LNshuti / avenue-one

Real Estate Valuation Automated

Avenue-One: Real Estate Valuation Automated

The purpose of this project is to demonstrates how to build and deploy a full stack application. The application covers all the areas necessary when building ML applications from data gathering, model development, deployment, automation, and frontend development. By leveraging Python, SQL, and machine learning libraries, a real estate deal valuation model is built and deployed as an API endpoint on AWS. The application also incorporates an automated data pipeline, user-friendly frontend interface, and thorough testing and validation processes.

Step 1: Data Gathering and Preparation

1. Identify and acquire relevant real estate data sources, such as property listings, historical sales data, or market trends.
2. Preprocess and clean the data using Python and SQL to ensure consistency and data quality.

Step 3: Model Development and Training

1. Utilize Python and suitable machine learning libraries (e.g., scikit-learn, TensorFlow) to develop a real estate deal valuation model.
2. Split the dataset into training and testing sets for model validation.
3. Train the model using the training dataset, adjusting hyperparameters as needed.
4. Evaluate the model's performance using appropriate metrics (e.g., mean squared error, R-squared).

Step 4: Model Deployment on AWS

1. Set up an AWS account if not already available.
2. Utilize AWS services such as Amazon EC2 or AWS Lambda to deploy the trained model as an API endpoint.
3. Configure the necessary security settings and access control for the deployed API.

Step 5: Data Pipeline and Automation

1. Design and implement an automated data pipeline using AWS Glue or custom Python scripts to fetch and preprocess real estate data.
2. Schedule the data pipeline to run periodically (e.g., daily or weekly) to ensure up-to-date information for deal valuation.
3. Integrate the data pipeline with the model deployment, triggering valuation predictions based on the updated data.

Step 6: Frontend Development

1. Create a user-friendly frontend interface using Python web frameworks (e.g., Flask, Django) or JavaScript libraries (e.g., React, Vue.js) to allow users to input property details for valuation.
2. Connect the frontend interface with the deployed model API to retrieve valuation predictions.
3. Implement appropriate error handling and user feedback mechanisms.

Step 7: Testing and Validation

1. Develop unit tests and integration tests to ensure the accuracy and reliability of the automated valuation system.
2. Validate the valuation predictions against known property values or expert opinions to assess the model's performance.
3. Make necessary refinements or adjustments based on the testing and validation results.

Step 8: Documentation and Reporting

1. Document the project's architecture, data sources, model details, and deployment processes.
2. Create user guides or technical documentation to assist users or other team members in utilizing and maintaining the system.
3. Prepare a summary report highlighting the automation achieved, time savings, and improved accuracy compared to manual valuation methods.

Step 9: Deployment and Monitoring

1. Deploy the project code from the GitHub repository to an AWS environment (e.g., EC2 instance, Lambda function).
2. Set up appropriate monitoring and logging using AWS CloudWatch or other suitable services to track system performance, errors, and data pipeline execution.

Step 10: Maintenance and Continuous Improvement

1. Establish a maintenance plan to address bug fixes, updates, or enhancements.
2. Continuously monitor and evaluate the system's performance, incorporating user feedback and making improvements as needed.

Architecture

architecture

References

This project was influenced by the LLamacademy package(https://github.com/danielgross/LlamaAcademy). Special appreciation to Daniel Gross for making his work available.