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Data Science with less than a year in Machine Learning & AI.
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Assessing your cultural and operational fit
Highly motivated and skilled Data Science professional with a Master's degree in Data Science and a strong foundation in Mathematics. Experienced in developing ML-based systems, AI-powered applications, and deep learning models for various domains including sound detection, mental wellness, diabetes prediction, and fraud detection. Proficient in Python, TensorFlow, and various data manipulation and visualization libraries, with a passion for building intelligent solutions and enhancing data integrity.
Christ (Deemed) University
Master of Science · Data Science
August 1, 2024 – June 30, 2026
St. Joseph College
Bachelor of Science · Mathematics
August 1, 2021 – June 30, 2024
ML-Based AC Controller Using Sound Detection
November 1, 2025 – June 1, 2026
Developed an ML-based system to classify sound signals for intelligent AC control. Converted raw audio (.m4a) files to .wav, generated spectrograms, and extracted features using Librosa. Built and trained a CNN model on spectrogram data, achieving reliable classification accuracy across varied sound environments.
MINDFULL – AI-Powered Mental Wellness Companion
August 1, 2025 – June 1, 2026
Developed an AI-driven mental wellness system integrating LLMs, RAG pipelines, and sentiment analysis for personalized emotional support. Implemented ML components including a crisis detection classifier (70+ keyword signals) and an LLM-assisted reasoning module. Built a modular Flask backend with authentication, scalable API endpoints, and database models for journaling and mood analytics.
Diabetes Prediction Using Deep Learning
July 1, 2025 – June 1, 2026
Built deep learning models (ANN, LSTM, and Simple RNN) on the Pima Indians Diabetes Dataset to classify diabetic vs. non-diabetic individuals. Performed data preprocessing and visualization, including feature scaling and exploration of glucose level, BMI, and age-related trends. Trained models using early stopping and dropout layers to prevent overfitting and improve generalization on test data.
Fraudulent Insurance Claim Detection
March 1, 2025 – June 1, 2026
Performed data cleaning, encoding, and feature engineering on insurance claims data including demographics, incidents, and claim amounts. Built and evaluated models using Random Forest Classifier with feature selection (RFE, SelectKBest) and class weight tuning to handle data imbalance. Achieved 84% accuracy in identifying key fraud indicators like insured zip, claim type, and policy premium.
Tourism Guide Web Application
December 1, 2024 – June 1, 2026
Developed a full-stack tourism website integrating responsive frontend and Python backend for destination exploration, trip planning, and user personalization. Created and managed SQL database tables for countries, destinations, hotels, restaurants, and wishlist data, supporting efficient storage and retrieval. Implemented robust form validations for login, registration, contact, and payment workflows, enhancing data integrity and user experience.
Complete Data Science Bootcamp
Udemy.com
June 1, 2026 – Present
SQL Bootcamp
Udemy.com
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and foundational skill set in Data Science, aligning well with the target role. The diversity of projects, from sound classification to mental wellness companions and fraud detection, indicates a broad curiosity and willingness to explore different applications of data science. However, the lack of professional experience means cultural fit in a corporate environment is yet to be proven.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems. The academic nature of all projects suggests a strong learning aptitude and ability to apply theoretical knowledge. However, there is no information regarding collaboration, stress handling, or work attitude from the provided data.