My Portfolio

A Showcase of my projects and my abilities.

I am Vineet Dhaimodker

Developer at heart and a tech enthusiast.

Project 2
Regression and Classification using ML

Implemented Linear and Multiple regression, Polynomial curve fitting and regression for Univariate and Bivariate data sets. Also implemented classifiers like Bayes, Perceptron based, Logistic regression- based and SVM for Linearly separable, nonlinearly separable and Overlapping data sets.

Project 3
Airplane Tickets Booking and Management System

Developed website using HTML, CSS, PHP (frontend) & MySQL (backend) with relational databases (airlines, timing, destinations, ticket cost) & hosted it locally. Enabled users to enter the source and destination boarding points and book flights using the same tools.

Project 4
Named Entity Recognition Models for Healthcare

Trained a Named Entity Recognition (NER) model for Natural Language Processing (NLP) of clinical health records. Collected & cleaned as per application requirements using Python. Implemented NER models from SpaCy & SciSpaCy libraries, compared their accuracies by varying hyper-parameters. Observed SciSpaCy en_ner_bc5cdr_md model with pre-training and achieved more accuracy with an F1 score of 0.846 after 100 iterations.

Project 5
Parent-Teacher Communication Application

Developed application to enable efficient communication between parents & teachers with features like chat support, performance analysis, grading, etc. Built frontend using Google Android Studio (Java, XML) and Git Labs for version control; used Database Administrator to store student data (names, marks, blood group, grade, etc.). Implemented Restful API using Retrofit & Volley libraries that use HTTP requests to GET, PUT, POST and DELETE data from Server.

Project 6
Novel Intelligence Scale Based on EEG Data

Designed a novel intelligence scale using Wavelet Packet Transform for feature extraction & Hierarchical Extreme Learning Machine for classification. Tested the scale on students & analyzed EEG signals from the frontal cortex of the brain using an EEG cap and amplifier while answering a quiz . Collected & classified students as low , high intelligence, etc. based on data analysis in terms of ~256 data points. Achieved 80% training accuracy and 73.33% testing accuracy.

Project 7
Pac-Man

Over the course of this project, I studied numerous search algorithms, and applied them to a practical use. Implemented Depth First Search, Breadth First Search, Uniform Cost Search, A* search in order to reach and eat the precoded fruit. By comparing all 4 methods, we found that A* had a better performance as it computed the path cost with a heuristic.

Project 8
BURNOL: Sports Search Engine

In this project, our task is to build a web search engine Building a crawler to scrape data from a specific domain Indexing and Ranking the scraped data using Lucene Indexing and Ranking the collected data using Hadoop Building a web interface to demonstrate the functionality of oursearch engine