Understanding Categorical Data

Feature engineering is a crucial step in building a performant machine learning model. Understanding categorical variables and encoding those variables with the right encoding techniques is paramount during the data cleaning and preparation stage. A survey published on Forbes says that Data preparation accounts for about 80% of data scientists’ work. Data scientists spend 60% of their time cleaning and organizing data.

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How to plot a Decision Boundary for Machine Learning Algorithms in Python

Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque. A popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface.

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9 Python libraries for Data Scientists and Machine Learning Engineers

Data Science

As you may already know, Python  is a programming language that lets you work quickly and integrate systems more effectively. It is a general-purpose language, with a wide variety of applications, from web developing using Django or  Flask,  to data science using awesome libraries like Scipy, Scikit-Learn, Tensorflow and much more.

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