https://github.com/newking9088/real_estate_cost_estimation_and_property_recommendation

We've developed an intelligent real estate recommendation engine that achieves 95% accuracy in price predictions while personalizing property suggestions to individual preferences. By implementing the KNN algorithm, our system analyzes thousands of historical transactions to determine recommendation properties
https://github.com/newking9088/real_estate_cost_estimation_and_property_recommendation

automated-machine-learning descriptive-analysis descriptive-statistics mlops-workflow predictive-modeling recommender-system

Last synced: 29 days ago
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We've developed an intelligent real estate recommendation engine that achieves 95% accuracy in price predictions while personalizing property suggestions to individual preferences. By implementing the KNN algorithm, our system analyzes thousands of historical transactions to determine recommendation properties

• Host: GitHub
• URL: https://github.com/newking9088/real_estate_cost_estimation_and_property_recommendation
• Owner: newking9088
• License: mit
• Created: 2024-10-15T03:08:20.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-02-14T20:28:23.000Z (3 months ago)
• Last Synced: 2025-04-01T13:38:01.967Z (about 2 months ago)
• Topics: automated-machine-learning, descriptive-analysis, descriptive-statistics, mlops-workflow, predictive-modeling, recommender-system
• Language: Jupyter Notebook
• Homepage:
• Size: 32.4 MB
• Stars: 1
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# End-to-End Machine Learning Pipeline for Real Estate Valuation & Recommendation Engine

This project aimed to enhance property recommendations by predicting prices with high accuracy. Utilizing KNN and collaborative filtering algorithms, it achieved a 95% accuracy rate and a 5% margin of error, effectively tailoring property suggestions to customer preferences through the analysis of historical pricing data and user behaviors.

## Quick Link

[Blogpost](https://nycdatascience.com/blog/student-works/end-to-end-machine-learning-pipeline-for-real-estate-valuation-recommendation-engine/?aiEnableCheckShortcode=true)

## Robust Real Estate Valuation Model 🏡🧮

- Developed a highly accurate property valuation model with 95% accuracy, able to estimate prices within a 5% margin of error.

- Leveraged an ensemble of advanced machine learning algorithms including CatBoost, LightGBM, Random Forest, and AdaBoost.

- Achieved excellent performance metrics with R² scores above 0.90 on validation data.

## Automated Machine Learning Pipeline 🤖🔍

- Implemented a streamlined end-to-end ML pipeline for data ingestion, preprocessing, feature engineering, model training, and hyperparameter tuning.

- Ensured data integrity and prevented leakage by carefully sequencing preprocessing steps and storing pipeline parameters.

- Enabled seamless deployment by persisting the preprocessing pipeline and trained models.

## Comprehensive Feature Engineering and Selection 🔧🔍

- Derived new domain-specific features like house age, total square footage, number of bathrooms, and years since last remodel.

- Performed rigorous feature selection using statistical techniques like f_regression and correlation analysis to identify the most important predictors.

- Analyzed the associations between categorical features using Chi-square and Cramer's V to select the most significant ones.

## Intelligent Property Recommendation Engine 🔍🏠

- Implemented a nearest-neighbor based recommendation system to match properties based on user preferences and property characteristics.

- Leveraged the robust data transformation pipeline to create a rich feature space for property matching.

- Provided real-time property recommendations by efficiently computing multidimensional feature similarity.

## Insights into Price Drivers 💰📈

- Analyzed feature importance using the CatBoost model, revealing that 'TotalSqFt' and 'OverallQual' are the most influential factors, accounting for nearly 50% of the price impact.

- Identified other key drivers like 'TotalBaths', 'YrRemodAge', and 'HouseAge' that significantly influence property prices.