This repository contains the analysis of customer data aimed at segmenting customers into homogeneous groups based on their characteristics. The analysis was conducted as part of a project to understand customer behavior and improve personalized services.
The customer data used in this analysis was collected from donnes_client.csv . It includes information such as age, annual income, and spending score.
The data cleansing process involved handling missing values, removing duplicates, and addressing outliers to ensure data quality and reliability.
Normalization was performed to bring all features to the same scale, facilitating comparison and analysis.
The K-Means algorithm, a popular clustering technique, was applied to partition customers into distinct clusters based on their similarities.
Clusters were thoroughly analyzed to discern the characteristics and profiles of different customer groups, providing valuable insights for decision-making and strategy development.
The analysis results were visualized using appropriate graphs and charts to facilitate understanding and interpretation.
The segmentation analysis successfully grouped customers into homogeneous clusters, offering valuable insights for decision-making, targeted marketing, and service personalization.
Further analysis could include exploring additional clustering algorithms, incorporating more features for segmentation, and conducting predictive modeling to anticipate future customer behavior.
CHEKHCHOU Bilal