Developed a digital dashboard for GE Power using ElasticSearch and Kibana. This project was successfully completed as a part of Master's IT Capstone at RPI. Contributed to the project as Project Manager and Developer within the team.

Different dashboards created for visualizing the numerous Key Performance Indicators (KPIs), metrics, critical event countdowns. The visualizations within the dashboards included budgetary allocations for a particular project or for a specific Team, monthly costs and monthly savings for various applications and services used by the teams, yearly expenses by teams, etc.
Users: admin, solution_architect, executive_leader.
Tenants: admin_tenant, solution_architect_view, leader_view.
Technologies used: Elasticsearch, Kibana, Open Distro Plugin by Amazon (for user-based access control), Python Flask, Docker.

Tennis Analytics is the final project of the Course: Applied Analytics and Predictive Modeling.

Objective: Predicting the Future Women Tennis "STAR" players.
Definition of STAR: A singles tennis player who has won at least one grand slam title and is ranked no. 1 at least once.
Data: Women Tennis Association (WTA) data from years 1994 - 2018.
Modeling Approach: Using the ELO Rating System to assign each player a performance rating each time a player plays a match. The ELO Rating System (exemplified in above image) gets every time a player wins or loses a match.
Solution Strategy: Considering the first 5 year Elo Rating trends for the 9 existing "STAR" players: Victoria Azarenka, Venus Williams, Serena Williams, Naomi Osaka, Maraia Sharapova, Simona Halep, Angelique Kerber, Garbine Muguruza, Caroline Wozniacki. Using the existing "STAR" players trends and considering the ELO Ratings of new players who began player in/after 2014 and then comparing the trends and finding similar/better trends.
Results: Following are the predicted Future Women Tennis "STAR" players: -

* Aryna Sabalenka
* Amanda Anisimova
* Elise Mertens
* Ekaterina Alexandrova
* Darya Kasatkina

Goals: 1. To recommend new cuisines and restaurants to the users based on ratings that they might like. 2. To recommend features to the restaurants that would probably help restaurants increase their star ratings, thereby, the number of users and revenue.

Hypothesis: Hypothesis 1a (H1a): Recommending a new cuisine to a user based on clusters that are created using restaurant ratings (average rating/average number of stars). Hypothesis 1b (H1b): Providing top five restaurants (if available) in the chosen city based on the ratings given by users. Hypothesis 2 (H2): Identifying a correlation between the features provided by the restaurants to the ratings given by the users.

Course: Foundations of Human-Computer Interaction Usability Instructor: Dr. Miles A. Kimball

Types of testing and analysis learned and performed: User Analysis, Usability testing, Focus groups, Focus Interviews, Surveys.
What did I learn: Collaboration with developer team, the importance of usability on interfaces, team management, leadership.

The project consists of various approaches to solve the well-known NP-COMPLETE TRAVELING SALESMAN PROBLEM (TSP). Several approaches include using Genetic Algorithm with different Crossover Operators such as NCX (Neighbor-based Constructive Crossover), SCX (Sequential Constructive Crossover), WCX (Window-based Constructive Crossover), SPCX (Single Point Crossover), GCX (Greedy Constructive Crossover). Another approach is using the Simulated Annealing algorithms (DSA-CE&MAP & SA) for solving TSP. Enhanced algorithms created by us are:- NCX, DSA-CE&MAP. The simulation is done using a robot car which is controlled by the software present on the provided URL.

What I did: Worked on NCX and other crossover operators and simulated annealing algorithms with Dashmeet. Also, did coding for the same. Additionally, contributed to the code for the robot vehicle. The robot vehicle code was written in C using Arduino. Also, worked on the hardware part.
What I learned: Team-work, Collaboration, Cooperation, Leadership skills, Improved problem-solving skills, Management skills, Improved Research capabilities.

The project was aimed at building a university online electoral system for easing the election procedure. The project was fabricated using JSP, J2EE servlets using HTML, XML, css, javascript, jquery at front end and SQL database and wamp server at back end.

What I did: Completed most part of backend coding including JSP, servlets and JS and also helped in HTML pages for front end.

The project was aimed at managing the hospital database eradicating the drawbacks of paperwork. The project was developed using J2EE servlets with HTML, CSS, javascript at front end and PL/SQL Database management tool of wamp server at backend.

What I did: Backend coding which include Java servlets, J2SE and also designed the whole website.

The project was aimed at building a web-based real-time communication using WEBRTC technology and APIs. The project was assembled using javascript with HTML, CSS at front end.

What I did: Using webrtc API and understanding it's nuances inorder to implement it.

The project was aimed at easing the reservation, inquiry and catering process by handling the railway databases. The project was made in PHP with SQL database at backend and HTML, CSS, js at the front end.

What I did: Created and coded different modules. Managed the whole database using MYSQL. Additionally, created the whole "Catering" module and helped in other modules including reservation and enquiry.