Shubham T.

Key Skills

Software

No software listed

Top Subject Matter

Machine Learning classification/regression

model evaluation and pipeline development

Top Data Types

Text

Top Task Types

Classification

Freelancer Overview

AI/ML Intern (Sunfocus Solutions) — built and evaluated 5+ ML models and end-to-end training/validation pipelines. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scikit-learn. Education includes Bachelor of Computer Applications, Govt PG College Bilaspur (2025) and Higher Secondary (12th), GSSS Slapper (2022). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

AI/ML Intern - Sunfocus Solutions

TextClassification

Built and evaluated multiple machine learning classification and regression models using Python and Scikit-learn as an intern. Completed data preprocessing, feature engineering, and model evaluation using standard metrics to improve predictive performance. Developed end-to-end ML pipelines including EDA, cleaning, training, validation, and hyperparameter tuning. • Trained models and measured Accuracy, Precision, Recall, F1-Score, and RMSE. • Implemented pipelines with GridSearchCV for hyperparameter tuning. • Built relational database-backed workflows using MySQL and SQLite with optimized SQL. • Created Streamlit dashboards for visualization and real-time prediction analysis.

2024 - 2024

AI/ML Intern (Sunfocus Solutions) — built and evaluated 5+ ML models and end-to-end training/validation pipelines

Developed and evaluated multiple machine learning classification and regression models as part of an AI/ML internship workflow. Built end-to-end ML pipelines from exploratory data analysis through data cleaning, feature engineering, training, validation, and hyperparameter tuning using GridSearchCV. Applied quantitative metrics to assess and improve predictive performance across experimental iterations. • Performed data preprocessing and feature engineering for model readiness • Used accuracy, precision, recall, F1-score, and RMSE for model evaluation • Conducted iterative experimentation to reduce prediction error by ~15% • Wrote and optimized database queries (MySQL/SQLite) to support model-related data operations

2023 - 2024

Education

Work History