https://github.com/noorulhudaajmal/price-predictor

End-to-end house price prediction project using MLOps
https://github.com/noorulhudaajmal/price-predictor

house-price-prediction mlflow mlops mlops-workflow

Last synced: 26 days ago
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End-to-end house price prediction project using MLOps

• Host: GitHub
• URL: https://github.com/noorulhudaajmal/price-predictor
• Owner: noorulhudaajmal
• Created: 2024-09-07T18:28:28.000Z (4 months ago)
• Default Branch: master
• Last Pushed: 2024-09-13T02:19:46.000Z (4 months ago)
• Last Synced: 2024-09-13T14:51:16.056Z (4 months ago)
• Topics: house-price-prediction, mlflow, mlops, mlops-workflow
• Language: Jupyter Notebook
• Homepage:
• Size: 8.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
# House Price Prediction

This project is designed to predict house prices using an end-to-end machine learning 

(ML) pipeline. It incorporates MLOps principles to streamline the development, deployment, 

and maintenance of the model.

The application consists of three main components:

1. **Model Training:** A training pipeline that builds and registers a model using MLflow.

2. **Model Deployment:** A Flask API that serves predictions from the trained model.

3. **Frontend:** A Streamlit app that allows users to input data and get predictions from the Flask API.

## Key Features

1. **End-to-End Machine Learning Pipeline**

   This project implements an end-to-end ML pipeline, starting from data ingestion to 

   model deployment.

2. **MLOps Framework**

   The project uses MLflow to manage the machine learning lifecycle:

   - **Experiment tracking:** Tracks various experiments and stores metrics, parameters, and artifacts.

   - **Model registration:** The trained model is logged and registered in MLflow, making it available for deployment. 

   - **Model versioning:** New versions of the model can be tracked and compared.

   

3. **Pipeline Architecture**

   - **Pipeline Pattern:** The project uses a pipeline architecture to process data through various stages (e.g., preprocessing, model training, evaluation).

   - **Dependency Injection:** Components like the inference pipeline, feature engineering, and preprocessing are injected into the training and deployment pipelines, enhancing flexibility and allowing for easier testing.

4. **Model Deployment via Flask API**

   The model is deployed using a Flask API, which exposes an endpoint `/predict` to serve real-time predictions. The API uses an inference pipeline to load the model from MLflow.

5. **Streamlit Web App for Predictions**

   The project includes a Streamlit app to provide a user-friendly interface for making predictions. Users can input data via the app, which then communicates with the Flask API to get the price predictions.

## Getting Started

1. **Install Dependencies**

   ```shell

   pip install -r requirements.txt

    ```

   

2. **Train the Model**

   Run the training_pipeline.py to train the model and register it in MLflow:

   ```shell

   python pipelines/training_pipeline.py

   ```

   

3. **Start Flask API**

   After training the model, start the Flask API to serve the model:

   ```shell

   python pipelines/deployment_pipeline.py

    ```

   This starts the API at http://localhost:8000

4. **Run the Streamlit App**

      

    Launch the Streamlit app:

   ```shell

   streamlit run app.py

    ```

   The Streamlit app will run at http://localhost:8501

## Docker Setup

1. **Build Docker Image**

   To build the Docker image, run the following command in the project root:

   ```shell

   docker build -t price-predictor-app .

    ```

2. **Run Docker Container**

   

    After the image is built, you can run the container:

   ```shell

    docker run -p 8000:8000 -p 8501:8501 price-predictor-app

    ```

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