The PyBer Analysis is creating a summary DataFrame of the ride-sharing data by city type and company valued at $2.3 billion. The analysis of all the rideshare data from January to early May of 2019. The Detailed analysis is done on keeping some important metrics and KPI's that will help management to look deeper in the performance of company. The metrics we will be doing analysis is total number of rides, drivers, the total amount of fares, the average amount of payment per each ride and total weekly fares for each city type and further we divided the analyis into Developed environments (Urban, Suburban, Rural) to get better understanding where the company generates maximum revenue and the traffic. This analysis will also help company to find the target auidence and how to overcome different challenges that they might face in future.
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The Data : Ride Share Data
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Tools: Jupyter Notebook , Stack Overflow, Python Packages(Pandas, Matplotlib)
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Code: Code in Juypter Notebook
The analysis is performed on human settlements (Urban, Suburban, Rural) riders, frivers, fares and taking out average overall. These some key points will help Riding Sharing app Company to perform perfomance analysis and depict the strongest and weakest point of company.
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As per the pie chart analysis we can see the Urban riders constitue 68% of all rides, while suburban take up to 26% and rural 5%. This depicts that ride sharing company generates maximum revenue from Urban city where the riders use the ride sharing app.
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The Second chart says where the drivers use the App and drivers for the company maximum drivers like to drive in Urban areas as of other human settlement as they get maximum number of riders.
The overall PyBer fare summary is presented in the image below:
As all the analysis and graphs depicts that all the traffic is towards urban cities where the Comapny generates maximum revenue.
The analysis is been very modest and just counting high level metrics like avg rider and driver count.
We can do more deep analysis and help company to make more solid management decisions:
- In the app we can introduce the new feature where we have an option to count of people driving in the car.
- Right now we are just doing analyis on overall perfomance of city to do more deep analyis we should look for traffic hotspots(Hotels, Office Buildings) where maximum request comes to driver for rides .
- Analysis can be done on different days of week, peak times of a day, Holidays.
- We can look for the bus schedules and metro schedules and offer customer discounts and driver perks to increase incoming requests.