Sales-Analysis_Python

The project involved analyzing retail sales data of a US-based clothing brand to uncover monthly, statewise, and time-based trends. Python was used for data cleaning, normalization, visualization, and generating insights to support business decision-making.As part of the Course-End Project on Sales Analysis, I conducted an in-depth analysis of retail sales data for AAL, a prominent US-based clothing brand, using Python. The project involved transforming raw data into actionable insights to support strategic decisions related to inventory, marketing, and expansion. Using libraries like pandas, matplotlib, seaborn, and sklearn, I performed data cleaning, normalization, visualization, descriptive analysis, and time-based trend analysis. I explored monthly and statewise performance, identified seasonal sales patterns, and derived key business insights. This project strengthened my Python skills, enhanced my analytical thinking, improved my ability to interpret business data, and added a comprehensive, insight-driven analysis to my portfolio.The analysis revealed key sales trends over a three-month period. November recorded the highest overall sales and units sold, indicating a strong seasonal demand, possibly due to festive shopping. October showed moderate performance, while December saw a decline in both units and revenue, suggesting post-seasonal slowdown. Statewise analysis highlighted top-performing regions contributing significantly to overall sales, guiding potential focus areas for expansion. Monthly boxplots and descriptive statistics uncovered sales variability, with November showing the highest peak values and wider spread. Timewise analysis identified consistent high-performing days and potential low-demand periods, providing valuable inputs for inventory planning and marketing strategy alignment.