🎯 Aspiring Data Analyst | Python • SQL • Excel • Power BI
📊 Passionate about exploring data, finding insights, and creating clean visualizations.
- 📺 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.
- Languages: Python (pandas, numpy, matplotlib, seaborn), SQL
- Data Tools: Excel, Power BI, Jupyter Notebooks
- Other: Git & GitHub, VS Code
- 💼 [LinkedIn] https://www.linkedin.com/in/rufat-ahmad-zada-7084a319a/
- 📧 Email: rufat.ahmadzadeh@gmail.com
✨ Always learning, always exploring data!