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  • Leeds, United Kingdom
  • 15:31 (UTC -12:00)

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ankitdwivedi2001/README.md

Hi, I'm Ankit Dwivedi 👋

Business Analyst / Data Analyst · Leeds, UK
MSc Business Analytics and Decision Sciences, University of Leeds (Merit)

I turn business questions into clear, evidence-based answers, from gathering requirements (BRD/FRD, user stories, UAT) to cleaning data, running the analysis and presenting the findings.

Tools: SQL (MySQL) · Power BI (DAX, Power Query) · Looker Studio · Excel · Python · R · JIRA · Confluence

Featured projects

Project What it shows Tools
Online Retail Sales Analytics 541,909 rows cleaned and analysed; found one customer driving 98% of Netherlands revenue Excel · MySQL · Power BI
Football Sponsorship Trends Analysis 800 sponsorship deals analysed; weighted sum model ranked 22 categories to recommend a new partner Excel · Google Sheets · WSM
HR Attrition Insight Dashboard Requirements-led analysis of 1,470 employees; 36.6% first-year attrition (live dashboard) BRD/FRD · MySQL · Looker Studio
Payment Methods & Financial Behaviour MSc dissertation: 162-respondent UK/India survey with mediation analysis R
Financial Fraud Detection Caught 81 of 98 fraud cases with 17 false alarms in 56,864 transactions Python · SMOTE · Random Forest

Get in touch

🌐 Portfolio · 💼 LinkedIn · 📧 ankitdofficial01@gmail.com

Open to Business Analyst and Data Analyst roles in the UK.

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  1. online-retail-sales-analytics online-retail-sales-analytics Public

    Sales and customer analytics project — Excel cleaning, MySQL/SQL analysis, and a live-connected Power BI dashboard

  2. hr-attrition-insight-dashboard hr-attrition-insight-dashboard Public

    End-to-end HR analytics project — SQL, Excel, and an interactive Looker Studio dashboard analyzing employee attrition.

  3. Payment-Methods-and-Financial-Behavior Payment-Methods-and-Financial-Behavior Public

    Data-driven analysis of payment methods and their impact on spending awareness and financial management.

    R

  4. financial-fraud-detection financial-fraud-detection Public

    Fraud detection using Logistic Regression, SMOTE, and Random Forest

    Jupyter Notebook 1