Playtime Classification with Logistic Regression #868
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👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. |
UppuluriKalyani
approved these changes
Nov 10, 2024
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🎉🎉 Thank you for your contribution! Your PR #868 has been merged! 🎉🎉 |
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Related Issues or bug
Predicting user playtime categories in a gaming environment can provide insights into player behavior, enhancing personalized recommendations and improving retention strategies. This project aims to build a logistic regression model that accurately categorizes playtime data, enabling game developers to better understand their users and optimize gameplay experiences.
Fixes: #864
Proposed Changes
This project utilizes logistic regression to classify playtime data into various categories based on factors such as session length, frequency, and user demographics. By leveraging this model, we can analyze and predict playtime patterns, helping stakeholders in the gaming industry understand user engagement more effectively. Logistic regression, a supervised learning method, is chosen for its effectiveness in binary and multiclass classification tasks, especially for datasets where interpretability is important.