Analysis of E-commerce Sales Using Python

Introduction

This repository contains the analysis of an online store's sales data performed using Python, specifically leveraging the libraries pandas, matplotlib, and seaborn. The analysis aims to uncover patterns and trends in sales data, focusing on changes in sales values over time, the distribution of sales values, and the quantity of orders for each month and quarter. Additionally, the analysis explores the highest sales values per category and average prices per category.

Required Libraries

• Pandas
• Matplotlib
• Seaborn

Data Handling

• Importing Data with Pandas
• Cleaning Data
• Exploring Data through Visualizations with Seaborn and Matplotlib

Results

The analysis generates various visualizations and insights, including line plot, bar plots, bar charts and catplot.

I used:

• Line plot showcasing the changes in sales values over time.
• Bar charts presenting the quantity of orders for each month and quarter.
• Bar charts highlighting the highest sales values per category.
• Catplot displaying the average prices per category.
• Pie charts displaying the order statuses.

Dataset - Kaggle https://www.kaggle.com/datasets/kyanyoga/sample-sales-data