Customer Segmentation Analysis

OVERVIEW: This project involves analyzing customer data to perform segmentation based on income and spending patterns. The insights are visualized in a Power BI dashboard, and clustering is implemented using an ML model in Google Colab.

TOOLS AND TECHNOLOGIES-

• Google Colab: Building and training the machine learning model
• Python: Used for data cleaning, processing and clustering
• Power BI: Interactive data visualization

PROJECT WORKFLOW- This project involves multiple stages, from data cleaning to machine learning and visualization. Below is the complete workflow:

1. Data Cleaning and Preprocessing:

• The original dataset was cleaned and processed to handle missing values, remove duplicates, and ensure data consistency.
• Features like Annual Income, Spending Score, and Gender were prepared for analysis.

2. Clustering with K-Means:

• K-Means clustering was applied in Google Colab to segment customers into distinct groups based on income and spending patterns.
• The optimal number of clusters was determined using the Elbow Method.

3. Clustered Data Export:

• The processed dataset, enriched with cluster labels, was exported as a CSV file for further visualization.

4. Dashboard Creation in Power BI:

• The processed dataset was imported into Power BI.

• Interactive visualizations, such as cluster descriptions, spending patterns, gender distribution, and age group analysis, were created to extract key insights.

   Dashboard-
  

• Enhanced visuals with filters, slicers, and drill-down options to make the insights actionable.

HOW TO RUN THIS PROJECT-

1. MACHINE LEARNING MODEL-

• Open the python script in Jupyter Notebook or open the Colab file directly.
• Install required libraries.
• Run the notebook to train the K-means clustering model.
• Save the output data with cluster labels.

3. POWER BI-

• Import the processed dataset into Power BI.
• Use the .pbix file to open the pre-built dashboard or recreate the visuals using imported data.
• Review interactive dashboard for insights.

KEY INSIGHTS-

1. Customer Distribution-

• The majority of customers (81) belong to the "Average" cluster, indicating balanced income and spending patterns.
• High-income and high-spending customers account for 39 individuals, showcasing a key segment for premium products or services.
• The "Low Income, High Spend" cluster (23 customers) could signify customers who prioritize discretionary spending despite limited income.

2. Spending Behavior:

• The top-performing cluster by spending score is "High Income, High Spend," with an average score of 82.13, representing the most valuable customer group.
• The "Low Income, High Spend" cluster has a notable spending score of 79.36, which could indicate potential financial strain or a preference for high-value purchases.

3. Income-to-Spending Ratio:

• The overall income-to-spending ratio is 1.21, reflecting a moderate balance between income and spending across all clusters.
• Clusters with lower ratios may represent overspending or high-value-focused customer behaviors.

4. Gender Distribution:

• Males constitute a slightly higher proportion (56%) compared to females (44%), indicating potential for gender-focused marketing strategies.

5. Age Group Analysis:

• Customers aged 30-40 form the largest group (60 customers), followed by the 20-30 age group (45 customers). These age groups might be the primary target audience for marketing campaigns.
• The youngest (<20) and oldest (50-70) groups are the smallest segments, with only 17 customers each, suggesting minimal engagement from these demographics.

6. Income and Spending Correlation:

• The scatter plot demonstrates a clear pattern where higher income often correlates with higher spending scores. However, outliers (e.g., low-income, high-spend customers) highlight unique opportunities or risks.