{"id":12,"date":"2026-01-03T01:55:16","date_gmt":"2026-01-03T01:55:16","guid":{"rendered":"https:\/\/datawithabi.com\/services\/"},"modified":"2026-05-06T18:27:19","modified_gmt":"2026-05-06T18:27:19","slug":"projects","status":"publish","type":"page","link":"https:\/\/datawithabi.com\/en_gb\/projects\/","title":{"rendered":"Projects"},"content":{"rendered":"
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Projects<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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A selection of projects focused on product analytics, applied machine learning, and decision-support systems.<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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I Help Businesses<\/h6>\t\t\t\t<\/div>\n\t\t\t\t
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What I Do<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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I work at the intersection of data and product, helping teams define metrics, explore data, and translate insights into decisions that shape product direction.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t1. Product Analytics & Growth\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tDesigned a product analytics framework (event taxonomy, funnel KPIs, dashboards) to track user engagement and support growth decisions in an early-stage startup environment.\n\nDefined activation and conversion metrics, enabling structured analysis of user behavior and clearer prioritization of growth initiatives.\n\nFocus: product metrics, funnel analysis, decision support\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t2. AI Systems for Business Impact\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tDesigned a multi-agent AI system to transform unstructured educational data into structured, decision-ready outputs.\n\nBuilt a RAG-based pipeline for curriculum alignment and insight generation, with a focus on interpretability and real-world usability.\n\nFocus: applied AI systems, data pipelines, decision support\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t3. Lead Conversion & Growth\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h4>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\tAnalyzed multi-year marketing and referral data to identify drivers of lead conversion.\n\nTranslated analytical outputs into operational recommendations (e.g., response time improvements, consultation flow changes) to improve conversion outcomes.\n\nFocus: business impact, feature engineering, stakeholder translation\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t4. Computer vision & OCR\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Automated SAT Question Labeling: 2 Hours of Manual Work Reduced to Under 5 Minutes<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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An end-to-end computer vision pipeline that classifies Digital SAT questions by difficulty at 98\u201399% accuracy, making consistent, scalable instructional planning possible without touching question text.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Problem<\/strong>
Test prep instructors manually categorized hundreds of Digital SAT questions by difficulty for every practice test; a slow, inconsistent process that bottlenecked curriculum planning and introduced rater bias. With 6 official practice papers and ~90 pages each, this was unsustainable at scale.<\/p>

Approach<\/strong>
Built an end-to-end pipeline processing 588 question pages extracted from official College Board practice tests. The system converts PDF answer keys to high-resolution grayscale images, isolates the difficulty marker region using calibrated coordinates, and applies binary thresholding with contour detection to count filled circles,\u00a0 mapping 3+ circles to Hard, 2 to Medium, 1 to Easy.<\/p>

No text parsing, no NLP. The entire classification relies on visual structure.<\/p>

Insight<\/strong>
The College Board encodes difficulty as a visual pattern, filled circle count, in its answer keys. Once the right image region is isolated, this signal can be extracted programmatically with high reliability across all official test formats, making the approach robust and reusable.<\/p>

Impact<\/strong>
The pipeline processed ~540 questions across 6 practice tests, achieving 98\u201399% classification accuracy. Manual labeling time dropped from approximately 2 hours to under 5 minutes per test. The project also demonstrates IP-compliant data science: the repo ships with cached derived features rather than copyrighted source materials, making it fully shareable and reproducible.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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98\u201399% accuracy \u00b7 ~540 questions classified \u00b7 2 hrs \u2192 <5 mins per test<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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Python \u00b7 OpenCV \u00b7 OCR \u00b7 pdf2image \u00b7 Computer Vision \u00b7 Jupyter \u00b7 Reproducible Pipelines<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t5. Unsupervised ML & Equity Analytics\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Unsupervised Learning Surfaced Hidden Equity Gaps in 4,424 Student Trajectories\n<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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K-Means clustering and a fairness audit revealed that financial burden, not academic preparation, is the primary driver of dropout risk, pointing to where targeted interventions will have the most impact.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Problem<\/strong>
Educational institutions often have aggregate outcome data (graduation rates, GPA distributions), but lack visibility into the distinct pathways students take to reach those outcomes. Without segmentation, it’s impossible to identify at-risk groups early enough to intervene, or to design supports that target the right students.<\/p>

Approach<\/strong>
Applied K-Means clustering (k=8, selected via elbow method, Davies-Bouldin index, and interpretability triangulation) to a 4,424-student higher education dataset with 36 variables spanning academic performance, demographics, and financial indicators. Used UMAP for 2D cluster visualization and ran chi-square fairness audits on sensitive attributes (gender and age) to identify demographic skews before recommending any intervention.<\/p>

Insight<\/strong>
Financial burden and age, not academic preparedness, are the strongest differentiators between student success and dropout. Scholarship support emerged as the clearest protective factor across all clusters.<\/p>

For example, cluster 1 had a 40% dropout risk and comprised older learners carrying debt. In contrast, cluster 4 had a 89% success rate and encompassed younger students with scholarships.<\/p>

Impact<\/strong>
Delivered 8 cluster profiles and a Tableau dashboard designed for non-technical stakeholders, translating ML outputs into actionable intervention recommendations. The fairness audit framework demonstrates how to surface demographic skews before deploying any support program, reducing the risk of interventions that help some groups while inadvertently overlooking others.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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4,424 students \u00b7 8 clusters \u00b7 40% dropout risk in highest-risk cluster vs. 89% success in most-protected cluster<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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Python \u00b7 scikit-learn \u00b7 K-Means \u00b7 UMAP \u00b7 Tableau \u00b7 Chi-Square Testing \u00b7 Equity Analysis \u00b7 Jupyter<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t6. SQL & Database Design\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Tutor Management System: SQL Database That Turns Scheduling Chaos Into Operational Analytics<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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A 9-table normalized MySQL schema, covering students, tutors, sessions, packages, and payments, that answers three critical business questions in real time: what generates revenue, who is available, and which packages are going unused.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Problem<\/strong>
A tutoring business managing group sessions, prepaid packages, multi-subject tutors, and Stripe payments cannot scale on spreadsheets. Without a reliable data model, three questions that directly affect revenue go unanswered: which subjects generate the most income, which tutors are available and qualified for a specific student request, and which prepaid packages are expiring with sessions still unused.<\/p>

Approach<\/strong>
Designed a fully\u00a0normalized (3NF) relational schema in MySQL 8.0 with 9 interconnected tables: Students, Tutors, Subjects, Sessions, Session_Packages, Payments, Session_Enrollments, Tutor_Subject_Expertise, and Tutor_Availability. Applied Crow’s Foot ER modeling, composite indexing, and business-rule constraints, including group session size limits, a 24-hour cancellation policy, Stripe transaction ID tracking, and a generated column for package session credits that eliminates update anomalies.<\/p>

Three analytical queries were written to directly answer three business questions.<\/p>

Insight<\/strong>
Math\/Science sessions generated\u00a0 53% of total revenue. A single 5-table JOIN identifies the specific tutor(s) qualified, rated highly, and available. Sorting active packages by sessions remaining identifies exactly which students need a renewal reminder.<\/p>

Impact<\/strong>
Delivered an analytics-ready schema with enforced business rules that makes reliable operational reporting possible. This project demonstrates the ability to design the data infrastructure that makes dashboards and models trustworthy in the first place.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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9 tables \u00b7 3NF normalized \u00b7 3 business queries \u00b7 Math\/Science = 53% of total revenue<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t
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SQL \u00b7 MySQL 8.0 \u00b7 Database Design \u00b7 ER Modeling \u00b7 3NF Normalization \u00b7 Analytical Queries<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Projects A selection of projects focused on product analytics, applied machine learning, and decision-support systems. I Help Businesses What I Do I work at the intersection of data and product, helping teams define metrics, explore data, and translate insights into decisions that shape product direction. 1. Product Analytics & Growth Designed a product analytics framework […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"disabled","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"enabled","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-12","page","type-page","status-publish","hentry"],"yoast_head":"\nProjects - Abigail Data Science<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datawithabi.com\/en_gb\/projects\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Projects - Abigail Data Science\" \/>\n<meta property=\"og:description\" content=\"Projects A selection of projects focused on product analytics, applied machine learning, and decision-support systems. 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I Help Businesses What I Do I work at the intersection of data and product, helping teams define metrics, explore data, and translate insights into decisions that shape product direction. 1. Product Analytics & Growth Designed a product analytics framework […]","og_url":"https:\/\/datawithabi.com\/en_gb\/projects\/","og_site_name":"Abigail Data Science","article_modified_time":"2026-05-06T18:27:19+00:00","og_image":[{"width":1785,"height":885,"url":"https:\/\/datawithabi.com\/wp-content\/uploads\/2026\/05\/pipeline_overview-1.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/datawithabi.com\/projects\/","url":"https:\/\/datawithabi.com\/projects\/","name":"Projects - Abigail Data Science","isPartOf":{"@id":"https:\/\/datawithabi.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/datawithabi.com\/projects\/#primaryimage"},"image":{"@id":"https:\/\/datawithabi.com\/projects\/#primaryimage"},"thumbnailUrl":"https:\/\/datawithabi.com\/wp-content\/uploads\/2026\/05\/pipeline_overview-1-1024x508.jpg","datePublished":"2026-01-03T01:55:16+00:00","dateModified":"2026-05-06T18:27:19+00:00","breadcrumb":{"@id":"https:\/\/datawithabi.com\/projects\/#breadcrumb"},"inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datawithabi.com\/projects\/"]}]},{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datawithabi.com\/projects\/#primaryimage","url":"https:\/\/datawithabi.com\/wp-content\/uploads\/2026\/05\/pipeline_overview-1-1024x508.jpg","contentUrl":"https:\/\/datawithabi.com\/wp-content\/uploads\/2026\/05\/pipeline_overview-1-1024x508.jpg"},{"@type":"BreadcrumbList","@id":"https:\/\/datawithabi.com\/projects\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/datawithabi.com\/"},{"@type":"ListItem","position":2,"name":"Projects"}]},{"@type":"WebSite","@id":"https:\/\/datawithabi.com\/#website","url":"https:\/\/datawithabi.com\/","name":"Abigail Data Science","description":"Just another WordPress site","publisher":{"@id":"https:\/\/datawithabi.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datawithabi.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datawithabi.com\/#organization","name":"Abigail Data Science","url":"https:\/\/datawithabi.com\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datawithabi.com\/#\/schema\/logo\/image\/","url":"https:\/\/datawithabi.com\/wp-content\/uploads\/2026\/01\/cropped-Logo-Abi-Kambanis.png","contentUrl":"https:\/\/datawithabi.com\/wp-content\/uploads\/2026\/01\/cropped-Logo-Abi-Kambanis.png","width":272,"height":250,"caption":"Abigail Data Science"},"image":{"@id":"https:\/\/datawithabi.com\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/pages\/12","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/comments?post=12"}],"version-history":[{"count":22,"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/pages\/12\/revisions"}],"predecessor-version":[{"id":1027,"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/pages\/12\/revisions\/1027"}],"wp:attachment":[{"href":"https:\/\/datawithabi.com\/en_gb\/wp-json\/wp\/v2\/media?parent=12"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}