Sparks_Internship_LR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7577b1eb",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8597094e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767de677",
   "metadata": {},
   "source": [
    "# Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3217b453",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('sparks_data.csv')\n",
    "x=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482b38e0",
   "metadata": {},
   "source": [
    "# Splitting the dataset into test set and train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad3b1e11",
   "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": "markdown",
   "id": "4eb34330",
   "metadata": {},
   "source": [
    "# Training the Simple Linear Regression Model on Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88290e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa59c70",
   "metadata": {},
   "source": [
    "# Predicting the test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eccd3cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ded8e7",
   "metadata": {},
   "source": [
    "# Visualizing the training set results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad5cd4ac",
   "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": [
    "plt.scatter(x_train,y_train, color='red')\n",
    "plt.plot(x_train, regressor.predict(x_train), color='green')\n",
    "plt.title(\"Study Hours vs Score (Training set)\")\n",
    "plt.xlabel(\"Study Hours \")\n",
    "plt.ylabel(\"Score \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0bee0f",
   "metadata": {},
   "source": [
    "# Visualizing the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb4dc6d3",
   "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": [
    "plt.scatter(x_test, y_test, color='red')\n",
    "plt.plot(x_train, regressor.predict(x_train), color='blue')\n",
    "plt.title(\"Study Hours vs Score  (Test Set)\")\n",
    "plt.xlabel(\"Study Hours\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24709825",
   "metadata": {},
   "source": [
    "#  Predicting a new result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a228a90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[93.69173249]\n"
     ]
    }
   ],
   "source": [
    "hours = 9.25\n",
    "score = regressor.predict([[hours]])\n",
    "print(score)"
   ]
  }
 ],
 "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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}