@varsha , the basic difference between logistic regression and linear regression are as follows:
1. Linear regression is a regression model which means it will give continuous output. Logistic regression is a binary classification algorithm which means it will give discrete output.
2. In Linear Regression, residuals are assumed to be normally distributed whereas, Logistic Regression, residuals need to be independent but not normally distributed.
3.Linear Regression is used when the response variable in continuous, Logistic Regression is used when the response variable is categorical.
4. Linear Regression gives an degree 1 equation: y=mx+c , Logistic Regression gives an equation of the form: Y=e^x + e^-x.
5. In Linear Regression the coefficient interpretation is straightforward whereas, in Logistic Regression it depends on log ,inverse-log, binomial, etc.