T4-3-Linear Regression- Validación del modelo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dividir el dataset en conjunto de entrenamiento y testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('../GitHub/python-ml-course/datasets/ads/Advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6.,  6., 18., 39., 39., 37., 23., 17., 12.,  3.]),\n",
       " array([-2.43546302, -1.94153136, -1.44759971, -0.95366805, -0.45973639,\n",
       "         0.03419527,  0.52812692,  1.02205858,  1.51599024,  2.00992189,\n",
       "         2.50385355]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "a=np.random.randn(len(data))\n",
    "plt.hist(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc=np.percentile(a,80)\n",
    "check=(a<perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 40, 200)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training=data[check]\n",
    "testing=data[~check]\n",
    "len(training),len(testing),len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "lm1=smf.ols(formula='Sales~TV+Radio',data=training).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   652.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 09 Jan 2020</td> <th>  Prob (F-statistic):</th> <td>8.29e-77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:34:35</td>     <th>  Log-Likelihood:    </th> <td> -312.31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   160</td>      <th>  AIC:               </th> <td>   630.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   157</td>      <th>  BIC:               </th> <td>   639.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.8950</td> <td>    0.334</td> <td>    8.656</td> <td> 0.000</td> <td>    2.234</td> <td>    3.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0464</td> <td>    0.002</td> <td>   29.540</td> <td> 0.000</td> <td>    0.043</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.1830</td> <td>    0.009</td> <td>   19.829</td> <td> 0.000</td> <td>    0.165</td> <td>    0.201</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>47.828</td> <th>  Durbin-Watson:     </th> <td>   2.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 109.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.289</td> <th>  Prob(JB):          </th> <td>2.00e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.118</td> <th>  Cond. No.          </th> <td>    415.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.893\n",
       "Model:                            OLS   Adj. R-squared:                  0.891\n",
       "Method:                 Least Squares   F-statistic:                     652.7\n",
       "Date:                Thu, 09 Jan 2020   Prob (F-statistic):           8.29e-77\n",
       "Time:                        21:34:35   Log-Likelihood:                -312.31\n",
       "No. Observations:                 160   AIC:                             630.6\n",
       "Df Residuals:                     157   BIC:                             639.8\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.8950      0.334      8.656      0.000       2.234       3.556\n",
       "TV             0.0464      0.002     29.540      0.000       0.043       0.050\n",
       "Radio          0.1830      0.009     19.829      0.000       0.165       0.201\n",
       "==============================================================================\n",
       "Omnibus:                       47.828   Durbin-Watson:                   2.077\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              109.140\n",
       "Skew:                          -1.289   Prob(JB):                     2.00e-24\n",
       "Kurtosis:                       6.118   Cond. No.                         415.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es podible que el estadístico F suba porque depende de la cantidad de lo datos y del SSD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validación del modelo con el conjunto de testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      13.268112\n",
       "9      12.650138\n",
       "10      7.026195\n",
       "14     18.393992\n",
       "15     20.697969\n",
       "24      8.093942\n",
       "25     15.745385\n",
       "26     14.892954\n",
       "32      7.683787\n",
       "37     15.403326\n",
       "38      9.782182\n",
       "39     20.382267\n",
       "43     14.041141\n",
       "61     22.843704\n",
       "68     18.952487\n",
       "69     20.996549\n",
       "73      9.947755\n",
       "80     11.328742\n",
       "86     11.470478\n",
       "87     15.465101\n",
       "91      4.497779\n",
       "95     16.261240\n",
       "97     15.324877\n",
       "98     24.089502\n",
       "101    23.302821\n",
       "116    11.976486\n",
       "128    22.092274\n",
       "133    19.232937\n",
       "142    19.210554\n",
       "150    18.475017\n",
       "151    10.051665\n",
       "164    11.027925\n",
       "166    10.606242\n",
       "168    17.217132\n",
       "178    16.166731\n",
       "179    12.415795\n",
       "187    17.021734\n",
       "189     5.977528\n",
       "190    12.249829\n",
       "197    12.817165\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_pred=lm1.predict(testing)\n",
    "sales_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.3950503834748"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSD=sum((testing['Sales']-sales_pred)**2)\n",
    "SSD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RSE Desviación o error estándar de los residuos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "RSE=np.sqrt(SSD/(len(data)-2-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04634856273809606"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error=RSE/np.mean(testing.Sales)\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='../GitHub/python-ml-course/notebooks/resources/summary-lm.png')"
   ]
  },
  {
   "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": "3.6.8"
  },
  "toc": {
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