Effects of product network relationships on demand in russian ecommerce

pam_fit<- pam(main_ozon_dist,

diss = TRUE,

k = i)

sil_width[i] <- pam_fit$silinfo$avg.width

}

# Plot sihouette width (higher is better)

plot(1:12, sil_width,

xlab = "Number of clusters",

ylab = "Silhouette Width")

lines(1:12, sil_width)

#Now we understand, that we have to use 7 clasters, let`s do it!

#Cluster Interpretation

library(cluster)

library(ISLR)

pam_fit<- pam(main_ozon_dist, diss = TRUE, k = 7)

# IT works

main_ozon_tv[pam_fit$medoids, ]

#Plotting the results

library(Rtsne)

tsne_obj<- Rtsne(main_ozon_dist, is_distance = TRUE)

tsne_data<- tsne_obj$Y %>%

data.frame() %>%

setNames(c("X", "Y")) %>%

mutate(cluster = factor(pam_fit$clustering))

#Plot the results

ggplot(aes(x = X, y = Y), data = tsne_data) +

geom_point(aes(color = cluster))

#Save the segments inside our dataset

main_ozon_tv$segments = tsne_data$cluster

#Create dataset with relusts of our segmentation

seg_main_ozon=main_ozon_tv %>% group_by(segments) %>%summarise(avg_rating_main = mean(Rating_main), avg_price_main = mean(Price_main),avg_discout_main = mean(Discount_main),avg_review_main = mean(Reviews_main),avg_sales_main=(mean(Sales_main)))

main_ozon_tv$Brand = as.character(main_ozon_tv$Brand)

main_ozon_tv$Product.Name = as.character(main_ozon_tv$Product.Name)

seg_main_sales = main_ozon_tv %>% group_by(segments) %>% count(mean(Sales_main)) %>% filter(n==max(n))

seg_main_brand = main_ozon_tv %>% group_by(segments) %>% count(Brand) %>% filter(n==max(n))

seg_main

#Final cluster

seg_main_all = main_ozon_tv %>% group_by(segments) %>% count(Product.Name, mean(Sales_main),mean(Price_main), mean(Rating_main),mean(Discount_main), mean(Reviews_main)) %>% filter(n==max(n))

main_ozon_tv

#Numbers of our users by segments

main_ozon_tv%>% group_by(segments) %>% tally()

#Clustering analysis for also viewed products

library(dplyr) # for data cleaning

library(ISLR) # for college dataset

library(cluster) # for gower similarity and pam

library(Rtsne) # for t-SNE plot

library(ggplot2) # for visualization

# Remove college name before clustering and little bit prepare the data

av_ozon_tv<-ozon_tv[, c(-1, -4: -8, -14:-17)]

av_ozon_tv$Brand = as.factor(av_ozon_tv$Brand)

av_ozon_tv$Product.Name = as.factor(av_ozon_tv$Product.Name)

av_ozon_dist<- daisy (av_ozon_tv, metric = "gower", type = list(logratio = 3))

# Check attributes to ensure the correct methods are being used

summary(av_ozon_dist)

#Create matrix

av_df_mat<- as.matrix(av_ozon_dist)

# Output most similar pair

av_ozon_tv[which(av_df_mat == max(av_df_mat[av_df_mat != max(av_df_mat)]),

arr.ind = TRUE)[1, ], ]

#Choosing a clustering algorithm

#Calculate silhouette width for many k using PAM

sil_width<- c(NA)

for(i in 2:10){

pam_fit<- pam(av_ozon_dist,

diss = TRUE,

k = i)

sil_width[i] <- pam_fit$silinfo$avg.width

}

# Plot sihouette width (higher is better)

plot(1:10, sil_width,

xlab = "Number of clusters",

ylab = "Silhouette Width")

lines(1:10, sil_width)

#Now we understand, that we have to use 7 clasters, let`s do it!

#Cluster Interpretation

library(cluster)

library(ISLR)

pam_fit<- pam(av_ozon_dist, diss = TRUE, k = 7)

# IT works

av_ozon_tv[pam_fit$medoids, ]

#Plotting the results

library(Rtsne)

av_tsne_obj<- Rtsne(av_ozon_dist, is_distance = TRUE)

av_tsne_obj<- av_tsne_obj$Y %>%

data.frame() %>%

setNames(c("X", "Y")) %>%

mutate(cluster = factor(pam_fit$clustering))

#Plot the results

ggplot(aes(x = X, y = Y), data = av_tsne_obj) +

geom_point(aes(color = cluster))

#Save the segments inside our dataset

av_ozon_tv$segments = av_tsne_obj$cluster

#Create dataset with relusts of our segmentation

seg_av_ozon=av_ozon_tv %>% group_by(segments) %>% summarise(avg_sales_av=mean(SAL_av),avg_price_av = mean(AP_av),avg_rating_av = mean(AR_av),avg_discount_av = mean(AD_av),avg_number_of_reviews_av = (mean(ANR_av)))

av_ozon_tv$Brand = as.character(av_ozon_tv$Brand)

av_ozon_tv$Product.Name = as.character(av_ozon_tv$Product.Name)

seg_av_sales = av_ozon_tv %>% group_by(segments) %>% count(mean(SAL_av)) %>% filter(n==max(n))

seg_av_brand = av_ozon_tv %>% group_by(segments) %>% count(Brand) %>% filter(n==max(n))

#Final cluster

seg_av_all = av_ozon_tv %>% group_by(segments) %>% count(Product.Name, mean(SAL_av),mean(AP_av),mean(AR_av), mean(AD_av)) %>% filter(n==max(n))

av_ozon_tv

#Numbers of our users by segments

av_ozon_tv%>% group_by(segments) %>% tally()

seg_lm_ozon<-merge(seg_main_ozon,seg_av_ozon)

#LM for seg_mean_ozon + seg_av_prod

library(car)

LM_main<- lm(avg_sales_main ~ avg_review_main + avg_rating_main + avg_sales_av + avg_price_av + avg_rating_av + avg_discount_av + avg_number_of_reviews_av, data=seg_lm_ozon)

summary(LM_main)

#Clustering analysis for co-purchased products

library(dplyr) # for data cleaning

library(ISLR) # for college dataset

library(cluster) # for gower similarity and pam

library(Rtsne) # for t-SNE plot

library(ggplot2) # for visualization

# Remove college name before clustering and little bit prepare the data

cp_ozon_tv<-ozon_tv[, c(-1, -4: -14)]

#here I deleted CO-purchase coloumn (binary)

cp_ozon_tv$Brand = as.factor(cp_ozon_tv$Brand)

cp_ozon_tv$Product.Name = as.factor(cp_ozon_tv$Product.Name)

cp_ozon_dist<- daisy (cp_ozon_tv, metric = "gower", type = list(logratio = 3))

# Check attributes to ensure the correct methods are being used

summary(cp_ozon_dist)

#Create matrix

cp_df_mat<- as.matrix(cp_ozon_dist)

# Output most similar pair

cp_ozon_tv[which(cp_df_mat == max(cp_df_mat[cp_df_mat != max(cp_df_mat)]),

arr.ind = TRUE)[1, ], ]

#Choosing a clustering algorithm

#Calculate silhouette width for many k using PAM

sil_width<- c (NA)

for(i in 2:15){

pam_fit<- pam(cp_ozon_dist,

diss = TRUE,

k = i)

sil_width[i] <- pam_fit$silinfo$avg.width

}

# Plot sihouette width (higher is better)

plot(1:15, sil_width,

xlab = "Number of clusters",

ylab = "Silhouette Width")

lines(1:15, sil_width)

#Now we understand, that we have to use 11 clasters, let`s do it!

#Cluster Interpretation

library(cluster)

library(ISLR)

pam_fit<- pam(av_ozon_dist, diss = TRUE, k = 11)

# IT works

cp_ozon_tv[pam_fit$medoids, ]

#Plotting the results

library(Rtsne)

cp_tsne_obj<- Rtsne(cp_ozon_dist, is_distance = TRUE)

cp_tsne_obj<- cp_tsne_obj$Y %>%

data.frame() %>%

setNames(c("X", "Y")) %>%

mutate(cluster = factor(pam_fit$clustering))

#Plot the results

ggplot(aes(x = X, y = Y), data = cp_tsne_obj) +

geom_point(aes(color = cluster))

#Save the segments inside our dataset

cp_ozon_tv$segments = cp_tsne_obj$cluster

#Create dataset with relusts of our segmentation

seg_cp_ozon=cp_ozon_tv %>% group_by(segments) %>% summarise(avg_price_av = mean(AP_cp),avg_rating_av = mean(AR_cp),avg_number_of_reviews_av = (mean(ANR_cp)))

cp_ozon_tv$Brand = as.character(cp_ozon_tv$Brand)

cp_ozon_tv$Product.Name = as.character(cp_ozon_tv$Product.Name)

seg_cp_price = cp_ozon_tv %>% group_by(segments) %>% count(mean(AP_cp)) %>% filter(n==max(n))

seg_cp_brand = cp_ozon_tv %>% group_by(segments) %>% count(Brand) %>% filter(n==max(n))

#Final cluster

seg_cp_all = cp_ozon_tv %>% group_by(segments) %>% count(Product.Name, mean(AP_cp), mean(AR_cp), mean(ANR_cp)) %>% filter(n==max(n))

cp_ozon_tv

#Numbers of our users by segments

cp_ozon_tv%>% group_by(segments) %>% tally()

#First regression attempts

library(car)

LM_control<- lm(Sales_main ~ Price_main + Rating_main + Discount_main + Reviews_main, data=ozon_tv)

summary(LM_control)

LM_sales<- lm(Sales_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_tv)

summary(LM_sales)

LM_rating<- lm(Rating_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_tv)

summary(LM_rating)

LM_reviews<- lm(Reviews_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_tv)

summary(LM_reviews)

LogM_sales<- lm(log(Sales_main) ~ log(Discount_main), data=ozon_tv)

summary(LogM_sales)

#Descriptive statistics

install.packages("pastecs")

library(pastecs)

descriptive <- stat.desc(ozon_tv[, 4:13])

round(descriptive, 2)

head(descriptive)

descriptive

str(descriptive)

decriptive_table<- table(descriptive)

as.data.frame(descriptive)

descriptive

install.packages("ggpubr")

library(ggpubr)

#ratings

ggboxplot(ozon_tv, y = "Rating_main", width = 0.5)

#reviews

ggboxplot(ozon_tv, y = "Reviews_main", width = 0.5)

#Discount

ggboxplot(ozon_tv, y = "Discount_main", width = 0.5)

#Price

ggboxplot(ozon_tv, y = "Price_main", width = 0.5)

#Sales

ggboxplot(ozon_tv, y = "Sales_main", width = 0.5)

#histograms

#sales

gghistogram(ozon_tv, x = "Sales_main", bins = 9,

add = "mean")

gghistogram(ozon_tv, x = "Rating_main", bins = 9,

add = "mean")

gghistogram(ozon_tv, x = "Discount_main", bins = 9,

add = "mean")

#qqplots

ggqqplot(ozon_tv, x = "Reviews_main")

ggqqplot(ozon_tv, x = "Rating_main")

ggqqplot(ozon_tv, x = "Sales_main")

#Sales by brand

ozon_brands<- read.csv("Ozon_brands.csv", header=TRUE, sep=",")

ggboxplot(ozon_brands, x = "Brand", y = "Sales_main",

color = "Brand")

ggstripchart(ozon_brands, x = "Brand", y = "Sales_main",

color = "Brand",

add = "mean_sd")

ggbarplot(df, x = "Brands", y = "Sales",

color = "Eye", position = position_dodge(),

palette = c("brown", "blue", "gold", "green"))

#Fitting the model

#Regression without outliers

#Without outliers first attempt

ozon_outliers<- read.csv("Ozon_outliers.csv", header=TRUE, sep=",")

LM_sales_outliers<- lm(Sales_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_outliers)

summary(LM_sales_outliers)

LM_rating_outliers<- lm(Rating_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_outliers)

summary(LM_rating_outliers)

LM_reviews_outliers<- lm(Reviews_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_outliers)

summary(LM_reviews_outliers)

#Without outliers second attempt

ozon_outliers2 <- read.csv("Ozon_outliers2.csv", header=TRUE, sep=",")

LM_sales_outliers2 <- lm(Sales_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_outliers2)

summary(LM_sales_outliers2)

LM_rating_outliers2 <- lm(Rating_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_outliers2)

summary(LM_rating_outliers2)

LM_reviews_outliers2 <- lm(Reviews_main ~ .-ID -Brand -Product.Name -Co_purchase, data=ozon_outliers2)

summary(LM_reviews_outliers2)

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