Linear Regression-Student Percentage Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>HOUR</th>\n",
       "      <th>PERCENTAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.1</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.2</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.6</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HOUR  PERCENTAGE\n",
       "0   5.1          80\n",
       "1   3.1          71\n",
       "2   6.2          89\n",
       "3   6.6          93"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "dataset=pd.read_csv(\"test.csv\")\n",
    "dataset\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": [
    "dataset.plot(x=\"HOUR\",y=\"PERCENTAGE\",style='o')\n",
    "plt.title('HOUR VS PERCENTAGE')\n",
    "plt.xlabel('HOUR STUDIED')\n",
    "plt.ylabel(\"PERCENTAGE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array(dataset.drop(['PERCENTAGE'],1))\n",
    "y=np.array(dataset['PERCENTAGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": [
    "line=regressor.coef_*x+regressor.intercept_\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([89.17297297])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=regressor.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>89</td>\n",
       "      <td>76.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      89  76.857143"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y_test,'Predicted':y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.17297297297299 [6.2] 89\n"
     ]
    }
   ],
   "source": [
    "for x in range(len(pred)):\n",
    "    print(pred[x],x_test[x],y_test[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of hours=[[7.5]]\n",
      "Predictor percentage=97.21891891891892\n"
     ]
    }
   ],
   "source": [
    "HOUR=np.array([7.5])\n",
    "HOUR=HOUR.reshape((1,1))\n",
    "own_pred=regressor.predict(HOUR)\n",
    "print(\"No of hours={}\".format(HOUR))\n",
    "print(\"Predictor percentage={}\".format(own_pred[0]))"
   ]
  }
 ],
 "metadata": {
  "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": 4
}