🍷 Predict wine quality using machine learning with this Jupyter Notebook, featuring EDA, model training, and insightful visualizations.

📊 Analyze bank marketing campaign data to uncover client insights, conversion rates, and optimize strategies for term deposit offers.

🚗 Detect driver distractions in images for improved road safety by predicting behavior like texting, talking, and reaching behind the wheel.

🤖 Explore deep learning architectures like ANN, CNN, RNN, and LSTM to enhance your understanding of machine learning and neural networks.

🔍 Detect credit card fraud efficiently using advanced machine learning techniques, achieving high accuracy rates on a large dataset of transactions.

🔌 Connect any API to Claude with this flexible Python framework for seamless integration and data exchange across services.

📊 Explore data science with notebooks and scripts for EDA, statistics, and visualization, all in Portuguese and designed for easy replication.

📚 Explore a curated library for mastering Machine Learning, Deep Learning, and AI through free resources, courses, and tools for all levels.

📚 Explore a growing library of free resources for learning Machine Learning, Deep Learning, and AI, covering essential concepts to advanced techniques.

📊 Explore machine learning techniques through hands-on sessions, datasets, and code examples in this 2-day bootcamp by DJS Compute.

📝 Enhance handwritten digit recognition using a compact ResNet-inspired PyTorch model with advanced techniques like MixUp and CutMix.

🧠 Implement U-Net in PyTorch for effective binary image segmentation, focusing on brain tumor detection with a complete pipeline from data prep to evaluation.

Marvel character analysis using Python, focusing on roles and superpowers.

Classifies 500K+ political tweets from Northern Europe using NLP and machine learning to analyze political discourse.

Complete pipeline for Training Model Behavior in Agentic Systems

Developed a comprehensive exploratory data analysis (EDA) of a vehicle repairs dataset, uncovering patterns in repair types, costs, and vehicle platforms. Includes data cleaning, insights extraction, tag generation from free-text fields, and saving of cleaned datasets for further analysis.

Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.

This repository showcases my journey in the fascinating world of Data Analytics.

Structured EDA on 2015 US flight delay data. Clean, reproducible notebook using a 6-step data analysis framework for real-world datasets.

Spaceship Titanic project.