| --- |
| 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) |
