SalesTracknAnalytics is a web application built with Flask and React for tracking and analyzing sales data. It allows users to fetch and analyze sales data from a government website and provides insights and reports based on the data.
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Sales Data Retrieval: The application retrieves sales data from the government website using web scraping techniques. It fetches data for a specific month and year and processes it to create a structured dataframe.
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Data Analysis: The retrieved sales data is analyzed to generate reports and perform further operations. The application provides insights such as daily sales, total sales, sale count, and more.
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Remaining Cards Calculation: The application calculates the remaining cards based on the sales data. It fetches additional data from Excel files and calculates the probability of remaining cards for each source number.
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Card Status Tracking: The application tracks the status of the remaining cards by making requests to a government website. It determines whether a card is ported, taken, or pending.
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Data Visualization: The application presents the analyzed data and card status information in an interactive and user-friendly manner. It includes visualizations such as tables and charts to facilitate data interpretation.
To deploy the SalesTracknAnalytics project, follow these steps:
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Clone the repository:
git clone https://github.com/RoyalMamba/SalesTracknAnalytics.git -
Install the necessary dependencies for the Flask backend and frontend.
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Run the Flask backend:
python app.py -
Access the application through a web browser at
http://localhost:8080.
Note: Make sure to configure the necessary paths in the code according to your system setup.
Contributions to SalesTracknAnalytics are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more information.
The SalesTracknAnalytics project was inspired by the need to track and analyze sales data efficiently. Thanks to the open-source community for providing helpful libraries and frameworks used in this project.