intro_MLclassification1

This Jupyter Notebook serves as a comprehensive guide to performing linear support vector machine (LinearSVC) classification and calculating accuracy scores for machine learning tasks. It provides step-by-step instructions and code examples for building, training, and evaluating a LinearSVC classifier

intro_classicacao1: Train aglorithms, Define Features, LinearSVC, acurracy_score, model.fit(), model.predict()

intro_classicacao2: Open a CSV, .rename(), train_test_split(), value_counts()

classificação_2D: Seaborn, np.arange(), SVM(), Create random values with SVC, Using StandardScaler to improve the model

intro_Machine_learning_4: datatime, DummyClassifier, Graphviz, DecisionTreeClassifier