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Python Machine Learning

You're reading from   Python Machine Learning Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Length 454 pages
Edition 1st Edition
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Author (1):
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Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Toc

Table of Contents (15) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Machine Learning Algorithms for Classification FREE CHAPTER 3. A Tour of Machine Learning Classifiers Using Scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

Turning the movie classifier into a web application


Now that we are somewhat familiar with the basics of Flask web development, let's advance to the next step and implement our movie classifier into a web application. In this section, we will develop a web application that will first prompt a user to enter a movie review, as shown in the following screenshot:

After the review has been submitted, the user will see a new page that shows the predicted class label and the probability of the prediction. Furthermore, the user will be able to provide feedback about this prediction by clicking on the Correct or Incorrect button, as shown in the following screenshot:

If a user clicked on either the Correct or Incorrect button, our classification model will be updated with respect to the user's feedback. Furthermore, we will also store the movie review text provided by the user as well as the suggested class label, which can be inferred from the button click, in a SQLite database for future reference...

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