Overview

This is a device price classification system that uses Python and SpringBoot. Mainly the system includes two small projects:

• Python project: Allows you to predict the prices, allowing the sellers to classify the device's prices according to their characteristics.
• SpringBoot project: Contains a simple entity, and a few endpoints, to call the service from the Python project for a bunch of test cases, and store them.

The Python project analyzes the device's data and preprocesses and visualizes it to give a better understanding of the best ways for modeling it. The modeling was done inside the notebook in the model directory and the model is called inside a Flask API that takes an input device and outputs the price range from 0 to 3.

The SpringBoot project contains a simple H2 database with several operations such as adding and removing devices. It also calls the Flask API, which predicts input devices to the database. It contains the following endpoints:

• GET /api/devices/: Retrieve a list of all devices.
• GET /api/devices/{id}: Retrieve details of a specific device by ID.
• POST /api/devices: Add a new device.
• POST /api/predict/{deviceId}: Predicts the device price and inputs it into the device entity.

Installation:

After cloning the project using the following snippet:

git clone https://github.com/AmrSheta22/Devices-Price-Classification-System.git
cd Devices-Price-Classification-System

You will need to install the system on your device by setting up two components:

First Component: Python Flask API:

First install the requirements for the project using:

pip install -r requirments.txt

Then you will run the flask API:

cd flask_api
python api.py

Second Component: SpringBoot backend:

After you have your API up and running you can now install the SpringBoot project:

cd ../priceClassificationSystem
mvn clean install
mvn spring-boot:run

The system Now should be up and running!

Modelling and Testing:

The AdaBoost model used here achieves 93.5 percent validation accuracy and is tested on the test dataset inside the data folder. If you want to test the results of the model through the API, you can run the following code:

cd flask_api
python testing.py

The output should be a list of the first ten records of the test set predicted. It should also output a file in the data folder called test_predicted.csv containing all the predicted test records.