Ninad Madhab

Specialties : ML, AI, Data Science, Data Analysis, Web development & Designing ,Programming,Data structure, social media research, flexibility & multi-tasking, startups & online communities.

Experience

Project Trainee

ITC Infotech

Worked on Projects which included the following

  • Data Analysis and predications on Industrial Data
  • Web development using Django and ASP.NET framework
  • Windows services development using C#

June 2018 - July 2018

Intern

C-DAC,Pune

Internship was related to High performance Computing and Deep Learning Algorithms

December 2017 - January 2018

Web Developer

Local Food-Delivery Startup

Woked on a professional Website of a Food-Delivery Local Startup using PHP

October 2017 - December 2017

Education

Veer Surendra Sai University of Technology,Burla

Bachelor of Technology
Computer Science

GPA: 3.44

August 2016 - May 2020

Demonstration Multipurpose School

Intermediate

Percentage: 92.4% (CBSE)

April 2014 - March 2016

Demonstration Multipurpose School

Higher Secondary

CGPA: 10 (CBSE)

April 2009 - March 2014

Skills

Programming Languages & Tools
Workflow
  • Mobile-First, Responsive Design
  • Cross Browser Testing & Debugging
  • Cross Functional Teams
  • Agile Development & Scrum

Interests

Apart from being a web developer, I enjoy most of my time being outdoors.

When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the front-end web development world.

Projects

Description: This project is a work with a advertising data set, indicating whether or not a particular internet user clicked on an Advertisement on a company website. A model that will predict whether they will click on an ad based off the features of that user.

Description: The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

Description:Built a model that will determine the tone (neutral, positive, negative) of the text. The model was trained on the existing data (train.csv). The resulting model determined the class (neutral, positive, negative) of new texts (test data that were not used to build the model).

Description: This application is developed to download attachments from mail automatically using IMAP Server Methods.