ReshmaPatilPawar/AY-24-25DSBDA
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data ScienceMachine LearningPandasPythonScikit-learnStreamlit
viraj2700
PROFILE
Viraj2700
Developed a heart disease prediction model for the ReshmaPatilPawar/AY-24-25DSBDA repository, delivering an end-to-end machine learning pipeline using Python, scikit-learn, and Pandas. The project included a Jupyter Notebook for model training and evaluation, leveraging logistic regression to assess health risk based on provided datasets. Integrated a Streamlit user interface to enable real-time predictions, making the tool accessible for interactive use. All artifacts, including the dataset and training notebook, were organized and committed to ensure reproducibility. The work established a deployable, reproducible workflow, providing stakeholders with a practical health risk assessment tool and demonstrating core data science and ML lifecycle concepts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
2,310
Activity Months1
Your Network
57 peopleWork History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ReshmaPatilPawar/AY-24-25DSBDA focusing on feature delivery and impact. Key feature delivered: Heart Disease Prediction Model using logistic regression with a training notebook, dataset, and a Streamlit UI for predictions. Model training, evaluation, and accuracy metrics completed. Repository activity includes two commits. No major bugs fixed this month. Business value: provides a ready-to-deploy health risk prediction tool with reproducible artifacts and user-friendly interface. Technologies demonstrated: Python, scikit-learn, Pandas, Jupyter, Streamlit, Git, ML lifecycle concepts.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ReshmaPatilPawar/AY-24-25DSBDA focusing on feature delivery and impact. Key feature delivered: Heart Disease Prediction Model using logistic regression with a training notebook, dataset, and a Streamlit UI for predictions. Model training, evaluation, and accuracy metrics completed. Repository activity includes two commits. No major bugs fixed this month. Business value: provides a ready-to-deploy health risk prediction tool with reproducible artifacts and user-friendly interface. Technologies demonstrated: Python, scikit-learn, Pandas, Jupyter, Streamlit, Git, ML lifecycle concepts.
May 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data ScienceMachine LearningPandasPythonScikit-learnStreamlit
Repositories Contributed To
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