Objective

The primary goal of this analysis was to understand the distribution of trip categories, popular routes, and mileage patterns while addressing missing data effectively.

Steps and Techniques

1 . Data Cleaning and Preprocessing :

• Handled missing values in the dataset, particularly in the "PURPOSE" column, by replacing null values with "NOT AVAILABLE."
  
• Ensured data consistency by verifying data types and checking for duplicate or inconsistent records.

2 . Exploratory Data Analysis (EDA) :

• Summarized key statistics, such as total mileage and the proportion of trips categorized as "Business" or "Personal."
  
• Created plots using matplotlib and seaborn to identify trends in trip times, locations, and distances traveled.

3 . Insights Generation :

• Highlighted popular trip start and stop locations, indicating frequently visited areas.
  
• Analyzed the correlation between mileage and trip purpose to infer user behavior patterns.

Key Findings

Dominant Categories : Business trips dominated the dataset, with a significant portion of rides lacking a specified purpose.
Popular Routes : Frequently traveled routes were concentrated in urban areas, indicating a high demand for rides within city limits.
Mileage Trends : The average trip mileage provided insights into the general distance covered per ride.

Conclusion

This analysis showcases how raw transportation data can be transformed into actionable insights, enabling better understanding of user behavior and operational trends. These findings can support decisions in fleet management, route optimization, and customer engagement strategies.

Code link :

Click here to access the code