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Data Science with less than a year in Data Science & Machine Learning.
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Highly motivated and aspiring Data Scientist with a strong academic foundation in Computer Applications and Data Science. Eager to apply analytical and problem-solving skills to real-world challenges and contribute to data-driven decision-making. Seeking entry-level opportunities in Data science, Data engineering or anything related to problem solving where I can leverage my technical skills and learn from experienced professionals.
Goa Business School (Goa University)
Bachelor of Computer Applications (BCA) · Computer Applications
N/A – June 30, 2024
Goa Business School (Goa University)
Master of Science (M.Sc.) · Data Science
N/A – June 30, 2026
Goa University
Secondary School Certificate (SSC)
N/A – May 31, 2019
Goa University
Higher Secondary School Certificate (HSSC) · Science Stream
N/A – May 31, 2021
UCL Football Players Market Value Prediction
October 1, 2025 – April 30, 2026
Developed a data-driven model to predict football players' market value using statistical, machine learning, and deep learning techniques. Scraped data from online websites and preprocessed player performance data including age, goals, assists, minutes played, nationality, and average ratings. Performed data cleaning, feature engineering, and transformation to improve model accuracy and consistency. Implemented multiple models including Linear Regression, Random Forest, Gradient Boosting, and Deep learning models for comparative analysis. Evaluated model performance using metrics such as R² Score, MAE, RMSE, and MSE to identify the best-performing approach. Applied transfer learning techniques to enhance model generalisation across different player positions. Analysed results to identify key factors influencing player valuation in professional football. Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Matplotlib, Seaborn.
GU GPT
January 1, 2025 – March 31, 2025
Developed a Goa University GPT model to provide answers to university-related questions. Utilized R and Node.js and llama3.2 model. Scraped and processed a significant volume of university data for model training and knowledge base construction. Contributed to model building and ensured accurate information retrieval for student queries.
Vegetable Classification System
November 1, 2024 – December 31, 2024
Developed an end-to-end web application for classifying vegetables from uploaded images. Implemented a Convolutional Neural Network (CNN) model using TensorFlow and Keras for image classification. Trained the model on 200 images across 8 different vegetable classes, achieving 77% accuracy. Built a robust backend API with FastAPI to handle image uploads and serve model predictions. Designed an intuitive front-end interface using HTML, CSS, and JavaScript (jQuery) for seamless user interaction and real-time display of classification results. Utilized OpenCV for image preprocessing and TensorFlow Lite for optimized model inference.
Object Classifier
October 1, 2024 – October 31, 2026
Designed and implemented a real-time object classifier from scratch using FastAPI, HTML, and a YOLO model. Achieved high accuracy 98% precision in detecting and classifying objects within images.
View ProjectCultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse applications of data science, from market value prediction to object classification and conversational AI. This breadth of interest, combined with leadership experience in university events, suggests an adaptable and collaborative individual. The stated interest in staying updated with the latest tech aligns well with a culture of continuous learning. The academic nature of all projects means real-world industry cultural fit is yet to be proven.
Soft Skills & Operational Fit
The candidate's resume highlights problem-solving, analytical thinking, teamwork, communication, and adaptability as soft skills. Participation in event coordination and sports/cultural organizing roles suggests good organizational skills and a proactive attitude. These traits indicate a potential for good operational fit within a team-oriented data science environment, though practical application in a professional setting is yet to be demonstrated.