We have all been in situation where we go to a doctor in emergency and find that the consultation fees are too high. As a data scientist we all should do better. What if you have data that records 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 that.
Size of training set: 5961 records Size of test set: 1987 records
- Qualification: Qualification and degrees held by the doctor
- Experience: Experience of the doctor in 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
- Place: Area and the city where the doctor is located.
Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the predicted value and observed score values. The final score calculation is done in the following way: Submissions are evaluated on Root-Mean-Squared-Log-Error (RMSLE) error = RMSLE (error) Score = 1 – error
3rd Rank: 0.75759588
Featured in Analytics India Magazine: https://www.analyticsindiamag.com/how-a-business-analyst-a-data-scientist-a-technology-lead-solved-predict-a-doctors-consultation-fee-hackathon/