BIKE RENTAL PREDICTION MODEL 🚲📊

Project Summary 📝

The Bike Rental Prediction Model (ONGOING PROJECT) aims to create a model using historical data and Scikit-Learn's RandomForestRegressor that forecasts daily bike rental demand based on various factors. Helping the business optimize staffing, inventory management, and revenue.

Report Outline 🧾

• Project Summary
  • Features
  • Requirements
  • Installation
  • Usage
  • Project Structure
    • Notes

Tools and Technologies 🛠️

• Programming Languages: Python
• Libraries/Frameworks:
  • Data Manipulation & Analysis: Pandas, NumPy
  • Data Visualization: Matplotlib, Seaborn
  • Machine Learning: Scikit-Learn (RandomForestRegressor)
  • Data Preprocessing: Scikit-Learn, Pandas
  • Model Export: Joblib
  • Model Evaluation: Scikit-Learn (MSE, R-Squared)
• Data Storage: CSV files
• Version Control: Git
• Development Environment: VSCode

Project Steps 🛤

1. Data Collection:

   • Download data from the following sources:
     • Original Source: http://capitalbikeshare.com/system-data
     • Weather Info: http://www.freemeteo.com
     • Holiday Schedule: http://dchr.dc.gov/page/holiday-schedule
     • Data Downloaded for Model Development: https://www.kaggle.com/datasets/marklvl/bike-sharing-dataset?select=hour.csv

2. Exploratory Data Analysis (EDA):

   • Perform EDA to explore the distribution of bike rentals.
   • Visualize correlations and trends using Matplotlib and Seaborn.
   • Identify key features impacting bike rentals, such as weather, holidays, and seasonal changes.

3. Model Development:

   • Split the data into training and testing sets.
   • Train the RandomForestRegressor model and tune hyperparameters.
   • Evaluate model performance using metrics like Mean Squared Error (MSE) and R-squared.

4. Save Model for Future Use:

   • Use joblib to save the model for future deployment and predictions.

5. Documentation and Reporting:

   • Document the entire process, from data sources to model details.
   • Provide visualizations for actual vs. predicted values.

6. Future Work:

   • Update the dataset periodically and test the model with new data.
   • Experiment with different models and hyperparameters for better performance.
   • Consider real-time predictions in a deployment scenario.

Visualizations 📊

Notes 📌

• Data Sources: Original bike-sharing data, weather data, and holiday schedules were used to build this model.
• Model Updates: The model is designed for future improvements, such as using a more up-to-date dataset and experimenting with different models.
• Deployment: The model is exportable for deployment in production environments using joblib.
• Evaluation: Metrics such as MSE and R-squared were used to measure the model's performance.
• Maintenance: Future work will include refining the model with additional data and testing deployment scenarios for real-time predictions.