DataBytes-Organisation/Intelligent-IoT-Data-Management
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Correlation AnalysisData AnalysisJupyter NotebookMatplotlibNumPyPandas
Jainy13
PROFILE
Jainy13
Jainy Joy developed a Sensor Data Correlation Analysis Notebook for the DataBytes-Organisation/Intelligent-IoT-Data-Management repository, enabling analysts to examine relationships between sensor streams in industrial IoT environments. Using Python, Pandas, and Seaborn, Jainy implemented data loading, preprocessing, and time-series visualizations to facilitate exploratory analysis. The notebook features a pairplot for visualizing inter-sensor correlations and a lag-detection mechanism to identify maximum correlation between specific sensors, such as s1 and s2. Comprehensive documentation was provided to guide users through the analysis process. The work delivered a focused, well-documented tool for actionable cross-sensor insights, though no major bug fixes were required.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
348
Activity Months1
Your Network
12 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for DataBytes-Organisation/Intelligent-IoT-Data-Management: Delivered a Sensor Data Correlation Analysis Notebook enabling analysts to explore inter-sensor relationships through data loading, preprocessing, time-series visualizations, a pairplot of sensor correlations, and a lag-detection feature to identify maximum correlation between s1 and s2. Documentation and notebook were added, supporting cross-sensor insights and actionable analytics for IIoT operations.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for DataBytes-Organisation/Intelligent-IoT-Data-Management: Delivered a Sensor Data Correlation Analysis Notebook enabling analysts to explore inter-sensor relationships through data loading, preprocessing, time-series visualizations, a pairplot of sensor correlations, and a lag-detection feature to identify maximum correlation between s1 and s2. Documentation and notebook were added, supporting cross-sensor insights and actionable analytics for IIoT operations.
May 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Correlation AnalysisData AnalysisJupyter NotebookMatplotlibNumPyPandasSeaborn
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