Sentiment analysis is part of the NLP techniques that consists in extracting emotions related to some raw texts.

SmokePredict: An ML project analyzing health data to predict smoking behavior. EDA, Decision Tree, and Neural Network models explored.

You can find exercises and codes realized during this lecture

The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.

ROC-GLM for DataSHIELD

a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, also a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN.

Display and analyze ROC curves in R and S+

Machine learning course project on computer science master degree. Prediction of diabetes based on many features related to health habits and previous medical events. EDA phase, followed by 3 ML supervised models (naive Bayes, Decision Tree and Neural Network)

Desktop application helping with TPR/FPR calculations and visualize ROC curve based provided parameters.

Credit card transactions fraud detection using classic algorithms

Measure and visualize machine learning model performance without the usual boilerplate.

Develop a heart disease prediction system that can assist medical professionals in predicting heart disease status based on the clinical data of patients.

The project involves using machine learning techniques, like RandomForestClassifier and MLP, to predict whether a song will be popular or not based on its acoustic features. The input consists of various acoustic and metadata features, while the output is a binary classification.

This project aims to identify the inevitable trade-off between accuracy and safety when predicting poisonous mushrooms with ML.

This project focuses on predicting customer churn in an e-commerce setting using machine learning techniques.

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.

Our group project aimed to evaluate three predictive machine learning classification models to anticipate whether website visitors engage in transactions. This is done by analysing different attributes of website visitors including duration spent on different web pages, click rates, and bounce rates.

This repository contains many jupyter notebooks and output printouts chronicling my learning of basic Machine Learning concepts.

SerenaRosi's GitHub page