#### 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.
Preface
1. Bike Sharing Analysis
Free Chapter
2. Absenteeism at Work
3. Analyzing Bank Marketing Campaign Data
4. Tackling Company Bankruptcy
5. Analyzing the Online Shopper's Purchasing Intention
6. Analysis of Credit Card Defaulters
7. Analyzing the Heart Disease Dataset
8. Analyzing Online Retail II Dataset
9. Analysis of the Energy Consumed by Appliances
10. Analyzing Air Quality

# Clustering

Clustering is an unsupervised learning technique in which you group categorically similar data points into batches, called clusters. Here, we will be focusing on the k-means clustering method.

K-means clustering is a clustering algorithm based on iterations where similar data points are grouped into a cluster based on their closeness to the cluster centroid. This means that the model runs iteratively to find the cluster centroid.

The optimum number of clusters for a dataset is found by using the elbow method.

## Method to Find the Optimum Number of Clusters

The logic behind k-means clustering is to define a cluster in such a way that, within the cluster, the sum of square (WSS) is minimized. The smaller the value of WSS, the better the compactness of the cluster. The clusters that are compact have data points that are similar to one another. We will be using the elbow method to find the optimum number of clusters.

The elbow method gets its name from the arm...