A logistic regression model anticipates a dependent data variable by examining the connection between one or more pre-existing independent variables. Logistic regression, which is commonly used for classification tasks, has numerous advantages, but it also has some drawbacks. When working with high-dimensional datasets, overfitting of the model may occur, resulting in inaccurate conclusions. Since data preparation is a time-consuming procedure when employing logistic regression, data maintenance becomes difficult as well. One of the major drawbacks of logistic regression is that it cannot deal with non-linear problems.