Student at BML Munjal University
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BML Munjal university
Data Scientist
June 16, 2026 – Present
Qiskit_Max-Cut_and_TSP_Optimization
December 17, 2024 – December 17, 2024
This repository demonstrates Qiskit-based implementations of the Max-Cut and Traveling Salesman Problem (TSP). It compares classical and quantum approaches for solving combinatorial optimization challenges using tools like VQE and QAOA.
View ProjectSpeech_to_Speech_LLM_Bot
December 16, 2024 – December 16, 2024
An AI chatbot enabling real-time voice interactions with speech recognition, NLP, and text-to-speech. Powered by DialoGPT, it features a user-friendly GUI with webcam support for seamless communication.
View ProjectStock_Price_Prediction_App
October 17, 2024 – October 17, 2024
The Stock Price Prediction App is a mobile application built with Flutter that uses an ARIMA machine learning model deployed via a Flask API to predict stock prices based on historical data. It provides real-time stock trend forecasts displayed on an interactive line chart. The app is cross-platform, running on both Android and iOS.
View ProjectBig_Data_in_Healthcare
September 24, 2024 – October 18, 2024
The project aims to develop a model that predicts diseases based on user-reported symptoms. It will also recommend precautions and medications that can be taken as early preventive measures.
View ProjectSIH_Parsing_Social_Media_Feeds
September 14, 2024 – September 14, 2024
Developed a tool to parse social media feeds, extracting key data such as posts, comments, and sentiment analysis from various platforms. Utilized automated scraping techniques and NLP to provide insightful analysis and visualizations of user interactions.
View ProjectPython
August 6, 2024 – September 7, 2024
Python is a versatile, high-level programming language known for its readability and broad range of applications, from web development to data science. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
View ProjectUnderstanding-Classification-Simple-use-of-Machine-Learning-in-NLP
July 24, 2024 – July 24, 2024
This project uses the Sentiment140 dataset for Twitter sentiment analysis. It involves text preprocessing, vectorization (TF-IDF or Word Embeddings), and classification (Naive Bayes, Logistic Regression, SVM), utilizing pandas, scikit-learn, nltk, and visualization tools.
View ProjectExploring-Stemmers-and-Lemmatizers-in-Natural-Language-Processing
July 23, 2024 – July 23, 2024
This project involves comparing different stemming and lemmatization algorithms in Natural Language Processing. The goal is to implement and analyze the performance of various stemmers and lemmatizers on a set of diverse input texts using libraries like NLTK and SpaCy.
View ProjectNLP-Basic-Preprocessing-Techniques
July 22, 2024 – July 22, 2024
This assignment explores fundamental NLP preprocessing techniques: Tokenization at word and sentence levels, Stopword Removal to filter out common words, Frequency Distribution and Word Count to analyze word usage, and Converting text to lowercase for uniformity. These techniques are essential for preparing textual data for further analysis.
View ProjectCompareKart-website
December 2, 2022 – December 2, 2022
The comparing website which will compare the products that you want to compare
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards personal learning and exploration in data science, machine learning, and even quantum computing. This self-driven approach aligns with a culture that values continuous learning and initiative. However, the lack of team-based projects or professional experience beyond a future role makes it difficult to fully assess cultural fit regarding collaboration and workplace dynamics.
Soft Skills & Operational Fit
The provided data is insufficient to assess soft skills or operational fit. Project descriptions indicate an ability to work on diverse technical challenges, but collaboration, problem-solving approach, and communication style cannot be evaluated.