![]() ![]() When Machine Learning algorithms measure distances between data points, the results may be dominated by the magnitude (scale) of the features instead of their values. ![]() There are different methods to do feature scaling. The complete project on GitHub Feature Scalingįeature scaling refers to the process of changing the range (normalization) of numerical features. Run the complete notebook in your browser How improtant data preparation really is? We’re going to compare the performance of a model with and without data preprocessing. We’re going to look at three general techniques:įinally, we’re going to apply what we’ve learned on a real dataset and try to predict Melbourne housing prices. This guide will introduce you to the most common and useful methods to preprocess your data. A requirement for reaching your model’s full potential is proper cleaning, wrangling and analysis of the data. Garbage data in, garbage predictions out. Since this step is so early in the process, screwing up here will lead to useless models. I am here to shatter your dreams, you’ll most likely spend a lot more time on data preprocessing and exploration than any other step of your Machine Learning workflow. You might just want to train Deep Neural Networks (or your favorite models). I know, data preprocessing might not sound cool. Use your skills to preprocess a housing dataset and build a model to predict prices. TL DR Learn how to do feature scaling, handle categorical data and do feature engineering with Pandas and Scikit-learn in Python. ![]()
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