This project involves analyzing sales data to gain insights into sales performance, trends, and patterns. The analysis is conducted using Python, and the project includes data cleaning, data visualization, and statistical analysis. The data used in this project consists of sales records. It includes various attributes such as date, product, quantity sold, and sales amount. The analysis is divided into several steps: Data Cleaning: Cleaning the raw data to handle missing values, incorrect formats, and other issues. Data Visualization: Creating visualizations to understand the data and identify patterns. Statistical Analysis: Performing statistical tests to draw insights from the data. Results The results of the analysis are documented in the Sales_Analysis.ipynb notebook. Key findings include trends in sales performance, factors affecting sales, and recommendations for improving sales.