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Implemented self-serviced ML models for a real estate advisory firm which allowed personalized reporting


Client Background

The client is a real estate advisory firm that provides strategic and tactical advice regarding property investment, planning, and development.


  • The client wanted to create an easy-to-use tool for realtors to help analyze their end-customers and increase conversion ratio in the various lines of businesses – Buy, Sell, Rent, Lease, Break-Lease, Renew-lease.  
  • They wanted to bring the power of machine learning into their analytics application to improve the accuracy of the results and reduce the gaps between onsite and marketing people.


  • Designed and developed predictive analytics and self-service predictive analytics workflows blending third party and own datasets. 
  • Implemented customer segmentation using ML models which helps realtors in identifying target customers group for a better conversion ratio. 
  • Implemented self-serviced ML models which allowed realtors to have personalized reporting. 
  • Developed machine learning models to get the propensity of the customer to buy/sell a property. 
  • Developed workflow to refresh predictive algorithms on an on-demand basis without any manual efforts. 

Tools & Technologies

Python, scikit learn, AWS, MySQL, Java

Key benefits

  • Improved customer experience by providing more control through self-service predictive analytics. 
  • Increased prediction accuracy. 
  • Enabled realtors to engage effectively with each persona type, thus aiding a superior customer experience. 
  • Helped realtors in redirecting marketing efforts and campaigns in the right direction (focusing on the top 2% of prospects who are most likely to buy in a particular month) instead of reaching out to all the prospects. 
  • The machine learning pipeline helped them to build predictive models for new realtors quickly. 
Key Benefits - Machine Learning Analytics - Real Estate



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