Uber Data Analysis

[alo7lika]

Is your feature request related to a problem? Please describe. The current exploratory data analysis (EDA) on Uber ride data primarily focuses on visualizing trends and identifying patterns. However, it lacks advanced predictive analytics capabilities to forecast future ride demand based on historical data. This limitation makes it difficult for stakeholders to make informed decisions regarding pricing strategies and resource allocation during peak hours.

Describe the solution you'd like I would like to implement a predictive analytics feature that utilizes historical ride data to forecast demand for Uber rides in specific locations and times. This can involve developing machine learning models (such as time-series forecasting models) to predict future ride volumes, which would allow Uber to optimize pricing and driver allocation more effectively.

Describe alternatives you've considered

• Simple Statistical Analysis: Using basic statistical methods to estimate future demand based on averages and trends. However, this approach may not be as accurate or robust as machine learning models.
• Heat Maps: Creating heat maps to visualize demand patterns over time, but this would still not provide predictive capabilities.
• Moving Averages: Implementing moving averages for ride demand, but this approach may lag behind actual trends and not account for external factors.

Approach to be followed (optional)

• Data Collection: Gather historical Uber ride data, including timestamps, pick-up/drop-off locations, and ride volumes.
• Data Preprocessing: Clean and preprocess the data to handle missing values and format it for analysis.
• Feature Engineering: Create features that capture time of day, day of the week, and holiday effects, which may influence ride demand.
• Model Selection: Choose appropriate machine learning models for time-series forecasting, such as ARIMA or LSTM.
• Model Evaluation: Split the data into training and testing sets to evaluate the model's performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
• Visualization: Create visualizations to compare predicted vs. actual demand and identify trends.
• Additional context

Add any other context or screenshots about the feature request here. Implementing predictive analytics in the Uber Data Analysis project can significantly enhance the insights derived from the data, leading to better decision-making and improved operational efficiency for ride-sharing platforms. This feature could be beneficial for stakeholders looking to optimize their strategies based on data-driven forecasts.

ADD LABELS GSSOC EXT 24 AND HACKTOBERFEST ASSIGN ME THIS PROJECT

[github-actions[bot]]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊

[UppuluriKalyani]

@alo7lika you can do more good projects...try to come up with good ideas.

[github-actions[bot]]

Hello @alo7lika! Your issue #124 has been closed. Thank you for your contribution!