PranayN23/NFLResourceAnalysis
Oct 2024 – Apr 2025
3 Months active
Languages Used
CSVPythonJupyter Notebook
Technical Skills
CSV ManipulationData AnalysisData ProcessingPandasData EngineeringData Preprocessing
vihaanchadha
PROFILE
Vihaanchadha
Vikram Chadha developed data processing and machine learning pipelines for the PranayN23/NFLResourceAnalysis repository, focusing on NFL offensive line analytics. He engineered reusable Python scripts using Pandas to ingest and normalize multi-year CSV datasets, enabling year-over-year performance comparisons and BI-ready outputs. Vikram implemented a TensorFlow-based MLP model for player predictions, enhanced data visualization, and improved feature engineering for richer analysis. He also delivered a backend API endpoint to provide draft capital information, increasing data accessibility for teams and dashboards. His work demonstrated depth in data engineering, machine learning, and backend development, resulting in robust, extensible analytics infrastructure without reported bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
5
Lines of code
26,912
Activity Months3
Your Network
14 peopleSame Organization
@stu.naperville203.org4
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — Delivered a new Draft Capital Information API endpoint within PranayN23/NFLResourceAnalysis to fetch a player's draft capital information and draft rating by name, position, team, and year. This enhancement improves data accessibility for teams, scouts, and dashboards and supports faster, data-driven decision-making across analytics workflows.
1 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — Delivered a new Draft Capital Information API endpoint within PranayN23/NFLResourceAnalysis to fetch a player's draft capital information and draft rating by name, position, team, and year. This enhancement improves data accessibility for teams, scouts, and dashboards and supports faster, data-driven decision-making across analytics workflows.
April 2025
November 2024
7 Commits • 3 Features
Nov 1, 2024November 2024: Delivered end-to-end offensive line predictions capabilities and richer data assets in NFLResourceAnalysis, enabling data-driven insights for coaching and performance analytics.
November 2024
7 Commits • 3 Features
Nov 1, 2024November 2024: Delivered end-to-end offensive line predictions capabilities and richer data assets in NFLResourceAnalysis, enabling data-driven insights for coaching and performance analytics.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary: Delivered a reusable Python-based data processing script for NFL Offensive Line metrics, enabling multi-year analysis and BI-ready outputs. Key deliverables included an end-to-end data pipeline that ingests CSV data across multiple years, computes weighted averages for performance metrics, normalizes by block percentage, maps team names, and saves results to CSV while preparing data for year-over-year comparison by shifting previous metrics. The work centers on a single feature with commit bc92e15b21086623ea97dbb552a6108758bf8258 ("Finished OL"). No major bugs were reported; the deliverable stabilizes the data pipeline and provides a solid foundation for ongoing analytics and business decisions.
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary: Delivered a reusable Python-based data processing script for NFL Offensive Line metrics, enabling multi-year analysis and BI-ready outputs. Key deliverables included an end-to-end data pipeline that ingests CSV data across multiple years, computes weighted averages for performance metrics, normalizes by block percentage, maps team names, and saves results to CSV while preparing data for year-over-year comparison by shifting previous metrics. The work centers on a single feature with commit bc92e15b21086623ea97dbb552a6108758bf8258 ("Finished OL"). No major bugs were reported; the deliverable stabilizes the data pipeline and provides a solid foundation for ongoing analytics and business decisions.
October 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture77.8%
Performance75.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPython
Technical Skills
API DevelopmentBackend DevelopmentCSV ManipulationData AnalysisData EngineeringData PreprocessingData ProcessingData VisualizationDatabase InteractionFeature EngineeringFile ManagementJupyter NotebookMachine LearningMulti-Layer Perceptrons (MLP)Pandas
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