The dataset used is the SUV purchase decision (https://www.kaggle.com/arnabdata/suv-purchase-decision) from Kaggle.
This dataset contains details of 400 customers.
User ID
Gender
Estimated Income
Purchase Decision (0 = No; 1 = Yes)
The goal is fit a Classifier to the data and provide predictions for future customers and check which factors are affecting the person's mentality to purchase the SUV or not.
This will help the company understand which factors they need to take care of in order to attract more and more customers to buy their cars.
Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression[1] (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name.