AWS SageMaker Mobile Price Classification

Sagemaker test (mind that this is with older sagemaker v1 version)

Project Overview

This project builds a machine learning pipeline to classify mobile phone prices into different ranges using AWS SageMaker. The workflow includes:

• Data Preparation: Load and split data using scikit-learn
• Data Storage: Upload training/testing data to Amazon S3
• Model Training: Train RandomForest classifier using SageMaker's SKLearn container
• Model Deployment: Deploy trained model as a real-time inference endpoint
• Predictions: Make predictions using the deployed endpoint
• Cleanup: Delete endpoints to avoid unnecessary charges

Dataset

• File: mob_price_classification_train.csv
• Task: Multi-class classification (predicting price_range)
• Features: 20 mobile phone specifications
• Train/Test Split: 85% training, 15% testing

Requirements

• AWS Account with SageMaker access
• IAM role with SageMaker permissions
• Python 3.x with dependencies in requirements.txt
• S3 bucket for storing training data

WARNING (Testing only - IAM & Networking): Use least-privilege IAM roles and scoped policies; avoid granting broad/full access in production. Also avoid deploying resources into default/public subnets for production — prefer private subnets, NAT gateway or VPC endpoints, and properly scoped security groups.

Key Components

1. research.ipynb - Main notebook containing the complete SageMaker workflow
2. script.py - Training script executed by SageMaker
3. train-V-1.csv - Training dataset
4. test-V-1.csv - Testing dataset

Usage

Run the cells in research.ipynb to:

1. Load and explore the dataset
2. Split data into train/test sets
3. Upload data to S3
4. Train a RandomForest model on SageMaker
5. Deploy the model to an endpoint
6. Make predictions on test data
7. Clean up resources

Model Performance

The RandomForest classifier is trained with:

• n_estimators: 100
• random_state: 0
• Evaluation: Accuracy score and classification report on test data (15% of dataset)

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🧹 Cleanup (Important: Avoid AWS Costs)

⚠️ To prevent unexpected charges, clean up these AWS SageMaker resources after testing:

1. Delete SageMaker Endpoint

   • AWS Console → SageMaker → Endpoints → Select endpoint → Delete
   • This is critical as running endpoints incur hourly charges

2. Delete Endpoint Configuration

   • AWS Console → SageMaker → Endpoint Configurations → Select config → Delete

3. Delete Model

   • AWS Console → SageMaker → Models → Select model → Delete

4. Clean S3 Bucket

   • AWS Console → S3 → Select bucket → Delete all training/test data and artifacts
   • Delete the bucket if created specifically for this project

5. Remove IAM Role (if created specifically for this project)

   • AWS Console → IAM → Roles → Select role → Delete

6. Delete CloudWatch Logs (optional)

   • AWS Console → CloudWatch → Logs → Delete log groups related to SageMaker

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References

• MLOPS Udemy course from Krish Naik