Data Science — Classification

Logistic regression

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=1000, n_features=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LogisticRegression()
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.4f}")

Decision trees

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier(max_depth=5)
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)

Random forest

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)

SVM

from sklearn.svm import SVC

model = SVC(kernel='rbf')
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)

Evaluation metrics

from sklearn.metrics import classification_report, confusion_matrix

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

Mini Practice

1. Train different classifiers
2. Compare performance
3. Tune hyperparameters
4. Analyze confusion matrix

Up Next

Continue with Clustering - Grouping data.