Logistic Regression for Predictive Modeling with R

Master logistic regression in R by preparing the advertisement dataset: set the working directory, load the data, inspect age and salary to predict purchased, and convert purchased to a factor.

Select predictors such as age and salary, encode the target as a factor, and split the data into training and test sets in R using caTools for logistic regression.

Explore feature scaling for distance-based models by comparing standardization (z-scores) and normalization, and see how scaling brings age and salary data to comparable ranges in training and test sets.

Fit a logistic regression model to the training set using glm with a binomial family, formula purchase ~ ., and predict test set results.

Explore how logistic regression uses maximum likelihood to tune beta naught and beta one, and how predictions map to a confusion matrix: true positives, true negatives, false positives, false negatives.

Explore how a classifier predicts yes or no and interpret a confusion matrix with true positives, true negatives, false positives, and false negatives; learn accuracy, sensitivity, and precision.

Explore logistic regression predictions in R by using predict(type='response') to obtain probabilities, set a 0.5 threshold for class labels, and evaluate performance with a confusion matrix and the caret package.

Visualize the logistic regression training and test sets with scaled data, revealing the linear classifier's decision boundary and misclassifications via the confusion matrix.

Apply logistic regression to the diabetes dataset to predict diabetic outcomes, including encoding the target variable and preparing a train–test split with caTools.

Train a logistic regression model in R using glm with a binomial family on the diabetes dataset. Predict on the test set and review the confusion matrix and accuracy.

Learn to build logistic regression models by assessing variable significance, applying backward elimination (alpha 0.05) and forward selection, and comparing models in R.

Apply dimension reduction via backward elimination in logistic regression, removing variables based on p-values and monitoring residual deviance and AIC to select the final model.

Compare two confusion matrices to evaluate the final logistic regression model, using thresholded predictions and observed outcomes, and note accuracy improving from 74% to 76%.

Apply ROC curves to reduce false positives in medical prediction, balancing sensitivity and specificity while exploring thresholds and interpreting the area under the curve.

Learn to generate predictions and plot a roc curve in R, evaluate a logistic regression model with performance, and tune thresholds by true and false positive rates.

Set thresholds in logistic regression, evaluate with ROC curves and confusion matrices, reduce false positives, and use area under the ROC curve to gauge model performance in R.

Learn to plot ROC curves and compute the area under the curve in logistic regression with R. Apply backward elimination, threshold tuning, and R Markdown reports to share results.

Load the credit risk dataset in R, handle missing values and outliers, and apply logistic regression to predict loan eligibility. Split training and test sets and assess accuracy.

Encode the loan status as binary 0/1, prepare ROC curves, and preprocess by handling missing values and blank spaces in categorical variables using R.

Learn to clean a loan dataset in R by imputing missing values for marital status, dependents, and self-employed, and set correct levels for categorical variables before modeling.

Learn to handle applicant income as a numeric feature in logistic regression: check missing values, detect outliers with IQR and box plots, and impute with mean or median in R.

Learn to detect and replace outliers in applicant and co-applicant income using a Q3 plus 1.5 IQR threshold, replace with the mean, validate with box plots and summaries.

The lecture demonstrates cleaning the loan amount variable by identifying missing values, imputing with the median, and capping outliers using an IQR-based threshold, preparing data for modeling in R.

Learn to handle missing values in the loan amount term by replacing NAS with 360 and binning into categories, then convert to a factor for modeling.

Treat credit history as a categorical variable, convert it to a factor after handling missing values, remove loan id, and split the data into training and test sets before modeling.

Split the dataset into training and test sets with 80/20 ratio using ktools, then fit a logistic regression model (glm, binomial) and evaluate with a confusion matrix and roc auc.