Smartphone Price Predictor

This project builds a smartphone price range classifier that predicts whether a phone falls into the Budget, Lower Mid-Range, Upper Mid-Range or Premium category based on its hardware specifications. The model is trained using a Random Forest Classifier with scikit-learn on a dataset of 20 smartphone features, including RAM, battery, camera resolution, screen size and connectivity options. The trained model is deployed as an AWS SageMaker endpoint, exposing real-time inference via a Python Flask web application that handles user input and returns predictions. Users interact through a clean & responsive HTML/CSS/JS interface that accepts all 20 specifications and displays the predicted price range instantly. This end-to-end project demonstrates practical skills across data preprocessing, ML model training, cloud-based model deployment and full-stack web integration.

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Features

• Predicts smartphone price range in 4 categories:
  • Budget mobile phone
  • Lower mid-range phone
  • Upper mid-range phone
  • Premium phone
• Interactive web interface with real-time predictions
• Responsive design that works on desktop and mobile devices
• Handles 20 different smartphone specifications for prediction.

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Tech Stack

• Frontend: HTML, CSS & JS.
• Backend: Flask (Python)
• ML Model Deployment: AWS SageMaker
• AWS Services: AWS Sagemaker, AWS S3, AWS IAM and AWS CLI.

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Prerequisites

• Python 3.x
• AWS Account with appropriate credentials
• Boto3 library
• Flask

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Installation

1. Clone the repository

https://github.com/AdarshZolekar/Smartphone-Price-Predictor.git

2. Install required packages

pip install flask boto3

3. Configure AWS credentials

aws configure
AWS_ACCESS_KEY_ID='your-access-key'
AWS_SECRET_ACCESS_KEY='your-secret-key'
AWS_REGION='ap-south-1'

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Usage

1. Start the Flask application:

python App.py

2. Open a web browser and navigate to http://localhost:5000

3. Enter the smartphone specifications in the form:

   • Basic specifications (RAM, storage, processor, etc.)
   • Camera details
   • Screen specifications
   • Additional features (Bluetooth, WiFi, etc.).

4. Click "Predict Price Range" to get the prediction.

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Screenshots

Smartphone Price Predictor Web Interface

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License

This project is open-source under the MIT License.

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Contributions

Contributions are welcome!

• Open an issue for bugs or feature requests.

• Submit a pull request for improvements.