In this project, an e-commerce platform dataset was analyzed. The dataset was aimed to be cleaned, and the given dataset was analyzed to mine informational insights using Python and MySQL.
The project aimed to conduct a comprehensive analysis of an e-commerce platform using the provided dataset. The dataset contained information about the products, customer reviews, purchase history, seller details, categories, and other relevant details. Valuable insights about customer behavior, popular products, seller performance, customer satisfaction, and overall platform performance were sought to be uncovered through various data analysis techniques.
Task 1 Load the data
Task 2 Find the duplicate values
Task 3 Remove the duplicate values
Task 4 Find the null values
Task 5 Remove the null values
Task 6 Renaming the column names
Task 1 Find the sentiment of the review
Task 2 Processing the review
Task 3 Exporting the cleaned dataset
Task 4 Generate tables using the cleaned dataset
Task 1 How many values are there in the given dataset?
Task 2 Find out the unique brands in the given dataset
Task 3 Retrieve all records from the 'ecommerce' table where the brand is 'Amazon'.
Task 4 Retrieve all records from the 'ecommerce' table where the product reviews contain the word 'good' in their text.
Task 5 Provide a list of all products and their corresponding details from the 'ecommerce' table that belong to the 'Electronics' category
Task 6 Retrieve all records from the 'ecommerce' table where the products are categorized under 'Electronics' as their primary category and the brand is 'Flipkart'.
Task 7 Provide a summary of the number of positive and negative sentiments for each primary category in the 'ecommerce' table.
Task 8 Retrieve all records from the 'ecommerce' table where the sentiment in the product reviews is classified as 'positive'.
Task 9 Provide a summary report for each brand in the 'ecommerce' table, including the total number of positive and negative sentiments in product reviews, the total number of reviews, and the percentage of positive and negative sentiments for each brand.
Task 10 Retrieve a count of products for each primary category in the 'ecommerce' table
Task 11 Retrieve all records from the 'ecommerce' table where the product name contains the word 'Tablet' as a substring
Task 12 Count the number of product reviews in the 'ecommerce' table where the text contains the word 'Alexa' as a substring.