Predicting Agent Bonus in Insurance: A Comparative Analysis of Machine Learning Models

In this project, we embark on a journey into the realm of predictive analytics within the insurance sector, focusing on the prediction of Agent Bonus using advanced machine learning techniques. Our primary objective is to unravel the intricate relationship between various customer attributes and the resultant Agent Bonus. To achieve this, we employ three powerful regression models—Decision Tree, Linear Regression, and Random Forest—each offering distinct advantages in predicting this crucial aspect of insurance business.

Step 1: Data Understanding and Preprocessing

We begin by meticulously examining the dataset, understanding the significance of each column such as Age, CustTenure, EducationField, Gender, and more, in the context of predicting Agent Bonus. Data preprocessing entails addressing missing values, encoding categorical variables, and normalizing features to ensure the data is conducive for model training.

Step 2: Exploratory Data Analysis (EDA)

Through visualization and statistical analysis, we delve into the dataset's characteristics, uncovering correlations between customer attributes and Agent Bonus. We aim to understand how factors such as MonthlyIncome, Complaints, and CustCareScore influence the bonus, offering valuable insights for our predictive models.

Step 3: Model Building and Evaluation

Decision Tree Regression: We construct a decision tree that recursively segments the data into regions, allowing us to predict Agent Bonus based on feature splits. The tree's interpretability aids in deriving actionable insights.

Linear Regression: By employing linear regression, we establish a quantitative relationship between predictor variables and the predicted Agent Bonus. This model enables us to discern the individual impact of each attribute on bonus determination.

Random Forest Regression: Through an ensemble of decision trees, the Random Forest model aims to capture complex interactions among attributes. This approach often enhances predictive accuracy.

Step 4: Model Comparison and Evaluation

We evaluate the models using standard metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). Our comparison reveals which model best captures the nuances of the dataset, yielding predictions closest to the actual Agent Bonus values.

Step 5: Insights and Implications

The project culminates with insightful observations about the predictive power of each model, elucidating the attributes that exert the most influence on Agent Bonus determination. This information is invaluable for insurance companies seeking to optimize agent compensation strategies and enhance customer satisfaction.

By combining the prowess of machine learning with the intricacies of insurance dynamics, this project contributes to the evolving landscape of predictive analytics in the financial sector. It underscores the potential of data-driven decision-making and the relevance of advanced regression techniques in shaping the future of the insurance industry.