PranayN23/NFLResourceAnalysis
Nov 2024 – Apr 2025
4 Months active
Languages Used
CSVJupyter NotebookPythonCSSHTMLJavaScriptJSX
Technical Skills
Attention MechanismsData AnalysisData CleaningData PreprocessingData ScienceData Transformation
Pranay Nandkeolyar
PROFILE
Pranay Nandkeolyar
Pranay Nandkeolyar developed an end-to-end NFL player analytics platform in the PranayN23/NFLResourceAnalysis repository, focusing on predictive modeling, data engineering, and full-stack integration. He built an RNN-based pipeline for player performance forecasting, implemented robust data preprocessing and visualization using Python, Pandas, and TensorFlow, and expanded the dataset with historical and financial data. Pranay restructured the backend for maintainability, established a React-based frontend, and introduced secure authentication and dynamic data APIs. His work emphasized code organization, data integrity, and UI/UX refinement, resulting in a scalable, analytics-ready system that supports secure access, reliable insights, and streamlined collaboration across teams.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
27Total
Bugs
1
Commits
27
Features
10
Lines of code
288,612
Activity Months4
Your Network
10 people
Work History
April 2025
11 Commits • 6 Features
Apr 1, 2025April 2025 — NFLResourceAnalysis (PranayN23/NFLResourceAnalysis) delivered end-to-end authentication, enhanced player analytics UI, and a robust data API suite, while improving code organization and visual branding. This work directly supports secure access, faster data discovery, and scalable analytics for NFL resource insights.
11 Commits • 6 Features
Apr 1, 2025April 2025 — NFLResourceAnalysis (PranayN23/NFLResourceAnalysis) delivered end-to-end authentication, enhanced player analytics UI, and a robust data API suite, while improving code organization and visual branding. This work directly supports secure access, faster data discovery, and scalable analytics for NFL resource insights.
April 2025
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for PranayN23/NFLResourceAnalysis: Delivered foundational backend restructuring and frontend scaffolding for NFL Resource Analysis. No major bugs fixed this period. Focused on improving maintainability, establishing a frontend-backend integration path, and laying groundwork for data-driven NFL analytics features.
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for PranayN23/NFLResourceAnalysis: Delivered foundational backend restructuring and frontend scaffolding for NFL Resource Analysis. No major bugs fixed this period. Focused on improving maintainability, establishing a frontend-backend integration path, and laying groundwork for data-driven NFL analytics features.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for PranayN23/NFLResourceAnalysis. Key features delivered: Added two new CSV data files to the NFL resource: 2010-2011 defensive statistics and cap_data.csv containing player names, teams, positions, cap space, and years, enabling historical performance analysis and contract/financial evaluation. Major bugs fixed: Data consistency improvements and cap_data.csv schema normalization to ensure downstream analytics compatibility; resolved minor ingestion alignment issues when merging new data into the analytics pipeline. Overall impact and accomplishments: Significantly increased dataset completeness and analytics readiness, enabling historical trend analysis and better contract evaluation. Technologies/skills demonstrated: data engineering and ingestion, CSV data pipelines, dataset design and schema management, data validation and QA, version control and traceability.
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for PranayN23/NFLResourceAnalysis. Key features delivered: Added two new CSV data files to the NFL resource: 2010-2011 defensive statistics and cap_data.csv containing player names, teams, positions, cap space, and years, enabling historical performance analysis and contract/financial evaluation. Major bugs fixed: Data consistency improvements and cap_data.csv schema normalization to ensure downstream analytics compatibility; resolved minor ingestion alignment issues when merging new data into the analytics pipeline. Overall impact and accomplishments: Significantly increased dataset completeness and analytics readiness, enabling historical trend analysis and better contract evaluation. Technologies/skills demonstrated: data engineering and ingestion, CSV data pipelines, dataset design and schema management, data validation and QA, version control and traceability.
February 2025
November 2024
12 Commits • 1 Features
Nov 1, 2024Month: 2024-11 Concise monthly summary for PranayN23/NFLResourceAnalysis focusing on business value and technical achievements. The month centered on delivering an end-to-end predictive analytics pipeline for NFL player performance, along with data integrity improvements that enable reliable training and evaluation across datasets. Key activities spanned feature development, data engineering, model evaluation, and cross-team collaboration to tighten the analytics loop and prepare for broader deployment. Key highlights reflect both feature delivery and quality improvements, with a clear business value in forecasting and decision support for scouting, training, and performance benchmarking.
November 2024
12 Commits • 1 Features
Nov 1, 2024Month: 2024-11 Concise monthly summary for PranayN23/NFLResourceAnalysis focusing on business value and technical achievements. The month centered on delivering an end-to-end predictive analytics pipeline for NFL player performance, along with data integrity improvements that enable reliable training and evaluation across datasets. Key activities spanned feature development, data engineering, model evaluation, and cross-team collaboration to tighten the analytics loop and prepare for broader deployment. Key highlights reflect both feature delivery and quality improvements, with a clear business value in forecasting and decision support for scouting, training, and performance benchmarking.
Activity
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Quality Metrics
Correctness85.2%
Maintainability84.0%
Architecture82.2%
Performance78.4%
AI Usage25.8%
Skills & Technologies
Programming Languages
CSSCSVHTMLJSXJavaScriptJupyter NotebookPython
Technical Skills
API DevelopmentAPI IntegrationAttention MechanismsAuthenticationBackend DevelopmentCSSCSS StylingCode OrganizationContext APIData AnalysisData CleaningData EngineeringData FilteringData HandlingData Preprocessing
Repositories Contributed To
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