Automating Preprocessing & Model Saving: Create, Save, and Reuse Modules | PD Model Dev - 6
In this advanced tutorial, we're delving into the world of automation, making your predictive modelling workflow seamless and efficient. Join us as we create a robust preprocessing module, saving both the preprocessing steps and the trained models for future use.
🔧 Key Takeaways:
Automated Preprocessing: Learn how to automate and modularize your preprocessing tasks for consistent and efficient data preparation.
Module Creation: Witness the creation of a powerful preprocessing module that streamlines your data preparation steps.
Model Saving: Explore the process of saving trained models, allowing for easy integration and reuse in various applications.
🌐 Series Overview:
This is the sixth instalment of our PD Model Development Series, designed for data science enthusiasts, analysts, and professionals aiming to master predictive modelling and automation techniques.
🎓 Who is This For?
Data Scientists and Analysts
Professionals in Predictive Modeling
Anyone Seeking Advanced Automation Techniques
👍 Engage With Us:
If you find this content valuable, don't forget to like, share, and subscribe for more in-depth tutorials on predictive modelling and model monitoring. Share your thoughts and questions in the comments below – we love hearing from our community!
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Resources -
Developed script in the video - https://github.com/LEARNEREA/Data_Sci...
Script to understand the diff. in scores - https://github.com/LEARNEREA/Data_Sci...
Automated Pre-processing - https://github.com/LEARNEREA/Data_Sci...
Logistic saved model - https://github.com/LEARNEREA/Data_Sci...
Random Forest saved model - https://github.com/LEARNEREA/Data_Sci...
XGB saved model - https://github.com/LEARNEREA/Data_Sci...
Raw data utilised in the development - https://github.com/LEARNEREA/Data_Sci...
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