To automate tasks using machine learning models in Streamlit, you can use the following steps:
- Train a machine learning model on a dataset of data.
- Save the model in a file.
- Create a Streamlit app that allows users to input data.
- Use the machine learning model to make predictions on the input data.
- Display the predictions to the user.
Here is an example of how to automate the task of predicting the price of a house using a machine learning model in Streamlit:
- Train a machine learning model on a dataset of house prices.
- Save the model in a file called
- Create a Streamlit app called
import streamlit as st import pickle # Load the machine learning model model = pickle.load(open("house_price_model.pkl", "rb")) # Get the user input home_size = st.number_input("Enter the home size (square feet):") bedrooms = st.number_input("Enter the number of bedrooms:") bathrooms = st.number_input("Enter the number of bathrooms:") # Make a prediction prediction = model.predict([[home_size, bedrooms, bathrooms]]) # Display the prediction st.write("The predicted price of the house is $", prediction)
- Run the Streamlit app.
When the user inputs the home size, number of bedrooms, and number of bathrooms, the Streamlit app will use the machine learning model to make a prediction of the price of the house. The prediction will be displayed to the user.