PranayN23/NFLResourceAnalysis
Oct 2024 – Feb 2026
4 Months active
Languages Used
CSVPythonJupyter NotebookCSSJavaScriptTypeScript
Technical Skills
Data AnalysisData CleaningData ProcessingMachine LearningPandasPython
tandon35
PROFILE
Tandon35
Developed and maintained the NFLResourceAnalysis repository, delivering end-to-end data pipelines and predictive analytics for NFL player and team performance. Built robust backend and API features using Python, TypeScript, and React, including weighted average grade endpoints, free agency data pages, and roster visualizations sorted by PFF scores. Engineered machine learning workflows with TensorFlow and Keras, implementing RNN and LSTM models for player performance prediction based on historical data. Enhanced data quality through preprocessing, aggregation, and cleanup, while improving reproducibility and maintainability. Integrated data visualization and deep learning techniques to support data-driven insights for coaching, scouting, and strategic decision-making.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
19Total
Bugs
1
Commits
19
Features
10
Lines of code
2,526,532
Activity Months4
Your Network
128 peopleShared Repositories
10
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Focused on establishing a data-to-insight pipeline for NFL player performance using LSTM within the NFLResourceAnalysis repository. Delivered groundwork for data preprocessing, statistics aggregation, and a predictive-modeling setup based on historical data.
1 Commits • 1 Features
Feb 1, 2026February 2026: Focused on establishing a data-to-insight pipeline for NFL player performance using LSTM within the NFLResourceAnalysis repository. Delivered groundwork for data preprocessing, statistics aggregation, and a predictive-modeling setup based on historical data.
February 2026
April 2025
5 Commits • 3 Features
Apr 1, 2025April 2025 (PranayN23/NFLResourceAnalysis) delivered three core features and associated data-model improvements to drive deeper analytics, enhanced user navigation, and faster access to key datasets. The work strengthened business value by enabling weighted analytics, surfacing player performance insights by team and season, and improving access to free-agent information.
April 2025
5 Commits • 3 Features
Apr 1, 2025April 2025 (PranayN23/NFLResourceAnalysis) delivered three core features and associated data-model improvements to drive deeper analytics, enhanced user navigation, and faster access to key datasets. The work strengthened business value by enabling weighted analytics, surfacing player performance insights by team and season, and improving access to free-agent information.
November 2024
12 Commits • 5 Features
Nov 1, 2024November 2024 performance summary for PranayN23/NFLResourceAnalysis: Delivered comprehensive predictive analytics capabilities across player and team modeling, with robust data pipelines, enhanced visualization, and important data quality improvements. Achievements enabled more accurate forecasts of player PFF scores and team statistics, supporting data-driven coaching, scouting, and strategic decision making.
12 Commits • 5 Features
Nov 1, 2024November 2024 performance summary for PranayN23/NFLResourceAnalysis: Delivered comprehensive predictive analytics capabilities across player and team modeling, with robust data pipelines, enhanced visualization, and important data quality improvements. Achievements enabled more accurate forecasts of player PFF scores and team statistics, supporting data-driven coaching, scouting, and strategic decision making.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary focused on delivering business value through data engineering for ML-ready player performance insights. Completed an end-to-end NFL defensive data processing pipeline that cleans and merges defense data from multiple CSV sources, applies weighted averaging to create robust defensive features, and generates an ML-ready dataset for predicting player performance based on historical data and value metrics. The work improves data quality, reproducibility, and speed of ML model iteration across the NFLResourceAnalysis workflow.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary focused on delivering business value through data engineering for ML-ready player performance insights. Completed an end-to-end NFL defensive data processing pipeline that cleans and merges defense data from multiple CSV sources, applies weighted averaging to create robust defensive features, and generates an ML-ready dataset for predicting player performance based on historical data and value metrics. The work improves data quality, reproducibility, and speed of ML model iteration across the NFLResourceAnalysis workflow.
Activity
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Quality Metrics
Correctness81.6%
Maintainability81.0%
Architecture80.0%
Performance73.2%
AI Usage26.4%
Skills & Technologies
Programming Languages
CSSCSVJavaScriptJupyter NotebookPythonTypeScript
Technical Skills
API DevelopmentBackend DevelopmentData AggregationData AnalysisData CleaningData CleanupData ModelingData PreprocessingData ProcessingData VisualizationDatabase InteractionDeep LearningFeature EngineeringFile ManagementFlask
Repositories Contributed To
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