This project focuses on analyzing the Sample Superstore dataset using Python and Power BI. The objective is to perform data cleaning, exploratory data analysis (EDA), and create an interactive dashboard to uncover meaningful business insights.
The project was completed as part of the Week 1 Data Analytics Internship Task.
- Python
- Pandas
- Power BI
- VS Code
- Git & GitHub
Dataset: Sample Superstore Dataset
Total Records: 9,994
Features Included:
- Category
- Sub-Category
- Sales
- Profit
- Quantity
- State
- Region
- Segment
- Discount
- Ship Mode
The following data quality checks were performed:
- Checked for missing values
- Checked duplicate records
- Validated data types
- Verified dataset structure
- Missing Values Found: 0
- Duplicate Records Found: 17
The analysis included:
- Mean
- Median
- Standard Deviation
- Minimum & Maximum Values
- Sales Distribution
- Profit Distribution
- Quantity Analysis
- Sales vs Profit
- Category-wise Performance
- Region-wise Profit Analysis
- Total Sales: $2.30M
- Total Profit: $286.40K
- Total Quantity: 38K
- Sales by Category (Bar Chart)
- Category Distribution (Pie Chart)
- Profit by Region (Bar Chart)
- Sales by State (Bar Chart)
- Geographic Sales Map
- Sales vs Profit Scatter Plot
- Category Filter
- Region Filter
- Technology category generated the highest sales ($836K).
- West region generated the highest profit ($108K).
- California contributed the highest sales among all states.
- The dataset contains no missing values.
- A total of 17 duplicate records were identified.
- Average sales per transaction are approximately $230.
- Higher discounts often reduce profitability.
SampleSuperstore.csv
eda.py
Superstore_Sales_Dashboard.pbix
Dashboard_Screenshot.png
README.md
Successfully performed data cleaning, exploratory data analysis, and dashboard development to transform raw sales data into actionable business insights.
Devansh Gautam
Data Analytics Internship – Week 1 Project
