theanupamkumar1/ml-project

End to End MLProject

MLProject is a machine learning project that includes data ingestion, data transformation, model training, and model monitoring components. It is designed to be scalable and easy to use.

key data Components

The project is divided into several components, each responsible for a specific task:

• data_ingestion.py: Responsible for ingesting data.
• data_transformation.py: Transforms the ingested data.
• model_trainer.py: Trains a machine learning model on the transformed data.
• model_monitoring.py: Monitors the performance of the trained model.

Project Structure

• .dvc/: Contains DVC files for data versioning.
• .ebextensions/: Contains configuration files for AWS Elastic Beanstalk. The python.config file sets the WSGIPath to application:application.
• artifacts/: Contains serialized machine learning models and data files.
• mlruns/: Contains metadata about MLflow runs.
• src/: Contains the source code of the application.

Key Files

• application.py: The main entry point of the application.
• Dockerfile: Used to create a Docker image of the application.
• requirements.txt: Lists the Python packages that the project depends on.

Setup

To set up the project, you need to install the dependencies listed in the requirements.txt file. You can do this by running:

pip install -r requirements.txt

Usage

The main entry point of the application is application.py. You can run it with:

python application.py

Project Components

Data Version Control (DVC)

Your project uses DVC for data versioning. The .dvc directory and .dvcignore file are used for this purpose.

Elastic Beanstalk Configuration

The .ebextensions directory contains configuration files for AWS Elastic Beanstalk. The python.config file sets the WSGIPath to application:application.

Python Application

The application.py file is the main entry point of your application. It imports various components from the src directory and defines several routes for a Flask application.

Machine Learning Artifacts

The artifacts directory contains serialized machine learning models (model.pkl and preprocessor.pkl) and data files (raw.csv, test.csv, train.csv).

MLflow Runs

The mlruns directory contains metadata about MLflow runs. Each run has a unique ID and contains information such as the user who initiated the run, the start and end times, and the location of any artifacts produced during the run.

Dockerfile

The Dockerfile is used to create a Docker image of your application.

Requirements

The requirements.txt file lists the Python packages that your project depends on.

Source Code

The src directory contains the source code of your application. It includes various components such as data ingestion, data transformation, and model training.

link

https://math-score-prediction-1-b3h8ggc3gchydzaz.eastus-01.azurewebsites.net/