Multivariate forecasting models rely not exclusively on historical time series data but use additional features (multivariate = multiple input variables) such as moving averages or momentum indicators. The underlying assumption is that various variables increase the accuracy of a forecast by helping the model identify patterns in the historical data … Read more
Get ready to dive into the world of social media sentiment analysis with Python! In this article, we’ll be exploring how companies are using machine learning to analyze the sentiments expressed in Twitter comments and how you can do the same. We’ll be training and comparing different classification models using … Read more
Regression models based on recurrent neural networks (RNN) can recognize patterns in time series data, making them an exciting technology for stock market forecasting. What distinguishes these RNNs from traditional neural networks is their architecture. It consists of multiple layers of long-term, short-term memory (LSTM). These LSTM layers allow the … Read more
Most online stores welcome countless visitors every day, but only a fraction of those visitors will make a purchase. Purchase intention prediction is the process of using machine learning algorithms to predict the likelihood that a particular customer will make a purchase. This can be useful for many applications, such … Read more
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models … Read more