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

Univariate Stock Market Forecasting using Facebook Prophet in Python

Facebook Prophet

Have you ever wondered how Facebook predicts the future? Meet Facebook Prophet, the open-source time series forecasting tool developed by Facebook’s Core Data Science team. Built on top of the PyStan library, Facebook Prophet offers a simple and intuitive interface for creating forecasts using historical data. What sets Facebook Prophet … Read more

Using Pandas DataReader to Access Online Data Sources in Python

pandas datareader api tutorial

Pandas DataReader is a library that allows data scientists to easily read data from a variety of sources into a Pandas DataFrame. This is especially useful for accessing data that resides outside of their local development environment and needs to be accessed via APIs. The Pandas DataReader provides functions for … 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

Feature Engineering for Multivariate Stock Market Prediction with Python

feature engineering for stock market prediction, multivariate time series modelling

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