arvindkrishna87/STAT390_LegalAid_Fall2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
TextJupyter NotebookPython
Technical Skills
File ManagementData AnalysisData CleaningData EngineeringData ProcessingFile Handling
Sana Nanlawala
PROFILE
Sana Nanlawala
Sana Nanlawala developed a robust data ingestion and analytics pipeline for the arvindkrishna87/STAT390_LegalAid_Fall2025 repository, consolidating multi-source CSV and Excel datasets into unified DataFrames using Python and Pandas. She engineered directory-based loading, standardized time parsing, and implemented initial data exploration to identify schema inconsistencies, enabling reliable analytics on customer call data. Sana also created a reusable Jupyter Notebook for analyzing call drop-off and abandonment patterns, supporting data-driven insights. Her work included comprehensive documentation updates and improved repository organization, enhancing onboarding and reproducibility. The depth of her contributions established a maintainable foundation for future feature development and stakeholder collaboration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
4
Lines of code
7,551
Activity Months2
Your Network
93 people
Work History
October 2025
10 Commits • 3 Features
Oct 1, 2025October 2025 Monthly Summary — arvindkrishna87/STAT390_LegalAid_Fall2025 Overview: Delivered an end-to-end data ingestion and analytics pipeline for multi-source datasets, established a reusable notebook for call-drop analysis, and enhanced project documentation and organization. This focused work created business value through reliable data consolidation, faster insight generation, and clearer stakeholder communications. 1) Key features delivered - Unified Data Ingestion and Analytics for Multi-Source Datasets: Implemented directory-based loading to ingest CSV and XLSX sources into a single DataFrame, parsed 'Start time' to datetime, and performed initial data exploration to identify common/missing columns; enabled analytics across sources for call data. - New Jupyter Notebook for Customer Call Drop-Off Analysis: Added a dedicated notebook to extract call details from CSV/XLSX, analyze drop-offs, and identify abandonment patterns across different stages of the call flow. - Documentation and Presentation Assets Update and Project Organization: Updated PDFs, reorganized artifacts, moved files, renamed artifacts, and added new presentation PDFs to improve readability and reproducibility. 2) Major bugs fixed - Data ingestion robustness: Fixed inconsistencies in multi-source ingestion, ensured Start time is parsed consistently, and standardized columns across sources to reduce alignment issues. - Directory-based loading stability and data exploration reliability: Improved error handling and data quality checks to surface issues early. - Documentation and artifact organization fixes: Improved onboarding, reproducibility, and stakeholder communication by reorganizing and updating assets. 3) Overall impact and accomplishments - Accelerated data-driven decision-making by delivering a reusable ingestion and analytics pipeline across diverse sources. - Improved data quality and reliability, enabling faster and more accurate insights into call patterns and abandonments. - Strengthened project organization, documentation, and presentation readiness, reducing friction for stakeholders and enabling smoother handoffs. 4) Technologies and skills demonstrated - Python and Pandas for data wrangling, time parsing, and multi-source consolidation. - Jupyter Notebooks for exploratory analysis and reporting. - Data quality checks, reproducible project organization, and version control discipline. Note: Commit-level traceability is maintained for each feature; key commits include those associated with ingestion, notebook development, and documentation/organization updates.
10 Commits • 3 Features
Oct 1, 2025October 2025 Monthly Summary — arvindkrishna87/STAT390_LegalAid_Fall2025 Overview: Delivered an end-to-end data ingestion and analytics pipeline for multi-source datasets, established a reusable notebook for call-drop analysis, and enhanced project documentation and organization. This focused work created business value through reliable data consolidation, faster insight generation, and clearer stakeholder communications. 1) Key features delivered - Unified Data Ingestion and Analytics for Multi-Source Datasets: Implemented directory-based loading to ingest CSV and XLSX sources into a single DataFrame, parsed 'Start time' to datetime, and performed initial data exploration to identify common/missing columns; enabled analytics across sources for call data. - New Jupyter Notebook for Customer Call Drop-Off Analysis: Added a dedicated notebook to extract call details from CSV/XLSX, analyze drop-offs, and identify abandonment patterns across different stages of the call flow. - Documentation and Presentation Assets Update and Project Organization: Updated PDFs, reorganized artifacts, moved files, renamed artifacts, and added new presentation PDFs to improve readability and reproducibility. 2) Major bugs fixed - Data ingestion robustness: Fixed inconsistencies in multi-source ingestion, ensured Start time is parsed consistently, and standardized columns across sources to reduce alignment issues. - Directory-based loading stability and data exploration reliability: Improved error handling and data quality checks to surface issues early. - Documentation and artifact organization fixes: Improved onboarding, reproducibility, and stakeholder communication by reorganizing and updating assets. 3) Overall impact and accomplishments - Accelerated data-driven decision-making by delivering a reusable ingestion and analytics pipeline across diverse sources. - Improved data quality and reliability, enabling faster and more accurate insights into call patterns and abandonments. - Strengthened project organization, documentation, and presentation readiness, reducing friction for stakeholders and enabling smoother handoffs. 4) Technologies and skills demonstrated - Python and Pandas for data wrangling, time parsing, and multi-source consolidation. - Jupyter Notebooks for exploratory analysis and reporting. - Data quality checks, reproducible project organization, and version control discipline. Note: Commit-level traceability is maintained for each feature; key commits include those associated with ingestion, notebook development, and documentation/organization updates.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 focused on documentation and repository hygiene for arvindkrishna87/STAT390_LegalAid_Fall2025. Delivered non-functional improvements that standardize docs, improve onboarding, and reduce version-control noise, establishing a foundation for faster, reliable future feature work.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 focused on documentation and repository hygiene for arvindkrishna87/STAT390_LegalAid_Fall2025. Delivered non-functional improvements that standardize docs, improve onboarding, and reduce version-control noise, establishing a foundation for faster, reliable future feature work.
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonText
Technical Skills
Data AnalysisData CleaningData EngineeringData ProcessingFile HandlingFile ManagementMatplotlibPandasPythonSeaborn
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