Supervised learning is a type of machine learning in which a model is trained on labeled data. This means that the training data includes both the input data and the corresponding correct output, and the goal of the model is to make predictions on new data that it has not seen before. In supervised learning, the model is able to learn from the training data by finding patterns and relationships between the input and the output, and then using this knowledge to make predictions on unseen data. Some common examples of supervised learning tasks include classification, regression, and sequence labeling. Supervised learning is a powerful and widely-used technique in the field of machine learning, and it has many applications in areas such as natural language processing, computer vision, and speech recognition. Examples of supervised learning algorithms include regression, naive Bayes, decision trees, decision forests, and support vector machines.
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 model to learn patterns in … Read more
Online shopping has become a part of our daily lives, and online stores are continually seeking to improve their sales. One way to achieve this is by using machine learning to predict customers’ purchase intentions. This innovative process can help businesses understand their customers’ behavior and tailor their marketing strategies accordingly. In this article, we … 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 does not exist. Instead, there … Read more
Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more
Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there has been increasing interest in using machine learning and deep learning techniques to generate predictions, in addition to traditional methods such as technical and fundamental analysis. Python libraries like Keras and Scikit-Learn make it relatively … Read more
If you travel a lot, you’ve probably already experienced this – you’re in a hurry on your way to the airport trying to catch a flight, only to find out that your flight is delayed. Wasn’t it great to know when a flight will be delayed in advance? We can use past flight delay data … Read more
