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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Project Template Outcomes

  • 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

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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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