Identifying Product Bundles from Sales Data Using R Language

Identifying Product Bundles from Sales Data Using R Language

In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

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What will you learn

Understanding the problem Statement
Importing the dataset directly from Amazon AWS
What is Clustering
Performing basic EDA and Data-preprocessing
Plotting Boxplot and checking for identifying outliers
Fixing outliers using proper technique
Scaling and normalizing the dataset
Partition based methods, Hierarchical Methods, Model-based methods of clustering
Kmeans, k-median, k-mode agglomerative, divisive expectation-maximization based methods
Identifying different libraries used for Clustering
Steps involved in K-clustering
Applying different methods available for clustering
Applying the silhouette score on the clustering result
Visualizing the result by plotting graphs
Picking the best model for making predictions
Calculating probability related to testing data points for different clusters

Project Description

The weekly sales transaction dataset consists of weekly purchased quantities of 800 products over 52 weeks. Normalised values are provided too. The objective of this data science project in R is to find out product bundles that can be put together on sale. Typically Market Basket Analysis was used to identify such bundles, here we are going to compare the relative importance of time series clustering in identifying product bundles.

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Curriculum For This Mini Project

Introduction
03m
Installing Libraries
01m
Understand the Data Set
05m
Outliers
06m
Clustering Techniques
02m
Installing Library to implement KMeans
05m
Steps in KMeans Algorithm
11m
Implementing Kmeans
12m
Cluster Model Deployment
04m
Hierarchical Clustering - Agglomerative Method
10m
Hierarchical Clustering - Divisive Method
04m
Silhoutte Score
04m
Cluster Goodness using Silhoutte score
05m
Model Based Clustering
06m
Self Organizing Maps
07m
FactoExtra library for Visualization
01m
Other Clustering Methods
10m
Hierarchical Clustering
03m
Hopkins Statistics
03m
Determine Optimal Number of Clusters
03m
Clustering Validation Statistics
05m
Advanced Clustering
04m
Conclusion
08m