98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis.

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

Predicting Bank Term Deposit Subscribers using Decision Trees

Light-weight package for classification metrics computed on streams or minibatches of data. Mainly for area under the curve (AUC) of precision-recall (PR) or receiver operating characteristic (ROC) curves. Supports multi-class setting with either macro- or micro aggregation..

Run histogram-based gradient boosted trees binary classifier on generated data and interpret results with standard metrics, SHAP, and supervised clustering

Repository where it is intended to address the detection of patients who will be readmitted for problems with diabetes.

The company has collected historical customer and claims data and wants to use it to develop a machine learning model that can predict whether a customer will file an insurance claim in the next year.

Build repository for brambox - https://gitlab.com/eavise/brambox

Repository where it is intended to address the challenge of LATAM Airlines, which consists of predicting the probability of delay of flights that land or take off from the Santiago de Chile airport (SCL)

Develop a model to predict which retail customers will respond to a marketing campaign. Logistic Regression shows the best performance.

Identify which customer is willing to possess the insurance policy, so we campaign efficiently.

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.

Linear Regression, Logistic Regression, ML Pipeline

A hotel chain is having issues with cancellations. This project analyzes customer booking data to identify which factors significantly influence cancellations, build models using logistic regression and decision trees to predict cancellations in advance, and help formulate profitable policies for cancellations and refunds for the hotel group

Machine learning utility functions and classes.

Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk

Fully connected neural network analyzing sentiments in reviews for Amazon's Alexa.

Предиктивный анализ оттока клиентов

A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.

Search engine that queries for information to find the best results based on custom analysers and indexing techniques.