Logistic Regression

1. Understanding KNN and Logistic
Regression
With Examples and Iris Dataset Code
Explanation
By: [Your Name]

2. What is KNN?
• K-Nearest Neighbors (KNN) is a simple, non-parametric algorithm.
• Instance-based learning: no explicit training phase.
• Classification based on proximity in feature space.

3. How KNN Works
• 1. Choose a value for K (number of neighbors).
• 2. Calculate distance (e.g., Euclidean) from new point to training points.
• 3. Find K nearest neighbors.
• 4. Assign the most common class among the neighbors.

4. Example of KNN
• New fruit to classify: Shape = Round, Color = Red.
• Training examples: Apples (Round, Red), Bananas (Long, Yellow).
• Most neighbors suggest Apple -> Classified as Apple.

5. What is Logistic Regression?
• A statistical model for binary or multi-class classification.
• Estimates the probability of a data point belonging to a class.
• Uses a logistic (sigmoid) function to squeeze output between 0 and 1.

6. How Logistic Regression Works
• 1. Compute weighted sum of inputs.
• 2. Apply sigmoid activation function.
• 3. Predict class based on a threshold (e.g., 0.5).

7. Example of Logistic Regression
• Predict whether a student passes based on study hours.
• More study hours -> Higher probability of passing.
• Probability output: e.g., 0.8 -> Pass.

8. Logistic Regression - Iris Dataset
Code (Part 1)
• Load Iris dataset using sklearn.
• Split data into features (X) and labels (y).
• Prepare training and testing sets.

9. Logistic Regression - Iris Dataset
Code (Part 2)
• Initialize LogisticRegression model.
• Train model with training data.
• Predict outcomes on test data.
• Evaluate with accuracy score.

10. Code Walkthrough
• 1. Load data.
• 2. Preprocess: train-test split.
• 3. Train model.
• 4. Test model.
• 5. Measure and print accuracy.

11. Summary
• KNN: Distance-based, simple, effective.
• Logistic Regression: Probability-based, robust for classification.
• Both are key tools in machine learning!

12. Thank You!
• Any Questions?
• Happy Learning!