Machine Learning & Predictive Analytics
ML Engagement Recommender
Built ML recommendation engine using Random Forest, XGBoost, and Logistic Regression with PCA analysis and feature engineering. Implemented 50+ engineered features and comprehensive model evaluation.
Challenge
Optimize SA engagement strategies by predicting which customer engagement approaches will be most successful based on historical patterns.
Solution
Built ML recommendation engine using Random Forest, XGBoost, and Logistic Regression with PCA analysis and feature engineering. Implemented 50+ engineered features and comprehensive model evaluation.
Impact Metrics
Results
- • 53% Conversion Rate Improvement through predictive targeting
- • 89.1% Model Accuracy with XGBoost (87.3% Random Forest, 82.7% Logistic Regression)
- • 15-20% Win Rate Improvement in engagement success rates
- • 30% Reduction in misallocated engagement efforts
- • $500K+ Annual Impact through optimized strategies
Business Impact
Replaced intuition-based approaches with statistical models, enabling data-driven engagement decisions and significantly improving conversion rates.
Machine Learning Pipeline
A proven approach combining statistical rigor, automation, and AWS best practices.
Feature Engineering
Created 50+ engineered features from raw engagement data including engagement history, account characteristics, and temporal features
Model Training
Trained Random Forest, XGBoost, and Logistic Regression with 5-fold cross-validation and hyperparameter tuning
PCA Analysis
Principal Component Analysis for dimensionality reduction and pattern identification
Model Interpretability
SHAP values and feature importance analysis for transparent decision-making
Feature Importance
- • Previous Engagement Success Rate (0.23 importance)
- • Account Adoption Score (0.18 importance)
- • Industry Vertical (0.15 importance)
- • Account Size (0.12 importance)
- • Time Since Last Engagement (0.09 importance)
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