This project is POC for Smart India Hackathon 2020. This Django based website. It includes three machine learning models - Loan Prediction Model, Admission Prediction Model and Email Spam Detection Model

A simple model to predict the user's ability to pay the loan.

This repo is for derived from a competition from analytics vidhya for predicting loan using the data given.

This project predicts the likelihood of loan approval based on applicants' financial and demographic data.

The purpose of using machine learning in this case is to automate the loan application evaluation process by building a predictive model that can classify loan applications into two categories: `Approved` or ` Rejected'

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.

This is a simple AI project that predicts whether a loan applicant is eligible for a loan or not. The project is implemented in Python and uses machine learning algorithms to make predictions based on various factors such as age, income, credit score, and loan amount.

The aim is to build a simple classification model for the given case scenario. The problem statement and the supporting dataset are attached for your reference. After building the classification model we need to predict the feature for the new entry. Classifier: Logistic Regression; Programming language: Python; IDE: Google Colab

Loan Prediction using Regression is a classic binary classification problem in Machine Learning. The goal is to predict whether a Loan Applicant is likely to be approved or rejected based on certain features or attributes.

This project predicts loan approval outcomes (Approved/Rejected) using a PyTorch neural network. It includes data preprocessing, train/validation/test split, model training with BCEWithLogitsLoss, and inference with probability-based classification.

This project focuses on predicting the loan approval status of applicants using machine learning techniques.