Helmy Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression in 3 Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1- Import your packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as nb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2- Create your data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f9709dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cigarettes = nb.array([[1], [30], [60]])\n",
    "hours = nb.array([[7], [210], [420]])\n",
    "\n",
    "plt.scatter(cigarettes, hours)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3- Modeling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(cigarettes, hours)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 560.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L34SsatWgowpM2N4ZW6xYMXbu3KnkESHMjJ07d1KsWLGgQ5HCLDUV/vMfvzj3t9/6WTXT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\nvYhIlPv/Ggc5SlnfShIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f95074c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cigarettes_test = nb.linspace(1,80)\n",
    "hours_pred = model.predict(cigarettes_test[:,None])\n",
    "\n",
    "plt.scatter(cigarettes, hours)\n",
    "plt.plot(cigarettes_test, hours_pred,'r')\n",
    "plt.legend(['Predicted line','Observed data'])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}