In this video, see how to build Python code to train a model and test it locally.
- [Instructor] In this video we will build and test…a buying propensity model, locally.…When we say locally, we can either do it on our laptop,…or we can use some compute VMs on GCP to run the same.…We won't be using Cloud ML yet.…We do this locally, since model building…is an iterative process, and we do not want to use…expensive Cloud ML resources for this work.…I'm going to use the Cloud Shell…for the model-building effort.…
The code for this is available under propensity-local.py.…Let us review the code.…The code reads the web-browsing-data.csv file…in line number 12.…Then, it prints the details of the file.…It filters the predictive variables…to contain only reviews, bought together, compare similar,…warranty, and sponsored links in line number 19.…It also sets the target to the BUY variable.…
Data is split into training and testing datasets…in line number 23.…It then uses the naive_bayes algorithm…to perform the prediction.…Prediction results are printed from line 39 onwards.…Let us try to run the code now.…
- Evaluating the machine learning tools in GCP
- Understanding the predictive analytics process
- Building models
- Training models with jobs
- Building and running predictions
- Best practices for cost control, testing, and performance monitoring
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. ML Options in GCP
2. Cloud ML Basics
3. Model Building with Cloud ML
4. Predictions in Cloud ML
5. Cloud ML Best Practices
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