The Bike Demand Prediction App is a machine learning-based web application designed to forecast bike rental demand based on various features such as weather conditions, time of day, seasonality, and holidays. The app integrates a trained XGBoost model with real-time weather data from the OpenWeather API and is deployed using Streamlit Cloud.
🔗 Live Demo:
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✅ Real-time Weather Data: Fetches live weather conditions based on user input.
✅ ML Model Prediction: Used a XGBoost model to estimate bike demand.
✅ User-Friendly UI: Interactive inputs and a modern-themed dashboard.
✅ Custom Styling: Beautiful gradient background with a dark sidebar.
- Python 🐍
- Streamlit 🎨
- Pandas, NumPy ,scikit-learn,Matplotlib & Seaborn 🏗
- Machine Learning (XGBoost) 🤖
- OpenWeather API ☁
- CSS (for Styling) 🎨
git clone https:https://github.com/ydv2027/Bike_Demand_Predictor
cd bike-demand-prediction