Analysis of Sales Report of a Clothes Manufacturing Outlet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "\n",
    "library(readxl) # to read excel\n",
    "library(plyr) \n",
    "library(caTools)\n",
    "\n",
    "library(e1071) \n",
    "library(caret) \n",
    "library(randomForest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# load data \n",
    "attribset = read_excel('Attribute DataSet.xlsx')\n",
    "dresssale = read_excel('Dress Sales.xlsx')\n",
    "\n",
    "#remove Dress_ID column\n",
    "attribset_ = attribset[2:14] \n",
    "dresssale_ = dresssale[2:24]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Attributes dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the unique values for each columns\n",
    "#lapply(attribset[2:14], unique)\n",
    "\n",
    "# values checking\n",
    "# style \n",
    "attribset_$Style[attribset_$Style == 'sexy'] = 'Sexy'\n",
    "\n",
    "# Price\n",
    "attribset_$Price[attribset_$Price == 'low'] = 'Low'\n",
    "attribset_$Price[attribset_$Price == 'high'] = 'High'\n",
    "\n",
    "# Size\n",
    "attribset_$Size[attribset_$Size == 's'] = 'S' \n",
    "attribset_$Size[attribset_$Size == 'small'] = 'S'\n",
    "\n",
    "# Season \n",
    "attribset_$Season[attribset_$Season == 'spring'] = 'Spring'\n",
    "attribset_$Season[attribset_$Season == 'summer'] = 'Summer'\n",
    "attribset_$Season[attribset_$Season == 'Automn'] = 'Autumn'\n",
    "attribset_$Season[attribset_$Season == 'winter'] = 'Winter'\n",
    "\n",
    "# NeckLine \n",
    "attribset_$NeckLine[attribset_$NeckLine == 'sweetheart'] = 'Sweetheart'\n",
    "\n",
    "# SleeveLength\n",
    "attribset_$SleeveLength[attribset_$SleeveLength == 'sleevless'] = 'sleeveless' \n",
    "attribset_$SleeveLength[attribset_$SleeveLength == 'sleeevless'] = 'sleeveless' \n",
    "attribset_$SleeveLength[attribset_$SleeveLength == 'sleveless'] = 'sleeveless' \n",
    "attribset_$SleeveLength[attribset_$SleeveLength == 'threequater'] = 'threequarter' \n",
    "attribset_$SleeveLength[attribset_$SleeveLength == 'thressqatar'] = 'threequarter' \n",
    "attribset_$SleeveLength[attribset_$SleeveLength == 'urndowncollor'] = 'turndowncollar' \n",
    "\n",
    "# FabricType\n",
    "attribset_$FabricType[attribset_$FabricType == 'shiffon'] = 'chiffon'\n",
    "attribset_$FabricType[attribset_$FabricType == 'sattin'] = 'satin'\n",
    "attribset_$FabricType[attribset_$FabricType == 'wollen'] = 'woolen'\n",
    "attribset_$FabricType[attribset_$FabricType == 'flannael'] = 'flannel'\n",
    "attribset_$FabricType[attribset_$FabricType == 'knitting'] = 'knitted'\n",
    "\n",
    "\n",
    "# Decoration\n",
    "attribset_$Decoration[attribset_$Decoration == 'embroidary'] = 'embroidery'\n",
    "attribset_$Decoration[attribset_$Decoration == 'sequined'] = 'sequins'\n",
    "attribset_$Decoration[attribset_$Decoration == 'ruched'] = 'ruche'\n",
    "attribset_$Decoration[attribset_$Decoration == 'none'] = 'null'\n",
    "\n",
    "# Pattern Type\n",
    "attribset_$'Pattern Type'[attribset_$'Pattern Type' == 'none'] = 'null' \n",
    "attribset_$'Pattern Type'[attribset_$'Pattern Type' == 'leapord'] = 'leopard'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# factoring \n",
    "\n",
    "attribset_$Style = factor(attribset_$Style, \n",
    "                          levels = c('Sexy', 'Casual', 'vintage', 'Brief', 'cute', 'bohemian', 'Novelty', 'Flare', 'party', 'work', 'OL', 'fashion'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11))\n",
    "\n",
    "attribset_$Price = factor(attribset_$Price, \n",
    "                          levels = c('Low', 'High', 'Average', 'Medium', 'very-high'),\n",
    "                          labels = c(0,1,2,3,4))\n",
    "\n",
    "attribset_$Size = factor(attribset_$Size, \n",
    "                          levels = c('M', 'L', 'XL', 'free', 'S'),\n",
    "                          labels = c(0,1,2,3,4))\n",
    "\n",
    "attribset_$Season = factor(attribset_$Season, \n",
    "                          levels = c('Summer', 'Autumn', 'Spring', 'Winter'),\n",
    "                          labels = c(0,1,2,3))\n",
    "\n",
    "attribset_$NeckLine = factor(attribset_$NeckLine, \n",
    "                          levels = c('o-neck', 'v-neck', 'boat-neck', 'peterpan-collor', 'ruffled', 'turndowncollor', 'slash-neck', 'mandarin-collor', 'open', 'sqare-collor', 'Sweetheart', 'Scoop', 'halter', 'backless', 'bowneck', 'NULL'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15))\n",
    "\n",
    "attribset_$SleeveLength = factor(attribset_$SleeveLength, \n",
    "                          levels = c('sleeveless', 'Petal', 'full', 'butterfly', 'short', 'threequarter', 'halfsleeve', 'cap-sleeves', 'turndowncollor', 'capsleeves', 'half', 'turndowncollar', 'NULL'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11,12))\n",
    "\n",
    "attribset_$waiseline = factor(attribset_$waiseline, \n",
    "                          levels = c('empire', 'natural', 'null', 'princess', 'dropped'),\n",
    "                          labels = c(0,1,2,3,4))\n",
    "\n",
    "attribset_$Material = factor(attribset_$Material, \n",
    "                          levels = c('null', 'microfiber', 'polyster', 'silk', 'chiffonfabric', 'cotton', 'nylon', 'other', 'milksilk', 'linen', 'rayon', 'lycra', 'mix', 'acrylic', 'spandex', 'lace', 'modal', 'cashmere', 'viscos', 'knitting', 'sill', 'wool', 'model', 'shiffon'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23))\n",
    "\n",
    "attribset_$FabricType = factor(attribset_$FabricType, \n",
    "                          levels = c('chiffon', 'null', 'broadcloth', 'jersey', 'other', 'batik', 'satin', 'flannel', 'worsted', 'woolen', 'poplin', 'dobby', 'knitted', 'tulle', 'organza', 'lace', 'Corduroy', 'terry'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17))\n",
    "\n",
    "attribset_$Decoration = factor(attribset_$Decoration, \n",
    "                          levels = c('ruffles', 'null', 'embroidery', 'bow', 'lace', 'beading', 'sashes', 'hollowout', 'pockets', 'sequins', 'applique', 'button', 'Tiered', 'rivet', 'feathers', 'flowers', 'pearls', 'pleat', 'crystal', 'ruche', 'draped', 'tassel', 'plain', 'cascading'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23))\n",
    "\n",
    "attribset_$`Pattern Type` = factor(attribset_$`Pattern Type`, \n",
    "                          levels = c('animal', 'print', 'dot', 'solid', 'null', 'patchwork', 'striped', 'geometric', 'plaid', 'leopard', 'floral', 'character', 'splice'),\n",
    "                          labels = c(0,1,2,3,4,5,6,7,8,9,10,11,12))\n",
    "\n",
    "attribset_$Recommendation = sapply(attribset_$Recommendation, factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Style</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Price</dt>\n",
       "\t\t<dd>2</dd>\n",
       "\t<dt>Rating</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Size</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Season</dt>\n",
       "\t\t<dd>2</dd>\n",
       "\t<dt>NeckLine</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>SleeveLength</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>waiseline</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>Material</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>FabricType</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>Decoration</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>Pattern Type</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>Recommendation</dt>\n",
       "\t\t<dd>0</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Style] 0\n",
       "\\item[Price] 2\n",
       "\\item[Rating] 0\n",
       "\\item[Size] 0\n",
       "\\item[Season] 2\n",
       "\\item[NeckLine] 1\n",
       "\\item[SleeveLength] 0\n",
       "\\item[waiseline] 1\n",
       "\\item[Material] 1\n",
       "\\item[FabricType] 1\n",
       "\\item[Decoration] 1\n",
       "\\item[Pattern Type] 1\n",
       "\\item[Recommendation] 0\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Style\n",
       ":   0Price\n",
       ":   2Rating\n",
       ":   0Size\n",
       ":   0Season\n",
       ":   2NeckLine\n",
       ":   1SleeveLength\n",
       ":   0waiseline\n",
       ":   1Material\n",
       ":   1FabricType\n",
       ":   1Decoration\n",
       ":   1Pattern Type\n",
       ":   1Recommendation\n",
       ":   0\n",
       "\n"
      ],
      "text/plain": [
       "         Style          Price         Rating           Size         Season \n",
       "             0              2              0              0              2 \n",
       "      NeckLine   SleeveLength      waiseline       Material     FabricType \n",
       "             1              0              1              1              1 \n",
       "    Decoration   Pattern Type Recommendation \n",
       "             1              1              0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count of missing values in attribset_ dataset\n",
    "colSums(is.na(attribset_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the function.\n",
    "getmode <- function(v) {\n",
    "   uniqv <- unique(v)\n",
    "   uniqv[which.max(tabulate(match(v, uniqv)))]\n",
    "}\n",
    "\n",
    "# fill missing Value with mode\n",
    "attribset_$Price[is.na(attribset_$Price) ==TRUE] <- getmode(attribset_$Price)\n",
    "attribset_$Season[is.na(attribset_$Season) ==TRUE] <- getmode(attribset_$Season)\n",
    "attribset_$NeckLine[is.na(attribset_$NeckLine) ==TRUE] <- getmode(attribset_$NeckLine)\n",
    "attribset_$waiseline[is.na(attribset_$waiseline) ==TRUE] <- getmode(attribset_$waiseline)\n",
    "attribset_$Material[is.na(attribset_$Material) ==TRUE] <- getmode(attribset_$Material)\n",
    "attribset_$FabricType[is.na(attribset_$FabricType) ==TRUE] <- getmode(attribset_$FabricType)\n",
    "attribset_$Decoration[is.na(attribset_$Decoration) ==TRUE] <- getmode(attribset_$Decoration)\n",
    "attribset_$`Pattern Type`[is.na(attribset_$`Pattern Type`) ==TRUE] <- getmode(attribset_$`Pattern Type`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t500 obs. of  13 variables:\n",
      " $ Style         : Factor w/ 12 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 3 4 5 6 2 7 8 6 ...\n",
      " $ Price         : Factor w/ 5 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 2 3 1 1 3 3 3 1 ...\n",
      " $ Rating        : num  4.6 0 0 4.6 4.5 0 0 0 0 0 ...\n",
      " $ Size          : Factor w/ 5 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 2 2 1 1 3 4 4 4 ...\n",
      " $ Season        : Factor w/ 4 levels \"0\",\"1\",\"2\",\"3\": 1 1 2 3 1 1 1 2 3 1 ...\n",
      " $ NeckLine      : Factor w/ 16 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 1 1 1 2 1 1 2 2 ...\n",
      " $ SleeveLength  : Factor w/ 13 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 3 3 4 1 3 5 5 1 ...\n",
      " $ waiseline     : Factor w/ 5 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 2 2 2 1 3 2 1 2 ...\n",
      " $ Material      : Factor w/ 24 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 3 4 5 1 6 3 6 7 ...\n",
      " $ FabricType    : Factor w/ 18 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 2 1 1 2 2 3 3 1 ...\n",
      " $ Decoration    : Factor w/ 24 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 2 3 4 2 2 5 6 2 ...\n",
      " $ Pattern.Type  : Factor w/ 13 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 2 2 3 2 4 5 4 5 ...\n",
      " $ Recommendation: Factor w/ 2 levels \"1\",\"0\": 1 2 2 1 2 2 2 2 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "attribset_data <- data.frame(attribset_)\n",
    "str(attribset_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dresses dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following `from` values were not present in `x`: 41314\n",
      "The following `from` values were not present in `x`: 41373\n",
      "The following `from` values were not present in `x`: 41434\n",
      "The following `from` values were not present in `x`: 41495\n",
      "The following `from` values were not present in `x`: 41556\n",
      "The following `from` values were not present in `x`: 41617\n",
      "The following `from` values were not present in `x`: 41315\n",
      "The following `from` values were not present in `x`: 41374\n",
      "The following `from` values were not present in `x`: 41435\n",
      "The following `from` values were not present in `x`: 40400\n",
      "The following `from` values were not present in `x`: 41557\n",
      "The following `from` values were not present in `x`: 41618\n"
     ]
    }
   ],
   "source": [
    "# Update columns name in dresssale_ dataset\n",
    "\n",
    "dresssale_ = rename(dresssale_,c('41314'='2/9/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41373'='4/9/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41434'='6/9/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41495'='8/9/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41556'='10/9/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41617'='12/9/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41315'='2/10/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41374'='4/10/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41435'='6/10/2013'))\n",
    "dresssale_ = rename(dresssale_,c('40400'='8/10/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41557'='10/10/2013'))\n",
    "dresssale_ = rename(dresssale_,c('41618'='12/10/2013'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert all variable types to numeric\n",
    "dresssale_ <- as.data.frame(apply(dresssale_, 2, as.numeric))\n",
    "\n",
    "# mean row \n",
    "dresssale_ = as.matrix(dresssale_)\n",
    "k <- which(is.na(dresssale_), arr.ind=TRUE)\n",
    "dresssale_[k] <- rowMeans(dresssale_, na.rm=TRUE)[k[,1]]\n",
    "dresssale_ = as.data.frame(dresssale_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>29/8/2013</th><th scope=col>31/8/2013</th><th scope=col>2/9/2013</th><th scope=col>4/9/2013</th><th scope=col>6/9/2013</th><th scope=col>8/9/2013</th><th scope=col>10/9/2013</th><th scope=col>12/9/2013</th><th scope=col>14/9/2013</th><th scope=col>16/9/2013</th><th scope=col>...</th><th scope=col>26/9/2013</th><th scope=col>28/9/2013</th><th scope=col>30/9/2013</th><th scope=col>2/10/2013</th><th scope=col>4/10/2013</th><th scope=col>6/10/2013</th><th scope=col>8/10/2013</th><th scope=col>10/10/2013</th><th scope=col>12/10/2013</th><th scope=col>total_sales</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2114 </td><td>2274 </td><td>2491 </td><td>2660 </td><td>2727 </td><td>2887 </td><td>2930 </td><td>3119 </td><td>3204 </td><td>3277 </td><td>...  </td><td>3624 </td><td>3706 </td><td>3746 </td><td>3795 </td><td>3832 </td><td>3897 </td><td>3923 </td><td>3985 </td><td>4048 </td><td>75979</td></tr>\n",
       "\t<tr><td> 151 </td><td> 275 </td><td> 570 </td><td> 750 </td><td> 813 </td><td>1066 </td><td>1164 </td><td>1558 </td><td>1756 </td><td>1878 </td><td>...  </td><td>2942 </td><td>3258 </td><td>3354 </td><td>3475 </td><td>3654 </td><td>3911 </td><td>4024 </td><td>4125 </td><td>4277 </td><td>52256</td></tr>\n",
       "\t<tr><td>   6 </td><td>   7 </td><td>   7 </td><td>   7 </td><td>   8 </td><td>   8 </td><td>   9 </td><td>  10 </td><td>  10 </td><td>  10 </td><td>...  </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  223</td></tr>\n",
       "\t<tr><td>1005 </td><td>1128 </td><td>1326 </td><td>1455 </td><td>1507 </td><td>1621 </td><td>1637 </td><td>1723 </td><td>1746 </td><td>1783 </td><td>...  </td><td>1892 </td><td>1914 </td><td>1924 </td><td>1929 </td><td>1941 </td><td>1952 </td><td>1955 </td><td>1959 </td><td>1963 </td><td>39691</td></tr>\n",
       "\t<tr><td> 996 </td><td>1175 </td><td>1304 </td><td>1396 </td><td>1432 </td><td>1559 </td><td>1570 </td><td>1638 </td><td>1655 </td><td>1681 </td><td>...  </td><td>2156 </td><td>2252 </td><td>2312 </td><td>2387 </td><td>2459 </td><td>2544 </td><td>2614 </td><td>2693 </td><td>2736 </td><td>44077</td></tr>\n",
       "\t<tr><td>   4 </td><td>   5 </td><td>  11 </td><td>  13 </td><td>  13 </td><td>  13 </td><td>  16 </td><td>  18 </td><td>  19 </td><td>  20 </td><td>...  </td><td>  25 </td><td>  26 </td><td>  26 </td><td>  26 </td><td>  26 </td><td>  27 </td><td>  27 </td><td>  27 </td><td>  27 </td><td>  457</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllll}\n",
       " 29/8/2013 & 31/8/2013 & 2/9/2013 & 4/9/2013 & 6/9/2013 & 8/9/2013 & 10/9/2013 & 12/9/2013 & 14/9/2013 & 16/9/2013 & ... & 26/9/2013 & 28/9/2013 & 30/9/2013 & 2/10/2013 & 4/10/2013 & 6/10/2013 & 8/10/2013 & 10/10/2013 & 12/10/2013 & total\\_sales\\\\\n",
       "\\hline\n",
       "\t 2114  & 2274  & 2491  & 2660  & 2727  & 2887  & 2930  & 3119  & 3204  & 3277  & ...   & 3624  & 3706  & 3746  & 3795  & 3832  & 3897  & 3923  & 3985  & 4048  & 75979\\\\\n",
       "\t  151  &  275  &  570  &  750  &  813  & 1066  & 1164  & 1558  & 1756  & 1878  & ...   & 2942  & 3258  & 3354  & 3475  & 3654  & 3911  & 4024  & 4125  & 4277  & 52256\\\\\n",
       "\t    6  &    7  &    7  &    7  &    8  &    8  &    9  &   10  &   10  &   10  & ...   &   11  &   11  &   11  &   11  &   11  &   11  &   11  &   11  &   11  &   223\\\\\n",
       "\t 1005  & 1128  & 1326  & 1455  & 1507  & 1621  & 1637  & 1723  & 1746  & 1783  & ...   & 1892  & 1914  & 1924  & 1929  & 1941  & 1952  & 1955  & 1959  & 1963  & 39691\\\\\n",
       "\t  996  & 1175  & 1304  & 1396  & 1432  & 1559  & 1570  & 1638  & 1655  & 1681  & ...   & 2156  & 2252  & 2312  & 2387  & 2459  & 2544  & 2614  & 2693  & 2736  & 44077\\\\\n",
       "\t    4  &    5  &   11  &   13  &   13  &   13  &   16  &   18  &   19  &   20  & ...   &   25  &   26  &   26  &   26  &   26  &   27  &   27  &   27  &   27  &   457\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 29/8/2013 | 31/8/2013 | 2/9/2013 | 4/9/2013 | 6/9/2013 | 8/9/2013 | 10/9/2013 | 12/9/2013 | 14/9/2013 | 16/9/2013 | ... | 26/9/2013 | 28/9/2013 | 30/9/2013 | 2/10/2013 | 4/10/2013 | 6/10/2013 | 8/10/2013 | 10/10/2013 | 12/10/2013 | total_sales |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2114  | 2274  | 2491  | 2660  | 2727  | 2887  | 2930  | 3119  | 3204  | 3277  | ...   | 3624  | 3706  | 3746  | 3795  | 3832  | 3897  | 3923  | 3985  | 4048  | 75979 |\n",
       "|  151  |  275  |  570  |  750  |  813  | 1066  | 1164  | 1558  | 1756  | 1878  | ...   | 2942  | 3258  | 3354  | 3475  | 3654  | 3911  | 4024  | 4125  | 4277  | 52256 |\n",
       "|    6  |    7  |    7  |    7  |    8  |    8  |    9  |   10  |   10  |   10  | ...   |   11  |   11  |   11  |   11  |   11  |   11  |   11  |   11  |   11  |   223 |\n",
       "| 1005  | 1128  | 1326  | 1455  | 1507  | 1621  | 1637  | 1723  | 1746  | 1783  | ...   | 1892  | 1914  | 1924  | 1929  | 1941  | 1952  | 1955  | 1959  | 1963  | 39691 |\n",
       "|  996  | 1175  | 1304  | 1396  | 1432  | 1559  | 1570  | 1638  | 1655  | 1681  | ...   | 2156  | 2252  | 2312  | 2387  | 2459  | 2544  | 2614  | 2693  | 2736  | 44077 |\n",
       "|    4  |    5  |   11  |   13  |   13  |   13  |   16  |   18  |   19  |   20  | ...   |   25  |   26  |   26  |   26  |   26  |   27  |   27  |   27  |   27  |   457 |\n",
       "\n"
      ],
      "text/plain": [
       "  29/8/2013 31/8/2013 2/9/2013 4/9/2013 6/9/2013 8/9/2013 10/9/2013 12/9/2013\n",
       "1 2114      2274      2491     2660     2727     2887     2930      3119     \n",
       "2  151       275       570      750      813     1066     1164      1558     \n",
       "3    6         7         7        7        8        8        9        10     \n",
       "4 1005      1128      1326     1455     1507     1621     1637      1723     \n",
       "5  996      1175      1304     1396     1432     1559     1570      1638     \n",
       "6    4         5        11       13       13       13       16        18     \n",
       "  14/9/2013 16/9/2013 ... 26/9/2013 28/9/2013 30/9/2013 2/10/2013 4/10/2013\n",
       "1 3204      3277      ... 3624      3706      3746      3795      3832     \n",
       "2 1756      1878      ... 2942      3258      3354      3475      3654     \n",
       "3   10        10      ...   11        11        11        11        11     \n",
       "4 1746      1783      ... 1892      1914      1924      1929      1941     \n",
       "5 1655      1681      ... 2156      2252      2312      2387      2459     \n",
       "6   19        20      ...   25        26        26        26        26     \n",
       "  6/10/2013 8/10/2013 10/10/2013 12/10/2013 total_sales\n",
       "1 3897      3923      3985       4048       75979      \n",
       "2 3911      4024      4125       4277       52256      \n",
       "3   11        11        11         11         223      \n",
       "4 1952      1955      1959       1963       39691      \n",
       "5 2544      2614      2693       2736       44077      \n",
       "6   27        27        27         27         457      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sum all values on row on (total sales)\n",
    "dresssale_$total_sales = rowSums(dresssale_)\n",
    "head(dresssale_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marged data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Style</th><th scope=col>Price</th><th scope=col>Rating</th><th scope=col>Size</th><th scope=col>Season</th><th scope=col>NeckLine</th><th scope=col>SleeveLength</th><th scope=col>waiseline</th><th scope=col>Material</th><th scope=col>FabricType</th><th scope=col>...</th><th scope=col>X26.9.2013</th><th scope=col>X28.9.2013</th><th scope=col>X30.9.2013</th><th scope=col>X2.10.2013</th><th scope=col>X4.10.2013</th><th scope=col>X6.10.2013</th><th scope=col>X8.10.2013</th><th scope=col>X10.10.2013</th><th scope=col>X12.10.2013</th><th scope=col>total_sales</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0    </td><td>0    </td><td>4.6  </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>...  </td><td>3624 </td><td>3706 </td><td>3746 </td><td>3795 </td><td>3832 </td><td>3897 </td><td>3923 </td><td>3985 </td><td>4048 </td><td>75979</td></tr>\n",
       "\t<tr><td>1    </td><td>0    </td><td>0.0  </td><td>1    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>...  </td><td>2942 </td><td>3258 </td><td>3354 </td><td>3475 </td><td>3654 </td><td>3911 </td><td>4024 </td><td>4125 </td><td>4277 </td><td>52256</td></tr>\n",
       "\t<tr><td>2    </td><td>1    </td><td>0.0  </td><td>1    </td><td>1    </td><td>0    </td><td>2    </td><td>1    </td><td>2    </td><td>1    </td><td>...  </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  11 </td><td>  223</td></tr>\n",
       "\t<tr><td>3    </td><td>2    </td><td>4.6  </td><td>1    </td><td>2    </td><td>0    </td><td>2    </td><td>1    </td><td>3    </td><td>0    </td><td>...  </td><td>1892 </td><td>1914 </td><td>1924 </td><td>1929 </td><td>1941 </td><td>1952 </td><td>1955 </td><td>1959 </td><td>1963 </td><td>39691</td></tr>\n",
       "\t<tr><td>4    </td><td>0    </td><td>4.5  </td><td>0    </td><td>0    </td><td>0    </td><td>3    </td><td>1    </td><td>4    </td><td>0    </td><td>...  </td><td>2156 </td><td>2252 </td><td>2312 </td><td>2387 </td><td>2459 </td><td>2544 </td><td>2614 </td><td>2693 </td><td>2736 </td><td>44077</td></tr>\n",
       "\t<tr><td>5    </td><td>0    </td><td>0.0  </td><td>0    </td><td>0    </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>...  </td><td>  25 </td><td>  26 </td><td>  26 </td><td>  26 </td><td>  26 </td><td>  27 </td><td>  27 </td><td>  27 </td><td>  27 </td><td>  457</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllll}\n",
       " Style & Price & Rating & Size & Season & NeckLine & SleeveLength & waiseline & Material & FabricType & ... & X26.9.2013 & X28.9.2013 & X30.9.2013 & X2.10.2013 & X4.10.2013 & X6.10.2013 & X8.10.2013 & X10.10.2013 & X12.10.2013 & total\\_sales\\\\\n",
       "\\hline\n",
       "\t 0     & 0     & 4.6   & 0     & 0     & 0     & 0     & 0     & 0     & 0     & ...   & 3624  & 3706  & 3746  & 3795  & 3832  & 3897  & 3923  & 3985  & 4048  & 75979\\\\\n",
       "\t 1     & 0     & 0.0   & 1     & 0     & 0     & 1     & 1     & 1     & 1     & ...   & 2942  & 3258  & 3354  & 3475  & 3654  & 3911  & 4024  & 4125  & 4277  & 52256\\\\\n",
       "\t 2     & 1     & 0.0   & 1     & 1     & 0     & 2     & 1     & 2     & 1     & ...   &   11  &   11  &   11  &   11  &   11  &   11  &   11  &   11  &   11  &   223\\\\\n",
       "\t 3     & 2     & 4.6   & 1     & 2     & 0     & 2     & 1     & 3     & 0     & ...   & 1892  & 1914  & 1924  & 1929  & 1941  & 1952  & 1955  & 1959  & 1963  & 39691\\\\\n",
       "\t 4     & 0     & 4.5   & 0     & 0     & 0     & 3     & 1     & 4     & 0     & ...   & 2156  & 2252  & 2312  & 2387  & 2459  & 2544  & 2614  & 2693  & 2736  & 44077\\\\\n",
       "\t 5     & 0     & 0.0   & 0     & 0     & 1     & 0     & 0     & 0     & 1     & ...   &   25  &   26  &   26  &   26  &   26  &   27  &   27  &   27  &   27  &   457\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Style | Price | Rating | Size | Season | NeckLine | SleeveLength | waiseline | Material | FabricType | ... | X26.9.2013 | X28.9.2013 | X30.9.2013 | X2.10.2013 | X4.10.2013 | X6.10.2013 | X8.10.2013 | X10.10.2013 | X12.10.2013 | total_sales |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0     | 0     | 4.6   | 0     | 0     | 0     | 0     | 0     | 0     | 0     | ...   | 3624  | 3706  | 3746  | 3795  | 3832  | 3897  | 3923  | 3985  | 4048  | 75979 |\n",
       "| 1     | 0     | 0.0   | 1     | 0     | 0     | 1     | 1     | 1     | 1     | ...   | 2942  | 3258  | 3354  | 3475  | 3654  | 3911  | 4024  | 4125  | 4277  | 52256 |\n",
       "| 2     | 1     | 0.0   | 1     | 1     | 0     | 2     | 1     | 2     | 1     | ...   |   11  |   11  |   11  |   11  |   11  |   11  |   11  |   11  |   11  |   223 |\n",
       "| 3     | 2     | 4.6   | 1     | 2     | 0     | 2     | 1     | 3     | 0     | ...   | 1892  | 1914  | 1924  | 1929  | 1941  | 1952  | 1955  | 1959  | 1963  | 39691 |\n",
       "| 4     | 0     | 4.5   | 0     | 0     | 0     | 3     | 1     | 4     | 0     | ...   | 2156  | 2252  | 2312  | 2387  | 2459  | 2544  | 2614  | 2693  | 2736  | 44077 |\n",
       "| 5     | 0     | 0.0   | 0     | 0     | 1     | 0     | 0     | 0     | 1     | ...   |   25  |   26  |   26  |   26  |   26  |   27  |   27  |   27  |   27  |   457 |\n",
       "\n"
      ],
      "text/plain": [
       "  Style Price Rating Size Season NeckLine SleeveLength waiseline Material\n",
       "1 0     0     4.6    0    0      0        0            0         0       \n",
       "2 1     0     0.0    1    0      0        1            1         1       \n",
       "3 2     1     0.0    1    1      0        2            1         2       \n",
       "4 3     2     4.6    1    2      0        2            1         3       \n",
       "5 4     0     4.5    0    0      0        3            1         4       \n",
       "6 5     0     0.0    0    0      1        0            0         0       \n",
       "  FabricType ... X26.9.2013 X28.9.2013 X30.9.2013 X2.10.2013 X4.10.2013\n",
       "1 0          ... 3624       3706       3746       3795       3832      \n",
       "2 1          ... 2942       3258       3354       3475       3654      \n",
       "3 1          ...   11         11         11         11         11      \n",
       "4 0          ... 1892       1914       1924       1929       1941      \n",
       "5 0          ... 2156       2252       2312       2387       2459      \n",
       "6 1          ...   25         26         26         26         26      \n",
       "  X6.10.2013 X8.10.2013 X10.10.2013 X12.10.2013 total_sales\n",
       "1 3897       3923       3985        4048        75979      \n",
       "2 3911       4024       4125        4277        52256      \n",
       "3   11         11         11          11          223      \n",
       "4 1952       1955       1959        1963        39691      \n",
       "5 2544       2614       2693        2736        44077      \n",
       "6   27         27         27          27          457      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data <- data.frame(attribset_ ,dresssale_)\n",
    "head(merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t500 obs. of  37 variables:\n",
      " $ Style         : Factor w/ 12 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 3 4 5 6 2 7 8 6 ...\n",
      " $ Price         : Factor w/ 5 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 2 3 1 1 3 3 3 1 ...\n",
      " $ Rating        : num  4.6 0 0 4.6 4.5 0 0 0 0 0 ...\n",
      " $ Size          : Factor w/ 5 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 2 2 1 1 3 4 4 4 ...\n",
      " $ Season        : Factor w/ 4 levels \"0\",\"1\",\"2\",\"3\": 1 1 2 3 1 1 1 2 3 1 ...\n",
      " $ NeckLine      : Factor w/ 16 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 1 1 1 2 1 1 2 2 ...\n",
      " $ SleeveLength  : Factor w/ 13 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 3 3 4 1 3 5 5 1 ...\n",
      " $ waiseline     : Factor w/ 5 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 2 2 2 1 3 2 1 2 ...\n",
      " $ Material      : Factor w/ 24 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 3 4 5 1 6 3 6 7 ...\n",
      " $ FabricType    : Factor w/ 18 levels \"0\",\"1\",\"2\",\"3\",..: 1 2 2 1 1 2 2 3 3 1 ...\n",
      " $ Decoration    : Factor w/ 24 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 2 3 4 2 2 5 6 2 ...\n",
      " $ Pattern.Type  : Factor w/ 13 levels \"0\",\"1\",\"2\",\"3\",..: 1 1 2 2 3 2 4 5 4 5 ...\n",
      " $ Recommendation: Factor w/ 2 levels \"1\",\"0\": 1 2 2 1 2 2 2 2 1 1 ...\n",
      " $ X29.8.2013    : num  2114 151 6 1005 996 ...\n",
      " $ X31.8.2013    : num  2274 275 7 1128 1175 ...\n",
      " $ X2.9.2013     : num  2491 570 7 1326 1304 ...\n",
      " $ X4.9.2013     : num  2660 750 7 1455 1396 ...\n",
      " $ X6.9.2013     : num  2727 813 8 1507 1432 ...\n",
      " $ X8.9.2013     : num  2887 1066 8 1621 1559 ...\n",
      " $ X10.9.2013    : num  2930 1164 9 1637 1570 ...\n",
      " $ X12.9.2013    : num  3119 1558 10 1723 1638 ...\n",
      " $ X14.9.2013    : num  3204 1756 10 1746 1655 ...\n",
      " $ X16.9.2013    : num  3277 1878 10 1783 1681 ...\n",
      " $ X18.9.2013    : num  3321 1985 10 1796 1743 ...\n",
      " $ X20.9.2013    : num  3386 2106 10 1812 1824 ...\n",
      " $ X22.9.2013    : num  3479 2454 11 1845 1919 ...\n",
      " $ X24.9.2013    : num  3554 2710 11 1878 2032 ...\n",
      " $ X26.9.2013    : num  3624 2942 11 1892 2156 ...\n",
      " $ X28.9.2013    : num  3706 3258 11 1914 2252 ...\n",
      " $ X30.9.2013    : num  3746 3354 11 1924 2312 ...\n",
      " $ X2.10.2013    : num  3795 3475 11 1929 2387 ...\n",
      " $ X4.10.2013    : num  3832 3654 11 1941 2459 ...\n",
      " $ X6.10.2013    : num  3897 3911 11 1952 2544 ...\n",
      " $ X8.10.2013    : num  3923 4024 11 1955 2614 ...\n",
      " $ X10.10.2013   : num  3985 4125 11 1959 2693 ...\n",
      " $ X12.10.2013   : num  4048 4277 11 1963 2736 ...\n",
      " $ total_sales   : num  75979 52256 223 39691 44077 ...\n"
     ]
    }
   ],
   "source": [
    "str(merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 350  37\n",
      "[1] 150  37\n"
     ]
    }
   ],
   "source": [
    "# spliting dataset \n",
    "set.seed(100)\n",
    "\n",
    "spl = sample.split(merged_data$Recommendation, SplitRatio = 0.7)\n",
    "train = subset(merged_data, spl==TRUE)\n",
    "test = subset(merged_data, spl==FALSE)\n",
    "\n",
    "print(dim(train)); print(dim(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification - Predict recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First model (Naive Bayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   1   0\n",
       "         1 106  41\n",
       "         0  66 137\n",
       "                                          \n",
       "               Accuracy : 0.6943          \n",
       "                 95% CI : (0.6431, 0.7422)\n",
       "    No Information Rate : 0.5086          \n",
       "    P-Value [Acc > NIR] : 1.352e-12       \n",
       "                                          \n",
       "                  Kappa : 0.3869          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : 0.02033         \n",
       "                                          \n",
       "            Sensitivity : 0.6163          \n",
       "            Specificity : 0.7697          \n",
       "         Pos Pred Value : 0.7211          \n",
       "         Neg Pred Value : 0.6749          \n",
       "             Prevalence : 0.4914          \n",
       "         Detection Rate : 0.3029          \n",
       "   Detection Prevalence : 0.4200          \n",
       "      Balanced Accuracy : 0.6930          \n",
       "                                          \n",
       "       'Positive' Class : 1               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"---------------\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "             \n",
       "naive_predict  1  0\n",
       "            1 33 37\n",
       "            0 30 50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# naive bayes model\n",
    "naive_model = naiveBayes(Recommendation ~.,data = train) # build model\n",
    "confusionMatrix(train$Recommendation,predict(naive_model,train),positive = '1') # create confusion Matrix\n",
    "print('---------------')\n",
    "naive_predict = predict(naive_model,test) # predict test set\n",
    "table(naive_predict,test$Recommendation) # create table "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second model (Support Vector Machine) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   1   0\n",
       "         1   6 141\n",
       "         0   0 203\n",
       "                                          \n",
       "               Accuracy : 0.5971          \n",
       "                 95% CI : (0.5437, 0.6489)\n",
       "    No Information Rate : 0.9829          \n",
       "    P-Value [Acc > NIR] : 1               \n",
       "                                          \n",
       "                  Kappa : 0.047           \n",
       "                                          \n",
       " Mcnemar's Test P-Value : <2e-16          \n",
       "                                          \n",
       "            Sensitivity : 1.00000         \n",
       "            Specificity : 0.59012         \n",
       "         Pos Pred Value : 0.04082         \n",
       "         Neg Pred Value : 1.00000         \n",
       "             Prevalence : 0.01714         \n",
       "         Detection Rate : 0.01714         \n",
       "   Detection Prevalence : 0.42000         \n",
       "      Balanced Accuracy : 0.79506         \n",
       "                                          \n",
       "       'Positive' Class : 1               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"---------------\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           \n",
       "svm_predict  1  0\n",
       "          1  0  2\n",
       "          0 63 85"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Support vector machine\n",
    "svm_model = svm(Recommendation ~.,train) # build model\n",
    "confusionMatrix(train$Recommendation,predict(svm_model),positive = '1')# create confusion Matrix\n",
    "print('---------------')\n",
    "svm_predict = predict(svm_model,test) # predict test set\n",
    "table(svm_predict,test$Recommendation) # create table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third model (Random Forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   1   0\n",
       "         1 147   0\n",
       "         0   0 203\n",
       "                                     \n",
       "               Accuracy : 1          \n",
       "                 95% CI : (0.9895, 1)\n",
       "    No Information Rate : 0.58       \n",
       "    P-Value [Acc > NIR] : < 2.2e-16  \n",
       "                                     \n",
       "                  Kappa : 1          \n",
       "                                     \n",
       " Mcnemar's Test P-Value : NA         \n",
       "                                     \n",
       "            Sensitivity : 1.00       \n",
       "            Specificity : 1.00       \n",
       "         Pos Pred Value : 1.00       \n",
       "         Neg Pred Value : 1.00       \n",
       "             Prevalence : 0.42       \n",
       "         Detection Rate : 0.42       \n",
       "   Detection Prevalence : 0.42       \n",
       "      Balanced Accuracy : 1.00       \n",
       "                                     \n",
       "       'Positive' Class : 1          \n",
       "                                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"---------------\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                    \n",
       "randomForest_predict  1  0\n",
       "                   1 63  0\n",
       "                   0  0 87"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "randomForest_model = randomForest(x = train, y = train$Recommendation,ntree =800)# build model\n",
    "confusionMatrix(train$Recommendation,predict(randomForest_model),positive = '1') # create confusion Matrix\n",
    "print('---------------')\n",
    "randomForest_predict = predict(randomForest_model,test) # predict test set\n",
    "table(randomForest_predict,test$Recommendation )# create table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresstion model  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression (total sales and (Style+Season+Material+Price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = total_sales ~ Style + Season + Material + Price, \n",
       "    data = train)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       "-19944  -6145  -2112   1336 108668 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  11642.9     2468.6   4.716 3.63e-06 ***\n",
       "Style1       -4756.9     2019.4  -2.356   0.0191 *  \n",
       "Style2        2720.4     3733.0   0.729   0.4667    \n",
       "Style3       -2242.7     3960.1  -0.566   0.5716    \n",
       "Style4       -4555.6     3039.3  -1.499   0.1349    \n",
       "Style5       -7081.8     3740.8  -1.893   0.0593 .  \n",
       "Style6       -6932.0     6056.4  -1.145   0.2533    \n",
       "Style7      -11758.8    12815.6  -0.918   0.3596    \n",
       "Style8       -4119.9     3213.0  -1.282   0.2007    \n",
       "Style9       -3267.7     4069.5  -0.803   0.4226    \n",
       "Style11      -9315.9    12772.5  -0.729   0.4663    \n",
       "Season1       -873.4     2346.7  -0.372   0.7100    \n",
       "Season2       2392.6     1910.8   1.252   0.2115    \n",
       "Season3       -651.9     1953.2  -0.334   0.7388    \n",
       "Material1    12390.3     7521.3   1.647   0.1005    \n",
       "Material2      795.6     2245.2   0.354   0.7233    \n",
       "Material3    -2392.4     3317.6  -0.721   0.4714    \n",
       "Material4     3645.2     3142.3   1.160   0.2469    \n",
       "Material5    -2154.0     1885.9  -1.142   0.2543    \n",
       "Material6    -3599.0     5523.1  -0.652   0.5151    \n",
       "Material7    -2414.3     9136.3  -0.264   0.7918    \n",
       "Material8    -2431.6     9074.4  -0.268   0.7889    \n",
       "Material9     1963.7     7593.4   0.259   0.7961    \n",
       "Material10   -6027.6     5060.2  -1.191   0.2345    \n",
       "Material11    -592.8     7479.6  -0.079   0.9369    \n",
       "Material12   -1596.6     4288.7  -0.372   0.7099    \n",
       "Material13   -2840.7     7505.8  -0.378   0.7053    \n",
       "Material14   -7007.3    12938.8  -0.542   0.5885    \n",
       "Material15   -2251.5    12852.1  -0.175   0.8611    \n",
       "Material16   -6536.3    12741.0  -0.513   0.6083    \n",
       "Material17   -5259.4     9114.9  -0.577   0.5644    \n",
       "Material19   -1822.2    12776.2  -0.143   0.8867    \n",
       "Material20   -7964.3    12800.2  -0.622   0.5343    \n",
       "Material23   -3010.3     9273.1  -0.325   0.7457    \n",
       "Price1       -1941.8     3912.9  -0.496   0.6201    \n",
       "Price2         371.2     1628.8   0.228   0.8199    \n",
       "Price3       -4102.3     3571.7  -1.149   0.2516    \n",
       "Price4       -8178.1     4050.9  -2.019   0.0444 *  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 12560 on 312 degrees of freedom\n",
       "Multiple R-squared:  0.09177,\tAdjusted R-squared:  -0.01594 \n",
       "F-statistic: 0.852 on 37 and 312 DF,  p-value: 0.7162\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"not plotting observations with leverage one:\n",
      "  8, 68, 153, 162, 202, 257, 271\""
     ]
    },
    {
     "data": {
      "image/png": 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qWNyBNBQnQ8tkBBN1I+eSJIiE5sQao4w0SQEKGhjAQ5kTcKQcIGEaRoqsCWEaRo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\n8D9d8j0zzKDdrAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"not plotting observations with leverage one:\n",
      "  8, 68, 153, 162, 202, 257, 271\""
     ]
    },
    {
     "data": {
      "image/png": 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Y5UXNcOqfocaWM25ZaC4pbzs5LPIW3aK0aGd8VgvGTTew2p2FSXz0Y2ra12xd2c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\nAUICBAgJEPgP2kipAbvdF78AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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21AJEgMkSWL3V59qU2BUCobXcYLkSAx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\nC7tvFAnAA4gEIAAiAQjwH9VF026Gr6YDAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in abline(regressor_Sales):\n",
      "\"only using the first two of 38 regression coefficients\""
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# regression (total sales and (Style+Season+Material+Price))\n",
    "regressor_Sales = lm(formula = total_sales ~ Style+Season+Material+Price, data = train) # build model\n",
    "summary(regressor_Sales) # print model summary\n",
    "plot(regressor_Sales, pch = 16, col = \"blue\") # Plot the results\n",
    "abline(regressor_Sales) # Add regression line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression (total sales and Rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = total_sales ~ Rating, data = train)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       " -9076  -6020  -2686    812 114971 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)   2742.8     1305.0   2.102   0.0363 *  \n",
       "Rating        1282.6      323.7   3.962 9.02e-05 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 12210 on 348 degrees of freedom\n",
       "Multiple R-squared:  0.04316,\tAdjusted R-squared:  0.04041 \n",
       "F-statistic:  15.7 on 1 and 348 DF,  p-value: 9.022e-05\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cROX0fX9kEqq3DDem+irgqemFyiCZKzNrI1XkcaTa9Y0EZ7+O3uZXxasaCNx6mI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\nAYIECPwffN9yg8B4AOYAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RBgLCRAJ0ICBBgLiakddCIkQICxkACd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\nOJvH3e8vv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      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# regression (total sales and Rating)\n",
    "regressor_Rating = lm(formula = total_sales ~ Rating, data = train) # build model\n",
    "summary(regressor_Rating) # print model summary\n",
    "plot(regressor_Rating, pch = 16, col = \"blue\") # Plot the results\n",
    "abline(regressor_Rating) # Add regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " MAE: 7784.569 \n",
      " MSE: 274959077 \n",
      " RMSE: 16581.89 \n",
      " R-squared: 0.04042806"
     ]
    }
   ],
   "source": [
    "# evaluation\n",
    "original = test$total_sales\n",
    "pred = predict(regressor_Rating,test)\n",
    "predicted = pred\n",
    "d = original-predicted\n",
    "\n",
    "mse = mean((d)^2) # MSE\n",
    "mae = mean(abs(d)) # MAE\n",
    "rmse = sqrt(mse) # RMSE\n",
    "R2 = 1-(sum((d)^2)/sum((original-mean(original))^2)) # R^2\n",
    "\n",
    "cat(\" MAE:\", mae, \"\\n\", \"MSE:\", mse, \"\\n\", \"RMSE:\", rmse, \"\\n\", \"R-squared:\", R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "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
}