Preprocessing Data in Scikit-Learn: Part 1
Preprocessing Data in Scikit-Learn: Part 1
Preprocessing Data in Scikit-Learn: Part 1 is a tutorial that introduces the basics of data preprocessing with Scikit-Learn, a Python library that provides tools for machine learning and data analysis. Data preprocessing is the process of transforming raw data into a format that is more suitable for machine learning algorithms. It can involve tasks such as scaling, encoding, imputing, or selecting features. In this tutorial, we will learn how to use some of the common preprocessing functions and classes in Scikit-Learn, such as StandardScaler, OneHotEncoder, SimpleImputer, and SelectKBest. We will also learn how to apply these preprocessing steps to a real-world dataset, the Titanic dataset, which contains information about the passengers who survived or died in the Titanic disaster. We will use this dataset to perform a logistic regression, a binary classification algorithm, and evaluate its performance. By the end of this tutorial, we will have a better understanding of how to prepare our data for machine learning in Python with Scikit-Learn.
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