what is logistics regression

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 (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).

Here are the steps to perform logistic regression:

1. Data Collection: The first step in the process is collecting relevant data. This could be historical data, survey data, or any other type of data that might be relevant to your goal.

2. Data Preparation: Once you have collected the data, you will need to prepare it for analysis. This could involve cleaning the data, selecting relevant variables, and splitting the data into a training set and a test set.

3. Model Creation: After your data is prepared, you can create your logistic regression model. This involves selecting a suitable logistic function and estimating the parameters of the model using your training data.

4. Model Testing: Once your model has been created, you should test it using your test data. This will give you an idea of how well your model is likely to perform on new data.

5. Model Use: If your model tests well, you can then use it to make predictions or decisions based on new data.

6. Model Refinement: Over time, you may find that your model needs to be refined or updated. This could be due to changes in the data, changes in the underlying relationship between the variables, or simply because you have collected more data that can be used to improve the model.