using Drebin dataset to distinguish between malwares and not malwares

Rice Crop Yield Estimation Using Satellite Data - EY Open Science Data Challenge 2023

This repository contains the source code to reproduce the paper "Feature-based No-Reference Video Quality Assessment using Extra Trees".

End-to-End Used Car Price Prediction using Ensemble Learning | Extra Trees, Random Forest, Gradient Boosting | Python • Scikit-learn

Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries

Machine learning pipeline for predicting employee attrition using ensemble models and feature engineering.

Prediction of forest cover type in Python.

Mitsui Kaggle — ExtraTrees with single-lag & group-lag features

Credit Card Fraud Detection with Python. Implemented various classification algorithms in scikit-learn and Built a Neural Network in Tensorflow

End-to-end Data Science project on Amazon Sales. Features data cleaning, EDA, outlier detection, and predictive modeling using Python, Pandas, and Scikit-learn.

An AI-driven hotel classification system using Extra Trees with SHAP-based explainability. Includes Streamlit frontend and FastAPI backend for deployment.

Sampling Assignment: Download dataset, balance classes, apply ML models with different sampling techniques to evaluate performance.

Data Science Project (Autonomous Driving Dataset Analysis)

Predicting Appliance Energy use in Residential Buildings.

End-to-end machine learning pipeline for credit card fraud detection with risk scoring and investigation queue. Uses Extra Trees classifier with calibrated thresholds, explainability (permutation importance), drift monitoring, and a Streamlit dashboard. Human-in-the-loop decision support, not auto-blocking.

Decision Tree Classification on the Forest Cover Type dataset with overfitting analysis, hyperparameter tuning, feature importance and Random Forest comparison.

This project aims to predict the price of laptops based on their technical specifications using various machine learning models. The dataset includes attributes like brand, processor, RAM, memory type, GPU, screen size, and operating system.

Machine learning project predicting electricity consumption based on ASHRAE dataset.

Classical machine learning techniques to the problem of protein function prediction based solely on amino acid sequences. The task is formulated as a supervised multi-class classification problem, using functional annotations derived from the UniProt and Gene Ontology databases.

A Python tool for trading signals on global markets based on machine learning