Anmol Anand
Cockrell School of Engineering
Bachelor of Science, Electrical and Computer Engineering
Graduated May 2023
Technical Core
Data Science and Information Processing
Technical Skills
Languages
Python, Java, JavaScript, C++, SQL, HTML
Technologies
React, Node.js, Git, PostgreSQL, MatLab, Tableau, AutoCAD, Fusion360, Blender, MSOffice, GSuite, R
Certifications
SQL for Data Science, Natural Language Processing with Classification and Vector Spaces, Getting started with TensorFlow 2, Excel Fundamentals for Data Analysis, Retrieve Data with Multiple-Table SQL Queries
Work Experience
Magnodata
Data Intern
Manalapan, NJ
May '20 - Jul '20
- Extracted text from user-generated videos using third-party services.
- Developed NLP algorithms to perform sentiment analysis and extract product attributes from extracted text.
AT&T
Intern
Middletown, NJ
Oct '18 - Jan '19
- Built a robotic camera system to track the sway of telephone poles in the wind using RoboRealm.
- Filtered parts of a video to isolate the telephone pole from the rest of the background and utilized Javascript to determine the arc of the sway of the telephone pole.
Projects
AI-Powered Guide To Essential Books For Students
- Integrated with ChatGPT to generate age-appropriate recommended books.
- Extracted insights for every book recommendation along with questions & answers for every topic.
Shading Detection - John Deere (Senior Design Project)
- Analyzed over 10,000 raw data points from an Arduino, an antenna, and a GNSS breakout board to detect and predict signal fading using 25 features based on satellite position.
- Developed a CatBoost-based ML pipeline using various Python libraries, such as Sklearn and Pandas, to determine an accuracy value of 80% for the model's ability to categorize shade or unshaded.
Can ChatGPT write like a student? - ML Toolbox for Text Analysis
- Created a dataset of over 25,000+ essays by combining the Evaluating Student Writing Kaggle Data and a list of papers generated with the ChatGPT API to perform NLP techniques, such as perplexity, logistic regression, Naive Bayes, and Decision trees.
- Implemented n-grams and perplexity to classify if a human or AI wrote a given article with an accuracy of 85%.
What Makes a Song a Hit? - Data Science Principles Final Project
- Built various machine-learning models to predict song popularity using audio features and popularity indexes of over 5,000 songs from the Spotify API.
- Utilized linear regression, logistic regression, and KNN models and developed a KNN model that predicts popularity with 72.71% accuracy.
Shopee Kaggle Competition - Data Science Laboratory Final Project
- Used a Siamese Neural Network and Term Frequency-Inverse Document Frequency to classify images that got a Kaggle score of 0.721.