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Feature_Engineering_and_EDA.R
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Feature_Engineering_and_EDA.R
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################################################################################################################################################################
# :::::::: Feature Engineering ::::::::
################################################################################################################################################################
### Creating a function for Engineered KPI's
product_features <- function(data){
##1. KPI - List Price for all the products
data$list_price <- data$gmv/data$units
##2. KPI - Promotional Offer for all the Products
data$promotional_offer <- (data$product_mrp - data$list_price)/data$product_mrp
##3. KPI - Payment Mode Indicator
data$payment_ind <- ifelse(data$s1_fact.order_payment_type == "Prepaid",1,0)
##4. KPI - Prepaid Order Percentage
# Total Order Placed
total_order <- aggregate(payment_ind ~ Year+Month+week, data, FUN = NROW)
# Total Online Order
online_order <- aggregate(payment_ind ~ Year+Month+week, data = data, FUN = sum)
# Merge "total_order" and "online_order"
order_merged <- merge(total_order, online_order, by = c("Month", "Year", "week"), all.x = TRUE)
# Calculating the proportion of total online order from total order
order_merged$per_order <- order_merged$payment_ind.y/order_merged$payment_ind.x
# Removing columns
order_merged <- order_merged[,-c(4,5)]
# Adding "per_order" column in dataset
data <- merge(data, order_merged, by = c("Month", "Year", "week"), all.x = TRUE)
##5. KPI - Product Category
cluster <- aggregate(cbind(units,list_price, product_mrp)~product_analytic_vertical, data, mean)
if(nrow(cluster)>2){
cluster$units_1 <- scale(cluster$units)
cluster$list_price_1 <- scale(cluster$list_price)
cluster$product_mrp_1 <- scale(cluster$product_mrp)
k1 <- cluster[,-c(1:3)]
# Applying clustering algorithm
clust <- kmeans(k1, centers = 3, iter.max = 50, nstart = 50)
cluster$price_tag <- as.factor(clust$cluster)
cluster <- cluster[, c(1,8)]
# Adding columns generated from the clustering algorithm to the dataset
data <- merge(data, cluster, by=c("product_analytic_vertical"), all.x = TRUE)
k2 <- count(data, price_tag)[2]
levels(data$price_tag)[which(k2==max(count(data, price_tag)[2]))] <- "Mass_Product"
levels(data$price_tag)[which(k2==min(count(data, price_tag)[2]))] <- "Premium_Product"
levels(data$price_tag)[which(k2!=max(count(data, price_tag)[2]) & k2!=min(count(data, price_tag)[2]))] <- "Aspiring_Product"
}
else{
data$price_tag <- NA
data$price_tag$product_analytic_vertical <- factor(data$price_tag$product_analytic_vertical)
if(tapply(data$product_mrp, data$product_analytic_vertical, mean)[[1]] > tapply(data$product_mrp, data$product_analytic_vertical, mean)[[2]]){
data$price_tag[which(data$product_analytic_vertical == levels(data$product_analytic_vertical)[1])] <- "Aspiring_Product"
data$price_tag[is.na(data$price_tag)] <- "Mass_Product"
}
else{
data$price_tag[which(data$product_analytic_vertical == levels(data$product_analytic_vertical)[2])] <- "Aspiring_Product"
data$price_tag[is.na(data$price_tag)] <- "Mass_Product"
}
}
##6. KPI - Adstock
# Considering the adstock rate as 50%
adstock_rate = 0.50
# Creating the adstock for each media investment
df <- data.frame(week=1:53)
for(i in 3:ncol(media_investment_weekly_final)){
df[[paste0(colnames(media_investment_weekly_final)[i],"_adstock")]] <- stats::filter(x=media_investment_weekly_final[i],
filter=adstock_rate, method="recursive")
}
# Merging the adstock with the actual dataset
data <- merge(data, df, by = c("week"), all.x = TRUE)
## Converting the data into weekly format
# As we have data from July-2015 to June-2016, So we're considering June-105 as our base for week calculation/number
# i.e 1st week of July-2015 as 1 (instead of 26), 2nd week of July-2015 as 2 (instead of 27) and so on till June-2016
# Also, for Jan-2016 we'll consider the subsequent week number [i.e week number after Dec-2015 last week] (instead as 1st week)
data$week <- ifelse(data$week>=26, data$week-25, data$week+28)
# Filtering out the variables which are not necessary
data <- subset(data, select = -c(Month,Year,product_analytic_sub_category,month_date,Week_date))
# Creating two vectors which holds the numeric andcategorical variables
col_numeric <- c("week", "gmv", "units", "deliverybdays", "deliverycdays", "sla", "product_mrp", "product_procurement_sla")
col_factor <- c("product_analytic_vertical", "s1_fact.order_payment_type","wday", "is_special_sale_day","special_sale_day", "price_tag")
# Convering the continuous variables into numeric format and Categorical variables in to factors
data[,col_numeric] <- sapply(data[,col_numeric], as.numeric)
data[,col_factor] <- sapply(data[,col_factor], as.factor)
df_dummies <- data[,col_factor] ## Created a data frame which holds only categorical variables
# Creating dummy variables for categorical/factor attributes
dummies<- data.frame(sapply(df_dummies, function(x) data.frame(model.matrix(~x-1,data =df_dummies))[,-1]))
dummies <- as.data.frame(cbind(data[1], dummies))
# Aggregate the dummy variables data by weeks
dummies_aggregate <- aggregate(.~ week, dummies, sum, na.rm = TRUE)
# Aggregating the Actual dtaa variables by weeks
data <- data %>% group_by(week) %>% summarise(gmv = sum(gmv), units = sum(units), deliverybdays = mean(deliverybdays), deliverycdays = mean(deliverycdays),
sla = mean(sla), product_mrp = sum(product_mrp), product_procurement_sla = mean(product_procurement_sla),
Total_Investment = mean(Total_Investment), TV = mean(TV), Digital = mean(Digital), Sponsorship = mean(Sponsorship),
Content_Marketing = mean(Content_Marketing), Online_Marketing = mean(Online_Marketing), Affiliates = mean(Affiliates),
SEM = mean(SEM), Radio = mean(Radio), Other = mean(Other), NPS_Score = mean(`NPS Score`), holiday_count = mean(holiday_count),
list_price = sum(list_price), promotional_offer = sum(promotional_offer)/length(week), per_order = mean(per_order), TV_adstock= mean(TV_adstock),
Digital_adstock = mean(Digital_adstock), Sponsorship_adstock = mean(Sponsorship_adstock), Content_Marketing_adstock = mean(Content_Marketing_adstock),
Online_Marketing_adstock = mean(Online_Marketing_adstock), Affiliates_adstock = mean(Affiliates_adstock), SEM_adtock = mean(SEM_adstock),
Radio_adstock = mean(Radio), Other_adstock = mean(Other_adstock))
# Merging the Dummy and actual data variables in to one data frame
data <- merge(data, dummies_aggregate, by = c("week"), all.x = TRUE)
return(data)
}
### Calling the "product_features" function for the 3 Product subcategories to create the Engineered variables and
### Also to covet the whole data into weekly format
GamingAccessory_df <- product_features(eleckart_GamingAccessory)
HomeAudio_df <- product_features(eleckart_HomeAudio)
CameraAccessory_df <- product_features(eleckart_CameraAccessory)
#************************************
### Other/Advanced Engineered KPI's::
other_kpi <- function(data){
##7. KPI - Moving average
myfun1 = function(x) rollmean(x, k = 2, fill = NA, align = "right")
myfun2 = function(x) rollmean(x, k = 3, fill = NA, align = "right")
myfun3 = function(x) rollmean(x, k = 4, fill = NA, align = "right")
x <- data[,c("week", "list_price", "promotional_offer")]
x <- arrange(x, week)
x1<-x %>% mutate_each(funs(myfun1),list_price,promotional_offer) %>% data.frame()
x2<-x %>% mutate_each(funs(myfun2),list_price,promotional_offer) %>% data.frame()
x3<-x %>% mutate_each(funs(myfun3),list_price,promotional_offer) %>% data.frame()
# Imputing missing or NA values
x1 <- imputeTS::na.ma(x1, k=2, weighting = "simple")
x2 <- imputeTS::na.ma(x2, k=3, weighting = "simple")
x3 <- imputeTS::na.ma(x3, k=4, weighting = "simple")
x1$LP_MA1<-(x1$list_price)
x1$PO_MA1<-(x1$promotional_offer)
x2$LP_MA2<-(x2$list_price)
x2$PO_MA2<-(x2$promotional_offer)
x3$LP_MA3<-(x3$list_price)
x3$PO_MA3<-(x3$promotional_offer)
x4=cbind(x1[,-c(1:3)],x2[,-c(1:3)],x3[,-c(1:3)])
data_1 <- cbind(data, x4[,c(1,3,5,2,4,6)])
data <- data_1
k9 <- data
data$inc_LP_MA1<-(data$list_price - data$LP_MA1)/data$LP_MA1
data$inc_LP_MA2<-(data$list_price - data$LP_MA2)/data$LP_MA2
data$inc_LP_MA3<-(data$list_price - data$LP_MA3)/data$LP_MA3
data$inc_PO_MA1<-(data$promotional_offer - data$PO_MA1)/data$PO_MA1
data$inc_PO_MA2<-(data$promotional_offer - data$PO_MA2)/data$PO_MA2
data$inc_PO_MA3<-(data$promotional_offer - data$PO_MA3)/data$PO_MA3
# Deleting columns
data$LP_MA1<-NULL
data$LP_MA2<-NULL
data$LP_MA3<-NULL
data$PO_MA1<-NULL
data$PO_MA2<-NULL
data$PO_MA3<-NULL
##8. Lag Variables [For 'list_price', 'promotional_offer', 'gmv']
data <- data[with(data, order(week)),]
#Lag List Price (different period lags) [Lag of list price by 1st week, 2nd week, 3rd week]
data_dum <- slide(data, Var = "list_price", slideBy = -1)
data_dum <- slide(data_dum, Var = "list_price", slideBy = -2)
data_dum <- slide(data_dum, Var = "list_price", slideBy = -3)
#Lag Promotional Offer (different period lags) [Lag of discount(Promotional Offer) by 1st week, 2nd week, 3rd week]
data_dum <- slide(data_dum, Var = "promotional_offer", slideBy = -1)
data_dum <- slide(data_dum, Var = "promotional_offer", slideBy = -2)
data_dum <- slide(data_dum, Var = "promotional_offer", slideBy = -3)
#Lag gmv (different period lags) [Lag of gmv by 1st week, 2nd week, 3rd week]
data_dum <- slide(data_dum, Var = "gmv", slideBy = -1)
data_dum <- slide(data_dum, Var = "gmv", slideBy = -2)
data_dum <- slide(data_dum, Var = "gmv", slideBy = -3)
data <- data_dum
col1 <- c("list_price-1", "promotional_offer-1", "gmv-1")
col2 <- c("list_price-2", "promotional_offer-2", "gmv-2")
col3 <- c("list_price-3", "promotional_offer-3", "gmv-3")
data[, col1] <- imputeTS::na.ma(data[, col1], k=1, weighting = "simple")
data[, col2] <- imputeTS::na.ma(data[, col2], k=2, weighting = "simple")
data[, col3] <- imputeTS::na.ma(data[, col3], k=3, weighting = "simple")
#Incremental Lags
#Incremental Lags of List Price by 1 week, 2 week, 3 week
data$LP_lag_1_per <- (data$list_price - data$`list_price-1`)/data$`list_price-1`
data$LP_lag_2_per <- (data$list_price - data$`list_price-2`)/data$`list_price-2`
data$LP_lag_3_per <- (data$list_price - data$`list_price-3`)/data$`list_price-3`
data$LP_lag_1_per <- ifelse(is.na(data$LP_lag_1_per),0,data$LP_lag_1_per)
data$LP_lag_2_per <- ifelse(is.na(data$LP_lag_2_per),0,data$LP_lag_2_per)
data$LP_lag_3_per <- ifelse(is.na(data$LP_lag_3_per),0,data$LP_lag_3_per)
#Incremental Lags of Promotional Offer by 1 week, 2 week, 3 week
data$PO_lag_1_per <- (data$promotional_offer - data$`promotional_offer-1`)/data$`promotional_offer-1`
data$PO_lag_2_per <- (data$promotional_offer - data$`promotional_offer-2`)/data$`promotional_offer-2`
data$PO_lag_3_per <- (data$promotional_offer - data$`promotional_offer-3`)/data$`promotional_offer-3`
data$PO_lag_1_per <- ifelse(is.na(data$PO_lag_1_per),0,data$PO_lag_1_per)
data$PO_lag_2_per <- ifelse(is.na(data$PO_lag_2_per),0,data$PO_lag_2_per)
data$PO_lag_3_per <- ifelse(is.na(data$PO_lag_3_per),0,data$PO_lag_3_per)
#Incremental Lags of gmv by 1 week, 2 week, 3 week
data$GMV_lag_1_per <- (data$gmv - data$`gmv-1`)/data$`gmv-1`
data$GMV_lag_2_per <- (data$gmv - data$`gmv-2`)/data$`gmv-2`
data$GMV_lag_3_per <- (data$gmv - data$`gmv-3`)/data$`gmv-3`
data$GMV_lag_1_per <- ifelse(is.na(data$GMV_lag_1_per),0,data$GMV_lag_1_per)
data$GMV_lag_2_per <- ifelse(is.na(data$GMV_lag_2_per),0,data$GMV_lag_2_per)
data$GMV_lag_3_per <- ifelse(is.na(data$GMV_lag_3_per),0,data$GMV_lag_3_per)
#Removing the columns
data$`list_price-1` <- NULL
data$`list_price-2` <- NULL
data$`list_price-3` <- NULL
data$`promotional_offer-1` <- NULL
data$`promotional_offer-2` <- NULL
data$`promotional_offer-3` <- NULL
data$`gmv-1` <- NULL
data$`gmv-2` <- NULL
data$`gmv-3` <- NULL
return(data)
}
### Calling the "other_kpi" function for the 3 Product subcategories to create the advanced KPI's
GamingAccessory_final <- other_kpi(GamingAccessory_df)
HomeAudio_final <- other_kpi(HomeAudio_df)
CameraAccessory_final <- other_kpi(CameraAccessory_df)
#######################################################################################################################################################
# :::::::: EDA for 3 Sub-categories [gmv Vs Independent variables]::::::::
######################################################################################################################################################
#***************************************************************** GamingAccessory
### Releasing memory
gc()
### Response Curves ['gmv' w.r.t all the Independent variables]
GA <- GamingAccessory_final[,c(2:19,21:32,68:82)]
# Plotting the scatter plot of all the Independent variables w.r.t 'gmv'
GA_plots1 <- list() # new empty list
for (i in 2:45) local({
i <- i
p4 <- ggplot(GA,aes(x=GA[,i],y=GA[,1])) + geom_point() + geom_smooth(method = "loess") + theme_bw() +
labs(x= paste0("", colnames(GA[i])),y="GMV")
GA_plots1[[i-1]] <<- p4 # add each plot into plot list
})
# Plotting all the graphs
# Note: It takes few seconds to load, please hold in order to come up all the graphs
plot_grid(GA_plots1[[1]],GA_plots1[[2]],GA_plots1[[3]],GA_plots1[[4]],GA_plots1[[5]],GA_plots1[[6]],
GA_plots1[[7]],GA_plots1[[8]],GA_plots1[[9]],align ="h")
plot_grid(GA_plots1[[10]],GA_plots1[[11]],GA_plots1[[12]],GA_plots1[[13]],GA_plots1[[14]],GA_plots1[[15]],
GA_plots1[[16]],GA_plots1[[17]],GA_plots1[[18]],align ="h")
plot_grid(GA_plots1[[19]],GA_plots1[[20]],GA_plots1[[21]],GA_plots1[[22]],GA_plots1[[23]],GA_plots1[[24]],
GA_plots1[[25]],GA_plots1[[26]],GA_plots1[[27]],align ="h")
plot_grid(GA_plots1[[28]],GA_plots1[[29]],GA_plots1[[30]],GA_plots1[[31]],GA_plots1[[32]],GA_plots1[[33]],
GA_plots1[[34]],GA_plots1[[35]],GA_plots1[[36]],align ="h")
plot_grid(GA_plots1[[37]],GA_plots1[[38]],GA_plots1[[39]],GA_plots1[[40]],GA_plots1[[41]],GA_plots1[[42]],
GA_plots1[[43]],GA_plots1[[44]],align ="h")
#***************************************************************** HomeAudio
### Releasing memory
gc()
### ### Response Curves ['gmv' w.r.t all the Independent variables]
HA <- HomeAudio_final[,c(2:19,21:32,63:77)]
# Plotting the scatter plot of all the Independent variables w.r.t 'gmv'
HA_plots1 <- list() # new empty list
for (i in 2:45) local({
i <- i
p5 <- ggplot(HA,aes(x=HA[,i],y=HA[,1])) + geom_point() + geom_smooth(method = "loess") + theme_bw() +
labs(x= paste0("", colnames(HA[i])),y="GMV")
HA_plots1[[i-1]] <<- p5 # add each plot into plot list
})
# Plotting all the graphs
# Note: It takes few seconds to load, please hold in order to come up all the graphs
plot_grid(HA_plots1[[1]],HA_plots1[[2]],HA_plots1[[3]],HA_plots1[[4]],HA_plots1[[5]],HA_plots1[[6]],
HA_plots1[[7]],HA_plots1[[8]],HA_plots1[[9]],align ="h")
plot_grid(HA_plots1[[10]],HA_plots1[[11]],HA_plots1[[12]],HA_plots1[[13]],HA_plots1[[14]],HA_plots1[[15]],
HA_plots1[[16]],HA_plots1[[17]],HA_plots1[[18]],align ="h")
plot_grid(HA_plots1[[19]],HA_plots1[[20]],HA_plots1[[21]],HA_plots1[[22]],HA_plots1[[23]],HA_plots1[[24]],
HA_plots1[[25]],HA_plots1[[26]],HA_plots1[[27]],align ="h")
plot_grid(HA_plots1[[28]],HA_plots1[[29]],HA_plots1[[30]],HA_plots1[[31]],HA_plots1[[32]],HA_plots1[[33]],
HA_plots1[[34]],HA_plots1[[35]],HA_plots1[[36]],align ="h")
plot_grid(HA_plots1[[37]],HA_plots1[[38]],HA_plots1[[39]],HA_plots1[[40]],HA_plots1[[41]],HA_plots1[[42]],
HA_plots1[[43]],HA_plots1[[44]],align ="h")
#***************************************************************** CameraAccessory
### Releasing memory
gc()
### Response Curves ['gmv' w.r.t all the Independent variables]
CA <- CameraAccessory_final[,c(2:19,21:32,77:91)]
# Plotting the scatter plot of all the Independent variables w.r.t 'gmv'
CA_plots1 <- list() # new empty list
for (i in 2:45) local({
i <- i
p6 <- ggplot(CA,aes(x=CA[,i],y=CA[,1])) + geom_point() + geom_smooth(method = "loess") + theme_bw() +
labs(x= paste0("", colnames(CA[i])),y="GMV")
CA_plots1[[i-1]] <<- p6 # add each plot into plot list
})
# Plotting all the graphs
# Note: It takes few seconds to load, please hold in order to come up all the graphs
plot_grid(CA_plots1[[1]],CA_plots1[[2]],CA_plots1[[3]],CA_plots1[[4]],CA_plots1[[5]],CA_plots1[[6]],
CA_plots1[[7]],CA_plots1[[8]],CA_plots1[[9]],align ="h")
plot_grid(CA_plots1[[10]],CA_plots1[[11]],CA_plots1[[12]],CA_plots1[[13]],CA_plots1[[14]],CA_plots1[[15]],
CA_plots1[[16]],CA_plots1[[17]],CA_plots1[[18]],align ="h")
plot_grid(CA_plots1[[19]],CA_plots1[[20]],CA_plots1[[21]],CA_plots1[[22]],CA_plots1[[23]],CA_plots1[[24]],
CA_plots1[[25]],CA_plots1[[26]],CA_plots1[[27]],align ="h")
plot_grid(CA_plots1[[28]],CA_plots1[[29]],CA_plots1[[30]],CA_plots1[[31]],CA_plots1[[32]],CA_plots1[[33]],
CA_plots1[[34]],CA_plots1[[35]],CA_plots1[[36]],align ="h")
plot_grid(CA_plots1[[37]],CA_plots1[[38]],CA_plots1[[39]],CA_plots1[[40]],CA_plots1[[41]],CA_plots1[[42]],
CA_plots1[[43]],CA_plots1[[44]],align ="h")