Comparison of model selection methods for Boston dataset

Mind Foundry OPTaaS R Client

Simple python implementation of Approximate Bayesian Computation for Model Choice

Text Classification / Sentiment Analysis with Machine Learning

Use decision trees to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

The best model is determined using PCA evaluation for the Arrhythmia prediction

Build a predictive model to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Data Science - Support Vector Machine Work

This project was in collaboration with University Hospital Birmingham

A library for running Bayesian active model selection on human behavioral experiments

Trabalhos e notas de aula da disciplina MAC0460 - Introdução ao Aprendizado de Máquina, no primeiro semestre de 2021, no IME-USP.

Bike sharing systems are a new generation of traditional bike rentals where the whole process from membership, rental and return back has become automatic. Through these systems, users are able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around th…

My take on "Do not overfit! II" competition on Kaggle which challenges participants to avoid overfitting.

• Build an ML model to predict Hyundai used cars prices based on different column features. • Performed EDA (Exploratory Data Analysis), Feature engineering, and Feature selection and compared different models and performed Hyperparameter tuning using GridSearchCV.

Practical experience in hyperparameter tuning techniques using the Keras Tuner library. Hyperparameter tuning plays a crucial role in optimizing machine learning models, and this project offers hands-on learning opportunities. Exploring different hyperparameter tuning methods, including random search, grid search, and Bayesian optimization