In the field of E-commerce, Amazon acts as the most dominant global marketplace with significant sales across multiple categories. There have been a lot of studies done in the past to understand the sales pattern of Amazon sales. This research paper aims to perform an analysis of Amazon sales data to find new key trends, insights, sales patterns, and factors influencing sales. This analysis starts with data preprocessing to improve data quality and accuracy of the dataset. After this, EDA is performed to identify initial trends and correlations within the data. This study finds sales patterns, identifying periods of peak sales and the states where different product categories are trending. Amazon sales data consist of sales, sales trends over time, sales distribution by state, various product categories, and several other data points. Amazon sales data analysis concentrates on the process of analyzing customer behavior, sales, market trends, and several other factors to make effective, improved data-driven decisions. The main key to earning profits and for a successful business is to analyze different factors like revenue, sales over the year, etc. Analysis of sales data helps in improving the business, increasing their revenue and effectiveness of promotional strategies. With the rise of new technology and innovation, data analytics is helping the e-commerce industry to grow fast. Data analysis enables e-commerce businesses to understand customer behavior in a quite different manner and helps the businesses to improve the quality of services they provide and to enhance the weak areas of the business. Many analyses are performed to get insights from the analyzed data so that the business can make better decisions and can help in customer behavior analysis and satisfaction. This analysis of Amazon sales data would help in making better decisions and in improving the quality of services which can lead to successful business.