There seems to be no end to bad loans in the country. According to the Reserve Bank of India, the overall bad loans as of March 2021 stood at INR 8.35 lakh crore, compared to INR 8.96 lakh crore in March 2020.
In a bid to solve the loan defaulter problem, Deloitte, in partnership with MachineHack, is launching a hackathon for data scientists and machine learning practitioners called ‘Machine Learning Challenge,’ from November 29 to December 13, 2021. The winners of the hackathon will get a chance to win cash prizes worth up to INR 1 lakh.
Let the challenge begin!
The time is ripe to showcase your talent and technical prowess. So, what are you waiting for? Participate in the fortnight-long hackathon, and get a chance to win cash prizes, alongside improving their Global Leaderboard Rankings and becoming the ultimate MachineHack Grand Master. The challenge is open to data scientists, machine learning practitioners, analytics professionals, and tech enthusiasts. It is designed for participants who are intermediate level.
The challenge starts on November 29, 2021.
Problem Statement & Description
Banks run into losses when a customer doesn’t pay their loans on time. Because of this, every year, banks have losses in crores, and this also impacts the country’s economic growth to a large extent. In this hackathon, we look at various attributes such as funded amount, location, loan, balance, etc., to predict if a person will be a loan defaulter or not.
To solve this problem, MachineHack has created a training dataset of 67,463 rows and 35 columns and a testing dataset of 28,913 rows and 34 columns. The hackathon demands a few pre-requisite skills like big dataset, underfitting vs overfitting, and the ability to optimise “log_loss” to generalise well on unseen data.
Datasets will be made live on November 29, 2021, at 6:00 PM.
Submission Guidelines
- Sklearn models should support the predict() method to generate the predicted values.
- The participant should submit a .csv file with exactly 28,913 rows with 1 column (Loan status). The submission will return an Invalid Score if you have extra rows or columns.
- The file should have exactly 1 column.
Note: Do not shuffle the sequence of the test series.
If you are using pandas, use this submission code:
submission_df.to_csv(‘my_submission_file.csv’, index=False)