E-Commerce Sales Data Project

Phase 1: Data Analysis with Python

In this phase, we use Python to load and analyze a synthetic e-commerce sales dataset. The dataset contains 20,000 transactions with columns including OrderID, CustomerID, ProductID, Quantity, Price, OrderDate, Region, and TotalAmount.

Key steps in Python:

·       - Load the data using pandas.

·       - Clean and preprocess the data.

·       - Perform basic analysis like total sales, average order value, top-selling products, and regional performance.

Solution

1,  firstly import the right python package, then import the dataset and clean it.

Basic check for data validation,

2, Performing basic analysis like total sales, average order value, top-selling products, and regional performance.

Phase 2: Data Querying with SQL

In this phase, the cleaned dataset is loaded into a relational database. SQL is used to perform queries such as:

·       - Total sales per region.

·       - Monthly sales trends.

·       - Top 10 customers by sales.

·       - Most frequently ordered products.

Solution

Firstly we select the right database to use, then do basic analyses

·       Total sales per region.

·       Top 10 customers by sales.

·       Monthly sales trends.

··      Most frequently ordered products.

Phase 3: Data Visualization

In this phase, tools like Tableau, Power BI, or matplotlib/seaborn are used to create visualizations such as:

·       - Sales trends over time.

·       - Regional sales comparison.

·       - Heatmaps of product popularity.

·       - Customer segmentation by purchase behavior.

Solution

·       - Sales trends over time.

·       - Regional sales comparison.

·       product popularity.

Customer segmentation by purchase behavior.

Dataset Information

The dataset used for this project is stored as a CSV file: /mnt/data/ecommerce_sales_data.csv