OUHIB_Yann_ML_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Data Vizualisation et présentation des données\n",
    "Les données que j'ai choisi portent sur les prix de l'immobilier en tant que time series avec les variables explicatives suivantes :\n",
    "- le code postale\n",
    "- le nombre de chambres\n",
    "- Une maison ou un appartement (que j'ai modifié en 0 ou 1 dans le code ci-dessous)\n",
    "- la date\n",
    "\n",
    "Le but étant de prédire la varaible expliquée : le prix.\n",
    "\n",
    "On peut visualiser ci-dessous notre dataset qui varie de 2007 (début de la crise immoblière) à 2019.<br>\n",
    "Chaque histogramme représente la répartition des prix dans un mois.<br>\n",
    "Par exemple, en octobre 2015 on se rend compte que le prix des maisons a varié jusqu'à + de $2m ! <br>\n",
    "Egalement, on se rend compte à travers cette visualisation qu'il y a des valeurs abérantes qui seront ammenées à être corrigées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Use of `dtf$Month` is discouraged. Use `Month` instead.”"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Use of `dtf$MyData.price` is discouraged. Use `MyData.price` instead.”"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "execution_count": 24,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Title     : Project ML ING3\n",
    "# Objective : Question 1 et data viz\n",
    "# Created by: Ouhib yann\n",
    "# Created on: 24/03/2020\n",
    "# Data imported from https://www.kaggle.com/deltacrot/property-sales\n",
    "library(ggplot2)\n",
    "library(scales)\n",
    "library(leaps)\n",
    "library(MASS)\n",
    "library(zoo)\n",
    "library(lmtest)\n",
    "\n",
    "\n",
    "# Getting the data from csv, converting date, puting it all in df for easier manipulation\n",
    "# MyData <- read.csv2(\"raw_sales.csv\", header = TRUE)\n",
    "MyData <- read.csv2(\"raw_sales.csv\", header = TRUE)\n",
    "newDate <- as.Date(MyData$datesold, format = \"%d/%m/%Y\")\n",
    "dtf <- data.frame(newDate, MyData$postcode, MyData$propertyType, MyData$bedrooms ,MyData$price)\n",
    "\n",
    "dtf$Year <- format(dtf$newDate, \"%y\")\n",
    "dtf$Month <- format(dtf$newDate, \"%b\")\n",
    "dtf$Day <- format(dtf$newDate, \"%d\")\n",
    "\n",
    "dtf$MyData.propertyType <- replace(as.character(dtf$MyData.propertyType), dtf$MyData.propertyType == \"house\", 1) # If it's a house, equals 1\n",
    "dtf$MyData.propertyType <- replace(as.character(dtf$MyData.propertyType), dtf$MyData.propertyType == \"unit\", 0) # If it's an unit, equals 0\n",
    "\n",
    "\n",
    "ggplot(data = dtf, aes(x = dtf$Month, y =  scale(dtf$MyData.price), col = Year)) +  geom_line() + facet_wrap(~ dtf$Year) + ggtitle(\" Average distribution of housing market\") + ylab(\"Price in e+05\") + xlab(\"Months\")\n",
    "# matplot(dtf$Year, dtf$MyData.price, type = \"l\", xlab = \"dates\", ylab = \"house price\") + scale_x_discrete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Gestion des outliers\n",
    "Les outliers ont été supprimés ici en suivant la méthode des écarts inter-quartiles.<br>\n",
    "J'ai au début voulu partir sur un interval de confiance mais la nature des données fait que cette méthode est parfois trop discriminante.<br>\n",
    "On a ensuite le calcul de l'espérance, la médianne, l'écart-type ainsi que le min et max de la série <u>APRES</u> suppression des outliers.<br>\n",
    "Enfin, le calcul de corrélation entre les differentes features est ensuite présenté."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean =  565544.9"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Median =  540000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Highest price =  1056000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lowest price =  221000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Rcodepost-price =  -0.07051372"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RpropertyType-price =  0.3945655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rbedrooms-price =  0.5852167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rpostcode-bedrooms =  0.2619391"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rproperty-bedrooms =  0.6770023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rpostcode-property =  0.276096"
     ]
    }
   ],
   "source": [
    "# Title     : Project ML ING3\n",
    "# Objective : Question 2\n",
    "# Created by: Ouhib yann\n",
    "# Created on: 24/03/2020\n",
    "# Data imported from https://www.kaggle.com/deltacrot/property-sales\n",
    "\n",
    "\n",
    "\n",
    "# Removing outliers based on interquartiles range\n",
    "mu <- mean(dtf$MyData.price)\n",
    "sigma <- sqrt(sd(dtf$MyData.price))\n",
    "n <- nrow(dtf)\n",
    "q1 <- quantile(dtf$MyData.price, c(0.25))\n",
    "q3 <- quantile(dtf$MyData.price, c(0.75))\n",
    "born_Inf <- 0.5*q1\n",
    "born_Sup <- 1.5*q3\n",
    "\n",
    "df_cleaned <- subset(dtf, dtf$MyData.price > born_Inf & dtf$MyData.price < born_Sup)\n",
    "\n",
    "\n",
    "# stats of cleaned dtf\n",
    "clean_mean <- mean(df_cleaned$MyData.price)\n",
    "clean_median <- median(df_cleaned$MyData.price)\n",
    "clean_Max <- max(df_cleaned$MyData.price)\n",
    "clean_Min <- min(df_cleaned$MyData.price)\n",
    "\n",
    "\n",
    "cat(\"\\nMean = \", clean_mean )\n",
    "cat(\"\\nMedian = \", clean_median)\n",
    "cat(\"\\nHighest price = \", clean_Max)\n",
    "cat(\"\\nLowest price = \", clean_Min)\n",
    "\n",
    "\n",
    "# Correlations all related to price\n",
    "cat(\"\\n\\nRcodepost-price = \", cor(df_cleaned$MyData.postcode, df_cleaned$MyData.price))\n",
    "cat(\"\\nRpropertyType-price = \", cor(as.numeric(df_cleaned$MyData.propertyType), df_cleaned$MyData.price))\n",
    "cat(\"\\nRbedrooms-price = \", cor(df_cleaned$MyData.bedrooms, df_cleaned$MyData.price))\n",
    "\n",
    "# Correlations related to bedrooms\n",
    "cat(\"\\nRpostcode-bedrooms = \", cor(df_cleaned$MyData.bedrooms, df_cleaned$MyData.postcode))\n",
    "cat(\"\\nRproperty-bedrooms = \", cor(df_cleaned$MyData.bedrooms, as.numeric(df_cleaned$MyData.propertyType)))\n",
    "\n",
    "# Correlation related to postcode and property\n",
    "cat(\"\\nRpostcode-property = \", cor(df_cleaned$MyData.postcode, as.numeric(df_cleaned$MyData.propertyType)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "On constate certaines corrélation fortes qui sont très justes au regarde la réalite :\n",
    "- correlation entre le <i>type de propriété</i> et le <i>nombre de pièces</i> (une maison a souvent plus de pièces qu'un appartement)\n",
    "- correlation entre le prix et le nombre de pièces."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Regression multilinéaire et maximum de vraissemblance\n",
    "Dans ce qui suit, on a utilisé la fonction \"lm()\" déjà implémentée sous R afin d'effectuer une régression multi-linéaire sur les différents Xi de notre dataset.<br>\n",
    "En suite, l'utilisation du maximum de vraissemblance nous permet de comparer les 2 méthodes (à savoir que la focntion \"lm()\" se base sur la méthode des moindres carrés ordinaires).<br>\n",
    "J'ai préféré afficher le summary des 2 méthodes plutôt qu'un \"all equals\" pas forcément adapté."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + \n",
      "    df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, \n",
      "    data = df_cleaned)\n",
      "\n",
      "Coefficients:\n",
      "                    (Intercept)       df_cleaned$MyData.bedrooms  \n",
      "                      305148.25                        113480.71  \n",
      "     df_cleaned$MyData.postcode               df_cleaned$newDate  \n",
      "                        -288.63                            39.91  \n",
      "df_cleaned$MyData.propertyType1  \n",
      "                       34400.78  \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:  glm(formula = df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + \n",
      "    df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, \n",
      "    data = df_cleaned)\n",
      "\n",
      "Coefficients:\n",
      "                    (Intercept)       df_cleaned$MyData.bedrooms  \n",
      "                      305148.25                        113480.71  \n",
      "     df_cleaned$MyData.postcode               df_cleaned$newDate  \n",
      "                        -288.63                            39.91  \n",
      "df_cleaned$MyData.propertyType1  \n",
      "                       34400.78  \n",
      "\n",
      "Degrees of Freedom: 27863 Total (i.e. Null);  27859 Residual\n",
      "Null Deviance:\t    8.137e+14 \n",
      "Residual Deviance: 4.394e+14 \tAIC: 733400\n"
     ]
    }
   ],
   "source": [
    "# Title     : Project ML ING3\n",
    "# Objective : Question 3 et 4\n",
    "# Created by: Ouhib yann\n",
    "# Created on: 01/04/2020\n",
    "# Data imported from https://www.kaggle.com/deltacrot/property-sales\n",
    "\n",
    "\n",
    "# Mutliple linear regression\n",
    "multi.fit <- lm(df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, data = df_cleaned)\n",
    "print(multi.fit)\n",
    "\n",
    "# lm is an ordinary least square, so let's do max likelyhood - gml - on price\n",
    "maxLikeHood <- glm(df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, data = df_cleaned)\n",
    "print(maxLikeHood)\n",
    "\n",
    "# all.equal(multi.fit, maxLikeHood)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "On constate que les moindres carrés et le maximum de vraissemblance nous donnent tous deux les mêmes résultats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Optimisation du modèle retenu\n",
    "Dans la cellule ci-dessous, j'ai choisi d'afficher le résultat des modèles pour les 3 types de regressions pas à pas (forward, backward, stepwise) afin de les comparer.<br>\n",
    "Il convient de noter que le modèle prend 4 type de paramètres explicatifs qui sont ceux présentés au début du projet (date, nombre de chambres, code postale, maison//appartement).<br>\n",
    "Expliquons comment fonctionne les modèles :\n",
    "- (1) corespond en fait à un modèle avec 1 seule variable explicative retenue (la variable retenue est celle dont la colonne est rempli par un 'asterisque')\n",
    "- (2) corespond à un modèle avec 2 variables explicatives retenues (toujours notées par un 'asterisque')<br>\n",
    "- etc...\n",
    "\n",
    "On constate que les modèles forward, backward et stepwise ont tous retenu les mêmes variables expliquées par modèle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subset selection object\n",
       "Call: regsubsets.formula(df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + \n",
       "    df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, \n",
       "    data = df_cleaned, nvmax = 4, method = \"backward\")\n",
       "4 Variables  (and intercept)\n",
       "                                Forced in Forced out\n",
       "df_cleaned$MyData.bedrooms          FALSE      FALSE\n",
       "df_cleaned$MyData.postcode          FALSE      FALSE\n",
       "df_cleaned$newDate                  FALSE      FALSE\n",
       "df_cleaned$MyData.propertyType1     FALSE      FALSE\n",
       "1 subsets of each size up to 4\n",
       "Selection Algorithm: backward\n",
       "         df_cleaned$MyData.bedrooms df_cleaned$MyData.postcode\n",
       "1  ( 1 ) \"*\"                        \" \"                       \n",
       "2  ( 1 ) \"*\"                        \" \"                       \n",
       "3  ( 1 ) \"*\"                        \"*\"                       \n",
       "4  ( 1 ) \"*\"                        \"*\"                       \n",
       "         df_cleaned$newDate df_cleaned$MyData.propertyType1\n",
       "1  ( 1 ) \" \"                \" \"                            \n",
       "2  ( 1 ) \"*\"                \" \"                            \n",
       "3  ( 1 ) \"*\"                \" \"                            \n",
       "4  ( 1 ) \"*\"                \"*\"                            "
      ]
     },
     "execution_count": 13,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Subset selection object\n",
       "Call: regsubsets.formula(df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + \n",
       "    df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, \n",
       "    data = df_cleaned, nvmax = 4, method = \"forward\")\n",
       "4 Variables  (and intercept)\n",
       "                                Forced in Forced out\n",
       "df_cleaned$MyData.bedrooms          FALSE      FALSE\n",
       "df_cleaned$MyData.postcode          FALSE      FALSE\n",
       "df_cleaned$newDate                  FALSE      FALSE\n",
       "df_cleaned$MyData.propertyType1     FALSE      FALSE\n",
       "1 subsets of each size up to 4\n",
       "Selection Algorithm: forward\n",
       "         df_cleaned$MyData.bedrooms df_cleaned$MyData.postcode\n",
       "1  ( 1 ) \"*\"                        \" \"                       \n",
       "2  ( 1 ) \"*\"                        \" \"                       \n",
       "3  ( 1 ) \"*\"                        \"*\"                       \n",
       "4  ( 1 ) \"*\"                        \"*\"                       \n",
       "         df_cleaned$newDate df_cleaned$MyData.propertyType1\n",
       "1  ( 1 ) \" \"                \" \"                            \n",
       "2  ( 1 ) \"*\"                \" \"                            \n",
       "3  ( 1 ) \"*\"                \" \"                            \n",
       "4  ( 1 ) \"*\"                \"*\"                            "
      ]
     },
     "execution_count": 13,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Subset selection object\n",
       "Call: regsubsets.formula(df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + \n",
       "    df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, \n",
       "    data = df_cleaned, nvmax = 4, method = \"seqrep\")\n",
       "4 Variables  (and intercept)\n",
       "                                Forced in Forced out\n",
       "df_cleaned$MyData.bedrooms          FALSE      FALSE\n",
       "df_cleaned$MyData.postcode          FALSE      FALSE\n",
       "df_cleaned$newDate                  FALSE      FALSE\n",
       "df_cleaned$MyData.propertyType1     FALSE      FALSE\n",
       "1 subsets of each size up to 4\n",
       "Selection Algorithm: 'sequential replacement'\n",
       "         df_cleaned$MyData.bedrooms df_cleaned$MyData.postcode\n",
       "1  ( 1 ) \"*\"                        \" \"                       \n",
       "2  ( 1 ) \"*\"                        \" \"                       \n",
       "3  ( 1 ) \"*\"                        \"*\"                       \n",
       "4  ( 1 ) \"*\"                        \"*\"                       \n",
       "         df_cleaned$newDate df_cleaned$MyData.propertyType1\n",
       "1  ( 1 ) \" \"                \" \"                            \n",
       "2  ( 1 ) \"*\"                \" \"                            \n",
       "3  ( 1 ) \"*\"                \" \"                            \n",
       "4  ( 1 ) \"*\"                \"*\"                            "
      ]
     },
     "execution_count": 13,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Title     : Project ML ING3\n",
    "# Objective : Question 5 et 6\n",
    "# Created by: OUHIB Yann\n",
    "# Created on: 16/04/2020\n",
    "# Data imported from https://www.kaggle.com/deltacrot/property-sales\n",
    "\n",
    "\n",
    "modBack <- regsubsets(df_cleaned$MyData.price~ df_cleaned$MyData.bedrooms + df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, data = df_cleaned, nvmax = 4, method = \"backward\") # backward regression\n",
    "modForw <- regsubsets(df_cleaned$MyData.price~ df_cleaned$MyData.bedrooms + df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, data = df_cleaned, nvmax = 4, method = \"forward\") # forward regression\n",
    "modStep <- regsubsets(df_cleaned$MyData.price~ df_cleaned$MyData.bedrooms + df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, data = df_cleaned, nvmax = 4, method = \"seqrep\") # stepwise\n",
    "\n",
    "summary(modBack)\n",
    "summary(modForw)\n",
    "summary(modStep)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Validité du modèle obtenu\n",
    "Il s'agit ici de vérifier la cohérence du modèle et des estimateurs obtenus.<br>\n",
    "On va donc tester (uniquement pour la regression stepwise ici) l'autocorrélation des résidus, l'homoscédascité ainsi que donner le coefficient de détermination du modèle.\n",
    "On ne testera pas la normalité des résidus étant donné que les données n'ont aucune raison de suivre une lois normale vis à vis de la distribution (on peut d'ailleurs le constater dans la datavisualition partie 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=654296.9\n",
      "df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + df_cleaned$MyData.postcode + \n",
      "    df_cleaned$newDate + df_cleaned$MyData.propertyType\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Df  Sum of Sq        RSS    AIC\n",
      "<none>                                         4.3944e+14 654297\n",
      "- df_cleaned$MyData.propertyType  1 2.5014e+12 4.4195e+14 654453\n",
      "- df_cleaned$MyData.postcode      1 4.6127e+13 4.8557e+14 657076\n",
      "- df_cleaned$newDate              1 5.1063e+13 4.9051e+14 657358\n",
      "- df_cleaned$MyData.bedrooms      1 1.6715e+14 6.0660e+14 663277\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = df_cleaned$MyData.price ~ df_cleaned$MyData.bedrooms + \n",
      "    df_cleaned$MyData.postcode + df_cleaned$newDate + df_cleaned$MyData.propertyType, \n",
      "    data = df_cleaned)\n",
      "\n",
      "Coefficients:\n",
      "                    (Intercept)       df_cleaned$MyData.bedrooms  \n",
      "                      305148.25                        113480.71  \n",
      "     df_cleaned$MyData.postcode               df_cleaned$newDate  \n",
      "                        -288.63                            39.91  \n",
      "df_cleaned$MyData.propertyType1  \n",
      "                       34400.78  \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\tDurbin-Watson test\n",
      "\n",
      "data:  modStep\n",
      "DW = 1.4215, p-value < 2.2e-16\n",
      "alternative hypothesis: true autocorrelation is greater than 0\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coefficient de détermination =  0.459932"
     ]
    }
   ],
   "source": [
    "# Title     : TODO\n",
    "# Objective : TODO\n",
    "# Created by: moi\n",
    "# Created on: 17/04/2020\n",
    "\n",
    "\n",
    "#modBack <- stepAIC(multi.fit, trace = TRUE, direction = \"backward\") # backward regression\n",
    "#modForw <- stepAIC(multi.fit, trace = TRUE, direction = \"forward\") # forward regression\n",
    "modStep <- stepAIC(multi.fit, trace = TRUE, direction = \"both\")  # stepwise regression\n",
    "\n",
    "print(modStep)\n",
    "\n",
    "\n",
    "# Used to test the adequation to a normal distribution - Saphiro-Wilk distrib.\n",
    "#test_SW <- shapiro.test(modStep)\n",
    "#print(test_SW)\n",
    "\n",
    "# Used to test autocorrelation -  Durbin Watson test.\n",
    "test_DW <- dwtest(modStep)\n",
    "print(test_DW)\n",
    "\n",
    "# determination coefficient - used in the context of prediction of future outcomes.\n",
    "cat(\"\\nCoefficient de détermination = \", summary(modStep)$r.squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Conclusion\n",
    "On constate que le test d'autocorrélation est compris entre 0 et 2 ce qui est habituel pour une time serie.<br>\n",
    "Concernant les features que sont le type de propriété, code postale, date et nombre de chambres : AIC nous permet de savoir quelle feature a le moins d'impacte lorsqu'elle est retirée du modèle. Il s'agit ici du <i>type de propriété</i>, ce qui semble cohérent étant donnée qu'il s'agit juste d'un 0 ou 1 dans notre dataset et qui a pour unique information de qualifié de \"maison\" ou \"d'appartement\" le bien. Le feature avec le plus de poids étant le <i>nombre de chambres</i>, ce qui encore une fois est cohérent avec la réalité et ce que recherche la plupart des gens lors d'un achat de propriété.<br>\n",
    "Enfin, on constate que le coefficient de détermination est ~ 0.5 ce qui nous permet avec ce modèle d'expliquer 50% de la variance des données prédictibles."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R (R-Project)",
   "language": "r",
   "metadata": {
    "cocalc": {
     "description": "R statistical programming language",
     "priority": 10,
     "url": "https://www.r-project.org/"
    }
   },
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}