Personal Reflections:

Over the past 3 months, I had the opportunity to complete General Assembly’s Data Science Immersive bootcamp in Singapore. The nature of a coding bootcamp meant that it was an intense experience with a steep learning curve, covering machine learning topics like Feature Engineering, Decision Trees, Natural Language Processing, and Deep Learning Neural Networks, all in just 12 weeks! Nevertheless, the experience was an enriching one as it gave me the opportunity to code in Python day in and day out, completing coding assignments as well as working on personal data science projects for my portfolio. With the skills developed during this bootcamp, I am now truly confident in entering the world of data science as a full-time career.

Looking back on these past few months, this bootcamp has given me a strong understanding of the end-to-end data science process as well as familiarity with the programming languages and tools needed for data analysis and data modeling. Towards the end of the bootcamp, each student had the opportunity to work on a personal capstone project to showcase all the data science concepts and techniques that we have learned throughout the 12 weeks. In this post, I will be explaining my capstone project and the end-to-end process that I undertook to build my data science models.

Food Restaurant Recommendation System

For my capstone project, I wanted to build something that would be meaningful for people in their everyday lives. This ultimately led me to build a recommendation system model that could recommend people restaurants near their location based on restaurant reviews from other people, drawing on sentiment analysis that could potentially improve the recommendation suggestions that you see on common food delivery apps like Deliveroo, GrabFood, and FoodPanda.

#general-assembly #data-science #recommendation-system #yelp #singapore