Update README.md

README.md

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----
-license: mit
----
+---
+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)