,train_y,color='0')\n",
    "plt.plot(train_x, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting test_x dataset after training dataset\n",
    "pr=lr.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(20, 16.874397005544534),\n",
       " (54, 48.61111111111111),\n",
       " (35, 38.99392501851517),\n",
       " (67, 61.11345303148581),\n",
       " (95, 88.04157409075442),\n",
       " (81, 82.27126243519687),\n",
       " (60, 55.343141375928255)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "list(zip(test_y,pr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "0AAsPVA_6KmK"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing Library to find MSE\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.797834650875306"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(test_y,pr,squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = [[9.25]]\n",
      "Predicted Score = 91.407589223163\n"
     ]
    }
   ],
   "source": [
    "# predicting Scores wrt to Hours\n",
    "hour =[9.25]\n",
    "own_pr=lr.predict([hour])\n",
    "print(\"No of Hours = {}\".format([hour]))\n",
    "print(\"Predicted Score = {}\".format(own_pr[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicted Score = 91.407589223163\n",
    "Thank_You."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Linear Regression.ipynb",
   "provenance": [
    {
     "file_id": "1wzD9Aa7cc7kRwyXq8DeJ8H56mJInOMZN",
     "timestamp": 1544113281508
    }
   ],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}