---
language:
- en
license: apache-2.0
tags:
- tabular-regression
- house-prices
- regression
- real-estate
- scikit-learn
- tensorflow
pipeline_tag: tabular-regression
metrics:
- rmse
- mae
- r2
---

# House Price Prediction Model

## Model Summary

A regression model that predicts residential house prices based on structured tabular features such as square footage, location, number of rooms, and other property attributes. Built as part of a broader machine learning portfolio exploring supervised learning across regression tasks.

---

## Model Details

- **Developed by:** Chandrasekar Adhithya Pasumarthi ([@Adhithpasu](https://github.com/Adhithpasu))
- **Affiliation:** Frisco ISD, TX | AI Club Leader | Class of 2027
- **Model type:** Regression (Neural Network / Gradient Boosting — update as applicable)
- **Framework:** TensorFlow / Keras *(or scikit-learn — update as applicable)*
- **License:** Apache 2.0

---

## Intended Uses

**Direct use:**
- Predicting house sale prices from structured property data
- Exploring feature importance in real estate pricing
- Educational demonstrations of regression pipelines and feature engineering

**Out-of-scope use:**
- Production real estate valuation without domain expert review
- Generalization to housing markets with significantly different distributions than training data

---

## Training Data

Trained on a structured housing dataset containing features such as:
- Square footage (total, above ground, basement)
- Number of bedrooms and bathrooms
- Neighborhood / location
- Year built and year remodeled
- Garage, pool, and lot features

*(Update with your specific dataset — e.g., Kaggle's Ames Housing Dataset)*

---

## Evaluation

| Metric | Value |
|--------|-------|
| RMSE   | TBD   |
| MAE    | TBD   |
| R²     | TBD   |

*(Fill in with your actual test set results)*

---

## How to Use

```python
import tensorflow as tf
import numpy as np

# Load model
model = tf.keras.models.load_model("house_price_model")

# Example input — replace with your actual feature vector
# [sq_ft, bedrooms, bathrooms, year_built, lot_size, ...]
sample_input = np.array([[1800, 3, 2, 2005, 8500]])

# Predict
predicted_price = model.predict(sample_input)
print(f"Predicted house price: ${predicted_price[0][0]:,.2f}")
```

---

## Model Architecture

```
Input (tabular features)
→ Dense(256, relu) → BatchNormalization → Dropout(0.3)
→ Dense(128, relu) → BatchNormalization → Dropout(0.2)
→ Dense(64, relu)
→ Dense(1, linear)  ← regression output
```

*(Update to match your actual architecture)*

---

## Limitations & Bias

- Performance is tied to the geographic and temporal distribution of training data — may not generalize to all housing markets
- Does not account for macroeconomic factors (interest rates, market trends) that heavily influence real prices
- Outliers (luxury properties, distressed sales) may be predicted with lower accuracy

---

## Citation

```bibtex
@misc{pasumarthi2026houseprices,
  author    = {Chandrasekar Adhithya Pasumarthi},
  title     = {House Price Prediction Model},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Chandrasekar123/PredictingHousePrices}
}
```

---

## Contact

- GitHub: [@Adhithpasu](https://github.com/Adhithpasu)