This project aims to create a clustering model for a bresilian ecommerce website. After testing several clustering models woth RFM marketing chosen features, the goal was to test the selected model with further features.
Finally, offer a model management plan to see how often the model has to be update to stay accurate.
The business objectives of this project revolve around improving the understanding of customer behavior for Olist, an e-commerce platform.
- Customer Segmentation: By segmenting clients based on their purchasing behavior, Olist can tailor marketing strategies, leading to more effective campaigns and improved customer retention.
- Personalization: Understanding customer profiles allows for personalized recommendations and communications, which can enhance the overall customer experience and drive sales.
- Data-Driven Decisions: Implementing data analysis helps Olist make informed decisions about product offerings, pricing strategies, and customer engagement approaches.
- Improving Customer Experience: By analyzing customer data, Olist can identify pain points and opportunities within the customer journey, leading to a better overall experience.
- Mastering SQL: Query and manipulate data using SQL to extract insights from datasets.
- Understanding Clustering Algorithms: Understand unsupervised learning techniques, specifically clustering methods like K-means or hierarchical clustering, to segment customers based on their behaviors.
- Data Exploration: Develop skills in exploratory data analysis (EDA) to understand data distributions, find patterns, and identify outliers.
- Model Maintenance: Learn how to create, validate, and maintain machine learning models, ensuring they remain relevant over time as new data becomes available.
- Communication Skills: Prepare for and practice delivering clear and professional project presentations, effectively conveying findings and methodologies.
- Application of Learned Skills to Real-World Scenarios: Apply theoretical knowledge to practical situations in e-commerce, enhancing ability to derive actionable insights from complex datasets.
clustering algorithms, unsupervised learning, customer segmentation, SQL, Exploratory Data Analysis, Machine learning models, KMeans clustering, Hierarchical clustering, DBscan, density clustering, E-commerce analytics, Data-Driven marketing, Profiling, Maintenance.
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt