gradientboostingclassifier

A comparative analysis of machine learning and deep learning algorithms for fraud detection, featuring XGBClassifier, CatBoostClassifier, and LGBMClassifier, as well as ANN, CNN, RNN, LSTM, and Autoencoders for performance benchmarking

The goal is to find out the employees those who stay and those who leave the company in the upcoming year. Through the various process of selecting, manipulating, transforming data and build the ensemble models, we get a best accuracy for the employee turnover rate.

Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.

Prediction model for loan default assessment

Sports betting is the activity of predicting sports results and placing a wager on the outcome.

A research study on How do factors like alcohol consumption, age, ethnic background, and medical history affect the risk of developing Alzheimer's disease?

Отчет по финальному проекту "Отток пользователей" специализации "Машинное обучение и анализ данных"

HR application to predict if an employee is about to quit

This project uses supervised machine learning algorithms to build a model that predicts which IPL team has a higher probability of winning a match.

A ML classification model for detecting potential exoplanets from pre-processed lightcurve data

ReneWind operates wind farms. Unexpected turbine failures are presenting operational and financial problems. This project uses machine learning to develop a model that accurately predict component failure, which will give the firm more control over maintenance scheduling, costs and power generation.

News type Classification Machine Learning

Employee attrition prediction is the use of machine learning algorithms to predict whether an employee will leave their current company. The model uses historical data to learn the patterns and relationships between features and the likelihood of employee attrition

Predicting Startup Success: A Gradient Boosting classifier engineered as a Tier 1 Screening Tool. Prioritizes valid failure detection over vanity metrics to help investors filter high-risk opportunities. Corrects data leakage in legacy models to achieve realistic, valid predictions (AUC 0.75) on startup success.

Data fetched by wafers is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not apparently obliterating the need and thus cost of hiring manual labour.

Train different models to predict the conversion rate while maximizing the F1 score.

The online payment fraud analysis project follows several step approach from data preprocessing through model evaluation, result comparison and final model selection, using transaction patterns to identify fraud indicators including account draining, suspicious transfers, and balance inconsistencies.

Modelos de classificação de risco de crédito usando algoritmos de Métodos Ensemble

The model will detect whether you are actually the detected one or not.