Predicting App Rating on Google Play Store

This project uses machine learning to predict the rating of an app on the Google Play Store. The project first cleans and preprocesses the data, then it trains and evaluates several different machine learning models. The best model, XGBoost Classifier, achieves an accuracy of 75.85%.

What's unique

This code is interesting because it shows how to use a variety of machine learning models to solve a real-world problem. The code is also well-documented and easy to follow, making it a valuable resource for anyone who is interested in learning more about machine learning.👨🏼‍💻

Summary

• 🤖 Machine learning is used to predict the rating of an app.
• 📈 Data is cleaned and preprocessed before training the models.
• 🚀 Several machine learning models are trained and evaluated.
• 🏆 XGBoost Classifier is the best model, with an accuracy of 75.85%.

Key Findings

• The most important features for predicting the rating of an app are the number of reviews, the size of the app, and the number of installs.

• The best machine learning model for predicting the rating of an app is XGBoost Classifier.

• The model accuracy can be improved by using more data and by using a more complex machine learning model.

Libraries Used

• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• XGBoost

Contribute

Contributions are welcome! Please open a pull request if you have any improvements or suggestions.