ManasGupta

Code. Research. Design. Write.

About me

As a machine learning engineer, I have a great passion for what I do. I am dedicated to continually improve my skills and knowledge and my current research focus is in the domain of Federated Learning. I have a wide range of skills, focused around data analysis, computer vision, natural language processing, and software design.

In my free time, I love playing sports and trying out new things. I also enjoy helping others in their craft and working on personal projects that allow me to bring ideas to life. I value simplicity and am always looking for ways to improve and make things more efficient.

Maybe we can get to know each other more over a cup of chai?

I mainly work with Python (and sometimes C++) and I have extensive experience using PyTorch and Hugging Face for my machine learning projects. I also rely on Git and Docker for management and version control.

Contact

You can reach out to me at work.manasgupta@gmail.com or send me a Hi on Linkedin or even send a carrier pigeon (which, while not common, would be super awesome!)

Projects

Utilized machine learning techniques to identify key factors influencing movie revenue. Implemented various models from SciKit-Learn library and conducted feature importance and coefficient analysis to drive actionable insights.

Predictive modelling for movie revenue

Developed a food delivery app featuring a platform for donating and receiving items during the COVID-19 pandemic. Utilized database management skills to design a MySQL-powered backend and effectively solve issue of reduced donations.

Khidmat app

Designed and implemented a single player card game featuring graphics and artificial intelligence using the PyGame library. Computer opponent maximizes chances of winning through strategic decision-making based on available cards and user choices.

In Spades

Successfully implemented neural style transfer algorithm based on Gatys et al. (2015) using VGG-19 model and advanced techniques such as semantic segmentation.

Art Generation with Deep Learning

Developed and trained an L-layer neural network from scratch to classify images as cats or non-cats. Implemented loss function, forward propagation, and backward propagation for effective training.

Cat vs Non-Cat Classifier from scratch

Utilized PySpark to perform real-time analysis of e-commerce data, including data ingestion, transformation, and analysis. Applied advanced data manipulation and machine learning techniques to gain insights and support decision-making.

Real Time E-Commerce Data Analysis

Gathered travel data from Reddit's r/travel using the Reddit API, conducted geo analysis, and visualized findings. Investigated the impact of COVID-19 on travel patterns and identified popular destinations and months for travel.

Travelling the world through data

Conducted extensive analysis on the "Customer Personality Analysis" dataset from Kaggle using advanced techniques such as segmentation, clustering, and feature engineering. Analyzed and interpreted results to gain valuable insights.

Customer Personality Analysis