Predicitng_viability

Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for prediction.

Author: Gopalika Sharma

Summary:

AutoML helps by streamlining the machine learning process thereby allowing non-technical users to harness the power of machine learning. Here, I have built a mutli-page streamlit app which is split into 2 pages, the first page helps you load the data and run various binary classification models on them, while also calucating the accuracy and other classification metrics which can be chosen in the drop down menu, there is also a section for choosing one own's model parameters in order to fine tune a model. The second page is a user driven prediction application where any user can choose the features and hence get a prediction about the investment viability of the target for this use case, this can be modified for any other case/problem statement as well.

References:

1. https://www.section.io/engineering-education/streamlit-ui-tutorial/
2. https://medium.com/@abhijitjitan/building-titanic-survival-prediction-web-app-with-streamlit-a2f0a7b40288
3. https://towardsdatascience.com/how-to-build-a-streamlit-ui-to-analyze-different-classifiers-on-the-wine-iris-and-breast-cancer-25c03c482a27
4. https://www.geeksforgeeks.org/deploy-a-machine-learning-model-using-streamlit-library/
5. https://www.section.io/engineering-education/streamlit-ui-tutorial/
6. https://medium.com/analytics-vidhya/wine-quality-prediction-app-8ba8291d40f9
7. https://medium.com/analytics-vidhya/heart-disease-prediction-with-machine-learning-d22bc924d8ab
8. Multi-page implementation Web app - https://medium.com/@u.praneel.nihar/building-multi-page-web-app-using-streamlit-7a40d55fa5b4