Deployed a mobile price range prediction model using scikit-learn on AWS SageMaker. Trained a Random Forest classifier for accurate predictions based on key features. Streamlined deployment with a concise guide and seamless integration of AWS services.
https://guneet-kohli.medium.com/mobile-price-range-classification-using-aws-sagemaker-5ddaf9d59777
This project presents an end-to-end deployment of a Random Forest multi-class classifier model on AWS SageMaker for predicting the price range of mobile phones. The code and dataset can be found in this repository.
The primary goal of this project is to train and deploy a Random Forest multi-class classifier model using AWS SageMaker. The model predicts the price range of mobile phones. The implementation is based on Krish Naik’s tutorial "End-to-end Machine Learning Project Implementation Using AWS SageMaker".
- Development Environment: Visual Studio Code, Anaconda
- AWS Services: SageMaker, S3, IAM
- sagemaker-custom-script.ipynb: Jupyter Notebook containing the project implementation.
- script.py: Python script used for model training.
- requirements.txt: File listing the required packages for the project.
- mob_price_classification_train.csv: Dataset used for training the model.
- train-V-1.csv and test-V-1.csv: Train and test data files.
To install the necessary packages, run the following command:
- pip install -r requirements.txt
Follow the steps outlined in the sagemaker-custom-script.ipynb notebook for a detailed walkthrough of the project. This includes setting up the AWS environment, training the Random Forest model, and deploying it on SageMaker.
This project serves as a comprehensive guide for deploying a machine learning model on AWS SageMaker, emphasizing the importance of deploying models for real-world applications. For a step-by-step walkthrough, please refer to the provided notebook (sagemaker-custom-script.ipynb).