ReshmaPatilPawar/AY-24-25DSBDA
May 2025 – Feb 2026
3 Months active
Languages Used
HTMLJavaScriptPythonMarkdown
Technical Skills
Computer VisionFlaskMachine LearningTensorFlowWeb DevelopmentJupyter
Aditya Konda
PROFILE
Aditya Konda
Over a three-month period, contributed to the ReshmaPatilPawar/AY-24-25DSBDA repository by developing a web application for age and gender prediction using a Flask backend and a pre-trained TensorFlow model, enabling users to upload images and receive real-time predictions through a simple HTML interface. Enhanced project reproducibility and onboarding by implementing a Jupyter notebook that demonstrates K-Means clustering on the Iris dataset with a from-scratch algorithm and explanatory notes. Focused on clear documentation, updating the README to improve contributor guidance and usage clarity. Work emphasized practical machine learning, computer vision, and data analysis using Python, Jupyter, and web technologies.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
2,120
Activity Months3
Your Network
57 peopleWork History
February 2026
2 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for ReshmaPatilPawar/AY-24-25DSBDA. Delivered practical data science artifacts and improved project documentation to support onboarding and reproducibility. Key features delivered include a Jupyter Notebook implementing K-Means clustering on the Iris dataset with a from-scratch algorithm and explanatory notes, and an updated README reflecting the latest project scope and usage instructions. There were no major bugs fixed this month; focus was on feature delivery and documentation improvements. Overall impact: enhanced reproducibility, faster contributor onboarding, and clearer project guidance for users. Technologies/skills demonstrated include Python, Jupyter notebooks, basic clustering algorithm implementation from scratch, data science pedagogy, documentation best practices, and Git/version control.
2 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for ReshmaPatilPawar/AY-24-25DSBDA. Delivered practical data science artifacts and improved project documentation to support onboarding and reproducibility. Key features delivered include a Jupyter Notebook implementing K-Means clustering on the Iris dataset with a from-scratch algorithm and explanatory notes, and an updated README reflecting the latest project scope and usage instructions. There were no major bugs fixed this month; focus was on feature delivery and documentation improvements. Overall impact: enhanced reproducibility, faster contributor onboarding, and clearer project guidance for users. Technologies/skills demonstrated include Python, Jupyter notebooks, basic clustering algorithm implementation from scratch, data science pedagogy, documentation best practices, and Git/version control.
February 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025Month 2025-10 focus: improve documentation hygiene for ReshmaPatilPawar/AY-24-25DSBDA. Delivered a documentation-only README update, enhancing onboarding and contributor guidance; no code changes were made. This foundational work supports smoother knowledge transfer and sets up for upcoming feature work.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month 2025-10 focus: improve documentation hygiene for ReshmaPatilPawar/AY-24-25DSBDA. Delivered a documentation-only README update, enhancing onboarding and contributor guidance; no code changes were made. This foundational work supports smoother knowledge transfer and sets up for upcoming feature work.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary for ReshmaPatilPawar/AY-24-25DSBDA: Delivered a new Age and Gender Prediction Web App with a Flask backend and a pre-trained TensorFlow model. The app supports image uploads and displays predictions via a simple HTML interface; end-to-end flow from upload to result is in place. No major bugs reported this month; focused on feature delivery and establishing a baseline for future model enhancements. Impact: enables rapid prototyping of ML-powered features and adds a user-facing analytics capability for demos and experimentation. Technologies demonstrated: Python, Flask, TensorFlow, HTML/CSS, and ML model integration.
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary for ReshmaPatilPawar/AY-24-25DSBDA: Delivered a new Age and Gender Prediction Web App with a Flask backend and a pre-trained TensorFlow model. The app supports image uploads and displays predictions via a simple HTML interface; end-to-end flow from upload to result is in place. No major bugs reported this month; focused on feature delivery and establishing a baseline for future model enhancements. Impact: enables rapid prototyping of ML-powered features and adds a user-facing analytics capability for demos and experimentation. Technologies demonstrated: Python, Flask, TensorFlow, HTML/CSS, and ML model integration.
May 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability76.0%
Architecture80.0%
Performance72.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
HTMLJavaScriptMarkdownPython
Technical Skills
Computer VisionFlaskJupyterK-Means clusteringMachine LearningTensorFlowWeb Developmentdata analysisdocumentationmachine learning
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