The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behavior related data from which they are unable to arrive at a judgment if an applicant can be defaulter or not. Credit Risk assessment is a crucial issue faced by Banks nowadays which helps them to evaluate if a loan applicant can be a defaulter at a later stage so that they can go ahead and grant the loan or not. This helps the banks to minimize the possible losses and can increase the volume of credits. This project evaluates the perfect execution of credit risk problem to classify the potential customers who are eligible for lending loans. This is a basic classification problem with important business applications. The goal of this problem solution is to build a model that lending institution can use to help make the best financial decisions.

we have addressed the problem of classifying highly unbalanced data using supervised machine learning algorithms. Unbalanced data is ubiquitous in nature, it’s dealt with in a wide range of fields including but not limited to that of business, bioinformatics, engineering and banking sector. We have focussed here on Credit Risk, which is defined as risk of default on a debt that may arise from a borrower failing to make required payments. In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs. Usually in a Credit Risk problem, loan default is a rare phenomenon and that is why we have such unbalanced data.

This project aims to predict credit risk for individuals applying for loans, classifying whether they will default based on features such as age, income, employment length, loan amount, interest rate, percentage of income, credit length, home ownership, and loan intent.

Loan default prediction is an important aspect in banking industry. In Finance and Banking sector the losses incurred by this Industry due to loan defaults or we can say customer not paying back their loan is increasing drastically. In this study we have built a loan default prediction model on the data collected for borrowers of multiple states in the Unites States of America. The research focuses on constructing a model that would predict whether the borrower would repay the loan or would end up being a defaulter. The research uses Random Forest classifier, Adaboost classifier and Artificial neural network model to compare the performance of these classifiers. It also works on understating and obtaining the important features that are to be monitored carefully before sanctioning any such credits. Keywords: Credit risk analysis, Loan Default, Machine Learning, Random Forest classifier, Adaboost Classifier, Artificial Neural network.

The most important function of a bank is to accept deposits and provide loans using it. Bank attracts Credit risk by lending loans to its customers where Credit Risk is the probability of the counterparty defaulting on its loan. So it a pivotal function of a bank to seggregate the customers into good and bad credit applicants. So here, with the past year data, we'll classify the credit applicants using the ML algorithms.

People often save their money in the banks which offer security but with lower interest rates. Lending Club operates an online lending platform that enables borrowers to obtain a loan, and investors to purchase notes backed by payments made on loans. It is transforming the banking system to make credit more affordable and investing more rewarding. But this comes with a high risk of borrowers defaulting the loans. Hence there is a need to classify each borrower as defaulter or not using the data collected when the loan has been given.

Advanced ML project based on IBM Data Science course with XGBoost, ROC-AUC, and business case

Financial institutions need to identify loan default risks for portfolio health. Our goal: develop ML model to classify defaulters, improving credit assessment & risk mitigation. Approach uses loan details, borrower data (income, credit, demographics). Outcome: reliable system for informed lending, loss reduction. Notebook covers complete modeling.

This project focuses on building a high-performance credit-risk classifier and applying SHAP for interpretability. You will train a non-linear model, compute global and local SHAP values, analyze high-risk vs low-risk customer profiles, and provide insights explaining key drivers of loan default risk.

Credit Risk Scoring Project is an advanced machine learning project aimed at predicting the likelihood of a customer defaulting on their loan. This project uses a Random Forest Classifier to build a robust credit risk scoring model.

The Credit Risk Modeling project focuses on predicting the likelihood of a borrower defaulting on a loan using classification techniques. By analyzing historical data, such as credit history, income levels, loan amounts, and repayment behavior, the model classifies borrowers into risk categories.

Credit sight is an end-to-end AI-powered credit risk assessment system built for internal use by financial analysts. It predicts the probability that a loan applicant will default, classifies them into risk tiers, and recommends an action — Approve, Review, or Reject.

A Flask-based app that uses a LightGBM classifier to predict loan defaults with high accuracy. It efficiently handles large datasets, leveraging machine learning to assess borrower creditworthiness, calculate risk percentages, and help financial institutions minimize losses and prevent NPAs by optimizing lending strategies.

Banks earn a major revenue from lending loans. But it is often associated with risk. The borrower's may default on the loan. To mitigate this issue, the banks have decided to use Machine Learning to overcome this issue. They have collected past data on the loan borrowers & would like you to develop a strong ML Model to classify if any new borrower is likely to default or not. The dataset is enormous & consists of multiple deteministic factors like borrowe's income, gender, loan pupose etc. The dataset is subject to strong multicollinearity & empty values. This model is created to overcome these factors & build a strong classifier to predict defaulter

This project builds a predictive model to assess loan default risk using customer data. It applies Logistic Regression, Decision Trees, and Random Forests to classify applicants as low or high credit risk. Through EDA, feature engineering, and model evaluation, it showcases an end-to-end data science pipeline.

This project focuses on building a machine learning pipeline to predict the likelihood of loan default using real-world credit data. The goal is not only to classify applicants as high or low risk, but also to support data-driven decisions around loan approvals using expected loss calculations and a strategy table simulation.

People often save their money in the banks which offer security but with lower interest rates. Lending Club operates an online lending platform that enables borrowers to obtain a loan, and investors to purchase notes backed by payments made on loans. It is transforming the banking system to make credit more affordable and investing more rewarding. But this comes with a high risk of borrowers defaulting the loans. Hence there is a need to classify each borrower as defaulter or not.

The model is a Logistic Regression classifier.Its job is to answer one question:“Will this customer fully pay the loan or default?”It does this by:Turning customer details into numbers,Calculating a risk score Converting that score into a probability,Making a yes/no decision.

Several supervised machine learning models were trained to predict if a peer-to-peer (P2P) loan will be fully paid or charged-off. The P2P lending industry is a growing space and it is imperative that P2P platforms have a strong loan default risk model in order to attract investors as well as minimize future losses. The data came from Lending Club, an ex-P2P lender, from 2007 - 2018, which includes ~2.26 million loan samples with 151 unique features. The best performing model was a Random Forest classifier, tuned with a cross-validated grid search, that could correctly predict default on 90.67% of unseen data with a recall score of 77.25%. Please click on the 'Repo' link below if you want to read further.

Loan Default / Credit Risk Classifier

A data-driven solution to predict high-risk loan applicants and support smarter approval decisions for banks and financial institutions.

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Predict loan defaults using ML

Credit risk classifier, predicting default or non default loan applicant

Perform a gender based fairness audit on an implementation of the Home Credit Default Risk Kaggle Competition.

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Machine Learning model for predicting loan default risk using Decision Tree Classifier

Predicting loan default probability using Random Forest Classifier for financial risk assessment.

Loan Default Risk Based on Customer Behavior Using Random Forest Classifier and SMOTE