Machine learning (ML) pipelines consist of several steps to train a model.

• Data collection
• Data cleaning
• Feature extraction (labelling and dimensionality reduction)
• Model validation
• Visualisation

Data collection and cleaning are the primary tasks of any machine learning Pipeline.

House Prices: Advanced Regression Techniques

• train.csv - the training set

• test.csv - the test set

• data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here

• sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms Data fields Here's a brief version of what you'll find in the data description file.