Data Analysis + Power BI Dashboard with 10,000-row Sales dataset (Python EDA + Insights + Visualizations)
📊 Sales-Performance-EDA-Project Data Analysis + Power BI Dashboard with 10,000-row Sales dataset (Python EDA + Insights + Visualizations)
📝 Project Overview This project performs end-to-end Exploratory Data Analysis (EDA) on a 10,000-row sales dataset using Python and builds a fully interactive Power BI Sales Dashboard.
It covers: Data Cleaning Exploratory Data Analysis Sales & Profit Trends Customer Insights Category & Segment Analysis Discount Impact Dynamic Dashboard with slicers
📂 Project Files File Name Description sales_performance_10000.csv Main dataset with 10,000 sales rows EDA_Notebook.ipynb Python EDA Notebook (Pandas, Matplotlib, Seaborn) Sales_Performance_Dashboard.pbix Power BI Dashboard file Sales_Performance_Dashboard.pdf Exported PDF version Sales Dashboard.png Page 1 dashboard screenshot Sales Insights and Analysis.png Page 2 dashboard screenshot 🧹 1. Data Cleaning (Python)
Performed in EDA_Notebook.ipynb: ✔ Handled missing values ✔ Converted date column to datetime ✔ Extracted Year, Month for analysis ✔ Removed duplicates ✔ Checked data distributions ✔ Created summary statistics
📈 2. Exploratory Data Analysis (EDA)
Key EDA performed:
🔹 Sales Trends
Monthly Sales trend
Year-wise comparisons
Seasonality patterns
🔹 Category-Level Analysis
Top performing categories
Sub-category contribution
Profit margin analysis
🔹 Regional Performance
Region-wise sales
High & low revenue regions
🔹 Customer Insights
Top 10 customers by revenue
Customer segment contribution
🔹 Discount Analysis
Discount vs Sales relationship
Discount vs Profit impact
📊 3. Power BI Dashboard Page 1 — Sales Dashboard
KPIs (Total Sales, Profit, Orders, Discount)
Monthly Sales Trend
Sales by Region
Sales by Product Category
Profit by Category
Sales by Sub-Category
Interactive slicers (Region, Segment, Category)
📷 Dashboard Preview: Page 2 — Insights & Analysis
Top 15 Customers
Sales by Customer Segment
Discount vs Sales (Scatter)
Discount vs Profit (Scatter)
Sales vs Profit (Correlation)
Total Profit (Summary)
📷 Insights Page: 🔍 Key Insights
✔ Electronics is the highest-selling category. ✔ Consumer Segment contributes the most sales. ✔ South & East regions show strong performance. ✔ Discounts increase sales but reduce profit margins. ✔ Profit peaks in March, July, November. ✔ Top customers bring a significant portion of total revenue.
🧰 Tools & Technologies Used Tool Purpose Python (Pandas, Matplotlib, Seaborn) Data Cleaning + EDA Jupyter Notebook Data Analysis Power BI Dashboard + Insights GitHub Version control & hosting
🚀 How to Run This Project
- Run EDA Notebook Install dependencies: pip install pandas numpy matplotlib seaborn
Run in Jupyter: jupyter notebook Open: EDA_Notebook.ipynb
- Open Power BI Dashboard Use Power BI Desktop → Open file: Sales_Performance_Dashboard.pbix