Feature engineering helps make models work better. It involves selecting and modifying data to improve predictions. This article explains feature engineering and how to use it to get better results. What is Feature Engineering? Raw data is often messy and not ready for predictions. Features are important details in your data. They help the model […]
Our discussion so far has been anchored around the family of linear models. Each approach, from simple linear regression to penalized techniques like Lasso and Ridge, has offered invaluable insights into predicting continuous outcomes based on linear relationships. As we begin our exploration of tree-based models, it’s important to reiterate that our focus remains on […]
Few data science projects are exempt from the necessity of cleaning data. Data cleaning encompasses the initial steps of preparing data. Its specific purpose is that only the relevant and useful information underlying the data is retained, be it for its posterior analysis, to use as inputs to an AI or machine learning model, and […]
In our previous exploration of penalized regression models such as Lasso, Ridge, and ElasticNet, we demonstrated how effectively these models manage multicollinearity, allowing us to utilize a broader array of features to enhance model performance. Building on this foundation, we now address another crucial aspect of data preprocessing—handling missing values. Missing data can significantly compromise […]
Choosing a machine learning (ML) library to learn and utilize is essential during the journey of mastering this enthralling discipline of AI. Understanding the strengths and limitations of popular libraries like Scikit-learn and TensorFlow is essential to choose the one that adapts to your needs. This article discusses and compares these two popular Python libraries […]
This post will demonstrate the usage of Lasso, Ridge, and ElasticNet models using the Ames housing dataset. These models are particularly valuable when dealing with data that may suffer from multicollinearity. We leverage these advanced regression techniques to show how feature scaling and hyperparameter tuning can improve model performance. In this post, we’ll provide a […]
The battle against fraud has become more intense than it ever has been. As transactions become increasingly digital and complex, fraudsters are constantly devising new ways to exploit vulnerabilities in financial systems. And this is where the power of machine learning comes into play. Machine learning offers a robust approach to identifying and even preventing […]
One of the significant challenges statisticians and data scientists face is multicollinearity, particularly its most severe form, perfect multicollinearity. This issue often lurks undetected in large datasets with many features, potentially disguising itself and skewing the results of statistical models. In this post, we explore the methods for detecting, addressing, and refining models affected by […]
Artificial intelligence is not just altering the way we interact with technology; it’s reshaping the very foundations of machine learning. As we stand on the brink of innovative breakthroughs, understanding emerging AI technologies becomes essential to grasp their profound implications on future applications and industries. This exploration is not merely academic—it’s a guide to influencing […]
When training a machine learning model, you may sometimes work with datasets with a large number of features. However, only a small subset of these features will actually be important for the model to make predictions. Which is why you need feature selection to identify these helpful features. This article covers useful tips for feature […]
