Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, backup, re-embed (using any model) or access your vector data from any vector databases or repository.

Successfully developed a machine learning model which can accurately predict whether a firm will become bankrupt or not, depending on various features such as net value growth rate, borrowing dependency, cash/total assets, etc.

The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #1 - Lifestyle and Health Factors

Data was downloaded through Kaggle

BOM(Bill of Materials) Business Analyst Case Study Solution using Python, Pandas manipulation and Visualization Technique

Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #5 - mHealth and ML

Successfully established a machine learning model which can accurately predict whether an employee of a given company will leave it in the impending future or not, based on several employee details and employment metrics.

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Este análisis exploratorio de datos (EDA) realizado sobre el conjunto de datos de rendimiento estudiantil tiene como objetivo identificar y comprender los factores que influyen en el desempeño académico de los estudiantes. A través de la limpieza, transformación y visualización de datos, se busca descubrir patrones y relaciones significatvas.

Successfully created a machine learning model which can accurately predict the fare of a taxi trip based on several features such as trip duration, tip amount, etc.

This project consists on exploratory data analysis and the application of supervised learning models for classification using a Hepatocellular Carcinoma dataset. Second Semester of the First Year of the Bachelor's Degree in Bioinformatics at FCUP.

Data analysis, recommendation system & sentiment analysis for Airbnb listings in Athens, Greece

Explored a dataset of planes while learning PySpark commands.

The objective of this project is to predict the number of bicycles needed to be made available each hour in order to make the service as efficient as possible

Successfully established a machine learning model using PySpark which can accurately classify whether a bank customer will churn or not up to an accuracy of more than 86% on the test set.

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #2 - Early Prediction of Heart Failure

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #4 - ICU Mortality Prediction Using ML