Exploring the Penguins Dataset with Streamlit

In this chapter, we'll explore the [Penguins dataset](https://github.com/dataprofessor/data/blob/master/penguins_cleaned.csv{:target=_blank} and use it as a base to build an interactive ML application using Streamlit.

By the end of this chapter you will,

• Loading, preprocessing, and preparing the dataset for visualization
• Using Streamlit Expander to display:
  • Dataset Information
  • Machine Learning Model Features (X)
  • Prediction Target (species) variable (y)
• Creating interactive scatter plots to identify patterns and relationships

Download Dataset

Let us download the dataset locally,

mkdir -p "$TUTORIAL_HOME/data"
curl -sSL \
  -o data/penguins_cleaned.csv \
  https://raw.githubusercontent.com/dataprofessor/data/refs/heads/master/penguins_cleaned.csv

Displaying the Data

Edit and update the streamlit_app.py with the following code,

import streamlit as st

# import pandas to read the our data file
import pandas as pd

st.title("🤖 Machine Learning App")

st.write("Welcome to world of Machine Learning with Streamlit.")

# read the csv file
df = pd.read_csv("data/penguins_cleaned.csv")
df

Stage, commit and push the code to the repository. In a few seconds you’ll see that your deployed app on the Streamlit Community Cloud refreshes as the code changes.

Application Overview

As part of this machine learning application, we will be building a simple classification model to predict Penguin species (y) using input variables (X). Using Streamlit's interactive widgets, we'll display these variables to make our application user-friendly and intuitive.

This classification model will help us categorize penguins into their respective species based on their physical characteristics. The input variables and target variable will be presented through Streamlit's interface, allowing users to easily interact with and understand the prediction process.

Adding Our First Widget

Let us add our first Streamlit widget expander to allow expand and collapse of the data frame.

Edit and update the $TUTORIAL_HOME/streamlit_app.py with the following code,

import streamlit as st

# import pandas to read the our data file
import pandas as pd

st.title("🤖 Machine Learning App")

st.write("Welcome to world of Machine Learning with Streamlit.")

with st.expander("Data"):
    st.write("**Raw Data**")
    # read the csv file
    df = pd.read_csv("data/penguins_cleaned.csv")
    df

Displaying the Variables

Let us create and display the input features (X) and target (y), edit and update the $TUTORIAL_HOME/streamlit_app.py with the following code,

import streamlit as st

# import pandas to read the our data file
import pandas as pd

st.title("🤖 Machine Learning App")

st.write("Welcome to world of Machine Learning with Streamlit.")

with st.expander("Data"):
    st.write("**Raw Data**")
    # read the csv file
    df = pd.read_csv("data/penguins_cleaned.csv")
    df
    # define and display
    st.write("**X**")
    X = df.drop("species",axis=1)
    X

    st.write("**y**")
    y = df.species
    y

Data Visualization

Let us visualize the Penguins data using a scatter plot, edit and update the $TUTORIAL_HOME/streamlit_app.py with the following code,

import streamlit as st

# import pandas to read the our data file
import pandas as pd

st.title("🤖 Machine Learning App")

st.write("Welcome to world of Machine Learning with Streamlit.")

with st.expander("Data"):
    st.write("**Raw Data**")
    # read the csv file
    df = pd.read_csv("data/penguins_cleaned.csv")
    df
    # define and display
    st.write("**X**")
    x = df.drop("species", axis=1)
    x

    st.write("**y**")
    y = df.species
    y

with st.expander("Data Visualization"):
    st.scatter_chart(
        df,
        x="bill_length_mm",
        y="body_mass_g",
        color="species",
    )

Now that we have our variables and target displayed for reference, let's move to the next chapter where we'll explore Streamlit's interactive features.