Analyzing Sales Data with Python and Excel

• Conducted a full data cleaning and preparation process on a multi‑column sales dataset (CSV and XLSX formats) by converting raw fields into workable formats, including transforming the Date column into datetime objects and the Amount column into numeric values for reliable computation.
• Filtered and structured the dataset by selecting all records between January 1, 2024 and May 31, 2024, removing observations with missing Amount or Email values, filling other missing fields, and cleaning text entries in the Note column to ensure accuracy and consistency. Dropped irrelevant columns that added no analytical value.
• Performed detailed descriptive analysis across multiple business dimensions, including plan wise revenue and counts, lead wise revenue distribution, client segmentation and frequency counts, country wise revenue patterns, and user level metrics such as average revenue per user and standard deviation.
• Computed month wise revenue totals and calculated monthly growth rates to understand short term sales trends and detect emerging business momentum.
• Built visual outputs using AI generated charts, including pie charts for lead and client summaries and a line chart illustrating monthly growth rates. Applied conditional highlighting to emphasize the top three revenue sources across plans, countries, and leads.
• Automated Excel report creation by compiling all summary tables, charts, and highlighted insights into a structured Excel workbook for easy business review.
• Implemented automated email delivery using Python and the yagmail package to send the completed Excel summary report and key insights directly to specified recipients without manual effort.
• Identified core business insights including total revenue during the study period, top performing plans, countries, and platforms, detailed monthly growth figures, and customer retention patterns by measuring repeat purchase rates.
• Completed the end‑to‑end workflow of cleaning, analyzing, visualizing, summarizing, and distributing sales insights, demonstrating solid analytical thinking, strong Python data handling skills, and the ability to automate repetitive reporting processes efficiently.

Full Explanation in Bangla (video)