This project uses K-means clustering to identify distinct customer segments based on their income and spending patterns. The insights gained can be used to create more effective, personalized marketing campaigns.

* Data Cleaning and Preprocessing: Cleans raw customer data and prepares it for analysis.

* K-Means Clustering: Implements the K-means algorithm to group customers into segments with similar characteristics.

* Streamlit Web App: Provides an interactive user interface built with Streamlit for:

* Cluster Visualization: Displays the created customer segments with clear visual markers for clusters and their centroids.

* Exploratory Analysis: Includes basic visualizations (e.g., income distribution) and descriptive statistics.

* Insightful Commentary: Offers a dedicated area for sharing key takeaways and recommendations.

* Product Data Set.csv

* PRODUCT NUM (int): Unique product identifier

* PRODUCT CODE (str): Product code

* UNIT LIST PRICE (float): Original price of the product

* ... (other product-related columns)

* Transaction Data Set.csv

* CUSTOMER NUM (int): Unique customer identifier

* PRODUCT NUM (int): Product identifier (links to Product Data Set.csv)

* QUANTITY PURCHASED (int): Number of units purchased

* DISCOUNT TAKEN (float): Discount applied (0-1)

* ... (other transaction-related columns)

* Customer Data Set.csv

* CUSTOMERID (int): Unique customer identifier

* INCOME (str): Customer's income (with currency symbols)

* ... (other customer-related columns)

* `load_data()`: Reads the CSV files into pandas DataFrames.

* `clean_data()`: Prepares the income column for analysis by removing currency symbols and converting it to numeric format.

* `merge_data()`: Combines data from multiple DataFrames, calculates total spending per customer, and pivots the data to create customer spending profiles.

* `perform_clustering()`: Implements the K-means algorithm with user-specified features and a number of clusters.

* `plot_clusters()`: Visualizes the clusters on a scatter plot.

* `main()`: Coordinates data loading, preprocessing, clustering, and Streamlit app initialization.

* Add More Features: Consider other relevant features for clustering.

* Experiment with Algorithms: Try different clustering techniques (e.g., DBSCAN).

* Advanced Analysis: Calculate customer lifetime value (CLV) and use it for segmentation.

* Recommendation System: Build a simple recommendation system based on cluster membership.