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Devices Price Classification System

Project Overview

The Devices Price Classification System is an AI-driven application designed to predict mobile device prices based on their specifications. This system utilizes machine learning to classify prices into categories (0-3), helping sellers accurately price their products.

Project Components

This project consists of two main components:

  1. Mobile Phone Price Prediction API:

    • Provides endpoints to manage and predict mobile phone prices based on specifications.
    • Built using Django REST Framework.

  2. Mobile Device Price Classification System:

    • Implements machine learning algorithms to classify mobile device prices.
    • Built using Python and various machine learning libraries.

API Documentation

Overview

The API allows users to create, retrieve, and predict mobile phone records.

Base URL

  • http://your-domain.com/ or http://localhost:8000/

Endpoints

  1. List/Create Mobile Phones

    • URL: /api/devices/
    • Methods: GET, POST
    • Description: Retrieve a list of all mobile phones or create a new entry.

    GET Response Example:

    [
    {
        "id": 1,
        "battery_power": 1000,
        "blue": 1,
        "clock_speed": 2.2,
        ...
        "price_range": 3
    }
]

    POST Request Body Example:

    {
    "battery_power": 1000,
    "blue": 1,
    "clock_speed": 2.2,
    "dual_sim": 1,
    "fc": 8,
    "four_g": 1,
    "int_memory": 64,
    "m_dep": 0.7,
    "mobile_wt": 180,
    "n_cores": 8,
    "pc": 12,
    "px_height": 1920,
    "px_width": 1080,
    "ram": 4096,
    "sc_h": 15.5,
    "sc_w": 7.5,
    "talk_time": 20,
    "three_g": 1,
    "touch_screen": 1,
    "wifi": 1,
    "price_range": null
}

  2. Retrieve Mobile Phone

    • URL: /api/devices/<device_id>/
    • Methods: GET
    • Description: Retrieve an individual mobile phone record.

  3. Predict Price

    • URL: /api/predict/<device_id>/
    • Method: POST
    • Description: Predict the price and save the result in the device entity.

Data Models

The API uses the following data model for mobile phones:

Field Type Description
battery_power Integer Total energy a battery can store in mAh
blue Integer Has Bluetooth or not (0/1)
clock_speed Float Speed at which microprocessor executes
dual_sim Integer Has dual sim support or not (0/1)
... ... ...
price_range Integer Price range category (0-3)

Price Range Categories

  • 0: Low Cost
  • 1: Medium Cost
  • 2: High Cost
  • 3: Very High Cost

Machine Learning Implementation

Overview

The machine learning component of the project classifies mobile device prices based on their specifications.

Data Processing

  • Datasets are loaded from CSV files (train.csv and test.csv).
  • Null values are removed, and features are standardized.
  • Data is split into training (70%), validation (15%), and testing (15%).

Model Architecture

  • Algorithm: Logistic Regression
  • Performance: Achieved 97% accuracy on test data with an average confidence score of 0.92.

Model Persistence

Conclusion

This project provides a comprehensive solution for mobile device price classification, integrating a robust API with a powerful machine learning model. The system is designed to assist sellers in accurately pricing their products based on detailed specifications.

For further details, please refer to the individual documentation files: