Boost model performance with GridSearchCV and Pipeline in Scikit-Learn!
In this YouTube video, you will discover how to create an advanced soy milk adulteration model using FTIR data and Scikit-Learn's pipeline and gridsearch cv functions. The tutorial begins with data loading and preprocessing, followed by the construction of a support vector regression model utilizing the pipeline and gridsearch cv functions. Finally, you'll witness the evaluation of the model's performance on a test dataset.
This instructional content is ideal for individuals interested in developing food adulteration models through machine learning techniques. No prior experience in machine learning is necessary to follow along.
Key takeaways from this video include:
Loading and preprocessing FTIR data
Constructing a support vector regression model using the pipeline and gridsearch cv functions
Assessing the performance of a food adulteration model
github: https://github.com/chrisp33/Python-Fo...
coconut FTIR Pulication: https://www.sciencedirect.com/science...
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