This repository contains the code and resources for our machine learning project focused on analyzing user behavior in the Lernavi application. The goal of this project is to predict the number of sessions a user is likely to stay on the application based on their behavioral data.
In this project, we explore the challenge of predicting user engagement and session duration in the Lernavi application. We aim to uncover patterns and features that have an impact on user behavior and develop machine learning models to make accurate predictions.
We utilize a dataset collected from the Lernavi application, which includes various user behavioral data such as the number of sessions finished, the number of NEXT actions taken, and the mean difficulty of questions encountered. This dataset serves as the foundation for our analysis and modeling efforts.
Feel free to discover our presentation in pptx, our notebook and our research paper.
We welcome contributions to enhance the project. If you find any issues, have suggestions, or would like to add new features, feel free to open an issue or submit a pull request. We appreciate your feedback and contributions!
For any questions or inquiries related to the project, please contact us at [email protected] or [email protected]. We appreciate your interest and feedback.
Happy exploring and modeling user behavior in the Lernavi application!