Real-Estate-Price-Prediction-using-Deep-Learning

Overview

This project utilizes deep learning techniques to predict real estate prices based on various property features. By training a neural network model on historical data, the system aims to provide accurate price estimations for real estate properties.

---

Features

• Data Preprocessing: Cleans and prepares the dataset for training.
• Model Training: Implements a deep neural network using TensorFlow and Keras.
• Evaluation: Assesses model performance using metrics like Mean Absolute Error (MAE).
• Prediction: Generates price predictions for new property data.

---

Installation

1. Clone the Repository

Run the following command in your terminal to clone the project:

git clone https://github.com/sneha30404/Real-Estate-Price-Prediction-using-Deep-Learning.git
cd Real-Estate-Price-Prediction-using-Deep-Learning

2. Install Dependencies

Make sure you have Python 3.8 or later installed. Then, install the required packages:

pip install -r requirements.txt

---

Usage

1. Prepare the Dataset

• Place your dataset file (realestate_prices.csv) in the project directory.

2. Run the Training Script

To preprocess the data, train the model, and save the trained model to disk:

python train_model.py

3. Make Predictions

Use the trained model to make predictions on new data:

python predict.py --input new_data.csv --output predictions.csv

---

Contributing

Contributions are welcome! Please fork the repository and create a new branch for any feature additions or bug fixes. Submit a pull request for review.

---

License

This project is licensed under the MIT License. See the LICENSE file for details.

---

Contact

For any questions or support, please contact:

• Name: Sneha
• GitHub: sneha30404