SmartCart Customer Segmentation

Domain: Machine Learning | Unsupervised Learning Type: Customer Analytics and Clustering Language: Python

Project Overview SmartCart Customer Segmentation is a machine learning project that applies unsupervised learning techniques to analyze and group customers based on their behavioral and demographic attributes. The project aims to uncover hidden patterns within the SmartCart customer dataset to enable data-driven business strategies and personalized marketing.

Objectives

• Identify distinct customer segments within the SmartCart dataset
• Apply clustering algorithms to group similar customers together
• Visualize patterns and relationships within the data
• Provide actionable insights for business decision-making

Project Workflow

1. Data Importing - Loading and inspecting the customer dataset
2. Data Preprocessing - Handling missing values and data cleaning
3. Feature Engineering - Selecting and transforming relevant features
4. Exploratory Data Analysis - Statistical analysis of data distributions
5. Data Visualization - Heatmaps, distribution plots and correlation analysis
6. Encoding - Converting categorical variables into numerical format
7. Clustering - Applying unsupervised learning algorithms to segment customers
8. Cluster Analysis - Evaluating and interpreting the customer groups

Technologies Used

• Python
• Jupyter Notebook
• Pandas for data manipulation
• NumPy for numerical computation
• Matplotlib and Seaborn for data visualization
• Scikit-learn for preprocessing, encoding and clustering

Dataset File: smartcart_customers.csv Description: Customer data including behavioral and demographic attributes collected from the SmartCart platform.

Author Jalan Bamjan