Ola Bike Demand Analysis

Inspiration

Bike ride demand changes throughout the day depending on factors such as weather, temperature, humidity, wind speed, and season. Understanding these patterns can help ride-hailing services make better decisions about resource allocation and demand planning.

I built this project to explore how data analysis and machine learning can be used to understand bike demand patterns and predict potential ride demand.

What It Does

Ola Bike Demand Analysis is a machine learning project that analyzes demand patterns and predicts ride demand using relevant environmental and time-based features.

Key features include:

• Data Analysis: Explore data to identify patterns, trends, and relationships between variables.
• Data Visualization: Use charts and graphs to understand demand variations.
• Machine Learning: Train a Random Forest model to predict ride demand.
• Interactive Dashboard: Use a Streamlit interface to explore predictions by adjusting inputs such as temperature, humidity, wind speed, hour, and season.

How I Built It

I developed the project using Python and popular data science and machine learning libraries.

• Pandas and NumPy: For data processing and numerical operations.
• Matplotlib and Seaborn: For exploratory data analysis and visualizations.
• Scikit-learn: For building and training the Random Forest model.
• Streamlit: For creating an interactive web dashboard.
• Pickle: For saving and loading the trained machine learning model.

After preparing the data, I explored demand patterns, selected relevant features, trained the model, and saved it for use in the dashboard. I then connected the trained model to Streamlit so users could change input values and explore predicted demand.

Challenges I Faced

One of the main challenges was understanding how different factors influence ride demand and selecting useful features for the prediction model. Another challenge was integrating the trained machine learning model into an interactive Streamlit application.

I also had to ensure that the dashboard passed input features to the model in the correct format and displayed predictions clearly. Working through these challenges helped me better understand the complete machine learning workflow, from data preparation to model deployment.

What I Learned

Through this project, I strengthened my skills in:

• Exploratory data analysis and feature selection.
• Data visualization and interpreting patterns.
• Training and using machine learning models.
• Saving and loading trained models.
• Building interactive applications with Streamlit.
• Integrating data science workflows into a usable application.

What's Next

Future improvements could include evaluating additional machine learning models, improving prediction accuracy, adding model performance metrics, and deploying the dashboard publicly so users can interact with it online.

Built With

Python, Pandas, NumPy, Scikit-learn, Random Forest, Streamlit, Matplotlib, Seaborn, and Jupyter Notebook.

Built With

• data-analysis
• data-visualization
• jupyter
• jupyter-notebook
• machine-learning
• matplotlib
• numpy
• pandas
• python
• random-forest
• scikit-learn
• seaborn
• streamlit