SumedhaZaware/Data-Science-and-Big-Data-Analytics-SPPU-2019-Pattern

Covers end-to-end data science workflows including data preprocessing with pandas (missing value handling, outlier detection, normalization), supervised learning models (linear regression, logistic regression, Naïve Bayes), and natural language processing tasks (tokenization, TF-IDF). Emphasizes exploratory data analysis through statistical summaries and visualization using Seaborn and Matplotlib, with practical applications on datasets like Iris, Boston Housing, and Titanic. Includes Apache Spark-based big data analytics assignments in Scala alongside real-world projects analyzing COVID-19 vaccination datasets.

No commits in the last 6 months.