Hands-On Project

Build a Real-Time Housing Price Prediction System on
Azure Machine Learning

In this hands-on project, you will build an end-to-end, production-style housing price prediction system using Azure Machine Learning Studio. The system ingests real estate data, trains and evaluates multiple regression models using Automated Machine Learning (AutoML), and deploys the best-performing model as a real-time prediction service.

This project mirrors how real estate analytics and valuation models are built and operationalized in enterprise environments - from data ingestion to live inference.

Ideal for Professionals in:

Data Analyst / Data Scientist
Machine Learning Engineer
AI Engineer
ML Architect
Solutions Architect
Automation Engineer

💡 What You'll Gain

1. Central ML Control Plane: Azure Machine Learning Workspace

Create and manage an Azure ML Workspace as the system's backbone
Organize datasets, experiments, models, and endpoints in one place
Understand workspace-level governance and lifecycle management

2. Scalable Compute for ML Workloads

Provision and manage Azure ML Compute Instances & Clusters
Run experiments on scalable, cost-controlled infrastructure
Learn how compute choices impact training time and cost

3. Real-World Dataset Ingestion & Preparation

Import tabular housing datasets from Azure Blob Storage
Handle real-world features such as:
- Area, bedrooms, bathrooms
- Property attributes
- Location-related indicators
Prepare clean, model-ready data inside Azure ML Studio

4. Automated Machine Learning (AutoML) Experimentation

Run AutoML regression experiments to:
- Test multiple algorithms (Linear Regression, Decision Trees, Gradient Boosting, etc.)
- Automatically tune hyperparameters
- Select the best-performing model based on metrics
Eliminate guesswork while retaining enterprise-grade rigor

5. Model Evaluation & Selection Framework

Compare model performance using standardized metrics
Understand why a model wins—not just that it wins
Select production-ready models, not just "highest accuracy" ones

6. Real-Time Model Deployment as a Web Service

Deploy the selected model as a real-time REST API endpoint
Configure inference settings, scaling, and monitoring
Enable live predictions for applications and downstream systems

7. Live Prediction & Endpoint Testing

Consume the deployed endpoint using Python (Jupyter Lab)
Send new property data and receive real-time price predictions
Convert raw predictions into business-ready outputs

8. Production-Aware ML Lifecycle Management

Monitor endpoint provisioning and inference behavior
Handle quota, region, and compute constraints
Clean up Azure resources to avoid unnecessary costs

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