California house pricing

End to End project implementation from machine learning model development to deployment as a docker container to Heroku

Link: https://california-housepricing-810427063c20.herokuapp.com/

This project implements an end-to-end machine learning solution for predicting house prices in California. It consists of the following key components:

Linear Regression Model:

In the first step, a Linear Regression machine learning model is developed using Python. The model is trained on a dataset containing various features of houses, such as location, number of bedrooms, square footage, and more. The trained model is saved as a pickle file for later use.

Web Application:

A web application is created using the Flask framework. This application allows users to input information about a house and receive a predicted price based on the trained Linear Regression model. The web interface is intuitive and user-friendly, making it easy for anyone to get house price predictions.

Dockerization:

The entire web application is containerized using Docker. This means that you can package the application and its dependencies into a Docker image, ensuring consistent and reliable deployment across different environments.

Deployment on Heroku:

The Dockerized web application is deployed on the Heroku platform. Heroku provides a convenient and scalable platform for hosting web applications, making it accessible to users on the internet.

How to Use To use this project, follow these steps:

Setup

To set up this project locally, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/satishkolli1992/californiahousepricing.git

2. Create a virtual environment (recommended) and install the dependencies:

   cd californiahousepricing
   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate
   pip install -r requirements.txt

3. Run the Flask application locally:

   python app/app.py

   You can access the web application by opening a browser and navigating to http://localhost:5000.

Linear Regression Model

The Linear Regression model was developed using the Jupyter notebook californiahousepricing /Linear regression - ML Implementation - California Housing.ipynb.ipynb. The trained model is saved as californiahousepricing/regmodel.pkl. This model is used for making house price predictions in the web application.

Web Application

The web application is built using the Flask framework and allows users to input various features of a house and get a predicted price. The main files for the web application are located in the app/ directory. The application include a HTML templates (home.html) and Python code in app.py.

Dockerization

To run the application in a Docker container, use the provided Dockerfile. You can build the Docker image and run the container locally using Docker Desktop or a similar tool.

docker build -t californiahousepricing .
docker run -p 5000:5000 californiahousepricing

Deployment on Heroku

The web application is deployed on Heroku as a Docker container. To deploy your own version of this application on Heroku, follow these general steps:

1. Create a Heroku account and install the Heroku CLI if you haven't already.

2. Log in to Heroku using the CLI:

   heroku login

3. Navigate to the project directory and create a Heroku app:

   heroku create your-app-name

4. Push the Docker container to Heroku:

   heroku container:push web -a your-app-name

5. Release the container:

   heroku container:release web -a your-app-name

6. Open your Heroku app in the browser:

   heroku open -a your-app-name

Now, your California House Price Prediction web application is live on Heroku!

Feel free to customize and improve this project as needed. Happy coding!