Custonova

Intro

• This project consist of two type of applications a django application and an angular application.
• This implies that the backend project and the frontend must be running for you to be able to use your system.

This repository contains the code for a customer churn project using KMeans clustering and logosticregreasion to predict the probability of a customer to stay loyal to the shop or not .

The project uses the following technologies:

• Python - The programming language used to develop the project.
• Sklearn - A machine learning library used to train the AI model.
• Django - A web framework used to create the REST API.
• Angular - A web framework used to create the frontend UI.

The project is divided into the following three parts:

• Model training - This part of the project uses Sklearn and TensorFlow to train the AI model. The model is trained on a dataset of fraudulent and non-fraudulent transactions.
• REST API - This part of the project uses Django to create a REST API that exposes the AI model. The REST API can be used to get predictions from the AI model.
• Frontend UI - This part of the project uses Angular to create a frontend UI that consumes the REST API. The frontend UI allows users to input transaction data and get predictions from the AI model.

Backend

• Activate anaconda environment
• Run the following commands sequentially.

cd custonova && pip install -r requirements.txt && python manage.py makemigrations && python manage.py migrate && python manage.py createsuperuser && python manage.py runserver

Frontend

• run the following commands sequentially.

bash npm i -g @angular/cli or yarn global add @angular/cli cd front npm install or yarn ng serve -o --hmr

• enjoy