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