Linear Regression

Import and create the model:

from sklearn.linear_model import LinearRegression

your_model = LinearRegression

Fit:

your_model.fit(x_training_data, y_training_data)

.coef_: contains the coefficients
.intercept_: contains the intercept

Predict:

predictions = your_model.predict(your_x_data)
.score(): returns the coefficient of determination R²


Naive Bayes

Import and create the model:

from sklearn.naive_bayes import MultinomialNB

your_model = MultinomialNB()

Fit:

your_model.fit(x_training_data, y_training_data)

Predict:

# Returns a list of predicted classes - one prediction for every data point
predictions = your_model.predict(your_x_data)

# For every data point, returns a list of probabilities of each class
probabilities = your_model.predict_proba(your_x_data)

K-Nearest Neighbors

Import and create the model:

from sklearn.neigbors import KNeighborsClassifier

your_model = KNeighborsClassifier()

Fit:

your_model.fit(x_training_data, y_training_data)

Predict:

# Returns a list of predicted classes - one prediction for every data point
predictions = your_model.predict(your_x_data)

# For every data point, returns a list of probabilities of each class
probabilities = your_model.predict_proba(your_x_data)

K-Means

Import and create the model:

from sklearn.cluster import KMeans

your_model = KMeans(n_clusters=4, init='random')

n_clusters: number of clusters to form and number of centroids to generate
init: method for initialization
k-means++: K-Means++ [default]
random: K-Means
random_state: the seed used by the random number generator [optional]

Fit:

your_model.fit(x_training_data)

Predict:

predictions = your_model.predict(your_x_data)

Validating the Model

Import and print accuracy, recall, precision, and F1 score:

from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score

print(accuracy_score(true_labels, guesses))
print(recall_score(true_labels, guesses))
print(precision_score(true_labels, guesses))
print(f1_score(true_labels, guesses))

Import and print the confusion matrix:

from sklearn.metrics import confusion_matrix

print(confusion_matrix(true_labels, guesses))

Training Sets and Test Sets
from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)


train_size: the proportion of the dataset to include in the train split
test_size: the proportion of the dataset to include in the test split
random_state: the seed used by the random number generator [optional]
Robot Emoji
Happy Coding!

