Machine Learning- Data Science

[image] Day 3 of building ML models - Decision Trees & Random Forests. Predicted loan approval from 491 applications. Random Forest edged out a single Decision Tree (84.85% vs 83.84%), no overfitting on either.

[image] Day 2 of building ML models - Logistic Regression Built a Logistic Regression churn model, 80% accuracy sounded great until I checked recall and found it was missing half of the actual churners. Fixed it by tuning the decision threshold, not the model

[image] Day 6 of building ML models one at a time: Naive Bayes. Built a spam classifier on 5K+ SMS messages. 98.4% accuracy AND 91% recall on spam, no trade-off this time, unlike my churn and wine models. Full project:

[image] Day 4 of building ML models - K-Means Clustering Used K-Means to turn 200 anonymous mail custominto 5 marketing personas, no labels, just clustering by income and spending behavior. Full project:

[video] LATEST FROM STANFORD!!!