In this code, we first import the necessary libraries and load the data from a CSV file into a Pandas dataframe. We then split the data into features (X) and target (y), with the features being all of the columns except for credit_approved and the target being the credit_approved column.
Next, we split the data into training and test sets using the train_test_split function. This function shuffles the data and splits it into a training set and a test set, with the test_size parameter specifying the proportion of the data to be used for the test set.
We then create a logistic regression model using the LogisticRegression class, and train the model on the training data using the fit method. Finally, we test the model on the test data and calculate the accuracy of the model using the score method.
This is just one example of how you could perform a regression analysis using Python. There are many other libraries and techniques that you can use to perform regression analysis in Python.
There are many different types of regression methods, and the appropriate method to use can depend on the characteristics of your data and the goals of your analysis. Some of the most common types of regression methods include:
Logistic regression: This is a method for modeling the probability of a binary response variable based on one or more predictor variables. Logistic regression is used to predict the probability that an event will occur (e.g. a customer will churn) based on the values of the predictor variables.
Linear regression: This is a method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the data. Linear regression is used to predict a continuous response variable based on one or more predictor variables.
Polynomial regression: This is a method for modeling the relationship between a dependent variable and one or more independent variables by fitting a polynomial equation to the data. Polynomial regression can be used to model relationships between variables that are not linear.
Multivariate linear regression: This is a method for modeling the relationship between a dependent variable and multiple independent variables by fitting a linear equation to the data.
Nonlinear regression: This is a method for modeling the relationship between a dependent variable and one or more independent variables by fitting a nonlinear equation to the data. Nonlinear regression can be used to model complex relationships between variables.
Stepwise regression: This is a method for building a regression model by adding or removing predictor variables from the model based on statistical criteria.
These are just a few examples of the many types of regression methods that are available. There are also many variations and extensions of these methods that can be used in different situations.