App Success Predictor
Year
2025
Tech & Technique
Python, scikit-learn, Streamlit, Pandas, Machine Learning
Description
Built App Success Predictor using Python, scikit-learn, and Streamlit, implementing machine learning models to predict app success with 85% accuracy. Features real-time predictions and comprehensive analytics.
Developed a robust ML platform for forecasting app performance. Features dynamic input processing, instant success probability scoring, and actionable insights for developers.
Developed a robust ML platform for forecasting app performance. Features dynamic input processing, instant success probability scoring, and actionable insights for developers.
My Role
As the ML developer, I:
- Built predictive models with 85% accuracy using scikit-learn.
- Implemented data processing pipelines with Pandas.
- Created interactive dashboard using Streamlit.
- Developed real-time prediction system.
- Designed intuitive UI for data visualization.
- Built predictive models with 85% accuracy using scikit-learn.
- Implemented data processing pipelines with Pandas.
- Created interactive dashboard using Streamlit.
- Developed real-time prediction system.
- Designed intuitive UI for data visualization.
