Question 1

What is Streamlit in Machine Learning?

Answer

Streamlit is an open-source app and framework which allows you to interactively and intuitively display the results of your machine learning model. One of the biggest advantages of Streamlit is that it’s easy to use and doesn’t require you to have any front-end experience, so you can create an app even without knowing a single line of HTML or CSS. Another advantage of Streamlit is that you can easily share the results of your work with the community, and even with people who have no programming experience whatsoever. Once you have created an app with Streamlit you can share your results with others via a single link. People can also run their own experiments based on your app. Another way to use Streamlit for machine learning is by going directly to the streamlite.io site and browsing through their example apps. You can then upload your own data to the site and run different models with it, without having to write even a single line of code.

Question 2

How do I use Streamlit in Python?

Answer

Streamlit is open-source so anyone can create a Streamlit app.
1. To use it in Python you first need to install Streamlit. You can do so via pip with: pip install streamlit
2. Spend some time learning about the main concepts in Streamlit – if you’ve never worked with Streamlit before, it’s worth investing a little bit of time learning about the main concepts like how an app is created and how it’s run. You can do so directly on their site.
3. Creating the app. If you know how Streamlit works, you can proceed with creating your app. To do so:
a. First, create a Python script. For example my_app.py
b. You also need to import your required libraries like so:
import streamlit as st
import pandas as pd
import numpy as np
c. You can add a title to your app with:
st.title(My first streamlit app')
4. To run the app you can simply run the script from the command line with: streamlit run my_app.py
5. You can improve your script, by loading actual data and creating a machine learning model. You can further add options for your Streamlit app, like adding buttons, filters, charts, and much more. It’s up to you.

Question 3

How do you train an ML model in Python?

Answer

1. Get training data – either find a data set file or custom generate your data. You can do the former by downloading an open-source data set from sites like Kaggle, and the latter you can do directly using a Python script.
2. Load the data file into Python – if you’re data is in a separate file, like a csv you need to read it into Python. You can use pandas read_csv() to read a csv file as a pandas data frame
3. Preprocess the data – depending on the data and algorithm this might include:
a. Splitting the data into train test – you must always train your algorithm on a completely different data set than the testing data
b. Encoding the data to numerical values if they are categorical – some algorithms will have difficulty working with non-numerical values
c. Standardizing the data – so that no feature in the data carries more weight than the rest
d. A specific data modification if the algorithm requires it. For example, for an SVM, you’ll need to rescale all your values to be in the [-1,1] interval.
4. Choose your machine learning algorithm and create a machine learning model. This will depend on your data and your problem, i.e. classification, regression, or clustering tasks, large or small data sets will determine your choice of machine learning method.
5. Train your model on the training data using the fit() method.
6. After the fitting process is complete, your model is trained and ready to be tested on your test data
7. After you test your model, you can evaluate its performance and try to improve performance with the help of hyperparameter tuning or other techniques

What Kind of ML Model Are We Building?

Table of Contents

Part I: Building Your Machine Learning Model

Setting Up Your Project Structure

Building Your Model

Saving Your Model

Part II: Building the Streamlit App

Creating the Streamlit UI

Loading Your Saved Model & Making Real-Time Predictions

Deploying the Streamlit App

Deploying Machine Learning Models with Python and Streamlit: Next Steps

Santiago Víquez

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How to Deploy Machine Learning Models with Python & Streamlit