•Techniques employed to clean and visualize data during exploratory data analysis of the US Honey dataset. • Cleaning process involving handling missing values, removing duplicates, and standardizing data formats. • Utilization of Matplotlib and Seaborn Python libraries for visualizing dataset characteristics and relationships. • Matplotlib providing flexibility and customization for creating various visualizations. • Seaborn offering a higher-level interface with built-in themes for visually appealing graphics. • Usage of SQL language and SQLite database for data manipulations and aggregations. • Execution of SQL queries to extract subsets, calculate summary statistics, and generate derived variables. • SQLite's lightweight and self-contained nature facilitating storage and retrieval of the dataset.