We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.

End-to-End Machine Learning project I made as a machine learning intern @ Mentorness

Open source project for data preparation of LLM application builders

Prepping tables for machine learning

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

This project involves an extensive exploration of fake news detection using machine learning techniques. It encompasses data preprocessing, feature extraction, model training, and evaluation to classify news articles as real or fake. Through thorough analysis and model validation, the project aims to provide valuable insights into the effectiveness

The notebook contains Python code for various machine learning tasks and models. Here is an overview of its content:

Accurate image classification powered by InceptionV3 deep learning model. Quickly classify diverse images with high precision using TensorFlow.

Easy to use Python library of customized functions for cleaning and analyzing data.

The Triton backend that allows running GPU-accelerated data pre-processing pipelines implemented in DALI's python API.

DataCamp inetrmediate course on how and when to perform data preprocessing in any machine learning project to get the data ready for modeling

Laboratory 4 - Retrieval Information

This project develops a machine learning model to predict the salaries of baseball players based on their past performance.

Laboratory 1 - Retrieval Information

This project uses a Convolutional Neural Network (CNN) to classify images as happy or sad. It includes data preprocessing, model training, and prediction on new images.

A summative coursework for CSC8635 Machine Learning with Project

Detection of Human Edited Images using CNN, VGG16, Xception, ELA, Ensemble Learning.

Data cleaning and preprocessing before ML models application. EDA and visualizations for agriculture data in EU.

This project employs machine learning to forecast housing prices in California. By scrutinizing location, housing details, and demographics, it constructs various regression models like Linear Regression, KNN, Random Forest, Gradient Boosting, and Neural Networks. These models offer invaluable insights to optimize predictive real estate investment

Contains the code and data files for a data science project using individual player statistics data from the 2023-2024 NCAA basketball season.