Scikit-Learn is an essential tool for machine learning with Python

In this post, we’re going to go through the fundamental features of Scikit-Learn. We will approach theory and real world applications in another series.

Until then, this will serve as a way to get familiar with the tools we can use.

Loading Data

Before you begin writing any code, you need to understand where your data is coming from.

Common Data Sources:

CSV

Text

Web

Common Data Forms:

Pandas Dataframes

NumPy Arrays

SciPy Matrices

Train and Test

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=)

Pre-Processing

1. Standardize your data:

Scale -> transorm train -> transorm test

standardScaler().fit(x_train)

.transform(x_train)

.transorm(x_test)

2. Normalize your data:

Normalize -> transform x_train -> transorm test

Normalizer().fit(x_train)

.transform(x_train)

.transform(x_test)

3. Binarize your data:

Binarizer(threshold= int).fit(x_data)

.transform(x_data)

Handling Missing Data

Imputer(missing_values= int, strategy = 'mean', axis = int)

.fit_transform(x_data)

Supervised Learning

Estimators

KNN:

neighbors.KNeighborsClassifier(n_neighbors=5)

Linear Regression:

LinearRegression(normalize=True)

Naive Bayes:

GaussianNB()

Support Vector Machines:

SVC(kernel='linear')

Fitting Models:

your_lr.fit(X, y)

your_knn.fit(X_train, y_train)

your_svc.fit(X_train, y_train)

Predictors:

your_svc.predict(np.random.random((2,5)))

your_lr.predict(X_test)

your_knn.predict_proba(X_test)