1️⃣ What is Linear Regression used for?
To predict continuous values by fitting a line between input (X) and output (Y).
Example: Predicting house prices.
2️⃣ How does Logistic Regression work?
It uses the sigmoid function to output probabilities (0-1) for classification tasks.
Example: Email spam detection.
3️⃣ What is a Decision Tree?
A flowchart-like structure that splits data based on features to make predictions.
4️⃣ How does Random Forest improve accuracy?
It builds multiple decision trees and takes the majority vote or average.
Helps reduce overfitting.
5️⃣ What is SVM (Support Vector Machine)?
An algorithm that finds the optimal hyperplane to separate data into classes.
Great for high-dimensional spaces.
6️⃣ How does KNN classify a point?
By checking the 'K' nearest data points and assigning the most frequent class.
It's a lazy learner – no actual training.
7️⃣ What is K-Means Clustering?
An unsupervised method to group data into K clusters based on distance.
8️⃣ What is XGBoost?
An advanced boosting algorithm — fast, powerful, and used in Kaggle competitions.
9️⃣ Difference between Bagging & Boosting?
⦁ Bagging: Models run independently (e.g., Random Forest)
⦁ Boosting: Models learn sequentially (e.g., XGBoost)
🔟 When to use which algorithm?
⦁ Regression → Linear, Random Forest
⦁ Classification → Logistic, SVM, KNN
⦁ Unsupervised → K-Means, DBSCAN
⦁ Complex tasks → XGBoost, LightGBM
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