Homestays: A Data-Driven Approach to House Price Prediction

Project Description

Homestays is a data science project that empowers you to make informed decisions in the housing market. By leveraging the power of machine learning, Homestays analyzes a wide range of factors that influence house prices, aiming to predict their value with exceptional accuracy.

This project delves beyond simply providing a price tag. Homestays sheds light on the hidden patterns within the housing market, giving you valuable insights into what truly drives value. With Homestays, you can:

• Gain a competitive edge: Whether you're a buyer or seller, Homestays equips you with the knowledge to make strategic decisions.

• Invest with confidence: Homestays empowers you to identify undervalued properties and make informed investment choices.

• Navigate the market with clarity: Homestays unravels the complexities of the housing market, allowing you to approach your next move with greater understanding.

Prerequisites:

• Docker installed on your system. You can verify by running docker -v in your terminal. If not installed, refer to the official Docker documentation for installation instructions specific to your operating system https://docs.docker.com/engine/install/.

Steps:

1. Clone the project repository:

   Open your terminal or command prompt and navigate to the directory where you want to clone the project. Then, run the following command to clone the repository from GitHub:

   git clone https://github.com/Darshanroy/HomeStays.git

   This will download the project files to a local directory named "HomeStays".

2. Install dependencies (requirements.txt):

   Navigate into the downloaded project directory:

   cd HomeStays

   Then, install the required Python libraries listed in the requirements.txt file using pip:

   pip install -r requirements.txt

   This will download and install the necessary libraries for your project to run.

3. Train the model:

   Run the following command to train your machine learning model using the training.py script:

   python training.py

   This script will likely take some time to complete depending on the size and complexity of your dataset. The training process might involve data preparation, model training, and evaluation.

4. Run Python Application:

    python app.py   

   This will run the python flask application

5. Run the application:

   Once the model is trained, start the web application using the following command:

   docker run -p 5000:5000 flaskapp:v1

   This command will:

   • Run a Docker container based on the image named flaskapp:v1. This image likely contains your application code and the trained model.
   • Publish the container's port 5000 to your host machine's port 5000. This allows you to access the application running inside the container through your web browser.

6. Access the application:

   Open your web browser and navigate to http://localhost:5000 (or http://127.0.0.1:5000 if needed). This should launch your Homesatys house price prediction application.