Customer Segmentation for Targeted Marketing

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.

**Features**

* **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.

**How to Run**

1. **Install Dependencies:**
   ```bash
   pip install streamlit pandas numpy scikit-learn matplotlib

2. Get the Dataset:
   • Create or download datasets named 'Product Data Set.csv', 'Transaction Data Set.csv', and 'Customer Data Set.csv'.
   • Place them in the same directory as your project files.
3. Run the Streamlit App:

   streamlit run app.py  # Replace 'app.py' with your main script name if different

Dataset Structure

• 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)

Code Explanation

• 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.

Customization and Next Steps

• 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.

Feedback

I welcome any contributions, suggestions, or questions!