Machine Learning
Machine Learning
Predicting Gaming Engagement Level
A Classification Project
Visit kaggle to download the dataset that is made available by Mr. Rabie El Kharoua.
This project investigates the engagement levels of gamers, categorising them as low, medium, or high. Leveraging machine learning (ML) techniques, the primary objective is to identify the optimal classification ML model and hyperparameters for accurately predicting these engagement levels.
The project starts with Exploratory Data Analysis (EDA) to uncover patterns within the dataset and establish links among variables. Following EDA, six classification models—Decision Tree, Random Forest, Logistic Regression, k-Nearest Neighbours (kNN), Support Vector Machines (SVM), and Gradient Boosting—are evaluated based on performance metrics such as accuracy, precision, recall, F1 score and runtime.
The Gradient Boosting model is superior, not only in recall, but also in all other areas, for predicting engagement levels effectively.
This project is available at both my GitHub and kaggle pages.