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Overview
A supervised machine learning project that predicts house prices based on property features like location, size, age, and amenities. Includes full data analysis, feature engineering, model training, and evaluation.
Machine Learning
7 technologies · 6 key features · 3 outcomes
Challenge & Approach
Real estate pricing is complex and subjective. Buyers and sellers need objective, data-driven price estimates based on comparable property features.
Applied regression techniques including Linear Regression, Random Forest, and XGBoost on a housing dataset. Conducted EDA, handled missing values, engineered features, and selected the best model based on RMSE and R² scores.
Key Features
Technologies Used
Results & Impact
Best model achieved R² score of 0.89
Outperformed baseline by 34%
Clean reproducible ML pipeline
Lessons Learned
Feature engineering has more impact than model selection
Cross-validation prevents overfitting in regression tasks
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