tahira2k16/logistic-regression-projects

Logistic Regression Project

Logistic-Regression-for-Multiple-Variable

Regression Project In this project, the focus is on Multiple Linear Regression, a machine learning algorithm used to establish a linear relationship between one dependent variable and multiple independent variables. Linear Regression is suitable for supervised learning regression problems where the goal is to predict a continuous variable.

To clarify terminology, the project defines the terms as follows:

• Independent Variables:

  • Also known as Input variables.
  • Referred to as Feature variables or Predictor variables.
  • Denoted as X.
  • These are the variables that serve as inputs for the model.

• Dependent Variable:

  • Also known as Output variable.
  • Referred to as Target variable or Response variable.
  • Denoted as y.
  • This is the variable the model aims to predict.

In summary, the project employs Multiple Linear Regression, where there is one dependent variable (the target variable, y) and multiple independent variables (the feature variables or predictors, X). This type of regression allows for the modeling of relationships between the dependent variable and multiple input features, making it suitable for various practical applications in predictive modeling and data analysis.