State-of-the art Automated Machine Learning python library for Tabular Data

Time Series Ensemble Forecasting

This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

Learning with Subset Stacking

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This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset available on kaggle.

Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.

Kaggle Competition

Identify the type of disease present on a Cassava Leaf image

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease

Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.

A machine learning model to predict whether a customer will be interested to take up a credit card, based on the customer details and its relationship with the bank.

There are many studies done to detect anomalies based on logs. Current approaches are mainly divided into three categories: supervised learning methods, unsupervised learning methods, and deep learning methods. Many supervised learning methods are used for log-based anomaly detection.

EasyVisa Project

Εxercises for Machine Learning course in Faculty of Informatics of Aristotle's University of Thessaloniki

Machine Learning Application In The Medical Field Used for predicting the occurrence of stroke for the patients depends on the patient information

machine learning ensemble learning types in easy steps with examples

Prediction of market premiums for property damage and business interruption insurance products. Added natural hazard data and stacked 3 best models as the final model.