Developed an NLP-based machine learning application to classify text messages as spam or legitimate using TF-IDF vectorization. Trained and evaluated a supervised text classification model, achieving 96.68% accuracy. Built and deployed an interactive Streamlit application for real-time spam message prediction.
Prince Gautam
Programmer & Analyst Intern
Delhi, IndiaArtificial Intelligence, Machine Learning, and Data Science
2roles
38skills
1education
3credentials
About
AI/ML and Data Science professional with hands-on experience in Python, machine learning, deep learning, artificial intelligence, NLP, data preprocessing, exploratory data analysis, image classification, and model deployment.
Experience
Programmer & Analyst Intern
TRL FutureX
08/2026-Present
Developed Python programs for AI/ML applications and analyzed datasets using NumPy and Pandas. Performed data cleaning, data preprocessing, exploratory data analysis (EDA), and dataset preparation for machine learning workflows. Built and evaluated supervised machine learning models using classification and regression techniques. Developed deep learning models with TensorFlow and Keras, including Convolutional Neural Networks (CNNs) for image classification. Implemented model saving, loading, and management workflows for trained machine learning models. Applied an end-to-end ML workflow from data preparation and model training through deployment using Flask REST APIs.
Student Intern — Artificial Intelligence
Acmegrade
08/2024 – 09/2024
Gained practical exposure to Artificial Intelligence applications across multiple technology domains. Built foundational knowledge of Natural Language Processing (NLP), Speech Recognition, Machine Vision, and Expert Systems. Explored practical AI applications through technology-focused training and problem-solving exercises.
Education
PGDAV College, University of Delhi
Bachelor of Arts, Computer Science
Graduation: 2026
Skills
PythonSQLScikit-learnSupervised LearningClassificationRegressionModel TrainingModel EvaluationFeature EngineeringTensorFlowKerasPyTorchConvolutional Neural Networks (CNN)Image ClassificationPandasNumPyMatplotlibData CleaningExploratory Data Analysis (EDA)Data PreprocessingData VisualizationNatural Language Processing (NLP)Text PreprocessingTF-IDFText ClassificationFlaskREST APIsStreamlitDockerAWSGitGitHubJoblibJupyter NotebookGoogle Sheets APITwilio Voice APITwiMLWorkflow Automation
Projects
Handwritten Digit Recognition (MNIST)
Developed an image classification system to recognize handwritten digits from the MNIST dataset. Processed and prepared digit images for model training and evaluated performance on 10,000 test images, achieving 99.01% test accuracy across 10 digit classes.
Developed a supervised machine learning application to predict student pass/fail outcomes from academic and behavioral features. Performed data cleaning, exploratory data analysis, categorical encoding, and feature preprocessing. Trained and evaluated a Logistic Regression model and deployed an interactive Streamlit application.
Driver Pickup Reminder Agent
Developed an automated reminder system using Google Sheets API and Twilio Voice API that monitors pickup times, places outbound calls 30 minutes before scheduled pickup windows, logs reminder and call status, and provides audit tracking.