x̄ - > Advanced data analysis with risk identification R

 To perform advanced data analysis with risk identification, you can use various statistical and machine learning techniques in R. Here's an example case study with R code that demonstrates risk identification using logistic regression:

Case Study: Loan Default Prediction

1. Data Preparation:

   - Obtain a dataset containing information about loan applicants, including various features such as credit score, income, employment status, loan amount, etc.

   - Split the dataset into a training set and a test set.

2. Data Exploration:

   - Load the necessary packages:

     ```R

     library(dplyr)

     library(ggplot2)

     library(corrplot)

     ```

   - Explore the dataset by examining its structure and summary statistics:

     ```R

     # Load the dataset

     loan_data <- read.csv("loan_data.csv")

     # Overview of the dataset

     str(loan_data)

     summary(loan_data)

     ```

   - Visualize the relationships between variables and identify potential risk factors:

     ```R

     # Create a correlation matrix

     cor_matrix <- cor(loan_data[, c("CreditScore", "Income", "LoanAmount", "Default")])

     # Plot a correlation heatmap

     corrplot(cor_matrix, method = "color", type = "upper")

     ```

3. Data Preprocessing:

   - Handle missing values and outliers:

     ```R

     # Replace missing values with appropriate imputation techniques

     loan_data$CreditScore[is.na(loan_data$CreditScore)] <- mean(loan_data$CreditScore, na.rm = TRUE)

     # Identify and handle outliers

     outlier_threshold <- quantile(loan_data$LoanAmount, c(0.01, 0.99))

     loan_data$LoanAmount[loan_data$LoanAmount < outlier_threshold[1]] <- outlier_threshold[1]

     loan_data$LoanAmount[loan_data$LoanAmount > outlier_threshold[2]] <- outlier_threshold[2]

     ```

   - Encode categorical variables:

     ```R

     # Convert categorical variables into factors

     loan_data$EmploymentStatus <- as.factor(loan_data$EmploymentStatus)

     ```

4. Model Development - Logistic Regression:

   - Split the data into a training set and a test set:

     ```R

     set.seed(123)

     train_indices <- sample(1:nrow(loan_data), 0.7 * nrow(loan_data))

     train_data <- loan_data[train_indices, ]

     test_data <- loan_data[-train_indices, ]

     ```

   - Train a logistic regression model:

     ```R

     # Build the logistic regression model

     model <- glm(Default ~ ., data = train_data, family = "binomial")

     # View the model summary

     summary(model)

     ```

5. Model Evaluation:

   - Predict on the test set and evaluate the model performance:

     ```R

     # Make predictions on the test set

     test_data$predicted_prob <- predict(model, newdata = test_data, type = "response")

     # Create a binary prediction based on a probability threshold

     threshold <- 0.5

     test_data$predicted_default <- ifelse(test_data$predicted_prob >= threshold, 1, 0)

     # Evaluate the model performance

     confusion_matrix <- table(test_data$Default, test_data$predicted_default)

     accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)

     precision <- confusion_matrix[2, 2] / sum(confusion_matrix[, 2])

     recall <- confusion_matrix[2, 2] / sum(confusion_matrix[2, ])

     f1_score <- 2 * precision * recall / (precision + recall