Project Update: Ola Bike Demand Analysis
I've been working on my Ola Bike Demand Analysis project to explore how machine learning can help analyze and predict bike ride demand.
What I've built:
- Exploratory data analysis to identify demand patterns.
- Data visualizations to understand trends and relationships.
- A Random Forest machine learning model for demand prediction.
- An interactive Streamlit dashboard where users can adjust parameters such as temperature, humidity, wind speed, hour, and season.
Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and Streamlit. What I learned: This project helped me understand the end-to-end machine learning workflow, from data analysis and model training to integrating a trained model into an interactive application.
I'm continuing to strengthen my skills in data science, machine learning, and deployment, with future improvements focused on model evaluation and prediction quality.
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