The project, implemented using Python, forecasts store sales using historical unit sales data for thousands of items across multiple stores. Preprocessing involved handling missing values, encoding categories, and extracting date features, ensuring proper time-series formatting. Feature engineering included lag features, rolling stats, store-item patterns, and Ecuador national and local holidays, along with temporal patterns like trends and seasonality. ML models: The first used Linear Regression (scikit-learn) for baseline predictions, followed by Deep Learning (LSTM networks with TensorFlow) to improve accuracy by capturing complex temporal patterns.

🚧 This is a placeholder for an upcoming project. 🚧 I’m currently working on a new Data Science project that will be featured here soon. This section will showcase the problem, approach, tools used, and key results — just like my other projects. In the meantime, feel free to explore the other projects in my portfolio or check out my GitHub for recent activity. Stay tuned! Link to GitHub Repository