This repository contains a comprehensive Exploratory Data Analysis (EDA) and a Power BI dashboard for BMW car sales data. The analysis provides insights into sales trends, pricing, mileage, engine sizes, and regional distribution of BMW models.
| File | Description |
|---|---|
BMW_Car_Sales_Classification.csv |
Raw dataset containing BMW sales records with details like Model, Year, Region, Engine Size, Mileage, Price, Fuel Type, and Transmission. |
Car_Sales_EDA.ipynb |
Jupyter Notebook performing detailed EDA with visualizations using Matplotlib and Seaborn. |
BMW_Car_Sales_Classification.pbix |
Power BI dashboard for interactive visualizations and insights. |
- Python 3.x
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Power BI
The EDA covers:
- Yearly Sales Trends – Line chart showing total sales volume over years.
- Top 10 Models by Sales – Horizontal bar chart highlighting best-selling BMW models.
- Sales Distribution by Region – Pie chart showing geographic sales distribution.
- Average Price by Model – Bar chart comparing average prices across models.
- Average Mileage by Model – Bar chart showing mileage trends.
- Average Price by Mileage Category – Low, Mid, High mileage categories analyzed for pricing.
- Average Engine Size by Model – Comparison of engine sizes per model.
- Boxplots for Numerical Features – Detecting outliers and distribution patterns.
- Top Seller: BMW 7 Series leads in sales among all models.
- Sales Trends: Steady growth post-2012, peaking around 2022–2024.
- Price Stability: Minimal variation in average price across models.
- Mileage Trends: Higher mileage in larger flagship models; electric/hybrid cars have lower mileage.
- Engine Sizes: Generally consistent across models, showing standardization.
- Regional Sales: Market distribution is balanced across regions.
- Clone this repository:
git clone https://github.com/Hossam445/Car_Sales_EDA.git