What Is a Good Model?-Evaluating Classifiers-Generalizing Beyond Classification-A Key Analytical Framework:Expected Value-Evaluation, Baseline Performance ,and Implications for Investments in Data
Ranking Instead of Classifying - Profit Curves – ROC Graphs and Curves - The Area under the ROC Curve (AUC) - Cumulative Response and Lift Curves-Example: PerformanceAnalytics forChurnModeling
Example: Targeting Online Consumers with Advertisements -Combining Evidence Probabilistically - Applying Bayes’ Rule to Data Science - A Model of Evidence “Lift”-Example: Evidence Lifts from Facebook “Likes”
Toward Analytical Engineering: Targeting the Best Prospects for a Charity Mailing – Our Churn Example Revisited with Even More Sophistication- Assessing the Influence of the Incentive-From an Expected Value Decomposition to a Data Science Solution
Co-occurrences and Associations: Finding Items That Go Together-Profiling :Finding Typical Behavior- Link Prediction and Social Recommendation - Data Reduction, Latent Information, and Movie Recommendation - Bias, Variance, and Ensemble Methods - Data-Driven Causal Explanation and a Viral Marketing Example
Reference Book:
1. MattTaddy,“BusinessDataScience”,McGraw–HillEducationLLC,FirstEdition,ISBN–978-1-26-045278-5 2. TonyGuida,“BigDataandMachineLearninginQuantitativeInvestment”,JohnWiley&Sons,Ltd,FirstEdition,ISBN –9781119522195 3. HadleyWickham,“RforDataScience:Import,Tidy,Transform,Visualize,andModelData,O’ReillyMedia,Inc., FirstEdition,ISBN –978-1-491-91039-9 4. StephenKlosterman,“DataScienceProjectswithpython”,PacktPublishing, FirstEdition,ISBN–978-1-83855-102-5
Text Book:
FosterProvostandTomFawcett,“DataScienceforBusiness”,O’ReillyMedia,Inc.,FirstEdition,ISBN – 978-1-449-36132-7
