This project is a sales performance dashboard built using Streamlit. It allows users to visualize and analyze sales data stored in a PostgreSQL database. The dashboard provides key metrics, visualizations, and raw data to help understand sales performance over time.

• Interactive Dashboard: User-friendly interface to explore sales data.
• Key Metrics: Displays total revenue, total orders, average order value, and top category.
• Dynamic Visualizations:
  • Revenue Over Time: Line chart showing revenue trends.
  • Revenue by Category: Bar chart displaying revenue distribution across categories.
  • Top Products: Pie chart highlighting top-selling products.
• Data Filtering: Filter data by date range, category, and product.
• Raw Data View: Table view to explore the raw sales data.
• CSV Export: Export filtered data to CSV for further analysis.

1. Clone the repository:

   git clone https://github.com/realshubhamraut/Sales-Performance-Analytics-Dashboard.git
   cd Sales-Performance-Analytics-Dashboard 

2. Install the required packages:

   pip install -r requirements.txt

3. Set up PostgreSQL database:

   • Ensure PostgreSQL is installed and running on your machine.
   • Create a database named sales_db.
   • Update the DB_CONFIG dictionary in generate_data.py and app.py with your PostgreSQL credentials if they differ.

4. Generate and insert data:

   python generate_data.py

1. Run the Streamlit app:

   streamlit run app.py

2. Open your browser and navigate to http://localhost:8501 to view the dashboard.

• generate_data.py: Script to generate and insert sales data into the PostgreSQL database.
• app.py: Streamlit application to visualize and analyze sales data.
• requirements.txt: List of required Python packages.
• generated_data.csv: CSV file containing the generated sales data.
• README.md: Project documentation.

• Streamlit
• Pandas
• Matplotlib
• Psycopg2-binary
• Polars
• Numpy

• Streamlit
• Pandas
• Matplotlib
• Psycopg2
• Polars
• Numpy