Here you’ll find everything about feature engineering, whether it’s Python tutorials on engineering features for time series analysis or regression models.
Feature engineering is the process of extracting features from raw data that can be used in machine learning models. These features represent underlying relationships and patterns in the data that can help a model make more accurate predictions. Feature engineering is an essential part of building successful machine learning models and can require a combination of technical expertise, creativity, and domain knowledge.
In a previous article, we looked at the value proposition of generative AI and ChatGPT (What is the Business Value of OpenAI’s GPT Models (ChatGPT)?). A standout feature of ChatGPT is its ability to write in different styles and tones. Whether you’re aiming for a professional message, a friendly vibe, a sprinkle of humor and … Read more
Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients you put in. If your input data is poor, your predictions will be too. But with the right ingredients – in this case, carefully selected input features – you can create a model that’s both … Read more
Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! While traditional time series data provides valuable insights into historical trends, multivariate forecasting models utilize additional features to identify patterns and predict future price movements. This process, known as “feature engineering,” is a crucial step … Read more
