Amazon Sales Data Analytics Project

Project Overview

This project demonstrates an end-to-end Data Analytics workflow using Python, Oracle SQL, and Power BI. The objective is to transform raw Amazon sales data into meaningful business insights through data cleaning, SQL analysis, and interactive dashboard visualization.

Project Objectives

• Clean and preprocess raw Amazon sales data.
• Handle missing values and duplicate records.
• Perform exploratory data analysis (EDA).
• Analyze sales trends using Oracle SQL.
• Build an interactive Power BI dashboard.
• Generate actionable business insights for decision-making.

Technologies Used

• Python
• Pandas
• Matplotlib
• Oracle SQL
• Power BI
• CSV

Project Structure

Amazon-Sales-Data-Analytics │ ├── Data │ └── Amazon_Sale_Report_Cleaned.csv │ ├── Python │ └── amazon_sales_data_cleaning.py │ ├── SQL │ └── amazon_sales_analysis.sql │ ├── PowerBI │ └── Amazon_Sales_Dashboard.pbix │ ├── Images │ └── dashboard.png │ ├── README.md ├── requirements.txt ├── .gitignore └── LICENSE

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Data Cleaning

The dataset was cleaned using Python by performing:

• Handling missing values
• Removing duplicate records
• Data type conversion
• Handling invalid values
• Exporting cleaned dataset
• Exploratory Data Analysis (EDA)

SQL Analysis

Oracle SQL was used to perform business analysis such as:

• Monthly Revenue Analysis
• Top Performing Categories
• State-wise Revenue
• City-wise Revenue
• Order Status Analysis
• Quantity Sold Analysis
• Sales Performance Reports

Power BI Dashboard

The dashboard includes:

• Total Revenue KPI
• Total Orders KPI
• Total Quantity KPI
• Average Order Value
• Cancelled Orders
• Cancel Rate
• Monthly Revenue Trend
• Revenue by Category
• Sales by Category
• Top States by Revenue
• Order Status Distribution

Key Business Insights

• Maharashtra generated the highest revenue.
• Set category contributed the largest share of sales.
• Most orders were successfully shipped.
• Revenue peaked in April.
• Cancelled orders accounted for approximately 15% of total orders.

Dashboard Preview

Add your dashboard screenshot here after uploading.

Images/dashboard.png

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How to Run

1. Clone this repository.
2. Install required Python libraries.

pip install -r requirements.txt

3. Run the Python script.

python amazon_sales_data_cleaning.py

4. Execute SQL queries in Oracle SQL Developer.

5. Open the Power BI dashboard (.pbix) using Microsoft Power BI Desktop.

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Author

Kunal Shedge

Aspiring Data Analyst

Skills:

• Python
• Oracle SQL
• Power BI
• Data Analytics
• Data Visualization

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