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Build and assess a model: product reviews data

In this exercise, you will build a logistic regression using the reviews dataset, containing customers' reviews of Amazon products. The array y contains the sentiment : 1 if positive and 0 otherwise. The array X contains all numeric features created using a BOW approach. Feel free to explore them in the IPython Shell.

Your task is to build a logistic regression model and calculate the accuracy and confusion matrix using the test dataset.

The logistic regression and train/test splitting functions have been imported for you.

This exercise is part of the course

Sentiment Analysis in Python

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Exercise instructions

  • Import the accuracy score and confusion matrix functions.
  • Split the data into training and testing, using 30% of it as a test set and set the random seed to 42.
  • Train a logistic regression model.
  • Print out the accuracy score and confusion matrix using the test data.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the accuracy and confusion matrix
____

# Split the data into training and testing
X_train, X_test, y_train, y_test = ____(____, ____, ____=0.3, ____=42)

# Build a logistic regression
log_reg = ____._____

# Predict the labels 
y_predict = log_reg.predict(X_test)

# Print the performance metrics
print('Accuracy score of test data: ', ____(____, ____))
print('Confusion matrix of test data: \n', ____(____, ____)/len(y_test))
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