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