Duration:
80 hours
 
Project Overview:
Delve into the world of customer analytics by performing a comprehensive segmentation analysis using an E-Commerce Public Dataset. This project focuses on grouping customers based on purchasing behaviors to enhance marketing strategies, improve customer retention, and optimize resource allocation. You will engage in data cleaning, feature engineering, exploratory data analysis, clustering techniques, and model maintenance simulations, providing you with practical experience in handling real-world e-commerce data.
 
Key Learning Outcomes:
Data Cleaning & Preprocessing:
Master techniques to handle missing values, merge multiple datasets, address duplicates, and ensure data integrity, preparing the dataset for in-depth analysis.
Feature Engineering:
Create and optimize new features, including RFM (Recency, Frequency, Monetary) metrics, to better represent customer behaviors and enhance clustering performance.
Exploratory Data Analysis (EDA):
Conduct univariate, bivariate, and multivariate analyses to uncover patterns, correlations, and insights within the data.
Data Visualization:
Develop clear and informative visualizations using libraries like seaborn and matplotlib to communicate findings effectively.
Clustering Techniques:
Apply clustering algorithms such as K-Means to segment customers based on purchasing behaviors and determine the optimal number of clusters using evaluation metrics.
Model Evaluation & Optimization:
Assess and optimize clustering performance using metrics like Silhouette Coefficient, Elbow Method, and Davies-Bouldin Index.
Machine Learning Model Maintenance:
Simulate model performance over time to assess when retraining is necessary due to changes in customer behavior.
Data Standardization & Scaling:
Standardize or scale numerical data to prepare for clustering algorithms.
 
Tools & Libraries:
Programming Language: Python
Libraries: pandas, numpy, scikit-learn, seaborn, matplotlib, yellowbrick
 
Deliverables:
Data Preparation Notebook:
Documenting data exploration, cleaning, merging, and initial visualizations.
Feature Engineering Notebook:
Detailed creation of RFM features and any additional features used.
Exploratory Data Analysis Notebook:
Visualizations and analysis showing key insights from the data through univariate, bivariate, and multivariate analyses.
Clustering Analysis Notebook:
Code and explanations for determining the optimal number of clusters, applying K-Means clustering, and interpreting the results.
Alternative Clustering Methods Notebook:
Exploration of other clustering algorithms (DBSCAN, Hierarchical Clustering) and comparative analysis.
Model Maintenance Simulation Report:
Methodology and findings from the simulation over time, including Adjusted Rand Index (ARI) calculations and retraining recommendations.
Final Report and Presentation:
Comprehensive documentation of the entire project, including methodologies, findings, visualizations, and business implications.
Presentation Slides:
Summarized version of the project suitable for presenting to stakeholders.
 
Who Should Enroll:
Data analysts and scientists looking to enhance their skills in clustering and customer analytics.
Marketing professionals interested in leveraging data to inform strategies.
Students and enthusiasts eager to apply machine learning techniques to real-world data.
 
Why This Project? Understanding customer behavior is crucial for businesses aiming to personalize marketing efforts, improve customer retention, and optimize resources. By completing this project, you'll gain hands-on experience in:
Handling and analyzing real-world e-commerce data.
Applying clustering techniques to segment customers effectively.
Interpreting and visualizing data to derive actionable business insights.
Simulating model performance over time to understand the importance of model maintenance.