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MACHINE LEARNING

Case Study: Property Price Prediction

An ML model analyzing Bangladesh property market data to accurately predict real estate prices, enabling data-driven investment and valuation decisions.

Project Duration

4 Months

Team Size

4 Specialists

Prediction Accuracy

89% R² Score

Project Overview

Client

Real Estate Analytics Platform

Industry

Real Estate / PropTech

Services Provided

  • Data Collection & Cleaning
  • Feature Engineering
  • ML Model Development
  • API Development
  • Dashboard Visualization

Technologies

Python Scikit-learn XGBoost Pandas FastAPI React
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The Challenge

The Bangladesh real estate market lacked transparent, data-driven valuation tools. Property prices varied wildly based on subjective assessments, making it difficult for buyers, investors, and developers to make informed decisions. Existing methods relied on agent estimates with no systematic analysis of the factors that truly drive property values in different neighborhoods.

The Solution

We built a comprehensive ML pipeline that collected and cleaned thousands of property transaction records across Dhaka and major cities. Feature engineering captured location-specific factors, property characteristics, proximity to amenities, and market trend indicators. Multiple algorithms were evaluated before selecting XGBoost as the optimal model, achieving an 89% R² score on holdout data.

The trained model was deployed as a FastAPI service, with a React-based web interface allowing users to input property details and receive instant valuations with confidence intervals and key value drivers.
Property Price Prediction Dashboard

The Results

89%

Prediction accuracy

10K+

Properties analyzed

3s

Average valuation time

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CEO, Tonkin Corporation

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Mukit Ur Rahman

Senior Product Manager, Sheba xyz

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John Francis

CEO, Sixfold Digital

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