The machine learning algorithm specifically designed for binary classification problems is the Logistic Regression algorithm. Despite its name, logistic regression is primarily used for classification tasks where the outcome or dependent variable is binary (i.e., it can take one of two possible classes, such as 0 or 1, True or False, Yes or No, etc.).

Logistic regression works by modeling the probability that an instance belongs to a particular class. It uses a logistic function (sigmoid function) to map predicted values to probabilities between 0 and 1. Based on these probabilities, it assigns the instances to the most appropriate class, usually using a threshold (typically 0.5).

Though the name might suggest it's a regression algorithm, logistic regression is widely used for binary classification due to its simplicity, efficiency, and interpretability. For multiclass classification problems (more than two classes), extensions like "Multinomial Logistic Regression" or other algorithms like Support Vector Machines (SVM), Decision Trees, and Random Forests can be used.

For categorical variables, you should perform a classification task. I analysed mainly categorical variables. Concerning supervised models, I used binary logistic regressions for inference models (i.e., to understand how predictors affect the dependent variable) and classification gradient boosting decision trees (random forests are also a possibility) for predictive models (i.e. forecast the outcome). For unsupervised models (i.e. determining hidden patterns in data), I aggregate data by individuals calculating percentages and making numeric data to use K-mean clustering.

The Support Vector Machine (SVM) algorithm is specifically designed for binary classification problems, where it separates data points into two distinct classes using a hyperplane in a high-dimensional space.

Logistic Regression, Support Vector machine , Naive Baye's and K-nearest neighbours are first in place algorithm that deals with Binary classififcation problems when several independent variable affects single outcome variable where the outcome is Binary in 0 or 1 , else in Bool in Yes or No.

Support Vector Machine (SVM) is best algorithm for the binary classification. It is used to separate two classes by draw a line between classes called hyperplane. If data points are complex and can't be separate by hyperplane then K-Nearest Neighbour KNN will be gives better results.