🚴 Predicting Bike Rental Demand — My Streamlit ML App for Regression

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Hey Streamlit Community! :waving_hand:

I just launched my first data science app using Streamlit, and I’m really excited to share it with you all. This project predicts bike sharing demand using a regression model trained on real-world data.

:wrench: What I used:

  • Python (Pandas, Scikit-learn)
  • Regression models (Linear Regression, Random Forest, XGBoost)
  • Data from UCI Bike Sharing Dataset

:bar_chart: Features:

  • Interactive sidebar to input variables (e.g., temperature, humidity, hour)
  • Predicts rental demand in real-time
  • Model insights and visuals (feature importance, EDA charts)

:globe_with_meridians: Live App: Bike Demand Predictor · Streamlit
:laptop: GitHub Repo: ahardwick95 (Arkevious Hardwick)

Would love to hear your feedback on the interface, performance, or ways I could improve the UX. Thanks for building such an awesome framework!

— Arkevious Hardwick

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