Apply confidence-interval estimation to compare conversion rates across segments and present the statistical significance.

Evaluate Type I/II error trade-offs for a proposed test and recommend appropriate alpha and beta thresholds.

Conduct a two-sample t-test in Python/R, interpret p-values, translate outcomes into plain-language business recommendations, and analyze test power under varying sample sizes.

Build and diagnose multiple linear regression models with proper statistical validation and remediation techniques.

Apply advanced classification methods including gradient boosting and logistic regression while handling class imbalance for optimal performance.

Evaluate and remediate class imbalance using SMOTE while documenting performance impact on F1-score for comprehensive model validation.

Build cross-validated random forest models that achieve business-defined accuracy targets

Evaluate and monitor model drift using statistical metrics to ensure long-term reliability

Implement standardized cross-validation pipelines for multiple supervised algorithms and compare performance metrics

Assess feature selection techniques to balance model accuracy with interpretability

You will build a complete customer lifetime value (CLV) prediction pipeline for an e-commerce company. Starting from raw transaction data, you will conduct exploratory data analysis, execute a hypothesis test comparing customer segments, build and cross-validate a classification model, apply feature selection to balance accuracy and interpretability, and deliver an executive summary memo with actionable marketing recommendations. The project integrates data summarization, statistical inference, classification modeling, and supervised learning into a single end-to-end analytical workflow.