Key Responsibilities: Gained hands-on experience in data manipulation, data analysis, and statistical modeling using Python and SQL Developed machine learning models, including regression, classification, and clustering techniques. Performed data preprocessing tasks such as data cleaning, feature engineering, and handling missing values. Built and deployed interactive dashboards for data visualization using Seaborn and Matplotlib. Key Achievements: Created a predictive model for Cyberbullying detection, achieving an accuracy of 90% using [ML techniques like LinearSVC, TF-IDF vectorizer]. Developed a data pipeline to automate the collection, cleaning, and analysis of data, reducing processing time by 65%. Presented a final capstone project on Cyberbullying, demonstrating proficiency in machine learning algorithms and data storytelling.