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Can we use Logistic Regression to predict numerical(continuous) variable i.e Revenue of the Restaurant

I have been given a task to predict the revenue of the Restaurant based on some variables can i use Logistic regression to predict the Revenue data. the dataset is of kaggle Restaurant Revenue Prediction Project. PS :- I have been told to use Logistic regression i know its not the correct algorithm for this problem

Solution

  • Yes... You can.!!

    Prediction using Logistic Regression can be done for numerical variables. The data you have right now contains all independent variables, and the outcome will be a dichotomous (dependent variable, having value TRUE/1 or FALSE/0).

    You can then use it to determine the log odds ratio to find a probability(range 0-1).

    For a reference you can have look at this.

    -------------------UPDATE-------------------------------

    Let me give u an example of my last yr's wok.. we had to predict if a student can qualify in campus placement or not, given history data of 3 yrs of test results and their final success or failure. (NOTE : This is dichotomous, will talk about this later.)

    Sample data was, student's marks in academics, and aptitude test held at college, and their status as placed or not.

    But in your case, you have to predict the revenue (WHICH IS non-dichotomous). So what to do?? It seems that my case was simple, right?? Nope..!!

    We were not asked just to predict if the student will qualify or not, we were to predict the chances of individual student getting placed, which is not at all a dichotomous. Looks like your scenario right?

    So, what you can do is, first classify the data as for what input variables, what is the final output variable (that will help in revenue calculation).

    For eg: Use data to find out if the restaurant will go in profit or loss, then relate it with some algorithms to find out the approx revenue prediction.

    I'm not sure if there are already such algorithms (identical to your need) exists or not, but I'm sure you can do much better by putting more efforts on research an analysis on this topic.

    TIP: NEVER think in such way that "Will Logistic Regression ONLY solve my problem?" Rather expand it to, "What Logistic can do better if used with some other technique.?"