---
license: mit
tags:
- machine-learning
- house-price-prediction
- random-forest
- real-estate
- pakistan
- regression
datasets:
- real-estate
language:
- en
model-index:
- name: House Price Prediction Model
  results:
  - task:
      type: tabular-regression
    dataset:
      name: Pakistani Real Estate Dataset
      type: real-estate
    metrics:
    - type: r_squared
      value: 0.9133
---

# House Price Prediction Model

This is a trained machine learning model for predicting house prices using a RandomForest regression algorithm. The model takes into account various property features such as location, size, number of bedrooms/bathrooms, and property type to provide accurate price estimates.

## Model Details

- **Algorithm**: RandomForest Regressor
- **Features Used**: property_type, location, city, baths, purpose, bedrooms, Area_in_Marla
- **Performance**: R² Score of approximately 0.9133
- **Training Data**: Pakistani real estate market data

## Model Architecture

The model is a pre-trained RandomForest regressor that includes:
- Label encoders for categorical variables
- Feature engineering for property characteristics
- Preprocessing pipeline for input data transformation

## Usage

```python
import pickle
import pandas as pd
from huggingface_hub import hf_hub_download

# Download and load the model from Hugging Face
model_path = hf_hub_download(
    repo_id="RayyanAhmed9477/house-price-prediction-model",
    filename="house_price_model.pkl"
)

# Load the model
with open(model_path, 'rb') as f:
    model_data = pickle.load(f)

# Prepare input data
input_data = pd.DataFrame([{
    'property_type': 'House',
    'location': 'DHA Defence',
    'city': 'Lahore',
    'baths': 3,
    'purpose': 'For Sale',
    'bedrooms': 4,
    'Area_in_Marla': 5.0
}])

# Encode categorical variables
for col in ['property_type', 'location', 'city', 'purpose']:
    if col in model_data['label_encoders']:
        le = model_data['label_encoders'][col]
        try:
            input_data[col] = le.transform([str(input_data[col].iloc[0])])
        except ValueError:
            # Handle unknown categories by using the most frequent one
            input_data[col] = le.transform([le.classes_[0]])

# Make prediction
prediction = model_data['model'].predict(input_data[model_data['feature_columns']])[0]
print(f"Predicted Price: {prediction}")
```

## Dataset Information

The model was trained on a Pakistani real estate dataset containing property features and their corresponding prices. The model considers various factors like location, city, property type, and physical characteristics of the property.

## Intended Use

This model is designed to help:
- Real estate agents estimate property values
- Home buyers and sellers understand market prices
- Property investors evaluate investment opportunities

## Limitations

- The model is trained on Pakistani real estate data and may not generalize to other regions
- Predictions are based on historical data and market conditions may change
- Accuracy may vary for extremely unique or luxury properties

## Training Details

- **Algorithm**: RandomForest Regressor with 100 estimators
- **Features**: 7 input features (property type, location, city, baths, purpose, bedrooms, area)
- **R² Score**: 0.9133
- **Training Framework**: scikit-learn
- **Target Variable**: Property price in PKR

## Model Card Authors

- Rayyan Ahmed

## How to Cite

If you use this model in your research, please cite it as:

```
House Price Prediction Model by Rayyan Ahmed
Available at: https://huggingface.co/RayyanAhmed9477/house-price-prediction-model
```

## License

This model is licensed under the MIT License.