Machine Learning & Deep Learning Models

In this project, I designed and implemented various machine learning and deep learning models across multiple domains including statistical analysis, computer vision, and time series forecasting. My work involved data preprocessing, feature engineering, model selection, training, and evaluation using state-of-the-art techniques. I applied algorithms such as Random Forests, XGBoost, CNNs, and LSTMs, depending on the task. For computer vision, I used convolutional neural networks to classify and detect objects, while in time series projects, I applied statistical models and deep recurrent networks for accurate forecasting. Each solution was tailored to the dataset's specific characteristics and business objectives, with a focus on performance, interpretability, and real-world deployment.