This project is a Dash-based interactive dashboard that visualizes historical automobile sales data. The dashboard allows users to analyze sales trends, GDP, unemployment rates, advertising expenditure, and seasonal effects on different vehicle types during recession and non-recession periods.
The project leverages Pandas, Matplotlib, Seaborn, and Plotly for data analysis and visualization.
- Line Plots: Shows how automobile sales fluctuate year-to-year.
- Vehicle Type Trends: Multiple line plots comparing sales trends across vehicle categories.
- Bar Charts: Compare sales trends during recession and non-recession periods using Seaborn.
- GDP Subplots: Line plots comparing GDP variations during recession vs non-recession periods.
- Bubble Plots: Illustrate the impact of seasonality on automobile sales.
- Scatter Plots: Identify correlations between average vehicle price and sales volume during recessions.
- Pie Charts: Display advertising expenditure distribution by period and by vehicle type.
- Unemployment Analysis: Line plots showing the effect of unemployment rates on vehicle sales.
- Dropdowns to select vehicle type and year.
- Dynamic updates based on user selections.
- Organized layout with output container displaying selected statistics.
The project uses the Historical Automobile Sales dataset from IBM Developer Skills Network:
Columns include:
- Date, Year, Month, Recession, Consumer_Confidence, Seasonality_Weight
- Price, Advertising_Expenditure, Competition, GDP, Growth_Rate, unemployment_rate
- Automobile_Sales, Vehicle_Type, City
- Clone the repository:
git clone <repository-url>
cd <project-folder>
2. Create a virtual environment:
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
3. Install dependencies:
pip install -r requirements.txt
Running the Dashboard Locally
python app.py