R/tumi2.R
In Dikosh/mathcalc:
Defines functions
tumi2
#' @title Predicts Stock Price Movement for Given Stock Symbol
#'
#' @description This package predicts whether the stock price at tommorow's market close would be higher or lower compared to today's closing place.
#'
#' @param symbol
#'
#' @return NULL
#'
#' @examples stock_predict('AAPL')
#'
#' @export tumi2
#' @import data.table
tumi2 <- function(x){
df <- df[!is.na(df$phone),]
aia <- df %>% group_by(phone) %>% summarise(nn = n())
aia$nchar <- nchar(as.character(aia$phone))
aia_tiny <- aia[aia$nchar==16 | aia$nchar==17,]
aia_tiny$phone <- str_replace_all(aia_tiny$phone, "[[:punct:]]", " ")
aia_tiny$code <- substring(aia_tiny$phone, 3, 3)
aia_tiny <- aia_tiny[aia_tiny$code==7,]
aia_tiny <- aia_tiny[,1]
aia_tiny <- na.omit(aia_tiny)
aia_tiny$phone <- as.factor(aia_tiny$phone)
df <- df[!is.na(df$phone),]
df <- inner_join(df,aia_tiny)
phones <- df %>% group_by(phone) %>% summarise(nn=n())
phones$class <- ifelse(phones$nn>=10,'Перекуп','Не перекуп')
write.csv(phones,"dictionaries/phones.csv",row.names = F)
phones <- read.csv("dictionaries/phones.csv")
df <- inner_join(df,phones)
df <- df[df$class!="Перекуп",]
df$volume <- as.numeric(sapply(strsplit(df$volume_dvigatel, " "), "[", 1))
df$dvigatel <- sapply(strsplit(df$volume_dvigatel, " "), "[", 2)
df$nn <- NULL
df$data_dobavlenia_v_parser <- ymd_hms(df$data_dobavlenia_v_parser)
df$data_posl_obyavlenia <- ymd_hms(df$data_posl_obyavlenia)
df$data_dobavlenia_v_parser <- as.Date(df$data_dobavlenia_v_parser)
df$data_posl_obyavlenia <- as.Date(df$data_posl_obyavlenia)
df <- df[df$data_posl_obyavlenia!="2020-01-01",]
df$date_difference <- (as.integer(df$data_posl_obyavlenia - df$data_dobavlenia_v_parser))
ai <- df %>% group_by(City,Marka,Model,Year,phone) %>% summarise(schet = n())
df <- inner_join(df,ai)
gc()
gc(reset = TRUE)
df$opcii <- NULL
df$text <- NULL
sapply(df, function(x){sum(is.na(x))})
unique_cars = df[df$schet==1,]
not_unique_cars = df[df$schet!=1,]
not_unique_cars <- not_unique_cars %>% mutate_if(is.character,as.factor)
not_unique_cars <- not_unique_cars[!is.na(not_unique_cars$Model),]
numbs <- not_unique_cars %>% group_by(City,Marka,Model,Year,phone) %>% summarise(SUM_DIF = sum(date_difference))# ZVEZDOCHKA
maxs <- not_unique_cars %>% group_by(City,Marka,Model,Year,phone) %>% summarise(data_posl_obyavlenia = max(data_posl_obyavlenia),
dama = min(data_dobavlenia_v_parser))
data <- inner_join(not_unique_cars,maxs)
data <- inner_join(data,numbs)
data$data_dobavlenia_v_parser <- data$dama
data$dama <- NULL
data$date_difference <- data$SUM_DIF
data$SUM_DIF <- NULL
daud <- rbind(unique_cars,data)
daud <- daud[daud$date_difference<=quantile(data$date_difference,0.99),]
daud$Max_date <- max(daud$data_posl_obyavlenia)
daud$data_date <- as.integer(daud$Max_date-daud$data_posl_obyavlenia)
daud <- daud[!(daud$date_difference==7 & daud$kod_statusa==2),]
daud$sale <- ifelse((daud$kod_statusa==4 & daud$data_date>30 ) | (daud$kod_statusa==2 & daud$data_date>30),'yes','no')
days <- as.integer(max(daud$data_dobavlenia_v_parser)-min(daud$data_dobavlenia_v_parser))
daud <- daud[daud$date_difference<=days+40,]
daud$data_date <- NULL
daud$Max_date <- NULL
daud$schet <- NULL
df <- daud
df <- df[df$Year>=1990,]
df <- df[df$cena<30000000,]
df <- df[df$cena>900000,]
group_1 <- df %>% group_by(Marka) %>% summarise(Marka_n=n()) %>% arrange(-Marka_n)
group_1 <- group_1[1:round(nrow(group_1)*0.6),]
write.csv(group_1,"dictionaries/group1.csv",row.names = F)
group_1 <- read.csv("dictionaries/group1.csv")
df <- inner_join(df,group_1)
group_2 <- df %>% group_by(Marka,Model) %>% summarise(Marka_model_n=n()) %>% arrange(-Marka_model_n)
group_2 <- group_2[group_2$Marka_model_n>=15,]
write.csv(group_2,"dictionaries/group2.csv",row.names = F)
df <- inner_join(df,group_2)
df$phone <- NULL
df <- unique(df)
group_3 <- df %>% group_by(Marka,Model,Year) %>% summarise(Count = n(),
Mean = mean(cena),
Median = median(cena),
Third_quartile = quantile(cena,0.95),
First_quantile = quantile(cena,0.05),
Second_quantile = quantile(cena,0.2),
Third_quantile = quantile(cena,0.8))
group_3$difficulty_classificator <- ifelse(group_3$Count<=3,"Impossible_fit","Fittable")
write.csv(group_3,"dictionaries/group3.csv",row.names = F)
df <- inner_join(df,group_3)
df$anomaly_by_price <- ifelse(df$cena>=df$Third_quartile,"too_expensive",
ifelse(df$cena>=df$Third_quantile,"expensive",
ifelse(df$cena<=df$First_quantile,"too_cheap",
ifelse(df$cena<=df$Second_quantile,"cheap","normal"))))
prop.table(table(df$anomaly_by_price))
group_4 <- df %>% group_by(Marka,Model) %>% summarise(year_mean = mean(Year),
First_q_year = quantile(Year,0.05),
Second_q_year = quantile(Year,0.2),
Third_q_year = quantile(Year,0.8),
Fourth_q_year = quantile(Year,0.95))
write.csv(group_4,"dictionaries/group_4.csv",row.names = F)
df <- inner_join(df,group_4)
df$anomaly_by_year <- ifelse(df$Year>=df$Fourth_q_year,"too_new",
ifelse(df$Year>=df$Third_q_year,"new",
ifelse(df$Year<=df$Second_q_year,"old",
ifelse(df$Year<=df$First_q_year,"too_old","new"))))
prop.table(table(df$anomaly_by_year))
hist(df$Year)
df$Sell_classs <- ifelse(df$sale=='yes',"Продан","Неизвестно")
df$sale <- NULL
sales_table <- df %>% group_by(Marka,Model,Year) %>% filter(Sell_classs =="Продан") %>% summarise(Prodan = n(),
Prodan_days = mean(date_difference),
Prodan_3 = quantile(date_difference,0.9),
Prodan_1 = quantile(date_difference,0.1),
Prodan_cena = mean(cena))
sales_table_3 <- df %>% group_by(Marka,Model,Year) %>% summarise(Count = n(),
days = mean(date_difference),
q3 = quantile(date_difference,0.9),
q1 = quantile(date_difference,0.1),
cena = mean(cena))
sales <- left_join(sales_table_3,sales_table)
sales <- sales[sales$Count>10,]
sales$predictable <- ifelse(sales$Count<=10,"cant predict","can predict")
sales$Prodan_days <- as.integer(sales$Prodan_days)
sales$Sales_days_group <- ifelse(sales$Prodan_days<10,"less than 10 days",
ifelse(sales$Prodan_days<20,"less than 20 days",
ifelse(sales$Prodan_days<30,"less than 30 days","more than 30 days")))
sales_to_train <- select(sales,-c(Count,days,q3,q1,cena,Prodan))
write.csv(sales_to_train,"BIG/SALES_TO_TRAIN.csv",row.names = F)
write.csv(df,"BIG/df.csv",row.names = F)
df <- fread("BIG/df.csv")
colnames(df)
table(df$City)
df = df[df$City=="Алматы" | df$City=='Нур-Султан (Астана)' | df$City=="Шымкент",]
train <- select(df,c(Marka,Model,Year,Tip_kuzova,KPP,privod,raspolozhenie_rulya,cvet,cena,volume,dvigatel,City,
Mean,Third_quartile,First_quantile,Second_quantile,Third_quantile,difficulty_classificator,anomaly_by_price,anomaly_by_year,
year_mean,First_q_year,Second_q_year,Third_q_year,Fourth_q_year,Marka_model_n,Marka_n,id_car_in_kolesa,probeg_gr))
train <- train %>% mutate_if(is.character,as.factor)
str(train)
#sum(is.na(train$privod))
train <- na.omit(train)
testr <- train %>% group_by(Marka,Model,Year) %>% summarise(count = n())
testr <- testr[testr$count>5,]
testr$count <- NULL
testr <- unique(testr)
write.csv(testr,"dictionaries/group5.csv",row.names = F)
train <- inner_join(train,testr)
train <- unique(train)
train <- train %>% mutate_if(is.character,as.factor)
str(train)
write.csv(select(train,id_car_in_kolesa),"mini_id_checker.csv",row.names = F)
sales_to_train <- read.csv("BIG/SALES_TO_TRAIN.csv")
train <- inner_join(train,sales_to_train)
train <- na.omit(train)
checkers <- select(df,c(id_car_in_kolesa,date_difference,views,kod_statusa))
#train$City <- NULL
#train <- unique(train)
fwrite(checkers,"BIG/checkers.csv",row.names = F)
write.csv(train,"BIG/TRAIN.CSV",row.names = F)
additional_data <- df %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel) %>%
summarise(Sales = n())
train <- fread("BIG/TRAIN.CSV",stringsAsFactors = T)
IDs <- select(train,id_car_in_kolesa)
train$id_car_in_kolesa <- NULL
index <- createDataPartition(train$cena,p=0.7,list = F)
tr <- train[index,]
ts <- train[-index,]
dtrain <- data.matrix(select(tr,-c(cena)))
ctest <- data.matrix(select(ts,-c(cena)))
dtrain <- xgb.DMatrix(data=dtrain,label=tr$cena)
ctest <- xgb.DMatrix(data=ctest,label=ts$cena)
dfin <- data.matrix(select(train,-c(cena)))
dfin <- xgb.DMatrix(data=dfin,label=train$cena)
colnames(tr)
bst <- xgboost(data = dfin,
objective='reg:linear',
nrounds = 25,
# nfold = 5,
metrics = "rmse",
maximize = FALSE,
verbose = 0,
#early_stopping_rounds = 30,
max_depth=7)
pred <- predict(bst,ctest)
mean(ts$cena-pred)
ts$prognoz <- pred
summary(ts$cena-ts$prognoz)
ts$error <- (ts$cena-ts$prognoz)/(ts$cena+ts$prognoz)
ts$diff <- ts$cena - ts$prognoz
summary(ts$error)
ts$prognoz <- abs(ts$prognoz)
#importance <- xgb.importance(feature_names = colnames(select(tr,-c(cena))), model = bst)
#print(xgb.plot.importance(importance_matrix = importance, top_n = 15))
xgb.save(bst, 'xgb.model')
bst <- xgb.load('xgb.model')
Ids_test <- IDs[-index,]
ts$id_car_in_kolesa <- Ids_test
checkers <- fread("checkers.csv")
tsss <- inner_join(ts,checkers)
tsss$flag1 <- ifelse(tsss$prognoz<tsss$cena,"predicted less than fact","predicted is greater")
mean_views <- tsss %>% group_by(Marka,Model,Year) %>% summarise(mean_date_difference = mean(date_difference),
count = n())
mean_views
tsss <- inner_join(tsss,mean_views)
tsss$flag2 <- ifelse(tsss$mean_date_difference<=tsss$date_difference,"passive","active")
#tsss$flag3 <- ifelse(tsss$mean_views<tsss$views,"interesting","less interesting")
tsss$Recommended_buying_price <- ifelse(tsss$prognoz<=1000000,tsss$prognoz - 0.18*tsss$prognoz,
ifelse(tsss$prognoz<=2500000,tsss$prognoz - 0.15*tsss$prognoz,
ifelse(tsss$prognoz<=4500000,tsss$prognoz - 0.12*tsss$prognoz,
ifelse(tsss$prognoz<=6000000,tsss$prognoz - 0.12*tsss$prognoz,
ifelse(tsss$prognoz<=8000000,tsss$prognoz - 0.1*tsss$prognoz,
ifelse(tsss$prognoz<=10000000,tsss$prognoz - 0.1*tsss$prognoz,
ifelse(tsss$prognoz<=15000000,tsss$prognoz - 0.1*tsss$prognoz,
ifelse(tsss$prognoz>15000000,tsss$prognoz - 0.8*tsss$prognoz,"NAN"))))))))
tsss$Max_buying_price <- ifelse(tsss$prognoz<=1000000,tsss$prognoz - 120000,
ifelse(tsss$prognoz<=2500000,tsss$prognoz - 200000,
ifelse(tsss$prognoz<=4500000,tsss$prognoz - 350000,
ifelse(tsss$prognoz<=6000000,tsss$prognoz - 450000,
ifelse(tsss$prognoz<=8000000,tsss$prognoz - 550000,
ifelse(tsss$prognoz<=10000000,tsss$prognoz - 650000,
ifelse(tsss$prognoz<=15000000,tsss$prognoz - 900000,
ifelse(tsss$prognoz>15000000,tsss$prognoz - 1200000,"NAN"))))))))
Final <- tsss
#Final$CLASSS <- ifelse(Final$flag1=="predicted is greater" & tsss$flag2=="active","Predicted greater and active","NAN")
index <- createDataPartition(train$cena,p=0.7,list = F)
tr <- train[index,-c(30:34)]
ts <- train[-index,-c(30:34)]
colnames(tr)
str(tr)
dtrain <- data.matrix(select(tr,-c(Prodan_days)))
ctest <- data.matrix(select(ts,-c(Prodan_days)))
train_labs <- tr$Prodan_days
val_labs <- ts$Prodan_days
dtrain <- xgb.DMatrix(data=dtrain,label=train_labs)
ctest <- xgb.DMatrix(data=ctest,label=val_labs)
xgb <- xgboost(data = dtrain,
objective='reg:linear',
nrounds = 25,
# nfold = 5,
metrics = "rmse",
maximize = FALSE,
verbose = 1,
#early_stopping_rounds = 30,
max_depth=7)
xgb.save(xgb, 'xgb_liklik.model')
damn <- Final[,colnames(tr)]
ctest_damn <- data.matrix(select(damn,-c(Prodan_days)))
val_labs_damn <- (damn$Prodan_days)
ctest <- xgb.DMatrix(data=ctest_damn,label=val_labs_damn)
xgb_model <- xgb
xgb_val_preds <- predict(xgb_model, newdata = ctest)
Final$Sales_days_group_pred <- predict(xgb,ctest)
boxplot(Final$Sales_days_group_pred)
qplot(Final$Sales_days_group_pred,Final$Prodan_days)
dfd <- fread("BIG/TRAIN.CSV",stringsAsFactors = T)
train <- dfd
IDs <- select(train,id_car_in_kolesa)
train$id_car_in_kolesa <- NULL
dtrain <- data.matrix(select(train,-c(cena)))
dtrain <- xgb.DMatrix(data=dtrain,label=train$cena)
bst <- xgb.load("xgb.model")
train$prognoz <- predict(bst,dtrain)
train$error <- (train$cena-train$prognoz)/(train$cena+train$prognoz)
train$diff <- train$cena - train$prognoz
train$prognoz <- abs(train$prognoz)
summary(train$error)
train <- dfd
IDs <- select(train,id_car_in_kolesa)
rm(dfd)
man <- as.data.frame(train)[,colnames(tr)]
man <- data.matrix(man[,-29])
man <- xgb.DMatrix(data=man,label=train$Prodan_days)
train$Sales_days_group_pred <- predict(xgb,man)
train$prognoz <- predict(bst,dtrain)
train$error <- (train$cena-train$prognoz)/(train$cena+train$prognoz)
train$diff <- train$cena - train$prognoz
train$prognoz <- abs(train$prognoz)
colnames(train)
qplot(train$Prodan_days,train$Sales_days_group_pred)
checkers <- fread("BIG/checkers.csv")
train$id_car_in_kolesa <- IDs$id_car_in_kolesa
train <- inner_join(train,checkers)
train$flag1 <- ifelse(train$prognoz<train$cena,"predicted less than fact","predicted is greater")
mean_views <- train %>% group_by(Marka,Model,Year) %>% summarise(mean_date_difference = mean(date_difference),
count = n())
train <- inner_join(train,mean_views)
train$flag2 <- ifelse(train$mean_date_difference<=train$date_difference,"passive","active")
train$Recommended_buying_price <- ifelse(train$prognoz<=1000000,train$prognoz - 0.18*train$prognoz,
ifelse(train$prognoz<=2500000,train$prognoz - 0.15*train$prognoz,
ifelse(train$prognoz<=4500000,train$prognoz - 0.12*train$prognoz,
ifelse(train$prognoz<=6000000,train$prognoz - 0.12*train$prognoz,
ifelse(train$prognoz<=8000000,train$prognoz - 0.1*train$prognoz,
ifelse(train$prognoz<=10000000,train$prognoz - 0.1*train$prognoz,
ifelse(train$prognoz<=15000000,train$prognoz - 0.1*train$prognoz,
ifelse(train$prognoz>15000000,train$prognoz - 0.08*train$prognoz,"NAN"))))))))
train$Max_buying_price <- ifelse(train$prognoz<=1000000,train$prognoz - 120000,
ifelse(train$prognoz<=2500000,train$prognoz - 200000,
ifelse(train$prognoz<=4500000,train$prognoz - 350000,
ifelse(train$prognoz<=6000000,train$prognoz - 450000,
ifelse(train$prognoz<=8000000,train$prognoz - 550000,
ifelse(train$prognoz<=10000000,train$prognoz - 650000,
ifelse(train$prognoz<=15000000,train$prognoz - 900000,
ifelse(train$prognoz>15000000,train$prognoz - 1200000,"NAN"))))))))
fwrite(train,"BIG/PREDICTED_VALUES.csv",row.names = F)
df <- fread("BIG/df.csv")
Final <- fread("BIG/PREDICTED_VALUES.csv",stringsAsFactors = T)
# till here ok
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[1:(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[1:(nrow(TOTAL)*0.3),]
TOTAL_LIQUIDITY_DATAMART_3 <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
A <- sum(TOTAL_LIQUIDITY_DATAMART_1$Count)/sum(TOTAL_LIQUIDITY_DATAMART$Count)
B <- sum(TOTAL_LIQUIDITY_DATAMART_2$Count)/sum(TOTAL_LIQUIDITY_DATAMART$Count)
C <- sum(TOTAL_LIQUIDITY_DATAMART_3$Count)/sum(TOTAL_LIQUIDITY_DATAMART$Count)
rm(TOTAL)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model,Year) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
#qplot(TOTAL_LIQUIDITY_DATAMART_1$Count,TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$sales_Percent <- NULL
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.7)*10)+as.integer(A*100)
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$sales_Percent <- NULL
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.7)*10+as.integer(0.5*100)
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.7)*10+as.integer(0.3*100)
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
qplot(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS,TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2)
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$CLASS <- ifelse(TOTAL$Mean_price<2200000,"less than 2m",
ifelse(TOTAL$Mean_price<5500000,"less than 5m",
ifelse(TOTAL$Mean_price<10500000,"less than 10m","more than 10m")))
write.csv(TOTAL,"BI.csv",row.names = F)
TOTAL %>% group_by(Count_type) %>% summarise(Correlation = cor(M_SALES_DAYS,LIQ_MINI_2))
Diinislam1 <- TOTAL
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,Tip_kuzova) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
TOTAL <- TOTAL_LIQUIDITY_DATAMART
Diinislam2 <- TOTAL # Tip Kuzova
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,KPP) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
TOTAL <- TOTAL_LIQUIDITY_DATAMART
Diinislam3 <- TOTAL # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,raspolozhenie_rulya) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam4 <- TOTAL_LIQUIDITY_DATAMART # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,volume) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam5 <- TOTAL_LIQUIDITY_DATAMART # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,dvigatel) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam6 <- TOTAL_LIQUIDITY_DATAMART # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,cvet) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam7 <- TOTAL_LIQUIDITY_DATAMART # KPP
Diinislam7$CVET_coef <- Diinislam7$LIQ_MINI_2
Diinislam6$DVIGATEL_coef <- Diinislam6$LIQ_MINI_2
Diinislam5$VOLUME_coef <- Diinislam5$LIQ_MINI_2
Diinislam4$RUL_coef <- Diinislam4$LIQ_MINI_2
Diinislam3$KPP_coef <- Diinislam3$LIQ_MINI_2
Diinislam2$KUZOV_coef <- Diinislam2$LIQ_MINI_2
Diinislam2$LIQ_MINI_2 <- NULL
Diinislam3$LIQ_MINI_2 <- NULL
Diinislam4$LIQ_MINI_2 <- NULL
Diinislam5$LIQ_MINI_2 <- NULL
Diinislam6$LIQ_MINI_2 <- NULL
Diinislam7$LIQ_MINI_2 <- NULL
Diinislam1 <- select(Diinislam1,-c(COEF_1,COEF_2))
Diinislam2 <- select(Diinislam2,-c(COEF_1,COEF_2))
Diinislam3 <- select(Diinislam3,-c(COEF_1,COEF_2))
Diinislam4 <- select(Diinislam4,-c(COEF_1,COEF_2))
Diinislam5 <- select(Diinislam5,-c(COEF_1,COEF_2))
Diinislam6 <- select(Diinislam6,-c(COEF_1,COEF_2))
Diinislam7 <- select(Diinislam7,-c(COEF_1,COEF_2))
Diinislam1 <- select(Diinislam1,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam2 <- select(Diinislam2,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam3 <- select(Diinislam3,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam4 <- select(Diinislam4,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam5 <- select(Diinislam5,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam6 <- select(Diinislam6,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam7 <- select(Diinislam7,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam7 <- Diinislam7 %>% group_by(Marka,Model,Year,cvet) %>% arrange(-CVET_coef)
datamart <- inner_join(Diinislam7,Diinislam6)
datamart <- inner_join(datamart,Diinislam5)
datamart <- inner_join(datamart,Diinislam4)
datamart <- inner_join(datamart,Diinislam3)
datamart <- inner_join(datamart,Diinislam2)
datamart <- inner_join(datamart,Diinislam1)
data <- datamart[,c(1,2,3,4,6,8,10,12,14,5,7,9,11,13,15,16,17,18)]
data$Liquidity <- data$LIQ_MINI_2+data$CVET_coef+data$DVIGATEL_coef+data$VOLUME_coef+data$RUL_coef+data$KPP_coef+data$KUZOV_coef
df_dictionary <- df %>% group_by(Marka,Model,Year,Tip_kuzova,dvigatel,volume,KPP) %>% summarise(N=n())
df_dictionary <- df_dictionary[df_dictionary$N>2,]
data <- as.data.frame(data)
tochka <- inner_join(data,df_dictionary)
tocka <- tochka[tochka$Tip_kuzova!=c("лимузин"),]
tocka <- tocka[tochka$Tip_kuzova!=c("микроавтобус"),]
tocka <- tocka[tochka$Tip_kuzova!=c("фургон"),]
summary(tocka)
write.csv(tocka,"BIG/BI_complete.csv",row.names = F)
df <- fread("BIG/df.csv")
Final <- fread("BIG/PREDICTED_VALUES.csv",stringsAsFactors = T)
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,Tip_kuzova,KPP,raspolozhenie_rulya,volume,dvigatel,cvet) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days))
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=15,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=6,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel,cvet) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel,cvet) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.25+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.05)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.25+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.05)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.25+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.05)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2+30
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-10
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-30
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
Diinislam1 <- TOTAL
D <- Diinislam1 %>% group_by(Marka,Model) %>% top_n(10,TOTAL_LIQ)
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
r1 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]$Count
r1 <- round(round(r1)/1000)*1000
r2 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.5),]$Count
r2 <- round(round(r2)/10)*10
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=r1,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=r2,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.4+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.4+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.2)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-40
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-55
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$TOTAL_LIQ_Range <- range01(TOTAL$TOTAL_LIQ)*30-15
Diinislam2 <- TOTAL
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
r1 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]$Count
r1 <- round(round(r1)/10)*10
r2 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.5),]$Count
r2 <- round(r2)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=r1,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=r2,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.4+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.4+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.2)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-40
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-55
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$TOTAL_LIQ_Range <- range01(TOTAL$TOTAL_LIQ)*30-15
Diinislam3 <- TOTAL
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
r1 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]$Count
r1 <- round(r1)
r2 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.5),]$Count
r2 <- round(r2)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=r1,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=r2,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model,Year) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.4+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.4+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.2)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-40
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-55
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$TOTAL_LIQ_Range <- range01(TOTAL$TOTAL_LIQ)*30-15
Diinislam4 <- TOTAL
Diinislam4 <- Diinislam4[,c(1:3,15)]
colnames(Diinislam4) <- c("Marka",
"Model",
"Year",
"COEF_MMY")
Diinislam3 <- Diinislam3[,c(1:2,14)]
colnames(Diinislam3) <- c("Marka",
"Model",
"COEF_MM")
Diinislam2 <- Diinislam2[,c(1,13)]
colnames(Diinislam2) <- c("Marka",
"COEF_M")
Diinislam4 <- inner_join(Diinislam4,Diinislam3)
Diinislam4 <- inner_join(Diinislam4,Diinislam2)
Diinislam1 <- inner_join(Diinislam1,Diinislam4)
Diinislam1$NEW_Liq <- Diinislam1$TOTAL_LIQ+Diinislam1$COEF_MMY+Diinislam1$COEF_MM+Diinislam1$COEF_M
write.csv(Diinislam1,"BIG/liquidity.csv",row.names = F)
write.csv(Diinislam2,"BIG/liquidity1.csv",row.names = F)
write.csv(Diinislam3,"BIG/liquidity2.csv",row.names = F)
write.csv(Diinislam4,"BIG/liquidity3.csv",row.names = F)
Finka <- inner_join(Final,Diinislam1)
colnames(Finka)
Finka$views <- NULL
Finka$M_1_Q <- NULL
Finka$M_3_Q <- NULL
Finka$Prodan_1 <- NULL
Finka$Prodan_3 <- NULL
Finka$Count <- NULL
Finka$count <- NULL
Finka$First_q_year <- NULL
Finka$CLASSS <- NULL
Finka$year_mean <- NULL
Finka$Second_q_year <- NULL
Finka$COEF_1 <- NULL
Finka$COEF_2 <- NULL
Finka$LIQ_MINI_2 <- NULL
Finka$Prodan_cena <- NULL
Finka <- Finka %>% arrange(-NEW_Liq)
Finka <- Finka[Finka$anomaly_by_price!="too_expensive",]
Finka <- Finka[Finka$anomaly_by_year=='normal',]
Finka <- Finka[Finka$predictable=='can predict',]
Finka$anomaly_by_year <- NULL
Finka$predictable <- NULL
Finka <- Finka %>% mutate_if(is.character,as.factor)
Finn <- Finka %>% group_by(Marka,Model) %>% top_n(15,TOTAL_LIQ)
Finn$Count_type
qplot(Finn$Prodan_days,Finn$TOTAL_LIQ,color=Finn$Count_type)
Finn %>% group_by(Count_type) %>% summarise(cor(Finn$Prodan_days,Finn$TOTAL_LIQ))
Final <- fread("BIG/PREDICTED_VALUES.csv",stringsAsFactors = T)
BI <- fread("BIG/BI_complete.csv",stringsAsFactors = T)
df <- fread("BIG/df.csv",stringsAsFactors = T)
reliz <- inner_join(Final,BI)
gc()
colnames(reliz)
reliz$year_mean <- NULL
reliz$First_q_year <- NULL
reliz$Second_q_year <- NULL
reliz$Prodan_cena <- NULL
reliz$CLASSS <- NULL
reliz$Third_quartile <- NULL
reliz$First_quantile <- NULL
reliz$Marka_model_n <- NULL
reliz$Prodan_3 <- NULL
reliz$Prodan_1 <- NULL
reliz$Prodan_days <- NULL
reliz$N <- NULL
#reliz$kod_statusa <- NULL
data <- reliz
length(unique(data$id_car_in_kolesa)) # adverts
data$error <- (data$cena-data$prognoz)/(data$cena+data$prognoz)
summary(data$error)
data <- data[data$error>-0.3,]
data <- data[data$error<0.3,]
length(unique(data$id_car_in_kolesa)) # adverts
Lik <- read.csv("BIG/liquidity.csv")
Marka <- unique(Lik[,c(1,23)])
Marka_model <- unique(Lik[,c(1,2,22)])
Marka_model_YEAR <- unique(Lik[,c(1,2,3,21)])
data$ADVERT_ORDER <- ifelse(data$error>0.2,-10,
ifelse(data$error>0.1,-6,
ifelse(data$error>=0,-5,
ifelse(data$error<-0.2,10,
ifelse(data$error<-0.1,5,
ifelse(data$error<=0,2,"NAN"))))))
table(data$ADVERT_ORDER)
summary(range01(data$Sales_days_group_pred)*20-10)
data$Sales_days_group_pred_coef <- range01(data$Sales_days_group_pred)*20-10
data$PREDICTABLE <- ifelse(data$predictable=='can predict',10,-10)
data$predictable <- NULL
table(data$PREDICTABLE)
data$ACTIVITY <- ifelse(data$flag2=='active',10,-10)
data$flag2 <- NULL
data$flag1
data$SCORING <- data$Liquidity + data$Sales_days_group_pred_coef + data$ADVERT_ORDER + data$PREDICTABLE + data$ACTIVITY
t <- data %>% group_by(Marka,Model) %>% summarise(Lik = mean(Liquidity),
Scor = mean(SCORING))
write.csv(data,"BIG/BI_SCORING.csv",row.names = F)
}
Dikosh/mathcalc documentation built on Dec. 18, 2019, 7:42 a.m.
R Package Documentation
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Defines functions tumi2
#' @title Predicts Stock Price Movement for Given Stock Symbol
#'
#' @description This package predicts whether the stock price at tommorow's market close would be higher or lower compared to today's closing place.
#'
#' @param symbol
#'
#' @return NULL
#'
#' @examples stock_predict('AAPL')
#'
#' @export tumi2
#' @import data.table
tumi2 <- function(x){
df <- df[!is.na(df$phone),]
aia <- df %>% group_by(phone) %>% summarise(nn = n())
aia$nchar <- nchar(as.character(aia$phone))
aia_tiny <- aia[aia$nchar==16 | aia$nchar==17,]
aia_tiny$phone <- str_replace_all(aia_tiny$phone, "[[:punct:]]", " ")
aia_tiny$code <- substring(aia_tiny$phone, 3, 3)
aia_tiny <- aia_tiny[aia_tiny$code==7,]
aia_tiny <- aia_tiny[,1]
aia_tiny <- na.omit(aia_tiny)
aia_tiny$phone <- as.factor(aia_tiny$phone)
df <- df[!is.na(df$phone),]
df <- inner_join(df,aia_tiny)
phones <- df %>% group_by(phone) %>% summarise(nn=n())
phones$class <- ifelse(phones$nn>=10,'Перекуп','Не перекуп')
write.csv(phones,"dictionaries/phones.csv",row.names = F)
phones <- read.csv("dictionaries/phones.csv")
df <- inner_join(df,phones)
df <- df[df$class!="Перекуп",]
df$volume <- as.numeric(sapply(strsplit(df$volume_dvigatel, " "), "[", 1))
df$dvigatel <- sapply(strsplit(df$volume_dvigatel, " "), "[", 2)
df$nn <- NULL
df$data_dobavlenia_v_parser <- ymd_hms(df$data_dobavlenia_v_parser)
df$data_posl_obyavlenia <- ymd_hms(df$data_posl_obyavlenia)
df$data_dobavlenia_v_parser <- as.Date(df$data_dobavlenia_v_parser)
df$data_posl_obyavlenia <- as.Date(df$data_posl_obyavlenia)
df <- df[df$data_posl_obyavlenia!="2020-01-01",]
df$date_difference <- (as.integer(df$data_posl_obyavlenia - df$data_dobavlenia_v_parser))
ai <- df %>% group_by(City,Marka,Model,Year,phone) %>% summarise(schet = n())
df <- inner_join(df,ai)
gc()
gc(reset = TRUE)
df$opcii <- NULL
df$text <- NULL
sapply(df, function(x){sum(is.na(x))})
unique_cars = df[df$schet==1,]
not_unique_cars = df[df$schet!=1,]
not_unique_cars <- not_unique_cars %>% mutate_if(is.character,as.factor)
not_unique_cars <- not_unique_cars[!is.na(not_unique_cars$Model),]
numbs <- not_unique_cars %>% group_by(City,Marka,Model,Year,phone) %>% summarise(SUM_DIF = sum(date_difference))# ZVEZDOCHKA
maxs <- not_unique_cars %>% group_by(City,Marka,Model,Year,phone) %>% summarise(data_posl_obyavlenia = max(data_posl_obyavlenia),
dama = min(data_dobavlenia_v_parser))
data <- inner_join(not_unique_cars,maxs)
data <- inner_join(data,numbs)
data$data_dobavlenia_v_parser <- data$dama
data$dama <- NULL
data$date_difference <- data$SUM_DIF
data$SUM_DIF <- NULL
daud <- rbind(unique_cars,data)
daud <- daud[daud$date_difference<=quantile(data$date_difference,0.99),]
daud$Max_date <- max(daud$data_posl_obyavlenia)
daud$data_date <- as.integer(daud$Max_date-daud$data_posl_obyavlenia)
daud <- daud[!(daud$date_difference==7 & daud$kod_statusa==2),]
daud$sale <- ifelse((daud$kod_statusa==4 & daud$data_date>30 ) | (daud$kod_statusa==2 & daud$data_date>30),'yes','no')
days <- as.integer(max(daud$data_dobavlenia_v_parser)-min(daud$data_dobavlenia_v_parser))
daud <- daud[daud$date_difference<=days+40,]
daud$data_date <- NULL
daud$Max_date <- NULL
daud$schet <- NULL
df <- daud
df <- df[df$Year>=1990,]
df <- df[df$cena<30000000,]
df <- df[df$cena>900000,]
group_1 <- df %>% group_by(Marka) %>% summarise(Marka_n=n()) %>% arrange(-Marka_n)
group_1 <- group_1[1:round(nrow(group_1)*0.6),]
write.csv(group_1,"dictionaries/group1.csv",row.names = F)
group_1 <- read.csv("dictionaries/group1.csv")
df <- inner_join(df,group_1)
group_2 <- df %>% group_by(Marka,Model) %>% summarise(Marka_model_n=n()) %>% arrange(-Marka_model_n)
group_2 <- group_2[group_2$Marka_model_n>=15,]
write.csv(group_2,"dictionaries/group2.csv",row.names = F)
df <- inner_join(df,group_2)
df$phone <- NULL
df <- unique(df)
group_3 <- df %>% group_by(Marka,Model,Year) %>% summarise(Count = n(),
Mean = mean(cena),
Median = median(cena),
Third_quartile = quantile(cena,0.95),
First_quantile = quantile(cena,0.05),
Second_quantile = quantile(cena,0.2),
Third_quantile = quantile(cena,0.8))
group_3$difficulty_classificator <- ifelse(group_3$Count<=3,"Impossible_fit","Fittable")
write.csv(group_3,"dictionaries/group3.csv",row.names = F)
df <- inner_join(df,group_3)
df$anomaly_by_price <- ifelse(df$cena>=df$Third_quartile,"too_expensive",
ifelse(df$cena>=df$Third_quantile,"expensive",
ifelse(df$cena<=df$First_quantile,"too_cheap",
ifelse(df$cena<=df$Second_quantile,"cheap","normal"))))
prop.table(table(df$anomaly_by_price))
group_4 <- df %>% group_by(Marka,Model) %>% summarise(year_mean = mean(Year),
First_q_year = quantile(Year,0.05),
Second_q_year = quantile(Year,0.2),
Third_q_year = quantile(Year,0.8),
Fourth_q_year = quantile(Year,0.95))
write.csv(group_4,"dictionaries/group_4.csv",row.names = F)
df <- inner_join(df,group_4)
df$anomaly_by_year <- ifelse(df$Year>=df$Fourth_q_year,"too_new",
ifelse(df$Year>=df$Third_q_year,"new",
ifelse(df$Year<=df$Second_q_year,"old",
ifelse(df$Year<=df$First_q_year,"too_old","new"))))
prop.table(table(df$anomaly_by_year))
hist(df$Year)
df$Sell_classs <- ifelse(df$sale=='yes',"Продан","Неизвестно")
df$sale <- NULL
sales_table <- df %>% group_by(Marka,Model,Year) %>% filter(Sell_classs =="Продан") %>% summarise(Prodan = n(),
Prodan_days = mean(date_difference),
Prodan_3 = quantile(date_difference,0.9),
Prodan_1 = quantile(date_difference,0.1),
Prodan_cena = mean(cena))
sales_table_3 <- df %>% group_by(Marka,Model,Year) %>% summarise(Count = n(),
days = mean(date_difference),
q3 = quantile(date_difference,0.9),
q1 = quantile(date_difference,0.1),
cena = mean(cena))
sales <- left_join(sales_table_3,sales_table)
sales <- sales[sales$Count>10,]
sales$predictable <- ifelse(sales$Count<=10,"cant predict","can predict")
sales$Prodan_days <- as.integer(sales$Prodan_days)
sales$Sales_days_group <- ifelse(sales$Prodan_days<10,"less than 10 days",
ifelse(sales$Prodan_days<20,"less than 20 days",
ifelse(sales$Prodan_days<30,"less than 30 days","more than 30 days")))
sales_to_train <- select(sales,-c(Count,days,q3,q1,cena,Prodan))
write.csv(sales_to_train,"BIG/SALES_TO_TRAIN.csv",row.names = F)
write.csv(df,"BIG/df.csv",row.names = F)
df <- fread("BIG/df.csv")
colnames(df)
table(df$City)
df = df[df$City=="Алматы" | df$City=='Нур-Султан (Астана)' | df$City=="Шымкент",]
train <- select(df,c(Marka,Model,Year,Tip_kuzova,KPP,privod,raspolozhenie_rulya,cvet,cena,volume,dvigatel,City,
Mean,Third_quartile,First_quantile,Second_quantile,Third_quantile,difficulty_classificator,anomaly_by_price,anomaly_by_year,
year_mean,First_q_year,Second_q_year,Third_q_year,Fourth_q_year,Marka_model_n,Marka_n,id_car_in_kolesa,probeg_gr))
train <- train %>% mutate_if(is.character,as.factor)
str(train)
#sum(is.na(train$privod))
train <- na.omit(train)
testr <- train %>% group_by(Marka,Model,Year) %>% summarise(count = n())
testr <- testr[testr$count>5,]
testr$count <- NULL
testr <- unique(testr)
write.csv(testr,"dictionaries/group5.csv",row.names = F)
train <- inner_join(train,testr)
train <- unique(train)
train <- train %>% mutate_if(is.character,as.factor)
str(train)
write.csv(select(train,id_car_in_kolesa),"mini_id_checker.csv",row.names = F)
sales_to_train <- read.csv("BIG/SALES_TO_TRAIN.csv")
train <- inner_join(train,sales_to_train)
train <- na.omit(train)
checkers <- select(df,c(id_car_in_kolesa,date_difference,views,kod_statusa))
#train$City <- NULL
#train <- unique(train)
fwrite(checkers,"BIG/checkers.csv",row.names = F)
write.csv(train,"BIG/TRAIN.CSV",row.names = F)
additional_data <- df %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel) %>%
summarise(Sales = n())
train <- fread("BIG/TRAIN.CSV",stringsAsFactors = T)
IDs <- select(train,id_car_in_kolesa)
train$id_car_in_kolesa <- NULL
index <- createDataPartition(train$cena,p=0.7,list = F)
tr <- train[index,]
ts <- train[-index,]
dtrain <- data.matrix(select(tr,-c(cena)))
ctest <- data.matrix(select(ts,-c(cena)))
dtrain <- xgb.DMatrix(data=dtrain,label=tr$cena)
ctest <- xgb.DMatrix(data=ctest,label=ts$cena)
dfin <- data.matrix(select(train,-c(cena)))
dfin <- xgb.DMatrix(data=dfin,label=train$cena)
colnames(tr)
bst <- xgboost(data = dfin,
objective='reg:linear',
nrounds = 25,
# nfold = 5,
metrics = "rmse",
maximize = FALSE,
verbose = 0,
#early_stopping_rounds = 30,
max_depth=7)
pred <- predict(bst,ctest)
mean(ts$cena-pred)
ts$prognoz <- pred
summary(ts$cena-ts$prognoz)
ts$error <- (ts$cena-ts$prognoz)/(ts$cena+ts$prognoz)
ts$diff <- ts$cena - ts$prognoz
summary(ts$error)
ts$prognoz <- abs(ts$prognoz)
#importance <- xgb.importance(feature_names = colnames(select(tr,-c(cena))), model = bst)
#print(xgb.plot.importance(importance_matrix = importance, top_n = 15))
xgb.save(bst, 'xgb.model')
bst <- xgb.load('xgb.model')
Ids_test <- IDs[-index,]
ts$id_car_in_kolesa <- Ids_test
checkers <- fread("checkers.csv")
tsss <- inner_join(ts,checkers)
tsss$flag1 <- ifelse(tsss$prognoz<tsss$cena,"predicted less than fact","predicted is greater")
mean_views <- tsss %>% group_by(Marka,Model,Year) %>% summarise(mean_date_difference = mean(date_difference),
count = n())
mean_views
tsss <- inner_join(tsss,mean_views)
tsss$flag2 <- ifelse(tsss$mean_date_difference<=tsss$date_difference,"passive","active")
#tsss$flag3 <- ifelse(tsss$mean_views<tsss$views,"interesting","less interesting")
tsss$Recommended_buying_price <- ifelse(tsss$prognoz<=1000000,tsss$prognoz - 0.18*tsss$prognoz,
ifelse(tsss$prognoz<=2500000,tsss$prognoz - 0.15*tsss$prognoz,
ifelse(tsss$prognoz<=4500000,tsss$prognoz - 0.12*tsss$prognoz,
ifelse(tsss$prognoz<=6000000,tsss$prognoz - 0.12*tsss$prognoz,
ifelse(tsss$prognoz<=8000000,tsss$prognoz - 0.1*tsss$prognoz,
ifelse(tsss$prognoz<=10000000,tsss$prognoz - 0.1*tsss$prognoz,
ifelse(tsss$prognoz<=15000000,tsss$prognoz - 0.1*tsss$prognoz,
ifelse(tsss$prognoz>15000000,tsss$prognoz - 0.8*tsss$prognoz,"NAN"))))))))
tsss$Max_buying_price <- ifelse(tsss$prognoz<=1000000,tsss$prognoz - 120000,
ifelse(tsss$prognoz<=2500000,tsss$prognoz - 200000,
ifelse(tsss$prognoz<=4500000,tsss$prognoz - 350000,
ifelse(tsss$prognoz<=6000000,tsss$prognoz - 450000,
ifelse(tsss$prognoz<=8000000,tsss$prognoz - 550000,
ifelse(tsss$prognoz<=10000000,tsss$prognoz - 650000,
ifelse(tsss$prognoz<=15000000,tsss$prognoz - 900000,
ifelse(tsss$prognoz>15000000,tsss$prognoz - 1200000,"NAN"))))))))
Final <- tsss
#Final$CLASSS <- ifelse(Final$flag1=="predicted is greater" & tsss$flag2=="active","Predicted greater and active","NAN")
index <- createDataPartition(train$cena,p=0.7,list = F)
tr <- train[index,-c(30:34)]
ts <- train[-index,-c(30:34)]
colnames(tr)
str(tr)
dtrain <- data.matrix(select(tr,-c(Prodan_days)))
ctest <- data.matrix(select(ts,-c(Prodan_days)))
train_labs <- tr$Prodan_days
val_labs <- ts$Prodan_days
dtrain <- xgb.DMatrix(data=dtrain,label=train_labs)
ctest <- xgb.DMatrix(data=ctest,label=val_labs)
xgb <- xgboost(data = dtrain,
objective='reg:linear',
nrounds = 25,
# nfold = 5,
metrics = "rmse",
maximize = FALSE,
verbose = 1,
#early_stopping_rounds = 30,
max_depth=7)
xgb.save(xgb, 'xgb_liklik.model')
damn <- Final[,colnames(tr)]
ctest_damn <- data.matrix(select(damn,-c(Prodan_days)))
val_labs_damn <- (damn$Prodan_days)
ctest <- xgb.DMatrix(data=ctest_damn,label=val_labs_damn)
xgb_model <- xgb
xgb_val_preds <- predict(xgb_model, newdata = ctest)
Final$Sales_days_group_pred <- predict(xgb,ctest)
boxplot(Final$Sales_days_group_pred)
qplot(Final$Sales_days_group_pred,Final$Prodan_days)
dfd <- fread("BIG/TRAIN.CSV",stringsAsFactors = T)
train <- dfd
IDs <- select(train,id_car_in_kolesa)
train$id_car_in_kolesa <- NULL
dtrain <- data.matrix(select(train,-c(cena)))
dtrain <- xgb.DMatrix(data=dtrain,label=train$cena)
bst <- xgb.load("xgb.model")
train$prognoz <- predict(bst,dtrain)
train$error <- (train$cena-train$prognoz)/(train$cena+train$prognoz)
train$diff <- train$cena - train$prognoz
train$prognoz <- abs(train$prognoz)
summary(train$error)
train <- dfd
IDs <- select(train,id_car_in_kolesa)
rm(dfd)
man <- as.data.frame(train)[,colnames(tr)]
man <- data.matrix(man[,-29])
man <- xgb.DMatrix(data=man,label=train$Prodan_days)
train$Sales_days_group_pred <- predict(xgb,man)
train$prognoz <- predict(bst,dtrain)
train$error <- (train$cena-train$prognoz)/(train$cena+train$prognoz)
train$diff <- train$cena - train$prognoz
train$prognoz <- abs(train$prognoz)
colnames(train)
qplot(train$Prodan_days,train$Sales_days_group_pred)
checkers <- fread("BIG/checkers.csv")
train$id_car_in_kolesa <- IDs$id_car_in_kolesa
train <- inner_join(train,checkers)
train$flag1 <- ifelse(train$prognoz<train$cena,"predicted less than fact","predicted is greater")
mean_views <- train %>% group_by(Marka,Model,Year) %>% summarise(mean_date_difference = mean(date_difference),
count = n())
train <- inner_join(train,mean_views)
train$flag2 <- ifelse(train$mean_date_difference<=train$date_difference,"passive","active")
train$Recommended_buying_price <- ifelse(train$prognoz<=1000000,train$prognoz - 0.18*train$prognoz,
ifelse(train$prognoz<=2500000,train$prognoz - 0.15*train$prognoz,
ifelse(train$prognoz<=4500000,train$prognoz - 0.12*train$prognoz,
ifelse(train$prognoz<=6000000,train$prognoz - 0.12*train$prognoz,
ifelse(train$prognoz<=8000000,train$prognoz - 0.1*train$prognoz,
ifelse(train$prognoz<=10000000,train$prognoz - 0.1*train$prognoz,
ifelse(train$prognoz<=15000000,train$prognoz - 0.1*train$prognoz,
ifelse(train$prognoz>15000000,train$prognoz - 0.08*train$prognoz,"NAN"))))))))
train$Max_buying_price <- ifelse(train$prognoz<=1000000,train$prognoz - 120000,
ifelse(train$prognoz<=2500000,train$prognoz - 200000,
ifelse(train$prognoz<=4500000,train$prognoz - 350000,
ifelse(train$prognoz<=6000000,train$prognoz - 450000,
ifelse(train$prognoz<=8000000,train$prognoz - 550000,
ifelse(train$prognoz<=10000000,train$prognoz - 650000,
ifelse(train$prognoz<=15000000,train$prognoz - 900000,
ifelse(train$prognoz>15000000,train$prognoz - 1200000,"NAN"))))))))
fwrite(train,"BIG/PREDICTED_VALUES.csv",row.names = F)
df <- fread("BIG/df.csv")
Final <- fread("BIG/PREDICTED_VALUES.csv",stringsAsFactors = T)
# till here ok
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[1:(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[1:(nrow(TOTAL)*0.3),]
TOTAL_LIQUIDITY_DATAMART_3 <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
A <- sum(TOTAL_LIQUIDITY_DATAMART_1$Count)/sum(TOTAL_LIQUIDITY_DATAMART$Count)
B <- sum(TOTAL_LIQUIDITY_DATAMART_2$Count)/sum(TOTAL_LIQUIDITY_DATAMART$Count)
C <- sum(TOTAL_LIQUIDITY_DATAMART_3$Count)/sum(TOTAL_LIQUIDITY_DATAMART$Count)
rm(TOTAL)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model,Year) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
#qplot(TOTAL_LIQUIDITY_DATAMART_1$Count,TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$sales_Percent <- NULL
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.7)*10)+as.integer(A*100)
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$sales_Percent <- NULL
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.7)*10+as.integer(0.5*100)
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.7)*10+as.integer(0.3*100)
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
qplot(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS,TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2)
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$CLASS <- ifelse(TOTAL$Mean_price<2200000,"less than 2m",
ifelse(TOTAL$Mean_price<5500000,"less than 5m",
ifelse(TOTAL$Mean_price<10500000,"less than 10m","more than 10m")))
write.csv(TOTAL,"BI.csv",row.names = F)
TOTAL %>% group_by(Count_type) %>% summarise(Correlation = cor(M_SALES_DAYS,LIQ_MINI_2))
Diinislam1 <- TOTAL
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,Tip_kuzova) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
TOTAL <- TOTAL_LIQUIDITY_DATAMART
Diinislam2 <- TOTAL # Tip Kuzova
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,KPP) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
TOTAL <- TOTAL_LIQUIDITY_DATAMART
Diinislam3 <- TOTAL # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,raspolozhenie_rulya) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam4 <- TOTAL_LIQUIDITY_DATAMART # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,volume) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam5 <- TOTAL_LIQUIDITY_DATAMART # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,dvigatel) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam6 <- TOTAL_LIQUIDITY_DATAMART # KPP
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,cvet) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
TOTAL_LIQUIDITY_DATAMART$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART$Count))
TOTAL_LIQUIDITY_DATAMART$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART <- as.data.frame(TOTAL_LIQUIDITY_DATAMART)
TOTAL_LIQUIDITY_DATAMART$LIQ_MINI_2 <- ((TOTAL_LIQUIDITY_DATAMART$COEF_1*0.3+TOTAL_LIQUIDITY_DATAMART$COEF_2*0.7)*10)-5
Diinislam7 <- TOTAL_LIQUIDITY_DATAMART # KPP
Diinislam7$CVET_coef <- Diinislam7$LIQ_MINI_2
Diinislam6$DVIGATEL_coef <- Diinislam6$LIQ_MINI_2
Diinislam5$VOLUME_coef <- Diinislam5$LIQ_MINI_2
Diinislam4$RUL_coef <- Diinislam4$LIQ_MINI_2
Diinislam3$KPP_coef <- Diinislam3$LIQ_MINI_2
Diinislam2$KUZOV_coef <- Diinislam2$LIQ_MINI_2
Diinislam2$LIQ_MINI_2 <- NULL
Diinislam3$LIQ_MINI_2 <- NULL
Diinislam4$LIQ_MINI_2 <- NULL
Diinislam5$LIQ_MINI_2 <- NULL
Diinislam6$LIQ_MINI_2 <- NULL
Diinislam7$LIQ_MINI_2 <- NULL
Diinislam1 <- select(Diinislam1,-c(COEF_1,COEF_2))
Diinislam2 <- select(Diinislam2,-c(COEF_1,COEF_2))
Diinislam3 <- select(Diinislam3,-c(COEF_1,COEF_2))
Diinislam4 <- select(Diinislam4,-c(COEF_1,COEF_2))
Diinislam5 <- select(Diinislam5,-c(COEF_1,COEF_2))
Diinislam6 <- select(Diinislam6,-c(COEF_1,COEF_2))
Diinislam7 <- select(Diinislam7,-c(COEF_1,COEF_2))
Diinislam1 <- select(Diinislam1,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam2 <- select(Diinislam2,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam3 <- select(Diinislam3,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam4 <- select(Diinislam4,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam5 <- select(Diinislam5,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam6 <- select(Diinislam6,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam7 <- select(Diinislam7,-c(Count,Mean_price,M_3_Q,M_1_Q,M_SALES_DAYS))
Diinislam7 <- Diinislam7 %>% group_by(Marka,Model,Year,cvet) %>% arrange(-CVET_coef)
datamart <- inner_join(Diinislam7,Diinislam6)
datamart <- inner_join(datamart,Diinislam5)
datamart <- inner_join(datamart,Diinislam4)
datamart <- inner_join(datamart,Diinislam3)
datamart <- inner_join(datamart,Diinislam2)
datamart <- inner_join(datamart,Diinislam1)
data <- datamart[,c(1,2,3,4,6,8,10,12,14,5,7,9,11,13,15,16,17,18)]
data$Liquidity <- data$LIQ_MINI_2+data$CVET_coef+data$DVIGATEL_coef+data$VOLUME_coef+data$RUL_coef+data$KPP_coef+data$KUZOV_coef
df_dictionary <- df %>% group_by(Marka,Model,Year,Tip_kuzova,dvigatel,volume,KPP) %>% summarise(N=n())
df_dictionary <- df_dictionary[df_dictionary$N>2,]
data <- as.data.frame(data)
tochka <- inner_join(data,df_dictionary)
tocka <- tochka[tochka$Tip_kuzova!=c("лимузин"),]
tocka <- tocka[tochka$Tip_kuzova!=c("микроавтобус"),]
tocka <- tocka[tochka$Tip_kuzova!=c("фургон"),]
summary(tocka)
write.csv(tocka,"BIG/BI_complete.csv",row.names = F)
df <- fread("BIG/df.csv")
Final <- fread("BIG/PREDICTED_VALUES.csv",stringsAsFactors = T)
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year,Tip_kuzova,KPP,raspolozhenie_rulya,volume,dvigatel,cvet) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days))
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=15,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=6,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel,cvet) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year,KPP,Tip_kuzova,raspolozhenie_rulya,volume,dvigatel,cvet) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.25+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.05)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.25+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.05)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.25+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.05)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2+30
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-10
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-30
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
Diinislam1 <- TOTAL
D <- Diinislam1 %>% group_by(Marka,Model) %>% top_n(10,TOTAL_LIQ)
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
r1 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]$Count
r1 <- round(round(r1)/1000)*1000
r2 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.5),]$Count
r2 <- round(round(r2)/10)*10
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=r1,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=r2,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.4+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.4+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.2)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-40
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-55
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$TOTAL_LIQ_Range <- range01(TOTAL$TOTAL_LIQ)*30-15
Diinislam2 <- TOTAL
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
r1 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]$Count
r1 <- round(round(r1)/10)*10
r2 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.5),]$Count
r2 <- round(r2)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=r1,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=r2,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.4+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.4+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.2)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-40
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-55
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$TOTAL_LIQ_Range <- range01(TOTAL$TOTAL_LIQ)*30-15
Diinislam3 <- TOTAL
TOTAL_LIQUIDITY_DATAMART <- Final %>% group_by(Marka,Model,Year) %>%
summarise(Mean_price = mean(cena),
Count = n(),
M_3_Q = mean(Third_quartile),
M_1_Q = mean(First_quantile),
M_SALES_DAYS = mean(Prodan_days)) %>% arrange(-Count)
r1 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.2),]$Count
r1 <- round(r1)
r2 <- TOTAL_LIQUIDITY_DATAMART[round(nrow(TOTAL_LIQUIDITY_DATAMART)*0.5),]$Count
r2 <- round(r2)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART[TOTAL_LIQUIDITY_DATAMART$Count>=r1,]
TOTAL <- anti_join(TOTAL_LIQUIDITY_DATAMART,TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL[TOTAL$Count>=r2,]
TOTKA <- anti_join(TOTAL,TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTKA
rm(TOTAL,TOTKA)
rm(TOTAL_LIQUIDITY_DATAMART)
range01 <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))}
TOTAL_LIQUIDITY_DATAMART_1$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_1$Count))
TOTAL_LIQUIDITY_DATAMART_1$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_1$M_SALES_DAYS))-1)
additional_data <- df %>%
group_by(Marka,Model,Year) %>%
summarise(Count = n())
additional_data1 <- df %>% filter(Sell_classs=="Продан") %>%
group_by(Marka,Model,Year) %>%
summarise(Sales = n())
add <- left_join(additional_data,additional_data1)
add$sales_Percent <- add$Sales/add$Count
add$Count <- NULL
add$Sales <- NULL
add <- as.data.frame(add)
add <- add %>% mutate_if(is.character,as.factor)
add <- na.omit(add)
rm(additional_data,additional_data1)
TOTAL_LIQUIDITY_DATAMART_1 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_1)
TOTAL_LIQUIDITY_DATAMART_1 <- TOTAL_LIQUIDITY_DATAMART_1 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_1 <- inner_join(TOTAL_LIQUIDITY_DATAMART_1,add)
TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_1$COEF_1*0.4+TOTAL_LIQUIDITY_DATAMART_1$COEF_2*0.4+TOTAL_LIQUIDITY_DATAMART_1$sales_Percent*0.2)*100
TOTAL_LIQUIDITY_DATAMART_2$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_2$Count))
TOTAL_LIQUIDITY_DATAMART_2$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_2$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_2 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_2)
TOTAL_LIQUIDITY_DATAMART_2 <- TOTAL_LIQUIDITY_DATAMART_2 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_2 <- inner_join(TOTAL_LIQUIDITY_DATAMART_2,add)
TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_2$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_2$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_2$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_3$COEF_1 <- (range01(TOTAL_LIQUIDITY_DATAMART_3$Count))
TOTAL_LIQUIDITY_DATAMART_3$COEF_2 <- abs((range01(TOTAL_LIQUIDITY_DATAMART_3$M_SALES_DAYS))-1)
TOTAL_LIQUIDITY_DATAMART_3 <- as.data.frame(TOTAL_LIQUIDITY_DATAMART_3)
TOTAL_LIQUIDITY_DATAMART_3 <- TOTAL_LIQUIDITY_DATAMART_3 %>% mutate_if(is.character,as.factor)
TOTAL_LIQUIDITY_DATAMART_3 <- inner_join(TOTAL_LIQUIDITY_DATAMART_3,add)
TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2 <- (TOTAL_LIQUIDITY_DATAMART_3$COEF_1*0.2+TOTAL_LIQUIDITY_DATAMART_3$COEF_2*0.6+TOTAL_LIQUIDITY_DATAMART_3$sales_Percent*0.1)*100
TOTAL_LIQUIDITY_DATAMART_1$Count_type <- "good"
TOTAL_LIQUIDITY_DATAMART_2$Count_type <- "medium"
TOTAL_LIQUIDITY_DATAMART_3$Count_type <- "bad"
TOTAL_LIQUIDITY_DATAMART_1$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_1$LIQ_MINI_2
TOTAL_LIQUIDITY_DATAMART_2$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_2$LIQ_MINI_2-40
TOTAL_LIQUIDITY_DATAMART_3$TOTAL_LIQ <- TOTAL_LIQUIDITY_DATAMART_3$LIQ_MINI_2-55
TOTAL <- rbind(TOTAL_LIQUIDITY_DATAMART_1,TOTAL_LIQUIDITY_DATAMART_2,TOTAL_LIQUIDITY_DATAMART_3)
TOTAL$TOTAL_LIQ_Range <- range01(TOTAL$TOTAL_LIQ)*30-15
Diinislam4 <- TOTAL
Diinislam4 <- Diinislam4[,c(1:3,15)]
colnames(Diinislam4) <- c("Marka",
"Model",
"Year",
"COEF_MMY")
Diinislam3 <- Diinislam3[,c(1:2,14)]
colnames(Diinislam3) <- c("Marka",
"Model",
"COEF_MM")
Diinislam2 <- Diinislam2[,c(1,13)]
colnames(Diinislam2) <- c("Marka",
"COEF_M")
Diinislam4 <- inner_join(Diinislam4,Diinislam3)
Diinislam4 <- inner_join(Diinislam4,Diinislam2)
Diinislam1 <- inner_join(Diinislam1,Diinislam4)
Diinislam1$NEW_Liq <- Diinislam1$TOTAL_LIQ+Diinislam1$COEF_MMY+Diinislam1$COEF_MM+Diinislam1$COEF_M
write.csv(Diinislam1,"BIG/liquidity.csv",row.names = F)
write.csv(Diinislam2,"BIG/liquidity1.csv",row.names = F)
write.csv(Diinislam3,"BIG/liquidity2.csv",row.names = F)
write.csv(Diinislam4,"BIG/liquidity3.csv",row.names = F)
Finka <- inner_join(Final,Diinislam1)
colnames(Finka)
Finka$views <- NULL
Finka$M_1_Q <- NULL
Finka$M_3_Q <- NULL
Finka$Prodan_1 <- NULL
Finka$Prodan_3 <- NULL
Finka$Count <- NULL
Finka$count <- NULL
Finka$First_q_year <- NULL
Finka$CLASSS <- NULL
Finka$year_mean <- NULL
Finka$Second_q_year <- NULL
Finka$COEF_1 <- NULL
Finka$COEF_2 <- NULL
Finka$LIQ_MINI_2 <- NULL
Finka$Prodan_cena <- NULL
Finka <- Finka %>% arrange(-NEW_Liq)
Finka <- Finka[Finka$anomaly_by_price!="too_expensive",]
Finka <- Finka[Finka$anomaly_by_year=='normal',]
Finka <- Finka[Finka$predictable=='can predict',]
Finka$anomaly_by_year <- NULL
Finka$predictable <- NULL
Finka <- Finka %>% mutate_if(is.character,as.factor)
Finn <- Finka %>% group_by(Marka,Model) %>% top_n(15,TOTAL_LIQ)
Finn$Count_type
qplot(Finn$Prodan_days,Finn$TOTAL_LIQ,color=Finn$Count_type)
Finn %>% group_by(Count_type) %>% summarise(cor(Finn$Prodan_days,Finn$TOTAL_LIQ))
Final <- fread("BIG/PREDICTED_VALUES.csv",stringsAsFactors = T)
BI <- fread("BIG/BI_complete.csv",stringsAsFactors = T)
df <- fread("BIG/df.csv",stringsAsFactors = T)
reliz <- inner_join(Final,BI)
gc()
colnames(reliz)
reliz$year_mean <- NULL
reliz$First_q_year <- NULL
reliz$Second_q_year <- NULL
reliz$Prodan_cena <- NULL
reliz$CLASSS <- NULL
reliz$Third_quartile <- NULL
reliz$First_quantile <- NULL
reliz$Marka_model_n <- NULL
reliz$Prodan_3 <- NULL
reliz$Prodan_1 <- NULL
reliz$Prodan_days <- NULL
reliz$N <- NULL
#reliz$kod_statusa <- NULL
data <- reliz
length(unique(data$id_car_in_kolesa)) # adverts
data$error <- (data$cena-data$prognoz)/(data$cena+data$prognoz)
summary(data$error)
data <- data[data$error>-0.3,]
data <- data[data$error<0.3,]
length(unique(data$id_car_in_kolesa)) # adverts
Lik <- read.csv("BIG/liquidity.csv")
Marka <- unique(Lik[,c(1,23)])
Marka_model <- unique(Lik[,c(1,2,22)])
Marka_model_YEAR <- unique(Lik[,c(1,2,3,21)])
data$ADVERT_ORDER <- ifelse(data$error>0.2,-10,
ifelse(data$error>0.1,-6,
ifelse(data$error>=0,-5,
ifelse(data$error<-0.2,10,
ifelse(data$error<-0.1,5,
ifelse(data$error<=0,2,"NAN"))))))
table(data$ADVERT_ORDER)
summary(range01(data$Sales_days_group_pred)*20-10)
data$Sales_days_group_pred_coef <- range01(data$Sales_days_group_pred)*20-10
data$PREDICTABLE <- ifelse(data$predictable=='can predict',10,-10)
data$predictable <- NULL
table(data$PREDICTABLE)
data$ACTIVITY <- ifelse(data$flag2=='active',10,-10)
data$flag2 <- NULL
data$flag1
data$SCORING <- data$Liquidity + data$Sales_days_group_pred_coef + data$ADVERT_ORDER + data$PREDICTABLE + data$ACTIVITY
t <- data %>% group_by(Marka,Model) %>% summarise(Lik = mean(Liquidity),
Scor = mean(SCORING))
write.csv(data,"BIG/BI_SCORING.csv",row.names = F)
}
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