Bike-sharing systems play a vital role in urban transportation. This assignment aims to predict bike demand, crucial for system optimization and resource management.
- Overview
- Technologies Used
- Data Preparation
- Model Building and Analysis
- Conclusions
- Acknowledgements
- Contact
- Objective: This assignment employs linear regression to forecast bike-sharing demand based on environmental and temporal factors.
- Significance: Bike-sharing systems are integral to urban mobility, necessitating accurate demand predictions for optimal fleet management and infrastructure planning.
- Python: 3.12.2
- Jupyter Notebook: 7.4.2
- Anaconda: 2023.10
- Libraries:
- Numpy: 1.26.2
- Pandas: 2.1.4
- Plotly: 5.18.0
- Matplotlib: 3.6.2
- Seaborn: 0.12.2
- Statsmodels: 0.14.0
- Scikit-learn (Sklearn): 1.2.0
- Data Collection: Gathered dataset encompassing environmental and temporal features relevant to bike-sharing demand.
- Data Cleaning: Ensured data integrity by handling missing values and inconsistencies.
- Feature Engineering: Transformed data through encoding categorical variables and scaling numerical features.
- Feature Selection: Utilized Recursive Feature Elimination (RFE) for optimal feature selection, refining the model iteratively.
- Model Validation: Conducted residual analysis to validate assumptions and ensure model robustness.
- Prediction and Insights: Generated predictions using the finalized linear regression model, extracting actionable insights into bike-sharing demand dynamics.
- Data Integrity: Ensured data quality through rigorous cleaning and preparation.
- Visualization: Visualized data patterns to inform model development and interpretation.
- Model Performance: Developed a reliable linear regression model validated through thorough analysis and prediction accuracy.
- Operational Insights: Derived actionable insights to enhance bike-sharing system efficiency and service quality.
- Resources: Utilized resources from:
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