rfm

customer ID or Description was found to be missing.
It could be matched to the stock code and filled the value but the unit price for these rows was found to be missing as well hence they were deleted.

This process takes place by finding associations between your various items which customers invest their shopping baskets or carts.
This is known as association rule mining and the Apriori Algorithm is really a commonly

For the comparison of both these segmentation techniques, the usage of a snake plot and a heat map is done.
Snake plot is really a line plot that is found in Marketing analytics a lot and gives the idea of comparison of different segments.
For snake plots to work, the info should be normalized.
It plots each cluster’s average normalized value of each attribute.
For effective plotting, the dataframe should be melted so that the metric columns are split into two columns.
First, the name of the metric, and second, the current numeric value.
After pre-processing of data, the next step was to perform Exploratory Data Analysis .

• Because the data is transactional data, it is crucial to perform data pre-processing in order to make it ideal for further analysis.
• Many frequent patterns are left out when the minimum support value is raised because they fail to fulfill the threshold value.

The algorithm generates all the frequent patterns for several three segments and generates the guidelines by finding co-relations among frequent item sets with at the very least 70% confidence and identifies the association rules for both segments.
Market Basket Analysis is a data mining approach used to find out the buying patterns of customers and subsequently increase sales.

The RFM values are log scaled first and then normalized.
A log transformation is applied for each RFM value and the StandardScaler() library is used for standardization.
The elbow method is then used to learn the right number of clusters which have appear to be 3.Clusters are named 0,1,2.This validates the RFM segments created.
Our analysis shows the support values decrease or move towards less than or equal to 0.01, sometimes.
Sometimes the Apriori Algorithm fails to generate the frequent patterns.
This is because it gets in an infinite loop.