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Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost
LogoS is a design project about logos. In clean vector and organized as libraries to use. This one includes logos of Banks and Payment Gateways of Iran
Amazon SageMaker Solution for explaining credit decisions.
The Banking Industry Architecture Network e.V. (BIAN) model in Archimate 3
This is my HackerEarth Handle
Analytics labs notebooks for Statistics and Business School students
openLGD is a Python powered library for the statistical estimation of Credit Risk Loss Given Default models. It can be used both as standalone library and in a federated learning context where data remain in distinct (separate) servers
A Data science challenge - "Mekktronix Sales Forecasting" organised by ZS through Hackerearth platform. Rank: 223 out of 4743.
A Data-Driven Approach to Predict the Success of Bank Telemarketing
? coding exercises from leetcode, hackerrank, codesignal etc.
Customer churn prediction is the process of using machine learning models to identify customers who are likely to leave in the near future.