Predict Health Insurance Owners' who will be interested in Vehicle Insurance

Build and evaluate several machine learning algorithms to predict credit risk.

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

Developed Machine Learning Models to Predict Credit Risk

Prediction module for Tumor Teller - primary tumor prediction system

Different Techniques to Handle Imbalanced Data Set

To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.

Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the…

Predict Health Insurance Owners who will be interested in Vehicle Insurance

Credit Worthyness Analysis using Linear Regression

Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department’s prediction fails sometimes. In this project, I learn how to build a machine learning model which can predict whether there will be rainfall today or not based on some atmospheric factors.

Data Science Major Project Completed in IT Vedant Institute using Machine learning algorithms

This project trains and avaluates machine learning model to identify creditworthiness of borrowers and classify credit risk predictions for a peer-to-peer lending services company.

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.

Use different techniques to train and evaluate different machine learning models to predict credit risk with unbalanced classes

Credit_Risk_Analysis using Machine Learning

Python and sklearn are used to build and evaluate multiple machine learning models to predict credit risk.