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Logistic Regression Model for Binary Classification

Introduction

This is a logistic regression model for binary classification. It reads a CSV file containing input data with two attributes and a target class label, and pre-processes the data by removing unwanted columns and splitting it into training and test sets.

Functions

1. sigmoid(z): computes the sigmoid of an input z.

2. loss(Y, y_hat): computes the logistic regression loss.

3. gradients(X, Y, y_hat): computes the gradients of the loss w.r.t the weights.

4. normalize(X): normalizes the input data.

5. predict(X, w): predicts the class label of input data using the learned weights.

6. plot_decision_boundary(X, w): plots the decision boundary for the input data.

7. train(X, Y, epochs, eta): trains the logistic regression model on the input data using gradient descent. It returns the learned weights and the loss vs. epoch list.

Model Usage

1. Using the trained model, predict the output class labels for the test set.
2. Calculate the accuracy of the model on the test set by comparing the predicted class labels with the true class labels.

3. Visualize the decision boundary by plotting the test set along with the decision boundary line, which is the line that separates the positive and negative classes.

Conclusion

After training and evaluating the logistic regression model, the results can be interpreted and conclusions drawn.