AutoML Project

Description

The AutoML Project is designed to streamline the process of building and deploying machine learning models for small-scale businesses and data scientists. It offers a complete solution that covers the entire machine learning workflow, from data preparation to model deployment.

Key Features:

1- Data Preprocessing: The project automates data cleaning and preparation tasks, including handling missing values, detecting outliers, and normalizing features. This ensures that data is ready for analysis with minimal manual effort.

2- Feature Engineering: Advanced techniques are employed to extract and select important features, which enhances the performance of the machine learning models.

3- Model Training: The backend uses a structured pipeline to select and train machine learning models. It utilizes meta-learning to choose the most appropriate models and optimize their configurations based on the data.

4- Hyperparameter Tuning: The system applies the SMAC (Sequential Model-Based Algorithm Configuration) method to fine-tune hyperparameters, improving model accuracy and performance.

5- Model Evaluation and Prediction: Users can assess model performance and make predictions on new datasets.

6- Frontend Interface: Built with React, the frontend provides a user-friendly interface for data upload, project management, model evaluation, and predictions.

7- Project Management: Users can create and re-use projects, keeping track of different models and their performance metrics for ongoing improvements.

8- Integration and API: The frontend and backend communicate through an API, ensuring smooth data exchange and interaction between the user interface and the machine learning engine.

Running the Project

To set up and run the project, follow these steps:

1. Backend Setup

Navigate to the Backend Directory:

cd Auto_ML_Project/backend/autoAnalysisServer

Install Backend Dependencies: Ensure that you have a requirements.txt file in the parent directory with the necessary Python packages listed.
```
pip install -r ../requirements.txt
```
Run the Backend Server:
```
python manage.py runserver
```

2. Frontend Setup