How to predict the Revenue by using logistic Regression This is how my Training dataset look like i want to predict revenue of the Restraunt

I have been told to use logistic Regression to predict the revenue of the restaurant how can i achieve this using logistic Regression what is the good tutorial for how can i achieve it. 
 A: Revenue is a continuous (actually ratio scale) variable, so logistic regression is not appropriate.  You should look into (multiple) linear regression.  

I have been told to use logistic Regression to predict the revenue of
  the restaurant

Who told you? Ask him/her to show you how!
When you have fitted a linear regression, you should criticize the model with residual plots and so on. If that shows problems with linear regression you can come back here and ask.
A: People, let´s not confuse the Logit model with the logistic curve itself. Logistic curves can be used to model and forecast revenue. People do it all the time.
The Logit model is appropriate when you want to know the probability of a event, but what you want here is not that. You wanna fit your data into a logistic curve, estimating the parameters that best describe your reality.
The parameters can be estimated in different ways, but the most common is to use a computer program to minimize the sum of the square of residuals, the OLS estimator.
You can see a practical example here: https://courses.lumenlearning.com/ivytech-collegealgebra/chapter/build-a-logistic-model-from-data/
A: Your advisor is mistaken. Logistic regression is not suited for real-value prediction; it is suited for dichotomous 0/1, true/false, etc prediction. While you could reformulate the problem to a binary problem (sales increasing/decreasing) there's no good reason to do so. 
Even a cursory glance at the Wikipedia page for logistic regression will convince your advisor otherwise. 
A: I can disagree with most comments at my risk. 
In a more general setting, where a researcher would have a continuous dependent variable and binary independent variable, the logistic regression can be useful in a way, the the input can be the continuous variable and the output will be the binary (independent) variable. This modelling would contain the same estimate of the model but coefficient meaning seems tricky.
A: Logistic Regression is a classification Algorithm of Machine Learning. With Logistic Regression, we can predict binary classification problems(0 or 1). From your snapshot of dataset as far as I saw you want to predict revenue of the Restraunt which contains real values. To predict this type problems you have to use Regression type Algorithm such that Linear Regression , Support Vector for Regression (SVR) , Decision Tree Regression, Random Forest Regression  , Bayesian Ridge Regression, BayesianRidge etc.
