Breast Cancer Classification with Deep Learning - Keras and Tensorflow - Ending Note

Ending Note

David Landup
David Landup

Conclusion

That would wrap up this Guided Project. Thank you for coming along on the ride, and I hope that you've learned how you can put your Machine Learning skills to very good use in the field of Medical Diagnosis.

In this Guided Project - we've started out with exploring some of the applications of Machine Learning in medicine, followed by an introduction to the topic of the project. With an engineering mindset - we've considered the problem at hand, and why performing the task of classifying breast cancer is expensive and difficult, as well as how we could remedy this, and what the rewards and benefits of solving the problem are.

Then, we've loaded in the dataset and performed Exploratory Data Analysis, and got familiar with the data in the domain. Only then, we've delved into the standard Machine Learning Workflow, starting with Data Preprocessing. We've explored what Class Imbalance is, and whether it poses an issue for us, as well as cases in which it would. We've explored the possibilities of removing class imbalance for projects that would be negativelly affected by it, and considered the implications of what the imbalance would bring us, noting them down for making educated guesses down the line.

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