PranayN23/NFLResourceAnalysis
Oct 2024 – Nov 2024
2 Months active
Languages Used
CSVPythonJSON
Technical Skills
CSV ManipulationData AnalysisData EngineeringData ProcessingPandasData Science
siddarthnachannagari
PROFILE
Siddarthnachannagari
During two months on the PranayN23/NFLResourceAnalysis repository, M. Nachan developed a robust data engineering and machine learning pipeline for NFL running back performance analysis. He built Python scripts and Pandas-based workflows to process and aggregate multi-year player and team data from CSV sources, establishing a reproducible foundation for analytics. Leveraging TensorFlow, Keras, and Scikit-learn, he implemented RNN and MLP models to predict player outcomes and visualize team-level correlations, producing R²-based performance metrics and comparative barplots. His work enabled transparent, data-driven player valuation and strategic insights, with reproducible Jupyter notebooks and exportable visualizations supporting reliable, auditable decision-making processes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
3
Lines of code
16,748
Activity Months2
Your Network
108 people
Work History
November 2024
13 Commits • 2 Features
Nov 1, 2024November 2024 — NFLResourceAnalysis: Delivered end-to-end, ML-powered prediction and visualization capabilities for NFL running backs and team-level correlations. The work supports data-driven decision-making for player valuation and strategic insights for teams. Key deliverables include R^2-based performance metrics, RNN/MLP visualizations, and robust graphs with reproducible notebooks. Resolved critical RNN graph KeyError and produced barplots comparing predicted vs actual outcomes (PFF, Net EPA). The initiatives strengthened data reliability, analytics speed, and stakeholder confidence in model-driven recommendations.
13 Commits • 2 Features
Nov 1, 2024November 2024 — NFLResourceAnalysis: Delivered end-to-end, ML-powered prediction and visualization capabilities for NFL running backs and team-level correlations. The work supports data-driven decision-making for player valuation and strategic insights for teams. Key deliverables include R^2-based performance metrics, RNN/MLP visualizations, and robust graphs with reproducible notebooks. Resolved critical RNN graph KeyError and produced barplots comparing predicted vs actual outcomes (PFF, Net EPA). The initiatives strengthened data reliability, analytics speed, and stakeholder confidence in model-driven recommendations.
November 2024
October 2024
4 Commits • 1 Features
Oct 1, 2024October 2024: Delivered foundational data engineering work for NFL running back performance analysis in PranayN23/NFLResourceAnalysis. Implemented data processing tooling and datasets for 2018–2022, enabling consistent, auditable analytics and trend assessment. Key deliverables include rb_combine.py for weighted averages and YoY changes, rb_combined_df.py to merge raw CSVs into a single DataFrame, and additional CSVs aggregating data by player and by team for the same period. Four commits completed to create and aggregate the dataset.
October 2024
4 Commits • 1 Features
Oct 1, 2024October 2024: Delivered foundational data engineering work for NFL running back performance analysis in PranayN23/NFLResourceAnalysis. Implemented data processing tooling and datasets for 2018–2022, enabling consistent, auditable analytics and trend assessment. Key deliverables include rb_combine.py for weighted averages and YoY changes, rb_combined_df.py to merge raw CSVs into a single DataFrame, and additional CSVs aggregating data by player and by team for the same period. Four commits completed to create and aggregate the dataset.
Activity
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Quality Metrics
Correctness87.0%
Maintainability87.0%
Architecture82.4%
Performance84.8%
AI Usage22.2%
Skills & Technologies
Programming Languages
CSVJSONPython
Technical Skills
CSV ManipulationData AnalysisData EngineeringData ProcessingData ScienceData VisualizationDeep LearningKerasMachine LearningMatplotlibModel EvaluationPandasPythonPython ScriptingRNN
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