I’m a data enthusiast who loves exploring real-world datasets to answer business questions and deliver insights. I build SQL analytics solutions, conduct exploratory data analysis, create predictive models, and design dashboards that help people understand what’s happening behind the numbers — all in a way that’s practical, reproducible, and easy to follow.

I primarily work with:

• 📊 Python — data cleaning, analysis, modeling
• 🧠 Machine Learning — churn prediction, lifetime value
• 🛢️ SQL — advanced analytics queries that drive business decisions
• 📈 Dashboards & Visualization — making insights accessible

My projects tend to focus on customer behavior, revenue analysis, segmentation, and retention — the kinds of things analysts and data teams work with every day.

• E-Commerce Sales EDA (online-retail-ii-eda)
  Exploratory Data Analysis on a real online retail dataset — uncovering trends, seasonality, and customer patterns that set the stage for deeper analytics.

• SQL-Driven Analytics (online-retail-ii-sql-analytics)
  Business analytics written entirely in SQL — answering strategic questions about revenue, customer segments, and key performance metrics using real sales data.

• Customer Segmentation & Lifecycle (customer-segmentation-lifecycle-analysis)
  A pipeline that breaks customers into segments (e.g., high-value vs. at-risk) using RFM analysis and clustering, helping businesses tailor strategies by customer type.

• Churn Prediction & CLV Modeling (customer-churn-clv-online-retail-ii)
  Predicts which customers are likely to churn and estimates Customer Lifetime Value — combining modeling and business logic to guide retention decisions.

• Retention Strategy Simulation (customer-retention-strategy-online-retail-ii)
  Simulation of retention strategies + analysis of their effectiveness using business-relevant metrics.

• Customer Intelligence Dashboard (customer-intelligence-dashboard-online-retail-ii)
  An interactive dashboard (e.g., built with Streamlit) that lets you explore customer segments, churn risk, and revenue drivers — great for both self-service analysis and executive reporting.

Across these projects, you’ll see:

• Structured data pipelines that clean and prepare messy real-world data
• SQL queries designed for meaningful business insights
• Clear documentation and step-by-step notebooks
• Predictive models that solve real questions (like churn, segmentation, or value forecasting)
• Interactive interfaces that make exploration easy

Each repository has its own README with more detail on the problem, approach, insights, and results.

I’m always open to chats about analytics, data strategy, and new projects. If something here sparks an idea — drop a star ⭐, open an issue, or connect with me!

Thanks for stopping by 👀
— Hamim