Device Price Prediction System

This project integrates a Python-trained Logistic Regression Classifier model with a Spring Boot application to predict the prices of electronic devices based on their specifications. The model, trained in Python, is exposed as a Flask web service, which the Spring Boot application consumes to provide predictions through RESTful APIs.

Prerequisites

Java JDK 11 or later
Maven 3.6 or later
Python 3.7 or later
Flask
Pandas
Scikit-learn
NumPy

Setup Instructions

Setting Up the Python Service

Install the required Python packages:

pip install flask pandas scikit-learn numpy

Start the Flask server:

Navigate to the directory containing the Flask application.
Run the application:
```
python app.py
```

Setting Up the Spring Boot Application

1.Clone the repository:

git clone <repository-url>
cd <repository-directory>

2.Build the project with Maven:

mvn clean install

3.Run the Spring Boot application:

mvn spring-boot:run

API Endpoints

GET '/api/devices/': Retrieves a list of all devices.
GET '/api/devices/{id}': Retrieves details of a specific device by ID.
POST '/api/devices': Adds a new device.
POST '/api/predict/{id}': Predicts the price for a specific device and updates the device entity - with the predicted price.

Usage Example

To predict the price of a device using the system, send a POST request with the device ID:

curl -X POST http://localhost:8080/api/predict/{id}

Replace {id} with the actual ID of the device.