arvindkrishna87/STAT390_LegalAid_Fall2025
Sep 2025 – Dec 2025
4 Months active
Languages Used
TextJupyter NotebookPythonMarkdownNonePDF
Technical Skills
File ManagementData AnalysisData CleaningData EngineeringData ProcessingFile Handling
Sana Nanlawala
PROFILE
Sana Nanlawala
Sana Nanlawala developed and maintained data analysis pipelines for the arvindkrishna87/STAT390_LegalAid_Fall2025 repository, focusing on telephony call data. Over four months, Sana consolidated multi-source datasets using Python and Pandas, engineered reusable Jupyter Notebooks for exploratory and outcome analysis, and implemented robust data ingestion with directory-based loading and time parsing. The work included standardizing documentation, improving repository hygiene, and enhancing onboarding materials, which streamlined collaboration and reproducibility. Sana’s approach emphasized data quality, error handling, and clear presentation, enabling faster insight generation and supporting data-driven decision-making. The depth of work established a maintainable foundation for ongoing analytics development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
25Total
Bugs
0
Commits
25
Features
10
Lines of code
16,155
Activity Months4
Your Network
94 peopleWork History
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025 – STAT390 LegalAid project: Delivered two major features and updated documentation for the arvindkrishna87/STAT390_LegalAid_Fall2025 repo. Focused on data-analysis notebook enhancements for call outcomes and customer-left scenarios, version and execution-count alignment, and the addition of a presentation PDF. Documentation was updated to reflect recent project changes. No major bugs were reported this month; stability improvements were achieved as part of notebook updates. The work strengthens data-driven decision making, accelerates stakeholder communication, and improves project clarity for ongoing development.
2 Commits • 2 Features
Dec 1, 2025December 2025 – STAT390 LegalAid project: Delivered two major features and updated documentation for the arvindkrishna87/STAT390_LegalAid_Fall2025 repo. Focused on data-analysis notebook enhancements for call outcomes and customer-left scenarios, version and execution-count alignment, and the addition of a presentation PDF. Documentation was updated to reflect recent project changes. No major bugs were reported this month; stability improvements were achieved as part of notebook updates. The work strengthens data-driven decision making, accelerates stakeholder communication, and improves project clarity for ongoing development.
December 2025
November 2025
11 Commits • 4 Features
Nov 1, 2025Monthly report for 2025-11 focusing on STAT390 LegalAid fall project (arvindkrishna87/STAT390_LegalAid_Fall2025). This period delivered feature work centered on data-driven telephony improvements, documentation enhancements, and repository hygiene. No major user-facing bugs were recorded in the provided data; the emphasis was on delivering a cohesive EDA framework, labeling workflow support, and clear developer/user guidance to strengthen future iterations and onboarding.
November 2025
11 Commits • 4 Features
Nov 1, 2025Monthly report for 2025-11 focusing on STAT390 LegalAid fall project (arvindkrishna87/STAT390_LegalAid_Fall2025). This period delivered feature work centered on data-driven telephony improvements, documentation enhancements, and repository hygiene. No major user-facing bugs were recorded in the provided data; the emphasis was on delivering a cohesive EDA framework, labeling workflow support, and clear developer/user guidance to strengthen future iterations and onboarding.
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
Correctness94.4%
Maintainability94.4%
Architecture93.6%
Performance92.0%
AI Usage20.8%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownNonePDFPythonText
Technical Skills
Data AnalysisData CleaningData EngineeringData ProcessingFile HandlingFile ManagementJupyter NotebookJupyter NotebooksMatplotlibPandasPythonSeaborndata analysisdata visualizationdocumentation
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