Credit Risk Classification

This repo contains the dataset and notebook for the kaggle restaurant reviews five class rating prediction

This repository holds the dataset and notebooks for the Amazon Books dataset 4 class Rating prediction

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.

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.

Objective: Address the classification problem behind predicting credit risk

Using various techniques to train and evaluate a model based on loan risk. Also, using a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

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.

We used a dataset that included birth and personal data as well as Autism Spectrum Quotient test scores to train machine learning algorithms to predict autism. We used Logistic Regression, Neural Network Models and Keras Tuner with Random Oversampling to train one with 90% accuracy.

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.

Identifying rare event.

Predict Health Insurance Owners who will be interested in Vehicle Insurance

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

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

Testing various supervised machine learning models to predict a loan applicant's credit risk.

Logistic regression model with train_test_split data

Machine learning models for predicting credit risk in LendingClub dataset.

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