• Introduction
• Installation
• Project Description
• Requirements
• Screenshots
• Contributing
• License

Welcome to the Data Visualization project built with Django. This project aims to provide insights into the data_sales.xlsx dataset using various visualizations implemented in a Django dashboard.

To set up the project, follow these steps:

1. Clone the repository.

2. Navigate to the project directory:

   cd adidas-sales-visualization

3. Create virtual environment:

   python -m venv venv

4. Activate the environment: Windows:

   .\venv\Scripts\activate

   macOS/linux

   source venv/bin/activate

5. Install the required dependencies using the following command:

   pip install -r requirements.txt

6. Run the Django development server:

   python manage.py runserver

• First Step: Open the dataset/data_sales.xlsx dataset to gain insights into its structure and variables.
• Preparing Your Data: Describe any data preprocessing steps undertaken for better analysis.

• Retailer Analysis: Utilized bar charts to compare total sales and operating profit by retailer.
• Trends: Implemented line graphs to visualize trends over time for variables like Total Sales and Units Sold.
• Geographical Insights: Employed maps and choropleth maps to visualize sales by region or state.
• Product Analysis: Utilized pie charts and bar charts to display the distribution of sales among different products.
• Price Analysis: Created scatter plots to understand the relationship between Price per Unit and Units Sold or Total Sales.
• Sales Method Analysis: Visualized the distribution of sales by different sales methods using pie charts or bar charts.
• Additional Insights: Utilized various appropriate charts for additional variables.

• Consistency: Maintained a consistent color scheme and style across all visualizations.
• Readability: Ensured charts are easily readable with clear labels, legends, and titles.
• Highlight Key Insights: Used annotations, highlighting, or visual cues to emphasize important findings.

• Analysis: Provided brief analyses for each visualization, identifying trends, outliers, or interesting patterns.
• Insights: Summarized key insights and considered implications on Adidas's sales strategy and operations.

• Django==5.0.2
• numpy==1.26.4
• pandas==2.2.1
• python-dotenv==1.0.1
• sqlparse==0.4.4

If you'd like to contribute to this project, please follow our Contributing Guidelines.

This project is licensed under the MIT License - see the LICENSE.md file for details.