scikit-learn

 Scikit-Learn provides core machine learning tools. These include model evaluation functions like accuracy_score, data preparation tools like StandardScaler, and model selection helpers like train_test_split. It also features clustering algorithms like KMeans, classification models like SVC, and regression models like LinearRegression.

Evaluation and Preprocessing

Measures accuracy and error with accuracy_score and mean_squared_error
Builds confusion matrices with confusion_matrix
Scales data using StandardScaler and MinMaxScaler
Encodes labels with LabelEncoder
Fills missing data via SimpleImputer 

Selection and Clustering

Splits data using train_test_split
Tunes parameters with GridSearchCV
Validates models using cross_val_score
Groups data with KMeans
Uses hierarchical groups via AgglomerativeClustering 

Classification and Regression

Predicts classes with KNeighborsClassifier and SVC
Uses trees and Bayes via DecisionTreeClassifier and GaussianNB
Fits linear equations with LinearRegression and Lasso
Predicts values using RandomForestRegressor