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anuragchauhan21/README.md

Hi, I'm Anurag

About Me

I work on analytics projects using SQL, Python, Excel, and Power BI to extract, analyze, and interpret data into clear, actionable insights. My approach is based on logic, structure, and analytical thinking to understand business problems and support better data-driven decisions.

Featured Projects

Project Description Tools
Revenue Risk Analysis Analyzed Olist e-commerce data using SQL and Python to quantify revenue risk in customer retention, delivery delays, and order cancellations. SQL, Python
Retention Risk Analysis Analyzed customer churn patterns to identify high-risk customer segments, key churn drivers, and revenue exposure. Estimated revenue at risk and translated findings into retention-focused business recommendations. Python, Machine Learning, Power BI
Consumer Complaint Classification using NLP Built an NLP pipeline to classify 383K+ consumer complaint narratives into 11 financial product categories. Compared classical ML with BERT and selected the most practical approach under local computational constraints. Python, NLP, Scikit-learn, BERT

Tech Stack

  • Data Analysis: SQL • Python (Pandas, NumPy, matplotlib, seaborn, scikit-learn) • Excel • Power BI
  • Machine Learning: Regression Models • Classification Models • Feature Engineering • Model Evaluation
  • Tools: Jupyter Notebook • VS Code • Google Colab

Analytics Playground

Coding Practice & Problem Solving:

  • HackerRank: Gold Badge in SQL, Intermediate SQL Certificate, Bronze Badge in Python
  • LeetCode: SQL50 questions practice
  • Kaggle: Sharing my learnings projects and practice notebooks

Certifications & Achievements

  • SQL (Intermediate) Certification — HackerRank (2025)
  • SQL 5-Star Problem Solving Badge — HackerRank (2025)
  • SQL50 Badge — Leetcode (2025)
  • AWS Educate ML Foundations Badge — Credly
  • Introducing Generative AI with AWS — Udacity
  • Pandas — Kaggle

Pinned Loading

  1. consumer-complaint-classification-nlp consumer-complaint-classification-nlp Public

    End-to-end NLP project comparing TF-IDF + Logistic Regression and BERT for consumer complaint product classification using the CFPB complaint dataset.

    Jupyter Notebook

  2. Revenue_Risk_Analysis Revenue_Risk_Analysis Public

    Analyzed Olist e-commerce data using SQL and Python to quantify revenue risk in customer retention, delivery delays, and order cancellations.

    Jupyter Notebook

  3. coffee-retail-performance-analysis coffee-retail-performance-analysis Public

    Analyzed 149K+ coffee shop transactions to uncover product demand, sales performance, customer purchasing patterns, and location-level trends through an interactive Excel reporting solution.

  4. Retention-Risk-Analysis Retention-Risk-Analysis Public

    Performed churn analysis, prediction, and revenue-risk estimation, supported by an interactive Power BI dashboard for decision-focused insights.

    Jupyter Notebook