Data Visualization on Honey Production dataset using seaborn and matplotlib libraries

This project revolves around the exploration and visualization of a honey production dataset using Python's seaborn and matplotlib libraries. In the context of the declining honeybee population and its impact on American honey agriculture, the dataset provides insights into honey production supply and demand in the United States from 1998 to 2012. The primary objective is to leverage Python visualization libraries to investigate the data, comprehend trends, and draw meaningful conclusions. The attributes in focus include the number of honey-producing colonies, honey yield per colony, total production, price per pound, production value, stocks held by producers, year, and state. The project employs various visualization techniques such as pie charts, displot, scatterplots, boxplots, pairplots, and correlation plots to gain valuable insights into the honey production dynamics over the specified time frame.

Key Skills: Data Visualization | Exploratory Data Analysis (EDA) | Python Programming | Matplotlib | Seaborn | Statistical Analysis | Correlation Analysis | Scatterplot Interpretation | Boxplot Analysis | Pie Chart Visualization | Data Interpretation | Insight Generation

Libraries: Pandas | Numpy | Matplotlib | Seaborn