# Conclusion

Text classification is a fundamental machine learning problem with applications
across various products. In this guide, we have broken down the text
classification workflow into several steps. For each step, we have suggested a
customized approach based on the characteristics of your specific dataset. In
particular, using the ratio of number of samples to the number of words per
sample, we suggest a model type that gets you closer to the best performance
quickly. The other steps are engineered around this choice. We hope that
following the guide, the
[accompanying code](https://github.com/google/eng-edu/tree/master/ml/guides/text_classification),
and the
[flowchart](https://developers.google.com/machine-learning/guides/text-classification/step-2-5#figure-5)
will help you learn, understand, and get a swift first-cut solution to your text
classification problem.