Nikhil Bansal

This project ranks the stock advisor of CNBC intraday trading. Given the ranking of the stock advisors, I can save time by not diving into deep research on stocks and mostly followed the best stock advisors.

Roles and Responsibilities:
As this was for my personal use, I developed the web scrapping and data analytics part and in fact built the entire thing from scratch.

Technologies: Python, Beautiful Soup (for scrapping), Python Pandas (for analytics)

The aim of the project was to plan the best possible route for the traveler when there are multiple routes to go from city X to city Y and there is no direct route from city A to city B via a single mode of transport.

Roles and Responsibilities:
I orchestrated the results from four major transport APIs (airplane, train, car, and bus) and wrote an algorithm to combine the results when there is no direct mode of transportation from city X to city Y.

I also wrote the logic for sorting and filtering wherein if a user wants to sort the results based on price/time of travel or if the user wants to choose between different modes of transport than the user has the flexibility to do so.

Technologies: Spring Boot, AWS Lambda, Redis

Bounce search words are those where a user types in a word in a typeahead (autocomplete or autosuggest) box but upon getting the suggestions, he bounces away from the typeahead box or from the website because he does not like the suggestions.

It scrapes competitors' typeahead boxes for an input region name that has a bounce count smaller than ten. It then compares it using Python and sees which suggestion matches, which do not, and take action accordingly.

Roles and Responsibilities:
I scraped the competitor's website, gathered the data and analyzed it.

Technologies: Python, Beautiful Soup, Selenium Web Driver, Jupyter, Python, Pandas

This is an NP-complete problem.

Project Details:
· Uses the graph coloring algorithm (Greedy).
· Uses a genetic algorithm.
· Uses a FitNesse function to design the timetable possibilities based on constraints.
· The constraints are classified into hard and soft constraints.
· Hard constraints (the breaking of which results in an infeasible timetable) and soft constraints (which do not have to be met, but which leads to desirable timetables when
met).
· While using the graph coloring algorithm (Greedy), courses are assigned as nodes and time slots are used as colors. Edges are drawn to show the clashing courses, which cannot be taught at the same timeslot.

Roles and Responsibilities:
The whole project was developed by a team of three. I designed and developed the back-end part in Java and implemented Greedy and a genetic algorithm along with a MySQL database schema.

Technologies: Java, Spring, MySQL, Redis

I designed a standard platform for monitoring and logging 40 microservices in insurance.

The new piece of code acts as a servlet filter and intercepts request and response and does the required monitoring and logging boilerplate stuff for the API.

I also designed a common jar for data-access-layer so that all the microservices which interact with third-party APIs can leverage it and do not write redundant HTTP client code to interact with third-party APIs.