Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

A personal project where logistic regression is used to predict if a student dropped out.

ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.

Get an intuitive sense for the ROC curve and other binary classification metrics with interactive visualization.

Performed model evaluation using evaluation metrics such as accuracy, precision, recall, F1-score etc. Then model interpretation using feature importance, SHAP and LIME. Finally , evaluated model robustness and stability through techniques like bootstrapping or Monte Carlo simulations.

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)

Predicting Diabetes Mellitus Using Machine Learning Techniques 🩺🤖

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.