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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")

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

Classification

telecom.head()
# Import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier 

# Create arrays for the features and the target variable
y = telecom["churn"].values
X = telecom[["account_length", "customer_service_calls"]].values

# Create a KNN classifier with 6 neighbors
knn = KNeighborsClassifier(n_neighbors=6)

# Fit the classifier to the data
knn.fit(X, y)
import numpy as np

X_new = np.array([[30.0, 17.5],
                  [107.0, 24.1],
                  [213.0, 10.9]])

# Predict the labels for the X_new
y_pred = knn.predict(X_new)

# Print the predictions for X_new
print("Predictions: {}".format(y_pred))

Measuring model performance

# Import the module
from sklearn.model_selection import train_test_split

X = telecom.drop("churn", axis=1).values
y = telecom["churn"].values

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
knn = KNeighborsClassifier(n_neighbors=5)

# Fit the classifier to the training data
knn.fit(X_train,y_train)

# Print the accuracy
print(knn.score(X_test, y_test))
# Create neighbors
neighbors = np.arange(1, 13)
train_accuracies = {}
test_accuracies = {}

for neighbor in neighbors:
  
	# Set up a KNN Classifier
	knn = KNeighborsClassifier(n_neighbors=neighbor)
  
	# Fit the model
	knn.fit(X_train, y_train)
  
	# Compute accuracy
	train_accuracies[neighbor] = knn.score(X_train, y_train)
	test_accuracies[neighbor] = knn.score(X_test, y_test)
print(neighbors, '\n', train_accuracies, '\n', test_accuracies)
import matplotlib.pyplot as plt

# Add a title
plt.title("KNN: Varying Number of Neighbors")

# Plot training accuracies
plt.plot(neighbors, train_accuracies.values(), label="Training Accuracy")

# Plot test accuracies
plt.plot(neighbors, test_accuracies.values(), label="Testing Accuracy")

plt.legend()
plt.xlabel("Number of Neighbors")
plt.ylabel("Accuracy")

# Display the plot
plt.show()

Regression

Introducing to regression

Aca lo que hacemos es extraer columnas "X" y "Y" de acuerdo a lo que deseamos solo para esas dos columnas.

Los vectores deben estar en dos dimensione spara sckit learn los pueda procesar, por eso usamos .reshape y como se vera en la salida del codigo, solo "X" quedo convertida a una tupla que puede ser manejada por scikit learn

import numpy as np
import pandas as pd

# Create X from the radio column's values
X = advertising["radio"].values

# Create y from the sales column's values
y = advertising["sales"].values

# Reshape X
X = X.reshape(-1,1)

# Check the shape of the features and targets
print(X.shape,y.shape)

Vamos a crear el modelo de regresion

  • Tengamos presente que las predicciones solo las hacemos sobre los features o caracteristicas= "X" y que curiosamente podemos tener varias predicciones pues se esta usando el "X" y podriamos proyect mucho mas valores por medio de la linea "print(predictions[:#])"
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