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Mayank Ambalal Jain

About Me

My enthusiasm lies in solving problems with appropriate solutions. Always been a persistent learner, I am enhancing my skills to develop more useful web applications/projects. I hold strong technical skills and an academic background in Full-Stack development (MERN and Django), Software Engineering methodologies, and Machine Learning services. Additionally, I have a summer fellowship experience in the field of computer vision.

Academic Projects

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Developing

E-Commerce Web-App

Learning and Implementing full-stack skills by building a multi-client website with MERN stack and will use React Context API for state management.

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Todo App

Using TypeScript on both sides (server and client) to build a Todo App from scratch with React, NodeJS, Express, and MongoDB. Implemented Get, Add, Update and Delete Todos, Client-side with React and TypeScript, Fetch data from the API, Display a Todo & Fetch and Display data

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MyCampus Car Rental

A group project of 6 people implemented an android native application car rental for errands or pleasure for university people. My contribution to the project was achieving the search car,profile check, and check the reservation calendar function and designed UX/UI for the rental manager part.

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Gente and Cuidad Website

Created a multi-client, responsive, and data-dynamic website for an NGO using the Model-View-Controller. Programmed 10 various functionality such as login system, account registration, contact form, different functions for events part by using CRUD. Deployed the website on an AWS-based university’s cloud.

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Food Recipe Search

Developed a search interface to display food recipes based on the ingredients inserted by a user. Analyzed multiple NTLK method outcomes for preprocessing the dataset. Programmed TF-IDF from scratch on 180K+ recipes and stored the data in ".pkl" format for rendering search results in 0.2 seconds.

Academic Projects

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Learning

Prediction from Chest X-Ray Images using Pre-Trained Convolutional Neural Networks

Given an input chest X-ray image, the algorithm must detect whether the person has been infected or not. Learning under progress.

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Neural Networks Concepts

Developed, trained, and tested single layer, multi-layer, and CNN from scratch using Python, NumPy, TensorFlow, and Keras.

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Car Make-Model Classifier

Acquired 96% accuracy and 80% validation accuracy on predicting car make & model in a given image using YOLO v3 and VGG16.Used transfer learning for VGG16 and hyperparameter tuning for YOLO v3.•Implemented various image data preprocessing such as edge detection, gaussian blur, denoise, image segmentation, and digital image interpolation on a 60-40 split dataset.

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Cuisine Classification

Developed a 20 cuisine multiclass classifier that classifies food recipes based on ingredients by implementing the Multinomial Naive Bayes algorithm.Achieved 70% accuracy and a 50% F1 score on the test set by using Laplace Smoothing.Analyzed various combinations of NLTK methods such as Stopwords, Stemming, and Lemmatization for reducing file size.

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AI Concepts

Assignments on Max Connect 4, Wumpus World, Probabilities and Bayesian Networks. Executed a MaxConnect4Game game with an Artificial Intelligence opponent by applying a minimax algorithm with alpha-beta pruning.

Other Projects

More on GitHub

Other Projects

More on GitHub

My GitHub Calendar

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