Description : Using the various past credit history and other data of a client an automated process needs to be setup to give loans to the eligible candidates who have no or lower risk to default on payments. The metric used is AUC score.
Language : Python
Environment : Jupyter notebook Path : Home Credit Risk Default
Description : The banks with the intent of credit card were more focused on the number of customers using their credit service but the drawback of them not being able to pay back the credit in time was an issue that soon followed, a system was in need to effectively decide the credit limit to be allowed to a person based on his previous credit history
Language : Python
Environment : Jupyter notebook Path : Bank Credit Card Defaulter
Description : Whenever an individual/corporation applies for a loan from a bank, their credit history undergoes a rigorous check to ensure that whether they are capable enough to pay off the loan. We have to build a model to automate this process of loan defaulter prediction to help decide whether to issue the loan to the individual or corporation.
Language : Python
Environment : Jupyter notebook Path : Loan Defaulter prediction
Description: From the given IAMDataset which consists of images of handwritten words. Our task is to automate the process of recognising each word from the image file using deep learning.
Language : Python
Environment : Colab Notebook Path: Handwriting recognition