Arunkkumar Karthikeyan

Key Skills

Software

Label Studio

Top Subject Matter

Power Tools Object Detection

Data Analysis and Visualization

Natural Language Processing and Machine Learning

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Bounding Box

Classification

Computer Programming Coding

Object Detection

Segmentation

Freelancer Overview

I bring a strong background in data science and analytics, with hands-on experience in preparing and processing high-quality datasets for machine learning and AI applications. My expertise lies in data labeling, feature engineering, and data transformation, gained through projects such as Spotify Song Success Prediction, where I analyzed text-based data, and an Ingredient Recommender System that required labeling user reviews for recommendation models. These experiences honed my skills in organizing unstructured data into actionable formats for training AI models. Moreover, I have used the labelling software Label Studio for object detection algorithms. Additionally, I have developed data pipelines and visualizations using tools like Tableau, Dash Plotly, and Python, ensuring seamless integration of labeled datasets into analytics workflows. My ability to work with diverse data types—structured, semi-structured, and unstructured—positions me to effectively contribute to AI training data initiatives in domains such as text analysis, consumer behavior, and financial insights.

Labeling Experience

Data Labelling for Object Detection

Label StudioImageObject Detection

At Bosch Power Tools, I contributed to data labeling for object detection projects using Label Studio. My role involved annotating image data to train machine learning models, ensuring high-quality labeled datasets for improved model performance. This work supported the development of efficient AI-driven tools and systems for object recognition tasks.

At Bosch Power Tools, I contributed to data labeling for object detection projects using Label Studio. My role involved annotating image data to train machine learning models, ensuring high-quality labeled datasets for improved model performance. This work supported the development of efficient AI-driven tools and systems for object recognition tasks.

2023 - 2024

Education

Work History