Andres Robles Gil

Water Pollution Analysis with Machine Learning

This project is a comprehensive analysis of water pollution trends using advanced data science techniques and machine learning algorithms. By examining real-world datasets, we sought to understand the key factors that drive pollution patterns across different regions.

Data Engineering and Processing:

• Data Acquisition: Sourced large water pollution datasets from public repositories, including historical water quality records.
• Data Cleaning: Removed inaccuracies, handled missing values, and eliminated outliers.
• Feature Engineering: Generated new features to enhance the dataset’s predictive capabilities, such as pollution growth rates and seasonal fluctuations.
• Data Pipelines: Built automated data pipelines using Python libraries such as pandas and scikit-learn.
• Data Visualization: Visualized water quality metrics and pollution levels using matplotlib and seaborn.

Modeling Techniques:

• Linear Regression: Predicted future pollution levels based on historical trends and regional data.
• K-Means Clustering: Identified distinct pollution regions and high pollution zones.

Key AI/ML Contributions:

• Developed a predictive model using regression techniques with Python and scikit-learn.
• Applied unsupervised clustering to identify pollution trends across different regions.
• Deployed the model using Jupyter notebooks for replication of results.

View Water Pollution Project on GitHub

Thesis: Synthetic Data Generation for Grasp Quality Evaluation

This thesis focuses on generating synthetic data for evaluating grasp quality in robotic bin-picking tasks. We developed a data generation pipeline that utilizes Blender through its Python API for creating synthetic 3D environments, simulating various grasp conditions.

Data Engineering and Processing:

• 3D Scene Creation: Built synthetic 3D environments using Blender to simulate bin-picking scenarios for robotic grasping.
• Grasp Simulation: Used Isaac Sim via its Python library to simulate grasping tasks and evaluate robotic grasp performance.
• Grasp Quality Evaluation: Generated grasp quality maps based on object geometry, surface smoothness, and other physical factors.
• Data Labeling: Labeled the generated data for training machine learning models to predict optimal grasp positions.

Key AI/ML Contributions:

• Developed a custom Blender add-on through its Python API for synthetic data generation.
• Implemented Isaac Sim simulations to evaluate grasp efficiency.
• Deployed machine learning models using TensorFlow and a Hugging Face UNet model.
• Utilized Google Cloud for large-scale data generation and model training.
• Achieved over 95% grasp success rate across diverse environments.

View Thesis Project on GitHub

FairShare – Full-Stack Personal Finance Management Platform

FairShare is a full-stack personal finance management platform that empowers users to track single expenses, recurring bills, and installment payments seamlessly. Unlike traditional budgeting apps, FairShare introduces a daily financial dashboard that helps users stay consistent with their savings goals, one day at a time.

Built With:

• Frontend: React (dynamic tables, responsive design for desktop and mobile)
• Backend: FastAPI (secure JWT authentication, Redis caching)
• Database: PostgreSQL (optimized with indexes for fast queries)
• Deployment: Docker containers on AWS EC2, managed with Nginx reverse proxy

Key Features:

• Track all types of expenses: one-time, recurring, and installments.
• Daily savings dashboard for clear, goal-oriented financial management.
• Secure user login and session handling with JWT tokens.
• Performance boost with Redis-based backend caching.
• Mobile-first design ensuring seamless use across devices.
• Scalable deployment using Docker and Nginx on AWS EC2.

Links:

🔗 Visit FairShare Live
🔗 Frontend Repository
🔗 Backend Repository