by: Robby Alfardo Irfan, Learner by doing

Photo by Elena Mozhvilo on Unsplash

When we want to build our models, our data must be prepared first. Our preprocessed data may contain attributes with a mixture of scales for various quantities such as dollars, kilograms, and sales volume. It has multiple features spanning varying degrees of magnitude, range, and units. This is a significant obstacle as a few machine learning algorithms are highly sensitive to these features.

Feature Scaling is an essential step in the data analysis and preparation of data for modeling. Wherein, we make the data scale-free for easy analysis. …


by: Robby Alfardo Irfan, Learner by doing

Photo by Josh Boot on Unsplash

Real-world data is messy and often contains a lot of missing values. There could be multiple reasons for the missing values. But the main reason for missingness can be attributed to data that doesn’t exist, data that isn’t collected due to human error, and data that is deleted accidentally.

In this series, we will learn how to detect missing values and then go over some of the methods used for handling them. Particularly, focus on deletions andimputations .

The broad scope of handling missing value is deletions and imputations . …


by: Robby Alfardo Irfan, Learner by doing

Photo by Chris Liu on Unsplash

Hi there, this is me with my first project about Explanatory Data Analysis (EDA). It is an approach for data analysis that employs a variety of techniques (mostly graphical). It is used to discover patterns, spot anomalies, check assumptions or test a hypothesis through summary statistics and graphical representations.

In this series, we will learn how to perform EDA using data visualization. Particularly, focus on seaborn, a Python data visualization library based on matplotlib and integrates closely with pandas pandas data structures.

Seaborn packages helps you explore and understand your data. Its plotting…

Robby Alfardo Irfan

Passionate to always learn more about data

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