Linear Regression and Logistic Regression have a lot of similarities and differences. Linear Regression is used to solve regression problems while Logistic Regression is used to solve classification problems. Below is the comparison of both the algorithms:

**1. Definition**

**Linear Regression:**Predicts a continuous dependent variable based on the values of independent variables.**Logistic Regression:**Predicts a categorical / discrete dependent variable based on the values of independent variables.**2. Curve**

**Linear Regression:**Straight Line. Equation of Line:**y = mx + c****Logistic Regression:**Sigmoid Curve (S Curve): Equation of S Curve:**log(y/(1-y)) = mx + c****3. Output**

**Linear Regression:**Predicts a continuous variable (example: What will be the GDP of the country this year?)**Logistic Regression:**Predicts a categorical / discrete variable (example: Whether it will rain today or not?)**4. Accuracy Measurement Techniques**

**Linear Regression:**Mean Absolute Error, Mean Square Error, Root Mean Square Error, R Square Method, Adjusted R Square Method**Logistic Regression:**Confusion Matrix, Classification Report, Accuracy Score, F1 Score, Precision, Recall, ROC (Receiver Operating Characteristic), AUC (Area Under the ROC Curve)
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