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

Demonstrating how changes in input image resolution affect the algorithm's output

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

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 techniques.

Review score prediction using text on the Amazon Fine Food dataset

Machine learning app to identify 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.

Using six different machine learning algorithms to evaluate credit data and compare each model’s accuracy, precision, and recall scores in relation to the data’s credit risk.

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.

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.

What causes a shopper to hit "purchase"?

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

Credit Risk Analysis utilizing imbalanced classification machine learning models

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

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.

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.

Binary classification from a dataset with imbalanced target feature classes