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MachineHack Launches New Hackathon: Use Your Data Science Skills To Predict A Doctor’s Consultation Fee

MachineHack Launches New Hackathon: Use Your Data Science Skills To Predict A Doctor’s Consultation Fee


Analytics India Magazine’s hackathon platform MachineHack, has come up with a new and challenging hackathon for data science enthusiasts. The new hackathon challenge called Predict The Doctor’s Consultation Fee Hackathon, will challenge the MachineHack users to predict a doctor’s consulting fee based on the given data. The winners of the hackathon will win exciting prizes.

We have all been in situations where we go to a doctor in an emergency and find that the consultation fee is far too high. As a data scientist, we all can do better. What if you have the data about the important details about a doctor and you get to build a model to predict the doctor’s consulting fee? This is the hackathon that lets you do just that.



The hackathon consists of a training set of a size of 5,961 records and the test set has 1,987 records. The data set includes:

Qualification: Qualification and degrees held by the doctor

Experience: Experience of the doctor in a number of years

Rating: Rating given by patients

Profile: Type of the doctor

Miscellaeous_Info: Extra information about the doctor

Fees: Fees charged by the doctor

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Place: Area and the city where the doctor is located.

The hackathon participants have to make use of this data, build a model and predict the consulting fee charged by the doctor.

MachineHack Platform has received an overwhelming response from its users. At MachineHack, we are constantly trying to bring to its users a diverse set of challenges, through these hackathons. We recently concluded two hackathons called “Predict The Data Scientists Salary In India Hackathon” and “Visualisation is Beautiful: Data Science Salary Visualisation Challenge”, and announced prices for the winners. We talked to the winners of these hackathons to know how they solved the particular problem of the hackathon.


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