DataScience-ArtificialIntelligence/OOPsJava
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSVJavaPlain Text
Technical Skills
AlgorithmsCode DeletionCommand Line InterfaceData AnalysisData ProcessingData Structures
hariprasadkishan
PROFILE
Hariprasadkishan
Developed two core features for the DataScience-ArtificialIntelligence/OOPsJava repository, focusing on data analytics and codebase maintainability. Delivered a GDP sorting and data processing utility that loads, sorts, and saves GDP data using a Dual Pivot Quick Sort algorithm, leveraging Java and CSV handling for efficient data pipelines. Built the BookPublisherChoice program to filter book datasets by publication year and publisher, supporting publishing analytics. Enhanced onboarding and reproducibility by adding comprehensive documentation and sample datasets. Undertook targeted repository cleanup, removing deprecated components and outdated directories to streamline maintenance. No major bugs were reported, reflecting careful implementation and attention to code quality.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
2
Lines of code
3,307
Activity Months1
Your Network
11 peopleWork History
November 2024
8 Commits • 2 Features
Nov 1, 2024In 2024-11, delivered two core features in DataScience-ArtificialIntelligence/OOPsJava that advance data analytics capabilities, performed targeted cleanup to reduce technical debt, and enhanced documentation to support onboarding and reproducibility. Key outcomes include: GDP Sorting and data processing utility using Dual Pivot Quick Sort with a GDP data pipeline (load, sort by GDP, save CSV) with sample datasets and README; BookPublisherChoice program to filter BX-Books.csv by publication year and publisher with added docs; cleanup and removal of deprecated components (Duel Pivot/Percolation and Book Publisher directories) to streamline maintenance. Major bugs fixed: none reported; minor issues resolved during maintenance. Overall impact: improved data processing readiness for GDP analytics and publishing analytics, with a cleaner, more maintainable codebase. Technologies/skills demonstrated: Java, advanced sorting algorithms (Dual Pivot QuickSort), CSV handling, data pipelines, documentation, and codebase hygiene.
8 Commits • 2 Features
Nov 1, 2024In 2024-11, delivered two core features in DataScience-ArtificialIntelligence/OOPsJava that advance data analytics capabilities, performed targeted cleanup to reduce technical debt, and enhanced documentation to support onboarding and reproducibility. Key outcomes include: GDP Sorting and data processing utility using Dual Pivot Quick Sort with a GDP data pipeline (load, sort by GDP, save CSV) with sample datasets and README; BookPublisherChoice program to filter BX-Books.csv by publication year and publisher with added docs; cleanup and removal of deprecated components (Duel Pivot/Percolation and Book Publisher directories) to streamline maintenance. Major bugs fixed: none reported; minor issues resolved during maintenance. Overall impact: improved data processing readiness for GDP analytics and publishing analytics, with a cleaner, more maintainable codebase. Technologies/skills demonstrated: Java, advanced sorting algorithms (Dual Pivot QuickSort), CSV handling, data pipelines, documentation, and codebase hygiene.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability87.6%
Architecture82.6%
Performance82.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJavaPlain Text
Technical Skills
AlgorithmsCode DeletionCommand Line InterfaceData AnalysisData ProcessingData StructuresDocumentationFile DeletionFile I/OFile ManagementObject-Oriented ProgrammingRepository Cleanup
Repositories Contributed To
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