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Streamlit · Lesson 49 of 56

Classification Demo

Source: 14-Streamlit/classification.py

Start here — no coding background needed

What you will learn

Example ML demo — predict categories from data.

In simple words

Advanced demo: train a model, show results in Streamlit — try locally when ready.

Quick dashboards and demos — great for showing data without HTML/CSS.

Easy example — try this first

Easy example — run this first. Change values and press Run again.

Python

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference notes (from full bootcamp)

Optional — deeper detail for when you are ready

Reference script from the bootcamp repo. Read the code below; run a simplified version in the playground when marked runnable.

Example HCL
HCL
import streamlit as st
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

@st.cache_data
def load_data():
    iris = load_iris()
    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    df['species'] = iris.target
    return df, iris.target_names

df,target_names=load_data()

model=RandomForestClassifier()
model.fit(df.iloc[:,:-1],df['species'])

st.sidebar.title("Input Features")
sepal_length = st.sidebar.slider("Sepal length", float(df['sepal length (cm)'].min()), float(df['sepal length (cm)'].max()))
sepal_width = st.sidebar.slider("Sepal width", float(df['sepal width (cm)'].min()), float(df['sepal width (cm)'].max()))
petal_length = st.sidebar.slider("Petal length", float(df['petal length (cm)'].min()), float(df['petal length (cm)'].max()))
petal_width = st.sidebar.slider("Petal width", float(df['petal width (cm)'].min()), float(df['petal width (cm)'].max()))

input_data = [[sepal_length, sepal_width, petal_length, petal_width]]

## PRediction
prediction = model.predict(input_data)
predicted_species = target_names[prediction[0]]

st.write("Prediction")
st.write(f"The predicted species is: {predicted_species}")

Browser practice only — full example needs Python on your computer (files, Flask, threads, etc.).

Practice test — try yourself

Write code, press Check. Wrong answer shows the correct code to copy & run.

You learned "Classification Demo". Use print() to show: Done: Classification Demo

Hint: Use one print() with the exact text.

Python