Project Title: Wine Quality Prediction on AWS EMR

Description/Problem Statement:Developed a wine quality prediction machine learning (ML) model using Spark on AWS Elastic MapReduce (EMR). The project involved training the model in parallel on multiple EC2 instances and subsequently deploying it to predict wine quality in an application running on a single EC2 instance.

Skills Utilized:

• AWS (EMR, EC2)
• Spark
• Machine Learning
• Model Deployment

Solution:

• Utilized AWS EMR to create a Spark cluster for distributed data processing.
• Employed Spark to build a wine quality prediction ML model, leveraging its parallel computing capabilities across multiple EC2 instances for efficient model training.
• Trained the ML model on a dataset of wine features and quality ratings.
• Implemented model saving and loading functionalities to ensure seamless integration into the prediction application.
• Developed an application running on a single EC2 instance to perform wine quality prediction using the trained model.

Program Output: The application provided predictions for wine quality based on input features, allowing users to assess the quality of wine samples.