{"id":23879685,"url":"https://github.com/sayed-ashfaq/yulu-hypothesistesting","last_synced_at":"2026-04-17T04:32:12.005Z","repository":{"id":269242217,"uuid":"906832704","full_name":"sayed-ashfaq/Yulu-HypothesisTesting","owner":"sayed-ashfaq","description":"In this project, I analyzed Yulu bikes' usage patterns by performing data visualization and applying various hypothesis testing methods. 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With a mission to eliminate traffic congestion, Yulu provides shared, solo, and sustainable commuting solutions through a user-friendly mobile app.  \n\nYulu zones are strategically located near metro stations, bus stands, residential areas, office spaces, and other key locations to make first and last-mile connectivity smooth, affordable, and convenient.  \n\nRecently, Yulu has faced significant revenue dips and has engaged a consulting team to identify the factors driving the demand for their shared electric cycles in the Indian market.  \n\n---\n\n## Objective  \nThe primary goals of this analysis are to:  \n1. Identify significant variables that influence the demand for shared electric cycles.  \n2. Evaluate how well these variables explain the overall demand.  \n\n---\n\n## Dataset  \nThe dataset contains data on daily electric cycle rentals and related environmental and seasonal factors.  \n\n**Key Features**:  \n- **`datetime`**: Date and time of observation.  \n- **`season`**: Season of the year (1: Spring, 2: Summer, 3: Fall, 4: Winter).  \n- **`holiday`**: Whether the day is a holiday (0 = No, 1 = Yes).  \n- **`workingday`**: Whether the day is a working day (0 = No, 1 = Yes).  \n- **`weather`**: Weather conditions categorized as:  \n  - 1: Clear, Few Clouds, Partly Cloudy  \n  - 2: Mist, Cloudy, or Few Clouds  \n  - 3: Light Snow or Rain  \n  - 4: Heavy Rain or Snow  \n- **`temp`**: Temperature in Celsius.  \n- **`atemp`**: Perceived temperature in Celsius.  \n- **`humidity`**: Humidity percentage.  \n- **`windspeed`**: Wind speed.  \n- **`casual`**: Count of casual (non-registered) users.  \n- **`registered`**: Count of registered users.  \n- **`count`**: Total rental bike count, including both casual and registered users.  \n\n---\n\n## Process Overview  \n\n### 1. **Exploratory Data Analysis (EDA)**:  \n- Visualized relationships between demand (`count`) and features like `season`, `weather`, `temp`, and `windspeed`.  \n- Performed Bi-Variate Analysis to uncover trends and correlations.  \n\n### 2. **Statistical Testing**:  \n- **2-Sample t-Test**: Compared means of bike rentals across different groups (e.g., holidays vs. working days).  \n- **ANOVA**: Assessed differences in bike demand across seasons.  \n- **Chi-Square Test**: Evaluated relationships between categorical variables such as `weather` and `season`.  \n\n### 3. **Key Observations**:  \n- Seasonal variations significantly impact demand, with higher rentals observed in fall and summer.  \n- Weather conditions have a noticeable effect, with clear weather driving higher demand.  \n- Working days tend to see more registered users, while holidays attract more casual users.  \n\n---\n\n## Key Insights  \n\n1. **Seasonality in Demand**:  \n   - Rentals peak during favorable weather conditions (fall and summer seasons).  \n\n2. **Weather Influence**:  \n   - Clear weather positively correlates with increased bike rentals, while heavy rain or snow significantly reduces demand.  \n\n3. **User Behavior**:  \n   - Registered users contribute consistently higher rentals on working days, while casual users drive demand on holidays.  \n\n---\n\n## Tools and Libraries  \nThis project utilized the following tools:  \n- **Python**:  \n  - `Pandas` for data manipulation.  \n  - `Matplotlib` and `Seaborn` for data visualization.  \n  - `Scipy` and `Statsmodels` for statistical testing.  \n- **Jupyter Notebook**: For interactive analysis and documentation.  \n\n---\n\n## Repository Structure  \n- **`data/`**: Contains the dataset used for analysis.  \n- **`notebooks/`**: Jupyter Notebooks documenting the analysis process.  \n- **`visualizations/`**: Saved plots and charts.  \n- **`README.md`**: Overview of the project (this file).  \n\n---\n\n## Next Steps  \nFuture work could include:  \n1. **Feature Engineering**: Derive new features (e.g., lag variables or rolling averages) to enhance predictive insights.  \n2. **Predictive Modeling**: Use machine learning models to forecast bike demand.  \n3. **Deep Dive into User Segments**: Analyze behavioral patterns across casual and registered users for targeted marketing strategies.  \n\n---\n\n## Acknowledgments  \n- **Dataset Source**: Provided by Scaler for this analysis.  \n- **Python Libraries**: Thanks to the open-source Python community for providing versatile data analysis tools.  \n\n---\n\n## License  \nThis project is licensed for educational and non-commercial use only. If utilizing any part of this repository, please credit the author.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayed-ashfaq%2Fyulu-hypothesistesting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayed-ashfaq%2Fyulu-hypothesistesting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayed-ashfaq%2Fyulu-hypothesistesting/lists"}