House Price Prediction

This project predicts house prices using the California Housing Dataset with two machine learning models: Random Forest Regressor and a Neural Network built using TensorFlow/Keras.

Table of Contents

• Project Overview
• Models Used
• Installation
• Usage
• Results
• Contributing
• License

Project Overview

The goal of this project is to predict housing prices in California based on features such as:

• Median Income
• House Age
• Average Rooms per Household
• Population, etc.

Two models are implemented:

1. Random Forest Regressor: A robust ensemble learning technique.
2. Neural Network: A deep learning approach for non-linear relationships.

Models Used

Random Forest Regressor

• Trained with 1000 estimators.
• Achieved a Mean Squared Error (MSE) of 0.2514 on the test data.

Neural Network

• Built with a simple architecture of two hidden layers.
• Achieved a Mean Squared Error (MSE) of 0.2644 on the test data.

Installation

To run this project locally, clone the repository and install the required dependencies:

git clone https://github.com/Sudo_User/house-price-prediction.git
cd house-price-prediction
pip install -r requirements.txt