Loan Application Prediction through machine learning moldes : Logistic Regression, Random Forest, DecisionTree,

Loan Approval Prediction Problem Type Binary Classification Training Accuracy 84% Loan approval prediction is classic problem to learn and apply lots of data analysis techniques to create best classification model. Given with the data set consisting of details of applicants loan and status whether the loan application is approved or not. Basis o…

A simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.

It is a classification Problem where we are supposed to predict whether a loan would be approved or not.

Flask app for predicting loan grant. Model Deployed using Heroku.

This GitHub repository contains a Python script for a machine learning project focused on predicting loan approval using a Support Vector Machine (SVM) classifier.

This project focuses on predicting loan approval using machine learning algorithms. The model takes various customer features as input and predicts whether a loan application will be approved or not

Loan Application Evaluator, using ML approach, Flask and Heroku.

The project employs Flask-Login for user session management, bcrypt for password hashing, and Flask-Migrate for database migrations. It serves as an example of integrating machine learning functionality within a web application for loan eligibility determination

Developed ML Model to predict whether a loan will be approved or not, based on various parameter, such as Marital Status, Income, etc.

The goal of this project is to develop a predictive model for loan approval classification by following a comprehensive data science workflow.

Machine Learning Project - Loan Approval Prediction

The project aims to predict loan approvals based on various factors, leveraging machine learning models and data pipelines.

The Loan Approval Prediction project used supervised learning with a Decision Tree Classifier for binary classification (loan approved or not), achieving 90% accuracy with interpretable predictions based on historical data.

A loan approval machine learning model that predicts whether a loan request will be approved based on key features such as income, credit score, and employment history. The model was deployed as a web application using Flask, allowing users to input data and receive instant loan approval predictions.

Explore predictive modeling in this project by applying classification techniques to a loan approval dataset. Analyze and preprocess the data, then use models like K-Nearest Neighbors, Random Forest, SVC, and Logistic Regression to predict loan outcomes. Gain insights into approval factors and enhance prediction accuracy.

This repository is an attempt to reimplement the research conducted by Nazim Uddin and his teams

About Loan Approval Prediction Problem Type Binary Classification Training Accuracy 84% Loan approval prediction is classic problem to learn and apply lots of data analysis techniques to create best classification model. Given with the data set consisting of details of applicants loan and status whether the loan application is approved or not.

Loan approval predictive model using classical classifiers.

A project to predict loan approvals using 💡 Machine Learning models. Features 📊 data cleaning, 🔍 EDA, and 🧠 models like Logistic Regression and Decision Trees for accurate predictions.