Wk 6 R Pipeline — Portfolio Summary

Problem

Evaluate multiple analytical tasks in one reproducible workflow: subscription renewal prediction, insurance risk classification, credit risk scoring, and speech text pattern analysis.

Approach

• Logistic regression with glm for renewal propensity.
• Decision tree modeling with rpart for insurance category logic.
• Neural network classification with nnet on normalized features.
• Text mining with tm and document-term matrices for term prevalence insights.

Model stack snapshot

Outcome

• Final score: 100/100 on the Week 6 summative.
• Produced a consistent workflow across structured and semi-structured data.
• Generated interpretable findings and reusable scripts for future work.

Artifacts and proof

Neural network scoring output (nnet, normalized features) — representative sample from Exercise 3 (credit risk classification):

• Related course hub: DSC 550 — Data Mining
• Public portfolio parent: Dave Wilson · Base Layer Labs

# Simplified reproducible pipeline skeleton
model_glm <- glm(renewal ~ ., data = train_df, family = binomial())
model_tree <- rpart(risk_class ~ ., data = train_df)
model_nnet <- nnet(target ~ ., data = normalized_train, size = 5)

What I learned

• Feature normalization is critical for stable neural network training.
• Root split and variable importance in trees are related but not identical concepts.
• Text mining pipelines need strict preprocessing consistency to make output defensible.

Public-sanitized note

This summary omits full answer-key tables and local file-path details for public portfolio use.