Kaggle Competition No.1 Solution: Predicting Consumer Credit Default
27.5 هزار بار بازدید -
7 سال پیش
-
Contributed by Bernard Ong, Jielei
Contributed by Bernard Ong, Jielei Emma Zhu, Miaozhi Trinity Yu, Nanda Trichy Rajarathinam. They enrolled in the NYC Data Science Academy 12-Week Full Time Bootcamp taking place between July - September 2016. This is their fifth project - Capstone Project, which was due in the 12th week of the program.
Ranking #1 on Kaggle for Predicting Consumer Debt Default
The team participated in a Kaggle closed competition to predict consumer credit default. Banks continue to look for the best credit scoring algorithm and one of its tenets is to predict the probability of an individual defaulting on their loan or undergoing financial distress. With this information, banks can make better decisions and borrowers can also do better financial planning. This challenge allowed the team to employ the best of machine learning models and algorithms to accurately predict the probability of default. Besides the technical aspects of the project, the team also used an Agile process designed for machine learning to ensure tasks can be done in parallel, in small chunks, and to fail fast, and iterate fast. The team ultimately achieved the highest AUC score possible, through six highly tuned models. They not only reach the top tiers of the scoreboard, but also smashed through to garner the #1 top ranking in the challenge. This is the story of how they did it.
Capstone Project Blog Link: http://blog.nycdatascience.com/studen...
Ranking #1 on Kaggle for Predicting Consumer Debt Default
The team participated in a Kaggle closed competition to predict consumer credit default. Banks continue to look for the best credit scoring algorithm and one of its tenets is to predict the probability of an individual defaulting on their loan or undergoing financial distress. With this information, banks can make better decisions and borrowers can also do better financial planning. This challenge allowed the team to employ the best of machine learning models and algorithms to accurately predict the probability of default. Besides the technical aspects of the project, the team also used an Agile process designed for machine learning to ensure tasks can be done in parallel, in small chunks, and to fail fast, and iterate fast. The team ultimately achieved the highest AUC score possible, through six highly tuned models. They not only reach the top tiers of the scoreboard, but also smashed through to garner the #1 top ranking in the challenge. This is the story of how they did it.
Capstone Project Blog Link: http://blog.nycdatascience.com/studen...
7 سال پیش
در تاریخ 1396/03/17 منتشر شده
است.
27,553
بـار بازدید شده