Data Science Python

Course Content

Total learning: 23 lessons Time: 10 weeks

Part – 6 Data Cleaning outlier treatment

Part-11 Logistic Regression Analysis

  • Logistic Regression
  • Discriminate Regression Analysis
  • Multiple Discriminant Analysis
  • Stepwise Discriminant Analysis
  • Logit function
  • Test of Associations
  • Chi-square strength of association
  • Binary Regression Analysis
  • Profit and Logit Models
  • Estimation of probability using logistic regression,
  • Wald Test statistics for Model
  • Hosmer Lemshow
  • Nagurkake R square
  • Pseudio R square
  • Maximum likelihood estimation
  • Model Fit
  • Model cross validation
  • Discrimination functions
  • AIC
  • BIC (Bayesian information criterion)
  • Kappa Statistics
  • AIC
  • BIC
  • Error/ Confusion matrices
  • ROC
  • APE
  • MAPE
  • Lift Curve
  • Sensitivity
  • Misclassification Rating
  • Specificity
  • Maximum Absolute Error
  • Recall
  • Miss classification
  • Root Final Prediction Error
  • Gini Coefficient
  • Schwarz’s Bayesian Criterion
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