Linear Regression/LinearRegressionSingle Variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('ex1data1.txt')\n",
    "X=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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=0.3,random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train Linear Regression using Skicit Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "model=LinearRegression()\n",
    "model.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.88953046,  6.00803956,  4.21995151,  3.35897649, 19.97236945,\n",
       "        3.4552309 ,  2.00775355,  1.98129835,  3.45901021,  8.03800372,\n",
       "        3.42818518,  1.92425432,  3.04245892,  2.61079038,  3.60935606,\n",
       "        3.73123894,  3.42027224, 18.32364361,  5.64652453,  1.91858535,\n",
       "        5.19017234, 13.5558025 ,  2.8695553 ,  3.03749857,  1.93700951,\n",
       "        2.49374974,  6.07996464,  2.3700953 ,  2.07660793])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=model.predict(X_test)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the Training set"
   ]
  },
  {
   "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": [
    "plt.scatter(X_train,y_train,color='red')\n",
    "plt.plot(X_train,model.predict(X_train),color='blue')\n",
    "plt.xlabel('Population in 10,000')\n",
    "plt.ylabel('Profit in $10,000')\n",
    "plt.title('Training Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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='yellow')\n",
    "plt.plot(X_train,model.predict(X_train),color='blue')\n",
    "plt.xlabel('Population in 10,000')\n",
    "plt.ylabel('Profit in $10,000')\n",
    "plt.title('Test Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}