Book Image

The Data Analysis Workshop

By : Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev
Book Image

The Data Analysis Workshop

By: Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev

Overview of this book

Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you’ll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.
Table of Contents (12 chapters)
Preface
7
7. Analyzing the Heart Disease Dataset
9
9. Analysis of the Energy Consumed by Appliances

Missing Values

Most real-world datasets have instances with values that are NaN or blank. These are missing values. The significance of missing values depends on multiple factors: the number of missing values, the number of features that have missing values, the tasks that are going to be carried out on data, and so on.

If the data is going to be fed into a machine learning model, then missing values should be dealt with. While some algorithms are capable of learning and predicting from data with missing values, it obviously makes more sense to train a model on data without missing values. This ensures that the model will learn relationships and patterns accurately.

Additionally, if there are many missing values or missing values in significant features of a dataset, they should also be dealt with.

There are two main ways to deal with missing values: deleting the instances or columns that have them (if they aren't significant), or imputing them with other values.

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