Price Prediction Solution for Real Estate Business Using MLflow 2.0

Price Prediction Solution for Real Estate Business Using MLflow 2.0

Written by Hafsa Mustafa

Technical Content Writer

April 18, 2023

Business velocity can be determining factor for the success of a business. Here, the term ‘velocity’ simply means acting in time and reacting to changed realities when you should. The landscape of the commercial real estate industry tends to be quite fickle as it is known for undergoing dramatic changes in no time. Therefore, it is essential for a business that is thriving in real estate to stay on top of things. This can help the business to react to market fluctuations and meet the customer needs in a better way.

This blog discusses how Royal Cyber’s data engineering solution helped its real estate client in improving customer engagement and retention.

Learn all About MLOPs Workflow with the help of this E-Guide

Problem Statement

Real estate businesses can benefit a lot if they are able to predict price fluctuations for their clients. However, this is a very complex mission as it is not easy to build a dynamic framework that successfully integrates companies, property values, and people to answer frequently asked questions like, what is the price of this house? Which area is the most or least expensive?

Although efforts are still being made to build a machine learning solution that can provide answers to such basic questions, professionals often struggle to find support for the library and environment agnostic, track hyperparameters by using visualization or reproduce and exchange models.

Our Solution

Royal Cyber’s solution revolved around unifying the data and AI with a high-functioning machine learning pipeline while simultaneously raising cross-functional collaboration and operating speed to new heights. Our goal was to overcome the challenges related to model tracking, reproducibility, scalability, governance, and standard way of packaging and deployment with acceleration in ML workflow.

We enabled our client to get started with machine learning with iterative speed and accelerate the ML development workflow. Our solution enabled the client to build an efficient ML model which could accurately predict the prices for the properties. It can now take its models from virtually nothing to production in a short span of two months. Besides providing fast development, our solution is also cost-effective and helps the client save valuable resources.

Through the increased velocity of data organization and Databricks as its analytics backbone, the client is now able to identify and act on new product opportunities, thus improving its customer engagement levels and retention rates.

Solution Architecture

We began by dividing our client’s real-time dataset into several numerical and categorical features demonstrating details like location, availability, size, and society, which we analyzed during the development of the project.

Find Out How Royal Cyber Achieved Fraud Prediction for A Fintech Company

To build our solution, we used different technology platforms, including, Databricks, MLflow, Amazon S3, and Amazon VPC. First off, a Databricks workplace on AWS was created. Once the pipeline was imported and set up, we ingested the data into the pipeline. We utilized the new advanced features like MLflow Recipes for exploration and also to clean the data. We then trained the model with the help of our algorithm. We also evaluated the model and predefined the metrics. After evaluation, the model was loaded and registered. Finally, we used MLflow Model Serving to serve the model through UI and API.

Here’s a simplistic diagram to show our solution architecture.

Business Benefits

  • 2x reduction in the cost needed for the development of a machine learning model
  • 2x faster development of production-based machine learning models (in less than two months) owing to quick iterative development
  • The client was able to save its resources in terms of time, cost, and effort

Conclusion

Our solution shows how an ML pipeline developed using Databricks Managed MLflow can enable real estate businesses to process large volumes of data easily. By accurately predicting the prices of properties, our solution protects clients against significant financial losses. It is, in fact, a wise investment since it can be upgraded in the future in a way that is low-cost and less time-intensive.

Need Help?

Royal Cyber has bagged numerous awards over the years for providing efficient IT solutions to businesses belonging to diverse industries, including fintech, petroleum, healthcare, automotive, and retail. You can contact our certified data experts to get your queries answered or discuss the next smart solution for your business.

Do you want to get ahead of the curve?

Recent Posts

  • How to Write Test Cases: Introduction and Best Practices
    Learn to write effective test cases. Master best practices, templates, and tips to enhance software …
    Read More »
  • MuleSoft Admin Co-Pilot: Revolutionize Integration Management
    In today’s fast-paced digital landscape, seamless data integration is crucial for business
    Read More »
  • Revolutionizing Customer Support with Salesforce Einstein GPT for Service Cloud
    Harness the power of AI with Salesforce Einstein GPT for Service Cloud. Unlock innovative ways …
    Read More »