📑 Project Overview Designing a recommendation engine for Food.com using PySpark on AWS EC2 to enhance user engagement and increase business opportunities through Exploratory Data Analysis (EDA).

📊 Dataset Information - Recipes Data: `RAW_recipes_cleaned.csv`
- User Interactions Data: `RAW_interactions_cleaned.csv`

🚀 Project Workflow 1. Environment Setup: Configure AWS EC2 & install PySpark.
2. Data Exploration: Load and preprocess data using PySpark.
3. EDA & Feature Engineering: Analyze data patterns to prepare features for model building.

🛠️ Setup Instructions - Set up EC2 instance and install required dependencies.
- Download datasets.

🔍 Key Insights - High-rated recipes attract more interactions.
- Seasonal trends in recipe categories.

📌 Future Enhancements - Implement collaborative filtering models.
- Deploy solution using AWS SageMaker.