Within the dynamic world of short-term leases, correct pricing and actionable insights are very important for brand new Airbnb property homeowners to maximise income and visitor satisfaction. My mission addresses this want by growing an end-to-end, cloud-native knowledge pipeline that leverages machine studying and analytics to ship real-time pricing suggestions and deep market intelligence. This text particulars the enterprise use case, technical structure, AWS companies, and the challenges overcome throughout improvement.
Challenge info: https://github.com/jimmy-chen-1/AWS_Pipeline_Project
The first objective is to empower new Airbnb hosts with:
- Prompt, data-driven nightly worth suggestions primarily based on property options and market elements.
- Actionable analytics on property sort efficiency and worth tier segmentation to information operational and advertising and marketing methods.
On the core is a Ok-Nearest Neighbor (KNN) machine studying mannequin, skilled to foretell optimum nightly rental costs utilizing attributes like location, room sort, property sort, and 16 extra options. The mannequin is deployed behind an online interface, permitting customers to enter their itemizing particulars and obtain speedy, market-aligned pricing solutions.
Past prediction, the pipeline supplies two key analytical streams:
- Property-Sort Insights: Evaluation of buyer satisfaction metrics (accuracy, communication, location, worth) throughout property varieties to establish high-performing and underperforming segments.
- Worth Tier Evaluation: Segmentation of listings into finances, mid-range, and upper-mid-range bands, revealing which tiers ship the best worth and visitor satisfaction.
The answer is constructed on a modular, serverless, and scalable AWS structure, orchestrated utilizing AWS Step Capabilities. The workflow consists of three parallel processing flows:
- ML Mannequin Coaching and Deployment
- Assessment Rating Analytics
- Worth Tier Evaluation
Key Steps:
- Knowledge Ingestion: Uncooked Airbnb CSV recordsdata are uploaded to Amazon S3.
- ETL Processing: AWS Lambda triggers AWS Glue ETL jobs for knowledge cleansing and transformation. Glue Crawlers detect schema and register it within the Glue Knowledge Catalog.
- Structured Storage: Cleaned knowledge is saved in Amazon RDS (MySQL) for downstream analytics and querying.
Parallel Processing Flows:
- ML Path: Lambda features format knowledge, set off mannequin coaching, and deploy the mannequin to an EC2 Auto Scaling Group behind an Utility Load Balancer (ALB). The net frontend permits customers to get real-time worth predictions.
- Assessment Evaluation Path: Lambda extracts overview metrics, writes outcomes to RDS, and notifies through SNS. These insights help suggestions for property enhancements.
- Worth Tier Evaluation Path: Lambda segments knowledge into worth tiers, evaluates efficiency, and writes findings to RDS, uncovering high-value however underserved segments.
