* Collaborated on machine learning models including SVM, Linear Regression, Time Series, Logistic Regression, Decision Trees, Naive Bayes, Random Forest, and K-means clustering for predictive analytics.* Contributed to a Data Engineering project focused on ETL processes, backend queries, and pipeline building using AWS services like QuickSight, Redshift, Kinesis, and Lambda for real-time data automation and dashboarding.* Utilized Python, SQL, and PySpark to clean, analyze, and transform large datasets, ensuring high data quality and accuracy in final outputs.* Designed interactive dashboards in AWS QuickSight, providing real-time insights for stakeholders and improving business decision-making.* Gained hands-on experience in data automation, streamlining the data lifecycle from ingestion to reporting.

* Automated tasks and performed advanced data manipulation using Python.* Analyzed and visualized complex datasets using Pandas, Matplotlib, and Seaborn, helping interpret trends and derive insights.* Applied machine learning techniques with Scikit-learn, focusing on classification and regression models.* Gained introductory experience in deploying machine learning models on IBM Cloud and IBM Watson.