BoomBikes, a U.S.-based bike-sharing service, has faced a significant drop in revenues due to the COVID-19 pandemic, which has led to a struggle for sustainability in the current market conditions. To counteract this, BoomBikes aims to develop a strategic plan to boost revenue as soon as lockdown restrictions are lifted and the economy stabilizes.
BoomBikes, a U.S.-based bike-sharing service, has faced a significant drop in revenues due to the COVID-19 pandemic, which has led to a struggle for sustainability in the current market conditions. To counteract this, BoomBikes aims to develop a strategic plan to boost revenue as soon as lockdown restrictions are lifted and the economy stabilizes.
To prepare for post-pandemic conditions, BoomBikes wants to better understand the anticipated demand for shared bikes. By gaining insights into the needs of customers once normalcy returns, the company aims to position itself as a standout provider and achieve a competitive advantage over other service providers.
Problem Statement BoomBikes has engaged a consulting firm to identify the main factors influencing the demand for shared bikes in the U.S. market. The company wants to understand:
The significant variables that predict shared bike demand How effectively these variables explain variations in bike demand Using a dataset collected on daily bike usage across the U.S., BoomBikes intends to analyze the impact of various factors based on weather and consumer behavior.
Business Objective Our goal is to create a model that forecasts the demand for shared bikes, utilizing the available variables. This model will help management assess how demand responds to different factors, enabling the company to align its business strategy with expected demand levels and enhance customer satisfaction. Additionally, the model will provide valuable insights into demand patterns in a new market environment.
Proposed Solution Approach We will utilize multiple linear regression to address this problem, following these steps:
Data Review and Initial Exploration Data Visualization Data Preparation Splitting Data into Training and Testing Sets Model Building Analyzing Residuals on Training Data Making Predictions with the Final Model Model Evaluation Conclusion Assumptions As time is a key factor for the company’s future strategy, we will incorporate it into the analysis. The Plotly library will be used to create clean and interactive visualizations, including a range of exploratory plots.
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Temperature is a strong predictor of bike rentals: The analysis shows that temperature (represented by 'atemp') has a strong positive correlation with the total number of bike rentals ('cnt'). This suggests that warmer weather encourages more people to use bike-sharing services.
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The model successfully predicts bike rentals: The linear regression model built in the project achieves a good R-squared value, indicating that it explains a significant portion of the variance in bike rental counts. This suggests that the model can accurately predict the number of bike rentals based on the chosen features.
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Feature selection is crucial for model performance: The project demonstrates the use of Recursive Feature Elimination (RFE) and Variance Inflation Factor (VIF) to select relevant features and avoid multicollinearity. This step enhances the model's performance by reducing the number of variables and ensuring they are independent, thus improving the accuracy and interpretability of the model.
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Further model refinement is possible: While the model performs well, it shows some room for further improvement. The residual analysis indicates that the model's errors are mostly normally distributed, but some potential outliers or deviations from the expected pattern might exist. Investigating these and potentially incorporating different model types or transformations could lead to an even more accurate and robust prediction model for bike rentals.
- numpy: 1.26.4
- pandas: 2.2.2
- seaborn: 0.13.2
- pingouin: 0.5.5
- statsmodels: 0.14.4
This project was developed using various Python libraries and tools. We acknowledge the contributions of the developers and maintainers of the following:
- NumPy: For numerical computing and array operations.
- Pandas: For data manipulation and analysis.
- Scikit-learn: For machine learning algorithms and model selection.
- Matplotlib: For creating static, interactive, and animated visualizations.
- Seaborn: For statistical data visualization.
- Plotly: For interactive and web-based visualizations.
- Pingouin: For statistical analysis.
- Statsmodels: For statistical modeling. We express our gratitude to the open-source community for providing these valuable resources that made this project possible.
Created by @Aakashbhardwaj27 - feel free to contact me!