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Multivariate Datasets: Data Cleaning and Preparation, and Model Development with Python and Machine Learning
內容大綱
Data cleaning, data preparation, and model development are the crucial steps in data analytics. The first two steps aim to improve data quality for higher accuracy, improved productivity, and better efficiency in modelling and obtaining results. The last step, model development, seeks to improve accuracy of prediction, especially in predictive modelling. In this technical note, we use a sample to illustrate how to work with a multivariate dataset in Python. This dataset’s massive number of variables requires different approaches to data cleaning, preparation, and model development, such as data normalization and dimension reduction.
學習目標
This technical note enables students to do the following:<ul><li>Learn how to clean and prepare a multivariate dataset for model building.</li><li>Understand the basic of building a model.</li><li>Practice how to train, validate, and compare different models.</li></ul>