stacking-ensemble

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

🏆데이콘 AI해커톤 대회 우수상 솔루션🏆

Identify the type of disease present on a Cassava Leaf image

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

Data from a website that provides job reviews. The website wants to analyze texts and the corresponding rating that is provided by the user about startups. Based on the texts, try to verify if it corresponds to the score provided by the reviewer. the task helps the website to rank user's reviews or ratings

Full data science project workflow that placed me in the top 5% of all competitors (0.11931).

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

Ensemble Learning Methods using Python

Project work related to various hackathons

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.

Anomaly Detection in logs data using Unsupervised Machine Learning Algorithms

This project does the task of classifying whether a person is suffering from diabetes or not

This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data's Machine Learning courses

Algorithms used to confirm whether a celestial body is a planet or not.

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.

In this project, we reduced an imbalanced dataset to a balanced dataset using Under-sampling approach by applying Consensus Clustering using 'Simple Majority Voting' consensus function and further saw the increase in the accuracy of disease prediction by running multiple classifiers with bagging and boosting technique.

Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. To better understand this definition lets take a step back into ultimate goal of machine learning and model building. This is going to make more sense as I dive into specific examples and why Ensemble methods are used.

Notebook files for classification and detection of anomalous trend in time series inverter data

Application of learnings in the Machine Learning course , this project mainly gives first hand idea of elaborative exploratory data analysis performed on data sets and various advanced regressions models are used for predicting House Prices.

Based on data such as general bio-data, payment history, and subscriptions, this stacking-ensemble model predicts whether a customer continues to use the service or not (attrition) with an accuracy of 83.14%

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.

성균관대학교 우수학부생 연구학점제를 통해 작성한 논문입니다.

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 candidates more likely to have the visa certified.

Language classification problem: which identifies South Africa's 11 official Languages through text data