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.

A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis, NLP processing and ML, achieving the functionality of a Content based movie recommender system

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

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.

Data was downloaded through Kaggle

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

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.

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

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.

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.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

Prediction of happy Customers based on Happiness Survey Data

Explored a dataset of planes while learning PySpark commands.

An SQL-based exploration of COVID-19 data and vaccination progress using the Covid-Deaths dataset for insights into global pandemic trends.

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

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

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 #1 - Lifestyle and Health Factors

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.