The goal of the project is to analyze the data of a ecommerce to drive insights from data

Step 1 - The first step was to download all the csv files and load in Postgresql

Step 2 - Created tables from all the csv files with the required data types

Step 3 - Understand the data and try to find the meaning of all the coulumn and how the the tables and linked together

Step 4- Cleaning and transforming the data.

Step 5 - Drive insights and answer the questions

Step 6 - Quality assurance

• Which cities and countries have the highest level of transaction revenues on the site? country city revenue United States Sunnyvale 672.22999
• What is the average number of products ordered from visitors in each city and country? country city average_units_sold United States Sunnyvale 2.375 United States Chicago 2
• Is there any pattern in the types (product categories) of products ordered from visitors in each city and country?
  country v2productcategory city count_by_category United States Housewares Sunnyvale 168
• What is the top-selling product from each city/country? Can we find any pattern worthy of noting in the products sold? country city name count_by_country United States Sunnyvale SPF-15 Slim & Slender Lip Balm 69
• Find the total number of unique visitors by referring sites Answer: 2419
• What percentage of visitors are coming from which Channel grouing channelgrouping channelpercentage Organic Search 57.7 Direct 19.72 Referral 17.01 Paid Search 3.3 Affiliates 1.72 Display 0.84

Understanding the coulumn meaning was difficult as it does not have clear meanings and have multiple columns using same name in different sheets

Loading the data into postgre with correct data type was challlange

If i have more time i will check and ensure the correctness of the results as there are many fields which needs clear explananation