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EzFlow.ai

Welcome to EzFlow.ai , your go-to platform for effortlessly turning raw tabular data into powerful machine learning models! EzFlow.ai is an intuitive and user-friendly software product designed to democratize machine learning, making it accessible to everyone, regardless of their expertise.

EzFlow.ai

Process Flow / Architecture

User Interaction:

1. User uploads data through the UI.
2. Specifies ML options and preferences.

Tools:

• Frontend: Developed using React for an interactive and responsive UI.
• File Upload: Utilizing Dropzone library for handling data uploads.
• Communication: Axios for sending user configurations to the backend.

Backend Processing:

1. Backend (Flask) receives data and configurations.
2. Processes data (labeling, encoding, imputation) using Pandas.
3. Stores processed data in the database (PostgreSQL).

Tools:

• Backend: Flask (Python) for handling user requests and processing data.
• Database: PostgreSQL for storing processed data.
• Data Processing: Pandas for efficient data manipulation.

Machine Learning Model Training:

1. Backend triggers a virtual machine for model training.
2. Virtual machine (AWS EC2) loads processed data, preprocesses it further.
3. Trains machine learning models using Scikit-learn, TensorFlow, or PyTorch.
4. Stores results back in the database.

Tools:

• Virtual Machine: AWS EC2 for scalable and flexible computing resources.
• Containerization: Docker for creating isolated environments.
• Machine Learning: Scikit-learn, TensorFlow, or PyTorch for training models.

Result Display:

1. User accesses results through the UI.
2. Compares models and reviews predictions.

Tools:

• Frontend: React for dynamic result display.
• Communication: Axios for fetching results from the backend.
• Visualization: D3.js or Chart.js for presenting model comparisons.

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This comprehensive process flow involves a user-friendly frontend, a robust backend utilizing Flask and Pandas for data processing, a PostgreSQL database for efficient data storage, a scalable AWS EC2 virtual machine for model training, and popular machine learning libraries for building and training models.

How it Works

Frontend:

1. Build UI components for data upload, ML options selection, and result display.
2. Use a frontend framework to manage state and handle user interactions.

Tools:

• Framework: React for building an interactive UI.
• State Management: Use state management tools provided by the chosen framework.

Backend:

1. Set up a server using a web framework.
2. Implement API endpoints for data upload, processing, and model training.
3. Write data processing logic and integrate with machine learning libraries.
4. Store/retrieve data in/from the database.

Tools:

• Framework: Choose a web framework such as Flask or Express.
• API: Implement RESTful API endpoints.
• Database: Utilize a database system like PostgreSQL for data storage.

Database:

1. Create a database schema based on processed data requirements.
2. Implement CRUD operations for data storage and retrieval.

Tools:

• Database System: PostgreSQL or any suitable relational database.

Virtual Machine:

1. Provision a virtual machine on a cloud platform.
2. Containerize the machine learning environment using Docker.
3. Develop scripts for loading data, preprocessing, training models, and storing results.

Tools:

• Cloud Platform: AWS, Google Cloud, or Azure.
• Containerization: Use Docker for creating isolated environments.

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This section outlines the workflow from building the frontend UI to setting up a robust backend, creating a structured database, and leveraging a virtual machine for machine learning processes.

Code/File Structure: Has been modified check - https://github.com/anandr07/EzFlow.ai/blob/main/File_Structure.txt

project-root/ |-- frontend/ | |-- src/ | | |-- components/ | | | |-- DataUpload.js | | | |-- MLOptions.js | | | |-- ResultDisplay.js | | |-- state/ | | | |-- dataState.js | | |-- App.js | | |-- index.js |-- backend/ | |-- app/ | | |-- routes/ | | | |-- upload.js | | | |-- process.js | | | |-- train.js | | |-- controllers/ | | | |-- dataController.js | | | |-- modelController.js | | |-- models/ | | | |-- dataModel.js | | |-- database/ | | | |-- dbConfig.js | | | |-- migrations/ | | | | |-- ... |-- database/ | |-- migrations/ | | |-- ... |-- virtual_machine/ | |-- docker/ | | |-- Dockerfile_data_loader | | |-- Dockerfile_preprocessor | | |-- Dockerfile_model_trainer | |-- scripts/ | | |-- load_data.py | | |-- preprocess_data.py | | |-- train_model.py |-- README.md