https://github.com/moonmoonsamal/customer_purchase_behavior_analysis

Customer purchase analysis with SQL, Python, and PowerBI
https://github.com/moonmoonsamal/customer_purchase_behavior_analysis

cleaning-data cte eda manipulate-data normalization visualization

Last synced: 19 days ago
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Customer purchase analysis with SQL, Python, and PowerBI

• Host: GitHub
• URL: https://github.com/moonmoonsamal/customer_purchase_behavior_analysis
• Owner: MoonmoonSamal
• Created: 2024-07-10T17:19:06.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-31T09:16:28.000Z (5 months ago)
• Last Synced: 2024-07-31T10:36:53.129Z (5 months ago)
• Topics: cleaning-data, cte, eda, manipulate-data, normalization, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 9.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer_Purchase_Behavior_Analysis

I am Moonmoon Samal, a data science student at Masai School. This dataset was provided by Masai School for building a project on data analysis using SQL, Python, and Power BI.

**About Project**

End-to-end customer purchase behavior analysis and reporting system using SQL, Python, and PowerBI. This project will help you understand customer purchase patterns, key performance indicators, and generate insightful reports for business decision-making.

![Screenshot 2024-07-31 143731](https://github.com/user-attachments/assets/600a8097-0e04-4871-ba87-e458cf59c0eb)

**Problem Statement**

Dataset containing customer purchase information for an online retail company. The dataset includes the following details:

- Transaction ID

- Customer ID

- Customer Name

- Product ID

- Product Name

- Product Category

- Purchase Quantity

- Purchase Price

- Purchase Date

- Country

**The analysis will focus on the following questions:**

- Calculate total purchases, total revenue, and average purchase value.

- Identify top customers and their purchasing behavior.

- Analyze purchase trends over time (monthly, quarterly, yearly).

- Identify the top-performing product categories.

The analysis will be conducted using SQL, Python programming language and library like Pandas, Numpy, Matplotlib, Seaborn. and Power BI. The results of the analysis will be presented in a report and in a series of visualizations.

I am excited to get started and I look forward to sharing the results of my analysis with you.

Note(: Please refrain from directly copying my code)

Thank You.