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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- mae |
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- r_squared |
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pipeline_tag: tabular-regression |
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tags: |
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- regression |
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- price-prediction |
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--- |
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# Model Card for Infinitode/IHPPM-OPEN-ARC |
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Repository: https://github.com/Infinitode/OPEN-ARC/ |
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## Model Description |
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OPEN-ARC-IHPP is a CatBoostRegressor model developed as part of Infinitode's OPEN-ARC initiative. It was designed to predict accurate price points for India house and property rentals based on various factors. |
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**Architecture**: |
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- **CatBoostRegressor**: `iterations=2500`, `depth=10`, `learning_rate=0.045`, `loss_function="MAE"`, `eval_metric="MAE"`, `random_seed=42`, `verbose=200`. |
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- **Framework**: CatBoost |
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- **Training Setup**: Trained with 2500 iterations on the dataset split. |
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## Uses |
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- Predicting accurate price points for properties in India. |
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- Validating or measuring existing price points for properties. |
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- Researching property value and factors that influence price. |
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## Limitations |
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- May generate implausible or inappropriate results when influenced by extreme outlier values. |
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- Could provide inaccurate prices; caution is advised when relying on these outputs. |
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## Training Data |
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- Dataset: India House Rent Prediction dataset from Kaggle. |
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- Source URL: https://www.kaggle.com/datasets/pranavshinde36/india-house-rent-prediction |
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- Content: House type, locality, city, area, furnishing and room specifics along with the target rent value. |
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- Size: 7691 entries of properties in India. |
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- Preprocessing: Removed tiny area properties, extreme rent outliers, and `area_rate`. Also created "area buckets" for better performance. |
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## Training Procedure |
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- Metrics: MAE, R-squared |
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- Train/Testing Split: 85% train, 15% testing. |
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## Evaluation Results |
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| Metric | Value | |
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| ------ | ----- | |
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| Testing MAE | 3.86k | |
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| Testing R-squared | 0.9351 | |
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## How to Use |
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```python |
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def predict_user_rent(model, raw_df): |
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print("\n\n========== RENT PREDICTION ASSISTANT ==========\n") |
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print("Choose values for each feature below. For categorical vars, pick a number.\n") |
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sample = {} |
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# Menu |
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def choose_cat(col_name): |
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unique_vals = sorted(raw_df[col_name].unique()) |
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print(f"\n--- {col_name} ---") |
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for idx, val in enumerate(unique_vals): |
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print(f"{idx + 1}. {val}") |
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sel = int(input("Enter your choice number: ")) - 1 |
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return unique_vals[sel] |
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# Categorical |
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sample["house_type"] = choose_cat("house_type") |
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sample["locality"] = choose_cat("locality") |
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sample["city"] = choose_cat("city") |
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sample["furnishing"] = choose_cat("furnishing") |
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# Numeric values |
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def choose_num(col_name): |
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return float(input(f"\nEnter value for {col_name}: ")) |
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sample["area"] = choose_num("area") |
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sample["beds"] = choose_num("beds") |
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sample["bathrooms"] = choose_num("bathrooms") |
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sample["balconies"] = choose_num("balconies") |
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# area bucket |
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area_val = sample["area"] |
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area_bins = [0, 300, 600, 900, 1200, 2000, 5000, 100000] |
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area_bucket = np.digitize([area_val], area_bins)[0] - 1 |
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sample["area_bucket"] = area_bucket |
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# placeholder for rent_psf bucket (we don't know rent yet) |
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# so we use area only as a proxy for typical price density |
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sample["rent_psf_bucket"] = min(int(area_bucket), 19) |
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df_input = pd.DataFrame([sample]) |
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# Must match training encodings |
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for col in ["house_type", "locality", "city", "furnishing"]: |
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df_input[col] = df_input[col].astype(raw_df[col].dtype) |
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# Prediction |
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pred_log = model.predict(df_input)[0] |
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pred_rent = np.expm1(pred_log) |
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print("\n===================================") |
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print(f"Estimated Rent: ₹ {pred_rent:,.2f}") |
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print("===================================\n") |
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return pred_rent |
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# Uncomment to use interactively: |
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# predict_user_rent(model, df) |
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``` |
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## Contact |
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For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact. |
