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I'm a Statistics graduate with hands-on experience in data analysis, machine learning, and business intelligence. I build end-to-end data science projects — from data cleaning and feature engineering to model building and interactive dashboards. Recent work includes a CO2 emissions forecasting project using panel data from 16 countries (CatBoost model, R²=0.93), with SHAP interpretability analysis and a Streamlit dashboard. I've also built customer churn prediction models (AUC 0.84) and customer segmentation projects using clustering techniques (K-Means, RFM analysis). Tools I work with: Python (pandas, scikit-learn, CatBoost), SQL, Power BI, and Streamlit. I use AI tools to speed up my workflow, but I always verify outputs against primary sources and don't cut corners on data validation. I'm looking to help businesses with: - Data analysis and reporting (Power BI dashboards) - Predictive modeling (classification, forecasting) - Customer segmentation and clustering - Data cleaning and pipeline building Open to both short-term projects and ongoing collaboration. Let's talk about what you need.
