End-to-end analysis of car sales using Python, SQL, and Power BI to uncover sales trends, customer insights, and business opportunities.
Showcasing strong data cleaning, visualization, and analytical storytelling skills with real-world business impact.
- Overview
- Business Problem
- Dataset
- Tools & Technologies
- Project Structure
- Data Cleaning & Preparation
- Exploratory Data Analysis (EDA)
- Research Questions & Key Findings
- Dashboard
- How to Run This Project
- Final Recommendations
- Author & Contact
This project analyzes car sales performance using:
- Python for preprocessing and automation
- SQL for querying, KPI generation, and business logic
- Power BI for interactive dashboards
The goal is to uncover insights into sales performance, customer demand, and revenue drivers, enabling data-driven decision-making in the automotive sector.
Car dealerships often record sales transactions but lack the ability to interpret them effectively.
This project transforms raw sales data into actionable insights by identifying:
- Best-performing car models
- Seasonal sales patterns
- Dealer performance
- Revenue optimization opportunities
- Source: Car sales transaction records (orders, models, dealers, customers, revenue)
- Size: ~XX,XXX rows (depending on dataset version)
- Columns include: SaleID, Date, Dealer, CarModel, Category, Quantity, Price, CustomerID, Region
- Python → Data cleaning, preprocessing, and transformation
- SQL → KPI calculations, queries, and analysis
- Power BI → Interactive dashboards and visualization
- Excel/CSV → Raw data source
│ ├── Car_Sales_Queries.sql # SQL queries for KPIs & insights ├── car_sales_analysis.pbix # Power BI dashboard file ├── python_scripts/ # Python preprocessing scripts ├── data/ # Raw & cleaned datasets ├── images/ # Exported dashboard screenshots └── README.md # Project documentation
- Removed duplicates & missing values
- Standardized car model names and categories
- Added calculated fields: Total Sale Value, Profit Margin, Region-wise Sales
- Ensured consistency in date formats for time-series analysis
- Time Trends: Monthly & yearly sales performance
- Dealer Performance: Revenue contribution by dealership
- Car Models: Best & worst-selling cars
- Regional Analysis: Sales trends across different regions
Which car models generate the most revenue?
→ SUVs and Premium models contributed the highest revenue.
Which dealers perform best?
→ Dealer X and Dealer Y accounted for 40% of total sales.
When are sales highest?
→ Seasonal peaks during festive seasons and year-end promotions.
What is the average sales value per transaction?
→ Approximately ₹XX,XXX per transaction.
⭐ KPI Overview
📅 Sales Trends
🚘 Car Model Performance
🌍 Regional Insights
- Clone/download this repository
- Run Car_Sales_Cleaning.sql and Car_Sales_KPIs.sql in your SQL environment
- Open car_sales_analysis.pbix in Power BI Desktop
- Explore interactive dashboards for insights
📈 Dealer Strategy: Support underperforming dealerships with better inventory and marketing
🚘 Product Mix: Focus on high-demand SUVs and premium categories
📊 Seasonal Promotions: Align campaigns with festive and year-end spikes
💡 Future Work: Add customer demographics for targeted sales and marketing strategies
T V Shreyas Kumar
💼 LinkedIn: [www.linkedin.com/in/shreyas-kumar] ✉️ Email: Shreyas291103@gmail.com