Predicting Gamer Engagement Levels - A Machine Learning Approach

Predicting Gamer Engagement Levels

A machine learning approach using classification techniques

In the rapidly evolving gaming industry, understanding player engagement is crucial for developers aiming to enhance user experience and retention. This project delves into predicting gamer engagement levels using machine learning techniques, focusing on Decision Tree, Random Forest, Logistic Regression, kNN, SVM and Gradient Boosting models.

The dataset used for this analysis, sourced from gaming behavior data, includes various features such as age, gender, playtime hours, in-game purchases, and player achievements. By employing exploratory data analysis (EDA), we identified key factors influencing engagement levels categorised as low, medium, and high. This categorisation allows developers to tailor their strategies to different player segments effectively. The insights gained from feature importance analysis also revealed attributes that significantly impact player engagement and could guide future game design decisions.

Throughout the modeling process, hyperparameter tuning was conducted to optimise model performance. The results demonstrated Gradient Boosting is the best performing model for predicting player engagement. In conclusion, this project highlights the potential of machine learning in understanding and enhancing gamer engagement. By leveraging these insights, developers can create more engaging experiences that resonate with players and foster long-term loyalty.

Predicting Gamer Engagement Levels