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punitkumar-dtu/README.md

Python SQL Tableau Excel

Hi, I'm Punit Kumar πŸ‘‹

πŸŽ“ Mechanical Engineering student at Delhi Technological University (DTU)
πŸ“Š Aspiring Data Analyst passionate about solving business problems with data.

I build end-to-end analytics projects using SQL, Python, Tableau, and Excel to transform raw data into actionable business insights through data cleaning, analysis, and interactive dashboards.


πŸ› οΈ Tech Stack

Languages & Tools

  • SQL (MySQL)
  • Python (Pandas, NumPy, Matplotlib, Plotly)
  • Tableau
  • Microsoft Excel
  • Git & GitHub
  • statistical testing
  • Product Analysis
  • Web Scrapping

πŸ“‚ Featured Projects

πŸ“‰ Telecom Customer Churn Analysis

Python β€’ Pandas β€’ Plotly β€’ Excel

  • Analysed customer churn using merged subscription, customer, and support datasets.
  • Identified 22.63% customer churn and β‚Ή11,005 monthly revenue at risk.
  • Found that plan type and customer tenure were stronger churn indicators than support interactions.

πŸ“ž IBM Telco Churn Analysis

Python β€’ SQL β€’ Tableau

  • Analysed 7,043 customer records using an end-to-end Python β†’ SQL β†’ Tableau workflow.
  • Identified 26.54% churn and β‚Ή1,39,130.85 monthly revenue at risk.
  • Used RANK(), LAG(), and running totals to uncover high-risk customer segments.

πŸ’° Sales & Revenue Analysis

SQL β€’ Tableau

  • Analysed $2.30M in sales with a 12.47% profit margin.
  • Built an interactive Tableau dashboard to monitor KPIs.
  • Discovered that aggressive discounting reduced profitability despite strong sales.

πŸ›οΈ Customer Shopping Dashboard

Python β€’ SQL β€’ Tableau

  • Analysed purchasing behaviour of 3,900+ customers.
  • Built customer segments using RFM-style analysis.
  • Found that customers purchasing without discounts spent more on average, challenging common promotional assumptions.

E-commerce Event History Analysis

  • Analyzed e-commerce user behavior using Python, MySQL, and Tableau to evaluate engagement, funnel performance, and retention.
  • Built view β†’ cart β†’ purchase funnel and identified key user drop-off points and category-level performance.
  • Measured conversion rates and D7 retention and translated findings into product recommendations.
  • Applied AARRR, RICE, and CIRCLES frameworks to prioritize product improvements and proposed A/B testing for validation.

πŸ“« Connect With Me

πŸ“§ Email: punitsingh1819@gmail.com

πŸ“„ Resume: **


⭐ Thanks for visiting my GitHub! Feel free to explore my projects and connect with me.

Pinned Loading

  1. E-commerce-event-history E-commerce-event-history Public

    Product Analyst case study

    Python 1

  2. AB-Testing--Case-Study AB-Testing--Case-Study Public

    Jupyter Notebook

  3. sales-and-revenue-analysis sales-and-revenue-analysis Public

    Sales and revenue analysis using SQL and Tableau to uncover trends, KPIs, and actionable business insights.

    1

  4. IBM-Churn-Analysis IBM-Churn-Analysis Public

    End-to-end customer churn analysis using SQL, Python, and Tableau with actionable retention insights.

    Jupyter Notebook 1

  5. customer-shopping-dashboard customer-shopping-dashboard Public

    Interactive Tableau dashboard analysing customer purchase behaviour, sales trends, and business KPIs using retail data.

    Jupyter Notebook 1

  6. Telecom-Customer-Churn-Analysis Telecom-Customer-Churn-Analysis Public

    Customer churn analysis using Python, SQL, and Tableau to identify churn drivers and improve customer retention.

    Jupyter Notebook 1