A comprehensive guide to building a machine learning pipeline for classifying human activities from sensor data using Data Studio, TSFresh, and scikit-learn. The tutorial covers the complete workflow
from data preparation to model validation using the UCI HAR dataset.
Reasons to Read -- Learn:
how to build a complete end-to-end machine learning pipeline for sensor data classification, including practical steps for data preparation, feature extraction, and model validation
how to use TSFresh for extracting meaningful features from time-series data, including how to optimize feature selection by reducing from 1152 raw features to 120 significant features
how to effectively use the SensiML Data Studio for managing and visualizing time-series data, including techniques for data augmentation using sliding windows to double your training dataset size
8 min readauthor: Chris Knorowski
0
What is ReadRelevant.ai?
We scan thousands of websites regularly and create a feed for you that is:
directly relevant to your current or aspired job roles, and
free from repetitive or redundant information.
Why Choose ReadRelevant.ai?
Discover best practices, out-of-box ideas for your role
Introduce new tools at work, decrease costs & complexity
Become the go-to person for cutting-edge solutions
Increase your productivity & problem-solving skills
Spark creativity and drive innovation in your work