Logistic Regression using R in 10 easy steps!

Explore predictive modeling by translating business problems into statistical equations, selecting dependent and independent variables, preparing data, and validating logistic regression models with training and validation sets.

Explore binary logistic regression by estimating probabilities for outcomes like loan repayment or fraud, using a dichotomous dependent variable and dummy coding for categorical predictors.

Explore how to build a logistic regression model from a finance case study, identify influential variables, create dummy variables, and predict loan default probabilities using a data dictionary.

Partition the data into training and validation sets with random sorting, typically in a 70/30 split, and use cross-validation to build and test a logistic regression model on unseen data.

Perform univariate analysis for logistic regression by examining descriptive statistics and outliers, then impute missing values and cap or discard anomalies to improve model accuracy.

Perform bivariate analysis to assess the impact of an independent variable on the dependent variable using cross tables. Learn missing value treatment, imputation, and dummy variable creation for logistic regression.

Explore multicollinearity analysis to identify highly correlated independent variables, assess with vif and r-squared, and iteratively remove variables with vif above 1.5 to build a good model.

Build logistic regression model using forward and backward selection, maximum likelihood estimation, and diagnostics like deviance, AIC, and chi-square test to predict default probabilities.

Test the logistic regression model on validation data to assess overfitting and real-world performance by predicting probabilities, comparing with actual outcomes, and confirming data preparation steps.

Assess logistic regression model performance using confusion matrix metrics—accuracy, sensitivity, specificity, and precision—across model and validation data, and determine optimal cutoff thresholds via ROC AUC.

Build readable scorecards from logistic regression outputs that convert probabilities into scores within a defined range, covering type 1 and type 2 conventions for defaulters.