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MachineHack is back with another exciting hackathon for this weekend. The 7th instalment of the popular weekend hackathon series, and this time we challenge data scientists to predict the price of used electronic devices in MachineHack’s Used Electronics Price Prediction: Weekend Hackathon #7.
The challenge will start on June 5th Friday at 6 pm IST.
Problem Statement & Description
We live in a world that is driven by technology and electronic devices as gadgets have become a part of our daily life. It is near impossible to think of a world without smartphones or tablets. Like many kinds of goods or products, used electronic devices have a good demand in our country. In this hackathon, we challenge the data science community to predict the price of used electronic devices based on certain factors.
Given are 6 distinguishing factors that can influence the price of a used device. Your objective as a data scientist is to build a machine learning model that can predict the price of used electronic devices based on the given factors.
The unzipped folder will have the following files.
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- Train.csv – 2326 observations
- Test.csv – 997 observations
- Sample Submission – Sample format for the submission
Target Variable: Price
The datasets will be made available for download on June 5th, Friday at 6 pm IST
Below are the file formats for the provided data
The top 3 competitors will receive a cool AIM goodie bag and a free pass to the Rising 2020.
- One account per participant. Submissions from multiple accounts will lead to disqualification
- The submission limit for the hackathon is 10 per day after which the submission will not be evaluated
- All registered participants are eligible to compete in the hackathon
- This competition counts towards your overall ranking points
- You will not be able to submit once you click the “Complete Hackathon” button. You may ignore this feature
- We ask that you respect the spirit of the competition and do not cheat
- This hackathon will expire on 8th June, Monday at 7 am IST
The leaderboard is evaluated using RMSLE for the participant’s submission.
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A Computer Science Engineer turned Data Scientist who is passionate about AI and all related technologies. Contact: email@example.com