Logistic Regression for classification of data

Logistic Regression is a popular algorithm used for binary classification tasks in machine learning. It is a type of regression analysis used to predict the outcome of a categorical dependent variable based on one or more independent variables.

How Logistic Regression works

Logistic Regression works by using the logistic function, also known as the sigmoid function, to map predicted values between 0 and 1. This function is defined as:

f(x) = 1 / (1 + e^(-x))

where x is the linear combination of the input features and model coefficients.

Classification of data

Logistic Regression is commonly used for binary classification tasks, where the target variable has two possible outcomes. For example, predicting whether an email is spam or not spam, whether a customer will churn or not churn, etc.

Implementation in Python

Logistic Regression can be implemented in Python using the scikit-learn library. Here is an example code snippet:

        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split
        from sklearn.metrics import accuracy_score

        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

        model = LogisticRegression()
        model.fit(X_train, y_train)

        y_pred = model.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)

        print("Accuracy:", accuracy)
    

Conclusion

Logistic Regression is a simple and efficient algorithm for binary classification tasks. It is widely used in various industries for predicting outcomes based on categorical data.