Amazon Sales Dataset

Project Overview:

In the highly competitive world of online retail, understanding what customers like, how products perform, and market trends is critical for success. This project focuses on analysing a dataset with more than 1,000 Amazon product ratings and reviews to discover insights that can help boost product visibility, improve customer satisfaction, and ultimately increase sales.

Problem Statement:

The goal of this project is to dig into the dataset and find valuable insights to make smart business decisions. Here are the main questions we're exploring and what we've found so far:

1. Effect of Discounts on Ratings: We've looked at whether offering discounts affects how customers rate products. Surprisingly, it seems that changing the discount percentage doesn't have a big impact on product ratings. Other factors matter more.

2. Best-Rated Product Category: We've found that among all the types of products, Office Products have the highest average ratings. Customers really seem to like them.

3. Price and Rating Connection: We've checked if a product's price is linked to how customers rate it. Interestingly, there's no clear connection between price and rating. Customers consider many things when they give ratings.

4. Common Words in Reviews: Most reviews are positive, but we've also figured out the words people use most often when they're happy or unhappy with a product. This helps us understand what customers care about.

5. Rating Distribution: We've looked at how ratings are spread across products. Most products get ratings between 4 and 4.5, which tells us about overall customer satisfaction.

6. Most Reviewed Product and Its Rating: The product with the most reviews tends to get ratings between 4 and 4.5, showing that it's quite popular and well-liked.

This project aims to help businesses make better decisions based on these insights, ultimately leading to happier customers and increased sales.