In this project, I analyzed retail sales data to extract valuable business insights and visualize key trends. Using Python and its powerful data analysis libraries, I was able to clean, process, and interpret the dataset to support better decision-making.
Tools & Libraries:
Pandas & NumPy → Data cleaning, transformation, and statistical analysis
Matplotlib & Seaborn → Data visualization and trend analysis
Key Work Done:
Preprocessed and cleaned raw sales data for consistency and accuracy
Conducted exploratory data analysis (EDA) to identify sales patterns
Analyzed revenue by product category, store branch, and payment method
Identified best-selling products and customer purchasing behavior
Created meaningful visualizations to highlight trends and insights
Outcome:
The analysis revealed seasonal trends, top-performing products, and customer preferences, providing actionable recommendations to improve retail sales strategies
