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Blladerunner/README.md

👋 Hi, I'm Rufus

🎯 Aspiring Data Analyst | Python • SQL • Excel • Power BI
📊 Passionate about exploring data, finding insights, and creating clean visualizations.

🚀 My Projects

  • 📺 Netflix EDA – Exploratory Data Analysis of Netflix dataset.
  • 🎥 YouTube Trending EDA – Analysis of trending videos across countries.
  • 🏪 Superstore - Top-10 customers, AOV by category, monthly trend, region/segment profitability, ABC, discount impact.
  • 🎵 Chinook - Monthly revenue & MoM, top customers, genre share, support-rep performance, basket, cohorts, RFM.
  • 🎬 Sakila – Rentals KPIs: monthly revenue & MoM, top films/customers, staff performance, country breakdown, cohorts & RFM.
  • 📊 Sales Analysis (pandas) – Retail sales EDA with Python (pandas, matplotlib, seaborn). Includes monthly sales trend, top products, regional profitability, and sales vs profit analysis.
  • ☕ Coffee Chain Analysis – EDA of global coffee chain sales using Python (pandas, matplotlib, seaborn). Includes monthly sales trends, top products, category profitability, and sales vs profit correlation.
  • 👥 Customer Segmentation – RFM Analysis – RFM-based customer segmentation using Python (pandas, matplotlib, seaborn). Identifies loyal, new, at-risk, and lost customers with visual insights and data-driven scoring.
  • 💹 Crypto Market Analysis - Cryptocurrency market analysis using Python (pandas, matplotlib, seaborn) and the CoinGecko API.
    Includes price trends, volatility, trading volume, correlation matrix, and max drawdown analytics.
  • 🧑‍💼 HR Analytics – Workforce insights using SQL (SQLite). Includes department-level salary analysis, gender ratio, hiring & termination trends, and experience-based pay insights.
  • 💹 Finance Market Analytics - SQL + Python project for market data ETL and analytics. Uses yfinance to fetch stock prices, stores them in SQLite, and computes daily/monthly returns, top-performing months, and KPIs.
  • 💳 Credit Card Fraud Detection (Deep Project) – End-to-end fraud detection project on extremely imbalanced data. Focuses on precision-recall metrics, PR-AUC, decision threshold tuning, and business cost trade-offs. Includes error analysis, false positive/false negative investigation, and executive-level recommendations.

🛠️ Tools & Skills

  • Languages: Python (pandas, numpy, matplotlib, seaborn), SQL
  • Data Tools: Excel, Power BI, Jupyter Notebooks
  • Other: Git & GitHub, VS Code

🌍 Let's Connect

✨ Always learning, always exploring data!

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  1. netflix-eda-project netflix-eda-project Public

    Exploratory Data Analysis (EDA) of the Netflix Movies and TV Shows dataset using Python, pandas, matplotlib, and seaborn.

    Jupyter Notebook 1

  2. superstore-sql-analysis superstore-sql-analysis Public

    SQL analysis on Sample Superstore (SQLite)

    1

  3. chinook-sql-analysis chinook-sql-analysis Public

    SQL analysis on the Chinook (SQLite) dataset: revenue trends, top customers, genres, RFM, cohorts.

    1

  4. sales-analysis-pandas sales-analysis-pandas Public

    Exploratory Data Analysis (EDA) of retail sales using Python (pandas, matplotlib, seaborn). Includes data cleaning, visualizations, and business insights.

    Jupyter Notebook 1

  5. coffee_chain_analysis coffee_chain_analysis Public

    Exploratory Data Analysis of a Coffee Chain dataset using Python (pandas, matplotlib, seaborn).

    Jupyter Notebook 1

  6. customer_segmentation_rfm_analysis customer_segmentation_rfm_analysis Public

    Customer segmentation using the RFM model. Identifies loyal, at-risk and lost customers; includes visuals and actionable business insights.

    Jupyter Notebook 1