Insight Backend

Insight Backend is the server-side component of the Insight machine learning web application. It provides a RESTful API for dataset management, model training, and prediction.

Installation

To get started with Insight Backend, you'll need to clone this repository to your local machine and install its dependencies. Here's how to do that:

1. Clone the repository: git clone https://github.com/SandeepK1729/insight-backend.git
2. Install dependencies: pip install -r requirements.txt
3. Create .env file in Insight directory:

      SECRET_KEY=<your-secret-key>

4. Change DATABASES in Insight/settings.py to LOCAL_DATABASE
5. Run migrations: python manage.py migrate
6. Start the server: python manage.py runserver

Usage

Insight Backend provides a number of API endpoints for dataset management, model training, and prediction.

API Reference

Users

Request	Route	Action
POST	/api/register	Create a new user account
POST	/api/login	Log in to an existing account
POST	/api/logout	Log out of an existing account
GET	/api/user	Get info of current user
PUT	/api/user	Modifies current user
DELETE	/api/user	Delete current user account
PATCH	/api/user	Change password of current user
POST	/api/user/api_key	Generate new API key for user

ModelFiles

Request	Route	Action
GET	/api/models	Get all saved models
POST	/api/models	Prediction from model
GET	/api/model/<int:model_id>	Get info of specific saved model
PUT	/api/model/<int:model_id>	train the specific saved model
PATCH	/api/model/<int:model_id>	Modifies the specific saved model
DELETE	/api/model/<int:model_id>	Delete specific saved model
POST	/api/model/<int:model_id>	Prediction from specific model

To use Insight, you'll need to make requests to these endpoints using a client such as axios or fetch. You can also use the provided frontend application, which is available in the insight-frontend repository.

Supported Machine Learning Algorithms

Insight Backend currently supports the following machine learning algorithms:

1. Regression

   • Linear Regression
   • Polynomial Regression
   • Support Vector Regression
   • Decision Tree Regression
   • Random Forest Regression

2. Classification

   • Logistic Regression
   • K-Nearest Neighbors
   • Support Vector Machine
   • Naive Bayes
   • Decision Tree Classification
   • Random Forest Classification

3. Ensemble

   • AdaBoost
   • Gradient Boosting

Contributing

If you'd like to contribute to Insight Backend, you can fork the repository and submit a pull request with your changes. Please make sure that your changes are well-documented and tested before submitting a pull request.

Credits

Insight Backend was built using the following technologies:

• Python
• Django
• Django REST Framework

License

Insight Backend is released under the MIT License. Feel free to use, modify, and distribute the code however you like.

Updates

• add report generation in specific model file put i.e, training process

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