Welcome to my portfolio repository!
This is where I showcase some personal programming projects I have worked on or am currently working on. Each project is stored in its own directory or repository, and includes all relevant code, data, and documentation.
Below is a list of some of the projects included in this portfolio or other repositories. Click on the project name to explore the details.
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Basque Country Weather EDA : An Exploratory Data Analysis of weather data from the Basque Country during the last century. Focused on temperature, precipitation trends and extreme weather events.
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Battleship Video Game : This project is a custom Python implementation of the classic board game "Battleship".
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California Housing Prices : This project focuses on predicting California housing prices using supervised Machine Learning models and clustering properties with KMeans. The implementation emphasizes the use of pipelines to streamline preprocessing, modeling, and analysis.
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Databases Management with SQLite3 : This project consists of two distinct components centered on database management and practical SQL applications. One involves solving a murder mystery game in SQL City using queries, while the other focuses on designing and managing a database model for suppliers and the parts they provide.
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Diamonds Classifier with PySpark on DataBricks : This project demonstrates the use of PySpark in DataBricks to classify diamonds based on their numerical and categorical features. It focuses on leveraging Big Data tools and techniques for Machine Learning tasks.
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Heart Disease Classifier : Implementation of a Machine Learning model to predict the likelihood of a heart attack using various features such as medical history, lifestyle habits, and demographics.
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Laptop Price Predictor : This project develops a machine learning model to predict laptop prices based on features like specifications, brand, and storage, helping a retailer set optimal prices to improve sales and profitability.
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Mobile Phone Pricing : This project uses machine learning to analyze the market and predict mobile phone prices based on factors like RAM, storage, and screen resolution, helping a company set competitive prices for future models.
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SmartCab with Reinforcement Learning : A simulated autonomous taxi system built using Q-Learning and designed to optimize the transport of passengers. This project has been developed using OpenAI's Gymnasium.
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API Iris Flower Model : A simple lightweight RESTful API built with Flask for deploying a machine learning model on PythonAnywhere, trained on the Iris flower dataset. The API is hosted in https://elecomexp.pythonanywhere.com/, making it accessible from any device with an internet connection.
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Deep Learning : This repository contains a collection of Jupyter Notebooks exploring Deep Learning techniques with modern neural network architectures and workflows, including CNNs, Transfer Learning, Fine-Tuning, RNNs, Embeddings, NLP, preprocessing layers, and an introduction to generative AI for images.
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Gamma Rays Scattering : This project uses Monte Carlo simulations to study Gamma Ray transport through a medium, focusing on radiation-matter interactions and key coefficients like absorption, backscattering, and transmission.
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Smart Learn Toolbox : Utility functions and tools for Data Science, Machine Learning and Deep Learning projects. This collection is continuously evolving and serves as a reference and toolbox for personal and experimental use.
If you have any suggestions or improvements, feel free to open an issue or submit a pull request. Your contributions are always welcome!
If you have any questions or want to get in touch, please feel free to reach out to me at LinkedIn.
Thank you for visiting my portfolio! I hope you find these projects interesting and insightful.