Data Science
Model Demand for BikeSharing using Multiple Linear Regression
Create an optimum matchmaking algorithm to improve travel time in a ride-sharing service
Certified by
Role
Data Scientist
Industry
Education
No. of Subscribers
23
Level
Advanced
Time Commitment
Submit First Draft in 30 days
Duration
60 days
Tools you’ll learn
Here’s What You Work On
About the Company
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.
Explore
the following work techniques
R
Matchmaking algorithms
Ridesharing services
Bridging the gap
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
Apply
the following skills
Data Analysis
Data preparation
Hypothesis testing
Model formulation
Expected output
In this menternship, you will design a matchmaking algorithm for ride-sharing services to improve travel time.
Create
the following deliverables
Data preparation of the given dataset
Recommendations for improving travel time by optimizing matchmaking for ridesharing services
What you’ll need before starting
R, Geohashing algorithm
