Innovative Data Science Solutions & Machine Learning Applications
📊 Comprehensive Python library for automated exploratory data analysis with 25+ functions for EDA, outlier detection, and statistical summaries. Published on PyPI for global data science community with advanced statistical capabilities.
🏠 XGBoost model achieving 83% accuracy on 50M+ records for home loan default prediction. Advanced feature engineering and memory optimization reduced false positives by 22% and eliminated 1,200+ hours of manual review.
👥 Analyzed 1,200+ employee records using XGBoost achieving 92.5% accuracy with SHAP analysis. Deployed predictive hiring model for INX Future Inc boosting accuracy by 20% with comprehensive performance insights.
🔧 Dependency-isolated CSV analysis toolkit using Python to resolve library conflicts, enabling parallel execution of 5+ EDA tools via virtual environments. Advanced automation for seamless data profiling workflows.
📈 XGBoost classifier for automated lead qualification reducing manual review time by 30%. Identified critical business trends including 20.4% lead inflow on Mondays and 46% peak conversions in November.
🚗 Vehicle pricing strategy model using Lasso Regression achieving R²=0.917 and RMSE=1.987. Leveraged PCA for 95% variance retention in automotive pricing with luxury premium analysis of $6-7.3K.
Dive deeper into my data science journey by exploring my Jupyter notebooks, Kaggle competitions, and open-source contributions. Each repository tells a story of problem-solving, innovation, and continuous learning in the field of data science and machine learning.
Complete source code, detailed documentation, and comprehensive project implementations with step-by-step analysis
View GitHub ProfileData science competitions, datasets analysis, and machine learning challenges with innovative solutions
Explore Kaggle ProfileInteractive data analysis notebooks with visualizations, insights, and detailed methodology explanations
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