Скачать или смотреть Logistic Regression + Hyperparameter Tuning | Machine Learning with Python

🚀 In this tutorial, we move from theory to practice with a hands-on implementation of Logistic Regression in Python. You’ll also learn how to improve your model using hyperparameter tuning with GridSearchCV.

✅ What You’ll Learn:

Importing dataset and preprocessing steps (train-test split, scaling)

Implementing Logistic Regression with scikit-learn

Model training and making predictions

Evaluating performance with Accuracy, Precision, Recall, F1-score

Visualizing the decision boundary

Hyperparameter tuning with GridSearchCV

Parameters: C, penalty (L1, L2, ElasticNet), solver

Choosing the best model automatically

Comparing default vs tuned Logistic Regression performance

🎓 This video is part of the SMIT Data Science & Machine Learning Series with Code with Yasir Nawaz.

👉 By the end, you’ll know how to train, evaluate, and optimize Logistic Regression models like a pro.