AI-Powered Rental Pricing for a Housing SaaS

Overview

This project focuses on designing and delivering an AI-driven pricing engine for a housing SaaS platform.
The goal was to help housing professionals estimate fair and competitive rental prices using data, while keeping the solution deployable and easy to integrate into existing workflows.

Key Features

• Pricing Recommendation Engine
  Built a supervised regression model to estimate rental prices using location features, neighborhood signals, and historical listings.

• End-to-End ML Pipeline
  Implemented preprocessing, feature engineering, training, GridSearchCV tuning, and evaluation to ensure robust and reproducible results.

• API-First Delivery
  Exposed predictions through a FastAPI endpoint so the engine could be integrated into client tools (CRM / internal dashboards).

• Business-Ready Outputs
  Focused on clear outputs (recommended price + context) that can be understood by non-technical stakeholders.

Technologies Used

• Python, Pandas, NumPy
• Scikit-learn, XGBoost, Random Forest
• Seaborn, Matplotlib
• FastAPI, Docker

Challenges and Learnings

A key challenge was balancing performance with trust: the model needed strong accuracy, but recommendations also had to remain consistent and explainable for business users.
Data quality was also critical: handling missing values, validating inputs, and preventing leakage had a major impact on stability.

Outcome

The pricing engine reduced manual pricing effort and improved consistency across listings.
Its modular design supports future improvements such as retraining, adding new neighborhood signals, and integrating user feedback for continuous iteration.