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The project predicts the probability of loan default using various financial features of customer. I applied SMOTENN by combining SMOTE and Edited Nearest Neighbor (ENN) to handle class imbalance. Logistic Regression, Random Forest and CATBOOST models have been apllied and evaluated based on accuray, F1 score, ROC-AUC score.

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cat-boost-algorithm class-imbalance classification-problem finance fraud-detection loan-default-prediction logistic-regression machine-learning random-forest

loan-default-prediction's Introduction

Hi there! Welcome to my Github Profile!👋

I am Faseeh and I am passionate about all things related to data and machine learning systems. I have recently completed my masters degree in Data Analytics and located in London, UK. I am currently looking for the job opportunities to start my career as a data scientist.
Please check out my portfolio for my data science and data analytics projects, skills, and certificates.

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