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Supervised Learning with scikit-learn

Run the hidden code cell below to import the data used in this course.

# Importing pandas
import pandas as pd

# Importing the course datasets 
diabetes = pd.read_csv('datasets/diabetes_clean.csv')
music = pd.read_csv('datasets/music_clean.csv')
advertising = pd.read_csv('datasets/advertising_and_sales_clean.csv')
telecom = pd.read_csv("datasets/telecom_churn_clean.csv")

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from sklearn.neighbors import KNeighborsClassifier
X = churn_df[['total_day_charge', 'total_eve_charge']].values
y = churn_df['churn'].values
print(X.shape, y.shape)

knn = KNeighborsClassifier(n_neighbors=15)
knn.fit(X, y)

Classifying labels with unseen data

1 - Build a model 2 - Model learns from the labeled data we pass to it 3 - Pass the unlabeled data to the model as input 4 - Model predicts the labels of unseen data

  • labeled data = training data

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