Create app.py

app.py

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+import numpy as np
+import streamlit as st
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+
+# Load the Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create and train the linear regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Streamlit app
+st.title("Iris Linear Regression")
+
+# Accept user input
+sepal_length = st.number_input("Sepal Length", min_value=0.0, max_value=10.0, value=5.8, step=0.1)
+sepal_width = st.number_input("Sepal Width", min_value=0.0, max_value=10.0, value=3.0, step=0.1)
+petal_length = st.number_input("Petal Length", min_value=0.0, max_value=10.0, value=3.8, step=0.1)
+petal_width = st.number_input("Petal Width", min_value=0.0, max_value=10.0, value=1.2, step=0.1)
+
+# Make prediction
+user_input = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+prediction = model.predict(user_input)
+
+# Display the result
+st.write(f"The predicted iris species is: {iris.target_names[int(prediction[0])]}")