Recipe_Recommender_Assignment_EDA

• For this project, we will use PySpark in AWS (Amazon Web Series) using EC2 Instances.

• Step into the shoes of an ML engineer working at food.com. The job is to design a recommender system to recommend recipes to users based on their choice and the current recipe they are looking at.

• The recommendation engine is a way to increase the website's user engagement. If a user is shown relevant recipes, they are more likely to spend more time on your site reading about recipes. Higher user engagement will likely result in more business opportunities like collaborations, promotions, etc.

• The performance of a recommendation engine will significantly impact the revenue your recipe site can generate.

• Designing a recommender from scratch is a time-consuming task. In this project, it is to explore the data and create features that will be used to build the recommender.

• Below are the links of the csv data containing the information to perform EDA.

• The respective files are downlaoded and imported using respective libraries for analysis and visualzation.

raw recipe data

https://raw-recipes-clean-upgrad.s3.amazonaws.com/RAW_recipes_cleaned.csv

raw ratings data

https://raw-interactions-upgrad.s3.amazonaws.com/RAW_interactions_cleaned.csv