ALL SKLEARN MODELS (MAIN CATEGORIES)

✅ 1. Supervised Learning Models

📈 Regression (predict numbers)

Linear Regression
Ridge Regression
Lasso Regression
ElasticNet
Decision Tree Regressor
Random Forest Regressor
Support Vector Regression (SVR)
K-Nearest Neighbors Regressor

📉 Classification (predict labels)

Logistic Regression
Decision Tree Classifier
Random Forest Classifier
Support Vector Machine (SVC)
K-Nearest Neighbors (KNN)
Naive Bayes:
- GaussianNB
- MultinomialNB
- BernoulliNB
SGD Classifier
Perceptron
Ridge Classifier

🔍 2. Unsupervised Learning Models

📊 Clustering

K-Means
DBSCAN
Agglomerative Clustering
Mean Shift
Spectral Clustering
Birch

📉 Dimensionality Reduction

PCA (Principal Component Analysis)
Kernel PCA
Truncated SVD
t-SNE (for visualization)
Factor Analysis

🔎 Outlier Detection

One-Class SVM
Isolation Forest
Local Outlier Factor (LOF)

⚙️ 3. Model Selection & Optimization (not models, but important tools)

GridSearchCV
RandomizedSearchCV
cross_val_score
train_test_split

🧾 4. Ensemble Models (very important)

BaggingClassifier / BaggingRegressor
RandomForestClassifier / Regressor
AdaBoost
GradientBoostingClassifier / Regressor
VotingClassifier
StackingClassifier

📊 5. Linear & Generalized Models

LinearRegression
LogisticRegression
Ridge / Lasso / ElasticNet
SGDRegressor / SGDClassifier
Bayesian Ridge

🧠 6. Probabilistic Models

Naive Bayes (all variants)
Gaussian Process Regression
Gaussian Process Classification

🔧 7. Other Useful ML Tools (not models)

StandardScaler
MinMaxScaler
LabelEncoder
OneHotEncoder
PolynomialFeatures

🧠 SIMPLE STRUCTURE

📈 Supervised Learning

→ Regression + Classification models

🔍 Unsupervised Learning

→ Clustering + Dimensionality Reduction

🔥 Ensemble Learning

→ Combining multiple models

⚙️ Utility Tools

→ Preprocessing + tuning

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