Why Real World Data is Hard: Lessons from Building a Gujarat Real Estate Predictor

2026-07-26 · Source: AI on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Novice, short

Summary

A developer built the Gujarat Real Estate Price Predictor, a Streamlit web application designed to estimate average cost per square foot based on historical RERA filings. The project utilized messy public Gujarat RERA data, requiring extensive cleaning, including filtering extreme outliers, establishing realistic market price floors (e.g., above ₹1500/sq.ft), and applying log transformation to the target variable. The core model is a Gradient Boosting Regressor from Scikit-learn, handling categorical features via ordinal encoding. Key challenges involved resolving data quality issues, managing feature order consistency between the trained model and the Streamlit app, and troubleshooting deployment errors related to Scikit-learn version mismatches and file paths on Streamlit Cloud. The project highlights the significant effort required for data preparation in real-world scenarios.

Key takeaway

For Machine Learning Engineers tackling real-world datasets, prioritize robust data cleaning and validation before model training. You should anticipate significant time spent on data preparation, outlier handling, and feature engineering, as this directly impacts model stability. Ensure your deployment pipeline strictly matches feature order and library versions used during training to avoid common integration errors. Focus on getting a functional application deployed, as this iterative process yields invaluable practical lessons.

Key insights

Real-world data quality demands extensive cleaning and iterative problem-solving for successful machine learning model deployment.

Principles

• Data preparation dominates real-world ML projects.
• Deployment provides more value than endless iteration.
• Feature importance reveals true data drivers.

Method

Clean raw RERA CSV by filling missing values, removing outliers, creating derived features, dropping leakage columns, and encoding categoricals. Train a tuned Gradient Boosting model, save it, and deploy via Streamlit.

In practice

• Apply log transformation for skewed target variables.
• Force input DataFrame to match model's feature order.
• Pin specific library versions for cloud deployment.

Topics

• Real Estate Prediction
• Data Cleaning
• Gradient Boosting Regressor
• Streamlit Deployment
• Feature Engineering
• RERA Data

Code references

• shaan-is-him/gujarat-real-estate-mlPrediction

Best for: Machine Learning Engineer, AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.