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USA-RealEstate-Regression-RF
tomer7934/USA-RealEstate-Regression-RF
USA-RealEstate-Regression-RF is a machine learning model from tomer7934. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Student Name: Tomer Naimi Course: Intro to Data Science ---
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Updated Dec 10, 2025
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At a glance
- Access
- Public
- Created
- Dec 4, 2025
- Updated
- Dec 10, 2025
- SHA
- 6a72e29b
Model card
From the Hugging Face model README
Assignment #2: Advanced Modeling & Feature Engineering (USA Real Estate)
Student Name: Tomer Naimi Course: Intro to Data Science
Video Presentation (Part 4)
1. Data Integrity and Methodological Assessment (20%)
Critical Data Handling Decisions
| Action | Analytical Justification | Implication |
|---|---|---|
| Extreme Outliers (Capping) | Applied Capping at 99.5th Percentile to all numerical features. | Methodological Necessity: Stabilized the training data against massive outliers ($1B Price, 473 Beds), which would otherwise collapse any predictive model. |
| Skewness Correction | Performed Log Transformation on the target variable (price). | Mitigated the strong positive skew in the price distribution, essential for meeting Linear Regression's underlying assumptions. |
| Feature Augmentation (FE) | Introduced Clustering Feature (KMeans ID) and One-Hot Encoding (OHE). | Crucial Step: Overcame the zero-correlation problem by injecting complex, non-linear location information into the model. |
| Data Integrity | Imputed ~16% missing values with Mode/Median. | Preserved statistical power by avoiding the massive loss of approximately 850 data points. |
2. Model Training and Evaluation (20% & 20%)
Finding A: Regression Performance — The Power of Feature Engineering
| Model Name | R-squared ($R^2$) | Score Improvement | Critical Insight |
|---|---|---|---|
| Baseline (Linear Regression) | 0.3793 | N/A | Failure Confirmed: Only 38% of price variance is linearly explained. Validated the need for advanced modeling. |
| Improved (Random Forest) | 0.6019 | $\mathbf{+58%}$ Increase in R2 | The Winner: Captured non-linear interactions and proved the direct value of the Engineered Features (Clustering and OHE). |
Finding B: Feature Interpretation & The Price Mechanism
Visualization: Bar Plot of Feature Importances
- Key Insight: The primary drivers of property value are not generic features, but Human Factors.
- Conclusion: The most significant predictors were
bath(39% importance) and thezip_code(24% importance). This reinforces that price is determined by Convenience and Micro-Location rather than simple square footage.
Finding C: Classification Task Results — The Non-Learner
- Models: Logistic Regression / Decision Tree Classifier.
- Accuracy: $\approx 0.502$.
- Critical Evaluation: Despite perfectly balanced classes, the models failed to achieve any predictive power (equivalent to random guessing). The low F1 Score ($0.11$) and Recall ($\mathbf{0.06}$) on Class 1 (Expensive) demonstrate a catastrophic Failure to Differentiate high-value homes.
- Submission: The low performance justifies the submission of the Logistic Regression Model (as the baseline winner for this task) as a record of the model's limitations.
3. Final Submission Status
- Model Winner (Regression):
random_forest_improved_model.pkl(R2: 0.6019) - Model Winner (Classification):
logistic_regression_model.pkl(F1: 0.11) - Repository Content: Notebook, README, Data File, 4 Model files, 7 PNG visuals.
- Created
- Dec 4, 2025
- Updated
- Dec 10, 2025