Lecture 44:- Logistic Regression Code

Here's a simple example of how you can implement Logistic Regression using Python and the popular scikit-learn library for a binary classification problem. In this example, we'll use a mock dataset with two features.

pythonCopy code

import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # Generate mock data np.random.seed(0) X = np.random.rand(100, 2) # 100 samples, 2 features y = (X[:, 0] + X[:, 1] > 1).astype(int) # Create binary labels based on the sum of features # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create and train the Logistic Regression model model = LogisticRegression() model.fit(X_train, y_train) # Make predictions on the test set y_pred = model.predict(X_test) # Calculate accuracy accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy:.2f}")

This code snippet demonstrates the following steps:

1. Import necessary libraries (numpy, scikit-learn).
2. Generate mock data with two features and binary labels.
3. Split the data into training and testing sets.
4. Create a LogisticRegression model from scikit-learn.
5. Train the model on the training data.
6. Make predictions on the test set.
7. Calculate and print the accuracy of the model.

Remember that this is a basic example, and in a real-world scenario, you would preprocess your data, handle missing values, perform feature scaling, and potentially tune hyperparameters for better performance.

Also, please note that machine learning code can vary based on the specific dataset and problem you are working on. The provided code is just a starting point to help you understand the implementation of Logistic Regression.