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Adhithpasu/
PredictingHousePrices

Name: PredictingHousePrices
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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)
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

Adhithpasu/PredictingHousePrices