Upload FastagFraudDetectionProject.R

FastagFraudDetectionProject.R

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+############################### R Project  #################################
+
+######################### Fastag Fraud Detection ###########################
+
+## 1) Descriptive analysis
+## 2) Data Preprocessing
+## 3) Data Visualization
+## 4) Model Development
+## 5) Create Model Pipeline
+## 6) Model Deployment
+
+
+## 1) Descriptive analysis
+
+# Let's load dataset  
+df <- read.csv('FastagFraudDetection.csv')
+
+# Let's look at first 5 row
+head(df)
+
+# Let's look at dataframe with view 
+View(df)
+
+# Let's look at dataframe info
+str(df)
+
+# Let's look at summary of dataframe
+View(summary(df))
+
+# Dimensions (rows and columns)
+dim(df) 
+nrow(df)
+ncol(df)
+
+# Let's look at column names
+names(df) 
+colnames(df)
+
+# Let's look at target column
+tail(df[,'Fraud_indicator'])
+df[['Fraud_indicator']]
+df$Fraud_indicator
+
+# Let's look at vehicle columns
+df[, c("Vehicle_Dimensions", "Vehicle_Speed",'Vehicle_Plate_Number','Vehicle_Type')]
+
+# For select a first row
+df[1, ]  
+
+# For select first three rows
+df[1:3,]
+
+# Let's filter rows where vehicle type is bus
+head(df[df$Vehicle_Type == "Bus ", ],n = 2)
+
+# Let's use subset function
+nrow(subset(df, Transaction_Amount > 300))
+
+# Let's look at count of null  values over each columns
+null_counts <- colSums(is.na(df))
+null_counts
+
+# Okey, missing data is not in dataframe :)
+
+
+## 2) Data Preprocessing
+
+# Let's divide timestamp column to time and date 
+df[['Date','Time']] <- df$Timestamp.split()
+
+# Load necessary libraries
+#install.packages("tidyverse")
+#library(tidyverse)
+
+# Let's check for rows with infinite values
+rows_with_inf <- apply(df, 1, function(row) any(is.infinite(row)))
+View(df[rows_with_inf, ])
+
+# Let's split Timestamp into Date and Time
+df <- df %>%
+  separate(Timestamp, into = c("Date", "Time"), sep = " ") #-- Okey, Great 
+
+# Let's do great only first letter of words of vehicle type
+df$Vehicle_Type <- str_to_title(df$Vehicle_Type)
+
+# Let's remove fastag id 
+df <- df[, !colnames(df) %in% c('FastagID')]
+
+# Let's group transaction amount and amount paid column 
+group <- function(x) {
+  ifelse(x < 100, '<100',
+         ifelse(x < 200, '100-200',
+                ifelse(x < 300, '200-300', '300+')))
+}
+
+# Create a new column with transaction amount groups
+df$Transaction_Amount_Group <- group(df$Transaction_Amount)
+
+
+# SOLUTION 2: 
+
+# Let's define the amount paid groups
+cut_points <- c(0, 100, 200, 300, Inf)
+group_names <- c('<100', '100-200', '200-300', '300+')
+
+# Let's create a new column with amount paid groups
+df$Amount_Paid_Group <- cut(df$Amount_paid, breaks = cut_points, labels = group_names, include.lowest = TRUE)
+
+# Let's define the vehicle speed groups
+cut_points <- c(0, 30, 60, 80 , 100 , Inf)
+group_names <- c('<30', '30-60', '60-80', '80-100','100+')
+
+# Let's create a new column with transaction amount groups
+df$Vehicle_Speed_Group <- cut(df$Vehicle_Speed, breaks = cut_points, labels = group_names, include.lowest = TRUE)
+
+# Let's find ratio transaction amount and amount paid
+divide <- function(x) {
+  ifelse(is.na(x) | x == 0, 
+         0, 
+         round(df$Amount_paid / x, digits = 3)
+  )
+}
+
+df$Transaction_Amount_Ratio <- divide(df$Transaction_Amount)
+
+# Let's seperate Geographical_Location column to long and lat 
+df <- df %>%
+  separate(Geographical_Location, into = c("Longitude", "Latitude"), sep = " ") 
+
+# Let's define fraud column as numeric for calculate process
+df$Fraud_Number <- ifelse(df$Fraud_indicator == 'Fraud', 1, 0)
+
+# Let's look at columns
+colnames(df)
+
+# Let's add new columns whether weekend or weekday
+df$Date <- as.Date(df$Date, format = "%m/%d/%Y")
+df$weekend <- ifelse(weekdays(df$Date) %in% c("Saturday", "Sunday"), 1, 0)
+
+# Let's create column and add day of week
+df$weekdays <- weekdays(df$Date)
+
+# Let's create pm and am clock
+df$Time1 <- as.POSIXct(df$Time, format = "%H:%M")
+df$clock <- ifelse(hour(df$Time) >= 12 & hour(df$Time) < 24, 1, 0)
+df <- df[, !colnames(df) %in% c("Time1")]
+
+## 3) Data Visualization
+
+# • Scatter Plot
+plot(df$Transaction_Amount, df$Vehicle_Speed, main = "Transaction Amount & Vehicle Speed", xlab = "Amount", ylab = "Speed", col = "darkgreen", pch = 1, cex = 1.2, font.main = 9)
+
+# • Histogram
+hist(df$Amount_paid, main = "Amount Paid Group", xlab = "Groups", ylab = "Frequency", col = "green", border = "blue", font.main = 3)
+
+# • Bar Plot
+barplot(table(df$Vehicle_Type), main = "Frequency of Vehicle Types", xlab = "Vehicle Type", ylab = "Frequency", col = "blue")
+text(x = 1:length(counts), y = counts + 11, labels = counts, pos = 3, cex = 0.9, col = "black", xpd = TRUE)
+
+# • Bar Plot
+lane_type_avg <- aggregate(Fraud_Number ~ Lane_Type, data = df, mean)
+barplot(lane_type_avg$Fraud_Number, main = "Frequency of Fraud by Lane Type", xlab = "Lane Type",ylab = "Frequency", col = "blue", names.arg = lane_type_avg$Lane_Type)
+
+# • Box Plot 
+boxplot(df$Transaction_Amount, df$Amount_paid, names = c("Transaction Amount", "Amount Paid"), main = "Transaction Amount & Amount Paid", col = c("blue", "green"), border = "black", font.main = 3)
+boxplot(df$Vehicle_Speed, names = c("Speed"), main = "Vehicle Speed", col = c("green"), border = "black", font.main = 3)
+boxplot(df$Transaction_Amount_Ratio, names = c("Ratio"), main = "Transaction Amount Ratio", col = c("red"), border = "blue", font.main = 3)
+
+# • Pie Chart
+pie(table(df$Transaction_Amount_Group), labels = levels(factor(df$Transaction_Amount_Group)) , main = "Transaction Amount Group", col = rainbow(length(levels(factor(df$Transaction_Amount_Group)))), border = "darkred", font.main = 4)
+
+# • Pie Chart
+#install.packages("plotrix")
+#library("plotrix")
+pie3D(table(df$Transaction_Amount_Group), labels = levels(factor(df$Transaction_Amount_Group)) , main = "Transaction Amount Group", col = rainbow(length(levels(factor(df$Transaction_Amount_Group)))), border = "darkred", font.main = 4)
+
+# • Line Plot
+library("tidyverse")
+df$Date <- as.Date(df$Date, format = "%m/%d/%Y")
+daily_sum <- aggregate(Transaction_Amount ~ Date, data = df, sum)
+plot(daily_sum$Date, daily_sum$Transaction_Amount, type = "l", 
+     main = "Daily Sum of Transaction Amount", 
+     xlab = "Date", ylab = "Sum of Transaction Amount", 
+     col = "red", lwd = 2, font.main = 2)
+
+# Let's extract analyze by month
+df$MonthName <- format(df$Date, "%B")
+monthly_avg <- aggregate(Transaction_Amount ~ MonthName, data = df, mean)
+
+monthly_avg$MonthName <- factor(monthly_avg$MonthName, 
+                                levels = c("Yanvar", "Fevral", "Mart", "Aprel", "May", "İyun", 
+                                           "İyul", "Avqust", "Sentyabr", "Oktyabr", "Noyabr", "Dekabr"),
+                                ordered = TRUE)
+
+plot(monthly_avg$MonthName, monthly_avg$Transaction_Amount, type = "l", 
+     main = "Daily Sum of Transaction Amount", 
+     xlab = "Date", ylab = "Sum of Transaction Amount", 
+     col = "red", lwd = 2, font.main = 2)
+
+# • Scatter with ggplot2 Plot
+library(ggplot2)
+ggplot(df, aes(x = df$Transaction_ID, y = df$Transaction_Amount)) +
+  geom_point(color = "blue", size = 3) +
+  labs(title = "Transaction Id & Amount", x = "Transaction Id", y = "Amount") +
+  theme_bw()
+
+# • Bar plot with ggplot2
+vehicle_type_avg <- aggregate(Amount_paid ~ Vehicle_Type, data = df, mean)
+
+ggplot(vehicle_type_avg, aes(x = Vehicle_Type, y = Amount_paid)) +
+  geom_bar(stat = "identity", fill = "orange", color = "black") +
+  labs(title = "Average Amount Paid by Vehicle Type", x = "Vehicle Type", y = "Average Amount Paid") +
+  theme_bw()
+
+
+# Let's find transaction counts by longitude and latitude
+transaction_counts <- aggregate(Transaction_ID ~ Longitude + Latitude, data = df, FUN = length)
+
+# Plot the map
+install.packages("maps")
+library(maps)
+
+world <- map_data("world")
+transaction_counts$Longitude <- as.numeric(as.character(transaction_counts$Longitude))
+transaction_counts$Latitude <- as.numeric(as.character(transaction_counts$Latitude))
+
+# Plot the map
+ggplot() +
+  geom_polygon(data = world, aes(x = long, y = lat, group = group), fill = "lightgray", color = "black") +
+  geom_point(data = transaction_counts, aes(x = Longitude, y = Latitude, size = Transaction_ID), color = "red") +
+  labs(title = "Transaction Count by Location", x = "Longitude", y = "Latitude", size = "Transaction Count") +
+  theme_minimal()
+
+
+## 4) Model Development
+
+# Let's analyze needs : Analyzing Fastag fraud involves identifying 
+# patterns and trends in fraudulent transactions to enhance system 
+# security and user trust. By leveraging data analytics and machine 
+# learning, businesses can predict and prevent future fraudulent activities.
+# This proactive approach helps mitigate financial losses and ensures 
+# the integrity of the Fastag system. Ultimately, maintaining a secure 
+# and reliable Fastag system promotes user satisfaction and supports 
+# efficient toll collection.
+
+# Let's remove unnecessary columns from dataframe
+
+df <- df[, !colnames(df) %in% c("Time","Fraud_indicator","Date","Longitude","Latitude","Vehicle_Plate_Number","Transaction_ID")]
+# df <- df[,!colnames(df) %in% c('weekend',"weekdayscümə axşamı",'weekdaysçərşənbə axşamı','weekdaysşənbə')]
+
+# Let's dummy some columns
+library(dplyr)
+cols_to_dummy <- c("Vehicle_Type", "Lane_Type", "TollBoothID", "Vehicle_Dimensions", 
+                   "Transaction_Amount_Group", "Amount_Paid_Group", "Vehicle_Speed_Group", "weekdays")
+
+# Creating dummy variables
+df_dummies <- df %>%
+  select(all_of(cols_to_dummy)) %>%
+  model.matrix(~ . - 1, data = .) %>%
+  as.data.frame()
+
+# Combining the dummies with the original dataframe excluding the original columns
+df <- bind_cols(df %>% select(-all_of(cols_to_dummy)), df_dummies)
+
+# install.packages("randomForest")
+library(randomForest)
+library(caret)  
+
+# Split the data into training and testing sets
+set.seed(123)
+trainIndex <- createDataPartition(df$Fraud_Number, p = 0.8, list = FALSE)
+trainData <- df[trainIndex,]
+testData <- df[-trainIndex,]
+
+# Separate inputs and target for training
+trainInput <- trainData[, !colnames(trainData) %in% c('Fraud_Number')]
+trainTarget <- trainData$Fraud_Number
+
+# Train the random forest model
+rf_model <- randomForest(trainInput, trainTarget, ntree = 100, mtry = 3, importance = TRUE)
+
+# Predict on the test set
+testInput <- testData[, !colnames(testData) %in% c('Fraud_Number')]
+testTarget <- testData$Fraud_Number
+predictions <- predict(rf_model, testInput)
+binary_predictions <- ifelse(predictions >= 0.5, 1, 0)
+
+# Evaluate model performance
+binary_predictions <- factor(binary_predictions, levels = c(0, 1))
+testTarget <- factor(testTarget, levels = c(0, 1))
+# Create the confusion matrix
+conf_matrix <- confusionMatrix(binary_predictions, testTarget)
+
+# Variable importance
+importance(rf_model)
+varImpPlot(rf_model)
+
+# Let's remove non important columns
+df <- df[,!colnames(df) %in% c('weekend',"weekdayscümə axşamı",'weekdaysçərşənbə axşamı','weekdaysşənbə')]
+# --Let's again create model
+
+
+# Let's predict new_value
+new_data <- testInput[1,]
+View(new_data)
+predictions <- predict(rf_model, new_data)
+binary_predictions <- ifelse(predictions >= 0.5, 1, 0) # -------------------
+predictions
+
+# install.packages("pROC")
+# install.packages("ggplot2")
+# library(pROC)
+# library(ggplot2)
+
+# Let's calculate the ROC curve and AUC
+roc_obj <- roc(testData$Fraud_Number, predictions)
+auc_value <- auc(roc_obj)
+
+roc_df <- data.frame(
+  tpr = roc_obj$sensitivities, 
+  fpr = 1 - roc_obj$specificities, 
+  thresholds = roc_obj$thresholds
+)
+
+ggplot(roc_df, aes(x = fpr, y = tpr)) +
+  geom_line(color = "blue") +
+  geom_abline(linetype = "dashed", color = "red") +
+  labs(title = paste("ROC Curve (AUC =", round(auc_value, 3), ")"),
+       x = "False Positive Rate",
+       y = "True Positive Rate") +
+  theme_minimal() # Great ✅
+
+
+## 5) Create Model Pipeline
+
+# Function 1:
+# Let's create model deployment function
+data_preprocessing_function <- function(df){
+  df <- separate(df, Timestamp, into = c("Date", "Time"), sep = " ")
+  df$Vehicle_Type <- str_to_title(df$Vehicle_Type)
+  df <- df[, !colnames(df) %in% c('FastagID')]
+  group <- function(x) {
+    ifelse(x < 100, '<100',
+           ifelse(x < 200, '100-200',
+                  ifelse(x < 300, '200-300', '300+')))
+  }
+  df$Transaction_Amount_Group <- group(df$Transaction_Amount)
+  
+  cut_points <- c(0, 100, 200, 300, Inf)
+  group_names <- c('<100', '100-200', '200-300', '300+')
+  # Let's create a new column with amount paid groups
+  df$Amount_Paid_Group <- cut(df$Amount_paid, breaks = cut_points, labels = group_names, include.lowest = TRUE)
+  
+  cut_points <- c(0, 100, 200, 300, Inf)
+  group_names <- c('<100', '100-200', '200-300', '300+')
+  df$Vehicle_Speed_Group <- cut(df$Vehicle_Speed, breaks = cut_points, labels = group_names, include.lowest = TRUE)
+  divide <- function(x) {
+    ifelse(is.na(x) | x == 0, 
+           0, 
+           round(df$Amount_paid / x, digits = 3)
+    )
+  }
+  df$Transaction_Amount_Ratio <- divide(df$Transaction_Amount)
+  df <- separate(df, Geographical_Location, into = c("Longitude", "Latitude"), sep = " ") 
+  df$Fraud_Number <- ifelse(df$Fraud_indicator == 'Fraud', 1, 0)
+  df$Date <- as.Date(df$Date, format = "%m/%d/%Y")
+  df$weekend <- ifelse(weekdays(df$Date) %in% c("Saturday", "Sunday"), 1, 0)
+  df$weekdays <- weekdays(df$Date)
+  df$Time <- as.POSIXct(df$Time, format = "%H:%M")
+  df$clock <- ifelse(hour(df$Time) >= 12 & hour(df$Time) < 24, 1, 0)
+  df <- df[, !colnames(df) %in% c("Time1")]
+  
+  return(df)
+}
+
+# Function 2:
+# Let's create model deployment function
+model_deployment_function <- function(df, model_file){
+  df <- df[, !colnames(df) %in% c("Time","Fraud_indicator","Date","Longitude","Latitude","Vehicle_Plate_Number","Transaction_ID",'weekend',"weekdayscümə axşamı",'weekdaysçərşənbə axşamı','weekdaysşənbə')]
+  
+  cols_to_dummy <- c("Vehicle_Type", "Lane_Type", "TollBoothID", "Vehicle_Dimensions", 
+                     "Transaction_Amount_Group", "Amount_Paid_Group", "Vehicle_Speed_Group", "weekdays")
+  
+  df_dummies <- df %>%
+    select(all_of(cols_to_dummy)) %>%
+    model.matrix(~ . - 1, data = .) %>%
+    as.data.frame()
+  
+  df <- bind_cols(df %>% select(-all_of(cols_to_dummy)), df_dummies)
+  df_main <<- df
+  set.seed(123)
+  trainIndex <- createDataPartition(df$Fraud_Number, p = 0.8, list = FALSE)
+  trainData <- df[trainIndex,]
+  testData <- df[-trainIndex,]
+  
+  trainInput <- trainData[, !colnames(trainData) %in% c('Fraud_Number')]
+  trainTarget <- trainData$Fraud_Number
+  
+  rf_model <- randomForest(trainInput, trainTarget, ntree = 100, mtry = 3, importance = TRUE)
+  
+  testInput <- testData[, !colnames(testData) %in% c('Fraud_Number')]
+  testTarget <- testData$Fraud_Number
+  
+  predictions <- predict(rf_model, testInput)
+  binary_predictions <- ifelse(predictions >= 0.5, 1, 0)
+  
+  # Let's evaluate model
+  binary_predictions <- factor(binary_predictions, levels = c(0, 1))
+  testTarget <- factor(testTarget, levels = c(0, 1))
+  conf_matrix <- confusionMatrix(binary_predictions, testTarget)
+  print(conf_matrix)
+
+  # Save the model
+  saveRDS(rf_model, model_file)
+  
+  return(df)
+}
+
+
+
+# Let's applying the model_function to the dataframe
+df_processed <- data_preprocessing_function(df)
+
+# Let's create model and save model file as rds
+model_file <- "C:/Users/HP/OneDrive/İş masası/R Programming/rf_model.rds"
+processed_df <- model_deployment_function(df_processed, model_file)
+
+
+
+## 6) Model Deployment 
+
+# Load the saved random forest model
+load_model_function <- function(model_file) {
+  rf_model <- readRDS(model_file)
+  return(rf_model)
+}
+
+# Function to make predictions using the loaded model
+predict_with_model <- function(rf_model, new_data) {
+  # Preprocess new data
+  new_data <- separate(new_data, Timestamp, into = c("Date", "Time"), sep = " ")
+  new_data$Vehicle_Type <- str_to_title(new_data$Vehicle_Type)
+  new_data <- new_data[, !colnames(new_data) %in% c('FastagID')]
+  
+  group <- function(x) {
+    ifelse(x < 100, '<100',
+           ifelse(x < 200, '100-200',
+                  ifelse(x < 300, '200-300', '300+')))
+  }
+  new_data$Transaction_Amount_Group <- group(new_data$Transaction_Amount)
+  
+  cut_points <- c(0, 100, 200, 300, Inf)
+  group_names <- c('<100', '100-200', '200-300', '300+')
+  
+  new_data$Amount_Paid_Group <- cut(new_data$Amount_paid, breaks = cut_points, labels = group_names, include.lowest = TRUE)
+  new_data$Vehicle_Speed_Group <- cut(new_data$Vehicle_Speed, breaks = cut_points, labels = group_names, include.lowest = TRUE)
+  
+  divide <- function(x) {
+    ifelse(is.na(x) | x == 0, 0, round(new_data$Amount_paid / x, digits = 3))
+  }
+  new_data$Transaction_Amount_Ratio <- divide(new_data$Transaction_Amount)
+  
+  new_data <- separate(new_data, Geographical_Location, into = c("Longitude", "Latitude"), sep = " ")
+  new_data$Date <- as.Date(new_data$Date, format = "%m/%d/%Y")
+  new_data$weekend <- ifelse(weekdays(new_data$Date) %in% c("Saturday", "Sunday"), 1, 0)
+  new_data$weekdays <- weekdays(new_data$Date)
+  new_data$Time <- as.POSIXct(new_data$Time, format = "%H:%M")
+  new_data$clock <- ifelse(hour(new_data$Time) >= 12 & hour(new_data$Time) < 24, 1, 0)
+  
+  new_data <- new_data[, !colnames(new_data) %in% c("Time1", "Time", "Fraud_indicator", "Date", "Longitude", "Latitude", "Vehicle_Plate_Number", "Transaction_ID", "weekend", "weekdayscümə axşamı", "weekdaysçərşənbə axşamı", "weekdaysşənbə")]
+  
+  cols_to_dummy <- c("Vehicle_Type", "Lane_Type", "TollBoothID", "Vehicle_Dimensions", "Transaction_Amount_Group", "Amount_Paid_Group", "Vehicle_Speed_Group", "weekdays")
+  
+  # Ensure each categorical variable has at least two levels
+  for (col in cols_to_dummy) {
+    if (length(unique(new_data[[col]])) < 2) {
+      new_data[[col]] <- factor(new_data[[col]], levels = c(unique(new_data[[col]]), "dummy_level"))
+    }
+  }
+  
+  new_data_dummies <- new_data %>%
+    select(all_of(cols_to_dummy)) %>%
+    model.matrix(~ . - 1, data = .) %>%
+    as.data.frame()
+  
+  new_data <- bind_cols(new_data %>% select(-all_of(cols_to_dummy)), new_data_dummies)
+  
+  # List of columns to be checked and added if not present
+  cols_to_add <- c(
+    "Transaction_Amount", "Amount_paid", "Vehicle_Speed", "Transaction_Amount_Ratio", 
+    "clock", "Vehicle_TypeBus ", "Vehicle_TypeCar", "Vehicle_TypeMotorcycle", 
+    "Vehicle_TypeSedan", "Vehicle_TypeSuv", "Vehicle_TypeTruck", "Vehicle_TypeVan", 
+    "Lane_TypeRegular", "TollBoothIDB-102", "TollBoothIDC-103", "TollBoothIDD-104", 
+    "TollBoothIDD-105", "TollBoothIDD-106", "Vehicle_DimensionsMedium", 
+    "Vehicle_DimensionsSmall", "Transaction_Amount_Group100-200", 
+    "Transaction_Amount_Group200-300", "Transaction_Amount_Group300+", 
+    "Amount_Paid_Group100-200", "Amount_Paid_Group200-300", "Amount_Paid_Group300+", 
+    "Vehicle_Speed_Group100-200", "Vehicle_Speed_Group200-300", "Vehicle_Speed_Group300+", 
+    "weekdaysbazar ertəsi", "weekdayscümə", "weekdaysçərşənbə"
+  )
+  
+  # Add missing columns with value 0
+  missing_cols <- setdiff(cols_to_add, colnames(new_data))
+  if (length(missing_cols) > 0) {
+    new_data[, missing_cols] <- 0
+  }
+  
+  # Select input features
+  new_input <- new_data[, cols_to_add]
+  
+  # Make predictions
+  predictions <- predict(rf_model, new_input)
+  binary_predictions <- ifelse(predictions >= 0.5, 1, 0)
+  
+  return(binary_predictions)
+}
+
+
+
+# Example usage:
+model_file <- "C:/Users/HP/OneDrive/İş masası/R Programming/rf_model.rds"
+rf_model <- load_model_function(model_file)
+
+# Example new data
+new_data <- data.frame(
+  Transaction_ID = 1,
+  Timestamp = c("1/6/2023 11:20"),
+  Vehicle_Type = c("Car"),
+  FastagID = c("12345"),
+  TollBoothID = c("A-101"),
+  Lane_Type = c("Express"),
+  Vehicle_Dimensions = c("Medium"),
+  Transaction_Amount = c(150),
+  Amount_paid = c(110),
+  Geographical_Location = c("34.0522118, 40.7128"),
+  Vehicle_Speed = c(60),
+  Vehicle_Plate_Number = c("ABC123")
+)
+
+# Make predictions
+predictions <- predict_with_model(rf_model, new_data)
+print(predictions)
+
+View(predictions)
+
+colnames(df_main)