Data Science
Generate recommendations to improve travel time in Uberpool
Create an optimum matchmaking algorithm to improve travel time in a ride-sharing service
About this Menternship
Opportunity:
The idea for Uber was born on a snowy winter night in Paris when the founder could not get a cab. Now a household name, initially Uber was a simple answer to “what if you could request a ride from your phone?” Since its inception, it has evolved to deliver the best commute experience for its customers. From pre-booking, affordable prices, food delivery to ride-sharing, Uber has it all! Uber claims that movement is its power, and it has undoubtedly proven so.

Problem:
Ride-sharing comes with many advantages. It is easy on the environment, gives more earnings to the driver for the same distance and is inexpensive for the rider. However, it is not easy to design a ride-sharing application. An intelligent algorithm that chooses the optimum order of pickups and drop-offs such that the wait time and travel are minimal is incredibly hard to design. Data scientists of the top ride-sharing companies are constantly improving their algorithms to reduce travel time and deliver a better customer experience.

Deliverable:
In this menternship, you will design a matchmaking algorithm for ride-sharing services to improve travel time.
Why take up this Menternship?
On completing this Menternship, you will learn about
You will explore:
R 
Matchmaking algorithms
Ridesharing services

You will apply:
Data preparation 
Data analysis
Business intelligence

You will create:
Recommendations for improving travel time by optimizing matchmaking for ridesharing services.


Expected Output
  1. Report on the ride-sharing algorithms
  2. Data analysis on R
  3. Report Recommendations on improving travel time
  4. Video of Self
Generate recommendations to improve travel time in Uberpool
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