Work to support marketing by improving upon an existing “propensity to buy” model to create better offers.
With the increasing amount of restaurants, their user reviews, tips, suggestions etc, there is huge amount of data which when utilized in the right manner can do wonders for the businesses to create better tailored offers for people. With all that data coming in, we need proper insights from it. Based on facts, figures and deeper insights which lead to creating better offers for customers, will help the business grow faster and bring out custom offers for people based on their interests. Having this huge understanding into existing data can help them better plan their next steps effectively. Sparky is developed to leverage Data analytics and MLlib.
What it does
Sparky provides: -Analytics -Prediction -Alerts It uses the Yelp dataset provided, to gather insights on user info, reviews, ratings, tips and suggestions All this data analysis works with a machine learning algorithm to create tailored offers for people based on their interests.
Accomplishments that I'm proud of
This is the first time we are working with Apache Spark and data analysis and we are proud of learning these and competing the entry on time.
What I learned
For sure, we learnt that Apache Spark is a brilliant platform for big data analysis. Computations which took hours on other platforms that we have used, were completed in minutes on Apache Spark. Almost, all the technologies used (Spark, Scala) for analytics, were a first for us and so was a great learning experience.