Supported AI/ML and VR/AR data collection projects for Meta by managing, validating, and uploading large datasets to ensure data accuracy, integrity, and readiness for model training. Operated and maintained data collection systems, scanning tools, and secure data pipelines while upholding proprietary data security and compliance standards. Leveraged SQL, Python, and visualization tools to monitor data quality, troubleshoot technical issues, and streamline workflows for greater efficiency. Collaborated with cross-functional teams to optimize processes, implement automation, and enhance the reliability of large-scale data-driven initiatives.

Developed a model which answers the users' queries about the dataset. The model consists of 3 Machine Learning classification algorithms, Logistic Regression, Support Vector Machine, and Random Forest to predict the values.

Utilized libraries Pandas, NumPy, NLTK, Re, SKLearn, and Matplotlib libraries to design the algorithms.

The accuracies obtained for Logistic Regression, Support Vector Machine, and Random Forest Algorithm is 86%, 72%, and 82%, respectively.