California Housing Value Predictor

This project involves building a machine learning model to predict median house values in California using the California Housing dataset. The dataset was imported from Scikit-learn. The model was developed in Google Colab and deployed as an API on Modelbit.

Project Overview

1. Model Development:

   • Utilized the California Housing dataset from Scikit-learn.
   • Developed a predictive model in Google Colab.
   • Saved the trained model as a pickle file.

2. API Deployment:

   • Deployed the model as an API using Modelbit, allowing external access for predictions.

3. Web Interface:

   • Created an HTML and JavaScript interface that connects to the deployed API.
   • Users can input parameters such as median income, house age, average rooms, and more to get predictions.

4. Deployment:

   • The HTML page is deployed on GitHub Pages, making it accessible to users at California Housing Value Predictor.

How to Use

1. Open the deployed HTML page.
2. Enter the required parameters in the input fields.
3. Click the "Predict" button to receive the predicted median house value based on the input data.

Technologies Used

• Python (for model development)
• Scikit-learn (for data handling and modeling)
• Google Colab (for model training and development)
• Modelbit (for API deployment)
• HTML, CSS, JavaScript (for web interface)
• GitHub Pages (for deployment)

License

This project is licensed under the MIT License.