Predictive Maintenance: Predicting Machine Failure using Sensor Data with XGBoost and Python

Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine learning algorithms, businesses can predict equipment failures before they happen and optimize their maintenance cycles to improve efficiency and reduce costs. In this article, we’ll explore the use of machine … Read more

Feature Engineering and Selection for Regression Models with Python and Scikit-learn

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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 … Read more

Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

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Finding the perfect hyperparameters for your machine learning model can be like searching for a needle in a haystack – unless you use random search. This efficient method automates the process of hyperparameter tuning, so you don’t have to spend hours manually testing different configurations. Hyperparameters are model properties (e.g., … Read more

Stock Market Forecasting Neural Networks for Multi-Output Regression in Python

Multi-output regression Python Neural Networks

Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and … Read more

Multivariate Anomaly Detection on Time-Series Data in Python: Using Isolation Forests to Detect Credit Card Fraud

anomaly detection random isolation forests

Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on … Read more