Elevate-Labs-Internship-Task-2

Superstore Sales Analysis

Overview

This project analyzes the Superstore dataset to uncover key business insights using data cleaning, preprocessing, and interactive visualizations. The workflow includes data cleaning in Python, exploratory data analysis, and dashboard creation in Power BI.

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Table of Contents

• Overview
• Data Cleaning
• Visualization & Insights
• Key Findings
• How to Use
• Repository Structure
• Credits

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Data Cleaning

Performed in Python (see data_cleaning.py):

• Checked for and handled missing values (none found).
• Removed duplicate rows.
• Renamed columns to lowercase with underscores.
• Converted date columns (order_date, ship_date) to datetime format.
• Ensured correct data types for numeric columns (sales, profit, quantity, discount).
• Saved the cleaned data as cleaned_superstore.csv.

Summary Table:

Step	Action Taken
Missing Values	None found
Duplicates	Removed
Text Standardization	All object columns standardized
Column Renaming	Lowercase, underscores
Date Conversion	order_date, ship_date to datetime
Numeric Types	Ensured for sales, profit, etc.

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Visualization & Insights

Created in Power BI (Superstore_Dashboard.pbix):

• Sales Trends Over Time: Line chart (by month & category)
• Profit by Segment: Pie chart
• Top Customers by Sales: Horizontal bar chart
• Sales by Category & Segment: Stacked bar chart
• Sales vs. Profit: Scatter plot
• Discount Impact on Profit: Waterfall chart (steps: sales, discount, profit)
• Regional Performance: Interactive slicer/filter

Dashboard Screenshot:

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Key Findings

• Sales are rising, led by the Technology category.
• Profitability is highest in Technology; Furniture lags behind.
• Top 10 customers drive a significant portion of sales.
• Discounts reduce profit margins, especially in Furniture.
• West region outperforms others in both sales and profit.

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How to Use

1. Clone this repository

   git clone https://github.com/yourusername/superstore-analysis.git

2. Data Cleaning
   • Run data_cleaning.py to clean the raw Superstore data.
3. Visualization
   • Open Superstore_Dashboard.pbix in Power BI Desktop to explore the interactive dashboard.

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Repository Structure

├── Superstore Analysis Dashboard.pdf
├── Superstore Sales & Profit Analysis.pptx
├── TASK 2 DATA ANALYST.pdf
├── cleaned_superstore.csv
├── data_cleaning.py
├── README.md

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Credits

• Dataset: Kaggle Superstore CSV
• Data Cleaning & Analysis: Suraj Rajeshkumar Rajvanshi
• Dashboard: Suraj Rajeshkumar Rajvanshi