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Kaviya-Mahendran/README.md

Kaviya Mahendran

Data Analyst | Analytics Engineering | Data & AI | Healthcare Analytics

I build data-driven solutions across analytics, ETL, predictive modelling, business intelligence, and applied AI.

With 4+ years of experience, my work focuses on turning messy data into reliable pipelines, analytical models, decision-support dashboards, and practical data products.

What I Work With

Analytics & BI

  • SQL, Python, Pandas, NumPy
  • Power BI, DAX, Power Query
  • Statistical analysis, KPI design, data storytelling

Data Engineering

  • ETL/ELT pipelines
  • PostgreSQL, Supabase
  • Data quality and validation
  • Data modelling, Star Schema
  • Azure, Snowflake, AWS

Data & AI

  • Predictive modelling
  • Scikit-learn
  • SHAP and explainable AI
  • NLP and text analytics
  • Responsible AI and decision-support systems

Domain Focus

  • Healthcare analytics
  • Antimicrobial resistance (AMR) surveillance
  • Population health
  • Business and operations analytics

Featured Projects

Healthcare & Applied Data Products

  • Smart Food Safety System — ML + rule-based food safety decision-support prototype.
  • NHS Pharmacy Forecast — Analysis and forecasting of NHS prescribing data.
  • HVAC Operations Platform — Data-driven operations platform with dashboards and structured operational data.

Data Engineering & Analytics Architecture

  • Automated ETL Orchestration — Python ETL workflow with validation, quality checks, loading and observability.
  • Analytics Data Architecture — Dimensional modelling and analytics architecture for reliable reporting.
  • CRM Migration Simulator — Data migration, validation and reconciliation workflows.

Predictive Analytics & AI

  • Predictive Modelling Pipeline — Reproducible classification workflow with model comparison and SHAP explainability.
  • NLP Topic Modelling — Text preprocessing and unsupervised topic discovery.
  • Privacy-Aware Feature Engineering — Feature engineering approaches designed with privacy considerations.

My Growth Journey

Python & SQL → Data Analytics → ETL & Data Quality → Data Modelling → Predictive Analytics → Explainable AI → Production-Oriented Data Systems → Healthcare & AMR Analytics

I use this GitHub to document that progression through practical projects rather than isolated tutorials.

Currently Developing

  • Advanced SQL and analytics engineering
  • Microsoft Fabric and Azure data services
  • Production-grade data quality and testing
  • Healthcare and AMR surveillance analytics
  • Reproducible ML pipelines
  • Decision-support systems with responsible AI principles

Research Interests

Antimicrobial Resistance (AMR) Surveillance · Healthcare Data Analytics · Population Health · Data Quality · Explainable AI · Spatiotemporal Analytics

Connect

Building practical data systems, one project at a time.

Pinned Loading

  1. ETL_pipeline_for_Charity ETL_pipeline_for_Charity Public

    Complete ETL pipeline that merges donations, supporter records, and event data into a unified analytics ready schema with automated validation.

    Python

  2. analytics-data-architecture-design analytics-data-architecture-design Public

    Reference analytics data architecture demonstrating layered storage, star schema modelling, metadata governance, and system level design for scalable analytics.

    1

  3. automated-etl-orchestration automated-etl-orchestration Public

    Automated ETL pipeline with scheduling, logging, schema validation, and analytics ready transformations. Designed to simulate production grade data ingestion and processing workflows.

    Python 1

  4. nlp-topic-modelling-pipeline nlp-topic-modelling-pipeline Public

    This repository demonstrates an end to end, privacy aware NLP analytics pipeline for transforming unstructured text into interpretable, decision ready insights.

    Python 1

  5. predictive-modelling-pipeline predictive-modelling-pipeline Public

    Predictive modelling pipeline for customer or donor behaviour, with model comparison, ROC evaluation, and SHAP based interpretability using privacy safe features.

    Python 1

  6. privacy-aware-feature-engineering privacy-aware-feature-engineering Public

    Privacy first feature engineering pipeline transforming PII-rich customer data into analytics ready features without exposing personal identifiers.

    Python