Predictive Maintenance: Predicting Machine Failure using Sensor Data with XGBoost and Python
By using machine learning and Python, businesses can predict equipment failures before they happen and optimize their maintenance cycles.
#Multivariate Models
9 tutorials
Feature Engineering and Selection for Regression Models with Python and Scikit-learn
This guide provides tips on feature exploration, engineering, and selection for machine learning using Python and Scikit-Learn
Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python
Learn how to use Random Search to tune the model hyperparameters of a Random Forest with Python that predicts house sale prices.
Stock Market Forecasting Neural Networks for Multi-Output Regression in Python
This tutorial develops a multi-output regression model in Python that generates a multi-day stock market forecast for the S&P500
Multivariate Anomaly Detection on Time-Series Data in Python: Using Isolation Forests to Detect Credit Card Fraud
This article describes multivariate anomaly detection in the example of credit card fraud using Random Isolation Forests in Python
Predictive Policing: Preventing Crime in San Francisco using XGBoost and Python
This article predicts crime types in San Francisco with the XGboost classifier in Python and displays them on a crime map of SF
Customer Churn Prediction - Understanding Models with Feature Permutation Importance using Python
This tutorial shows how to build a customer churn prediction model in telecommunications. We will use Python and measure feature importance.
Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python
This tutorial shows how to model a multivariate time series using a recurrent neural network to forecast the stock market.
Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in Python
This article shows how to create a rolling multi-step forecast for a rising sine curve using Keras neural networks with lstm layers in Python