Developed a comprehensive data analysis and visualization project using Python's pandas and matplotlib libraries. The project focuses on analyzing sales data, cleaning and preparing the dataset, and creating various visualizations to uncover insights and patterns in the data.
Handled missing values and removed duplicates from the sales dataset
Created various plot types including histograms, line plots, box plots, and density plots
Explored dataset statistics, data types, and relationships between variables
Automated the data processing and visualization workflow using Python scripts
The project follows a structured data analysis workflow:
One of the main challenges was handling the dataset's inconsistencies, including missing values and
duplicate entries. This was addressed through systematic data cleaning processes using pandas functions
like dropna() and drop_duplicates().
Another challenge was selecting appropriate visualization types for different aspects of the data. This was solved by creating multiple visualization types (histograms, line plots, box plots, bar plots, and density plots) to effectively communicate different patterns and relationships in the data.