RFM-model

Customer satisfaction project built on data with information on RFM (stands for recency,frequenct,monetary).Dataset is from kaggle

Project Details

In this project, I applied the K-means clustering algorithm to analyze the dataset. To determine the optimal number of clusters, I used the elbow method. The elbow method is a technique that helps identify the appropriate number of clusters by evaluating the within-cluster sum of squares (WCSS) for different values of k.

I iterated over a range of k values and calculated the WCSS for each k. Then, I plotted a line graph with the number of clusters (k) on the x-axis and the corresponding WCSS on the y-axis. The graph resembled an elbow shape, and the 'elbow point' indicated the optimal number of clusters.

By visually inspecting the graph, I determined the value of k at the elbow point, which represents the point of diminishing returns in terms of decreasing WCSS. This value was selected as the optimal number of clusters for the K-means algorithm in this project.

Evaluation

For getting meaningful results, I used plotly for visualizations and named my customer segments as:

Loyal Spenders
Occasional Engagers
Rapid Repeaters
Potential Lost
High value Regulars

each one segment explanations can be seen on dashboard I created with this project which also presents the visual outputs.