[ACL2023] We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source LLMs. LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.

Neural Networks ensemble via majority voting in order to classify ships given non-satellite images. All the models have been trained using PyTorch with pretrained weights.

Comparison of classifier Algorithms on Diabetes Health Indicators Dataset.

Cognitive Robotics University Exam Project

This project aims to build a regression model that predicts the number of views for TED Talks videos on the TED website.

A complete pipeline for network intrusion detection comparing label encoding and one‑hot encoding, with SMOTE resampling, feature selection, and ensemble modeling using scikit‑learn and XGBoost, also this was phase one of our University's "CSAI 253- Machine Learning" course.

Price prediction and appartments recommendation

Various scripts for machine learning

Survana: software for survival analysis and biomarker discovery using ensemble methods.

The AdaBoost (Adaptive Boosting) algorithm is a popular ensemble method used in machine learning to improve the performance of weak classifiers. It combines multiple weak classifiers to create a strong classifier, focusing more on the misclassified instances in each subsequent iteration.

Comparison of classifier Algorithms on bank marketing Dataset

We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors.

This project employs machine learning algorithms to predict customer churn by analyzing historical customer data. It provides actionable insights to enhance customer retention. The models were fine-tuned using hyperparameter optimization and tackled data imbalance with SMOTE, achieving high F1-scores to drive targeted business strategies.

Predicting potential donors using various machine learning models for Charity

Random Forest library university project

Regression-PrediksiHargaRumahBoston-kaggle-ensamblemodel-supervisedlearning

Project on Trend Analysis on Pest Occurrence Using Meteorological Data - Information Systems and Business Intelligence (MEng), supervised by Prof. F. Amato, PhD A. Moccardi and PhD M. Fonisto (2024)

Ensamble Voting for Financial Time Series

Regression exercises and projects done at alx training

Credit Risk Analysis utilizing imbalanced classification machine learning models