We will use a Kaggle dataset about credit card fraud detection. This dataset consists of transactions made by credit cards in September 2013 by European cardholders. It contains 492 frauds out of a total of 284,807 transactions. You can see that it is highly unbalanced, where the positive class (frauds) Read more…
In the last article, we learned how to train a Machine Learning model like Linear Regression or XGBoost to forecast Time Series data. We had to reframe the dataframe as a supervised learning problem. You can read about this process here. To explain the process we used Forex data, specifically Read more…
So far we have been talking about classical approaches when forecasting time series data. However, it is essential to explore alternative techniques that involve advanced methodologies such as machine learning and deep learning. There are mixed views regarding the accuracy of these last techniques. Some say that these advanced techniques Read more…
Ensemble Learning is a powerful method used in Machine Learning to improve model performance by combining multiple individual models. These individual models, also known as “base models” or “weak learners,” may have limitations such as high variance or high bias. There are two main types of ensemble models: There are Read more…