First Machine Learning Portfolio Project with different version

Modernize your Machine Learning Workflow with Low Code Tools & Quickly Integrate a Machine Learning Model in your Chatbot & Deploy to Whatsapp

This project aims to create a robust machine learning model that accurately predicts an applicant's eligibility for a loan based on various features such as income, credit history, and marital status.

End-to-end ML pipeline model and drift monitoring for Loan Eligibility classification.

This project focuses on developing Machine Learning (ML) models to predict loan eligibility, which is vital in accelerating the decision-making process and determining if an applicant gets a loan or not.

Loan Prediction Analytics vidhya Hackathons. Predict Loan Eligibility for Dream Housing Finance company.Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. Used Machine Learning model to Achieve maximum Accuracy.

machine learning project using logistic regresion and random forest with hyper tuned

𝐋𝐨𝐚𝐧 𝐄𝐥𝐢𝐠𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧

End-to-end ML pipeline model and drift monitoring for Loan Eligibility classification.

Credit Approval using Xgboost and GridSearch CV

This project is an interactive Streamlit application that predicts customer loan approval using a Random Forest Classifier. It features dataset upload, automated preprocessing, label & one-hot encoding, SMOTE oversampling, exploratory data analysis (EDA), performance metrics, confusion matrix visualization, and ranked prediction results.

Final Year major project

Minor Machine Learning project on Loan Prediction

It is a Machine Learning based project in which we aim to find out that whether a person is eligible for loan or not, based on different attributes.

An automated system to analyze customer financial data and provide preliminary eligibility decisions for various banking products, including car loans, personal loans, premium credit cards, and lines of credit. The system will generate estimated decisions based on predefined criteria, such as income, credit history, and existing liabilities.

📊 Predict loan eligibility using advanced ML techniques, combining multiple models for accurate insights based on financial and demographic data.