Credit risk is an unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. Use imbalanced-learn and scikit-learn libraries to build and evaluate machine learning models using resampling.

Built and evaluated several machine-learning models to predict credit risk using free data from LendingClub.

Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.

Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.

Credit risk analysis using scikit-learn and imbalanced-learn.

Predict credit risk with machine learning techniques.

Credit Risk Analysis utilizing imbalanced classification machine learning models

Use Python and Scikit-learn and Imbalanced-learn to predict credit risk. Compare the strengths and weaknesses of machine learning models. Assess how well a model works.

Predict credit risk with machine learning models by using different techniques to train and evaluate models with unbalanced classes.

Predict credit risk using a variety of Resampling Models and algorithms.

A case study utilizing supervised machine learning. (In order for the code to work, unzip the csv file in the Resources folder)

Final project for the end of the course in collaboration with Alessandro Zanzi.

Supervised machine learning models built and evaluated to predict credit loan risk. Resampling and ensemble techniques applied to the logistic regression classifier models using Scikit-learn, Imbalanced-learn, Pandas, and NumPy libraries in Python.

Conception and deployment of a credit-scoring model, API and interactive dashboard

Data visualization of the NYC restaurant data, and data analysis to gauge if a restaurant located in a high-income area receives a higher health inspection grade. Uses Python (Pandas, Scikit-learn, Imbalanced-learn), PostgreSQL, SQLAlchemy, Tableau, JavaScript (Plotly.js library), HTML, CSS, and Bootstrap.

Machine learning models for predicting credit risk in LendingClub dataset.

Testing different supervised machine learning algorithms to predict credit risk

Utilizing Machine Learning to Analyze and Assess Credit Risk