Handling categorical features to preprocess before building machine learning models. Techniques of encoding categorical features to numeric.
Dealing with categorical features is a common thing to preprocess before building machine learning models. In real-life data science scenario, it means that the dataset has an attribute stored as text such as days of the week(Monday, Tuesday,.., Sunday), time, colour(Red, Blue, …), or place names, etc.
Getting Started with scikit-learn Pipelines for Machine Learning: Building a pipeline from the ground up. (All code in this post is also included in this GitHub repository.)
We’ll explore the breast cancer dataset and create a model that classifies tumors. Scikit-Learn provides clean datasets for you to use when building ML models. And when I say clean, I mean the type of clean that’s ready to be used to train a ML model. The best part? The datasets come with the Scikit-Learn package itself. You don’t need to download anything. Within just a few lines of code, you’ll be working with the data.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
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