Analysis using Spark

Spark

Data analysis of a satisfaction survey using RDD and PySpark

FlexiTools Web Page

HTML, CSS, JavaScript

Static web page developed in HTML, CSS, and some JavaScript

Time Series Projects

Python

Application of SARIMA, Prophet, and LSTM models in time series for a temporal sample of the monthly number of passengers (air passengers) (Project 1) and the quantity of products and subtotals in a retail store (Project 2).

Java API Project

Java, Spring Boot

The current project is based on the design and implementation of an API using the Spring Boot framework for a growing store. Through a layered software architecture, the API provides a modular structure that separates the system's responsibilities into different logical layers.

Algorithmic trading

Algorithmic trading system that automates the entire process, from extracting real-time market data (EUR/USD) via the OANDA API to executing trades based on a simple moving average strategy. Data is processed in an Azure VM and stored in a MySQL database, enabling continuous strategy optimization.

Cleaning and Preparing Data for ML Projetcs

A well-executed combination of data cleaning and preparation not only improves the quality of the input data but also enhances model performance, leading to more accurate and robust machine learning systems.

How to Approach Machine Learning Projects

The machine learning process starts with understanding the problem and classifying the model type. Next, the data is cleaned, explored, and split into training and test sets, followed by training a baseline model. Finally, advanced strategies are experimented with, hyperparameters are tuned, and results are interpreted to improve predictions.