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pip install streamlit
import streamlit as st
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Page Title
st.title("Machine Learning Life Cycle in Streamlit")
# Buttons for each stage
if st.button("1. Data Collection"):
st.header("Data Collection")
st.write("Using Iris dataset for demonstration.")
data = load_iris(as_frame=True)
st.write(data.frame.head())
elif st.button("2. Data Preprocessing"):
st.header("Data Preprocessing")
st.write("Splitting the data into train and test sets.")
data = load_iris(as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
st.write(f"Train size: {len(X_train)}; Test size: {len(X_test)}")
elif st.button("3. Model Training"):
st.header("Model Training")
st.write("Training a Random Forest Classifier.")
data = load_iris(as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)
st.write("Model trained successfully.")
elif st.button("4. Model Evaluation"):
st.header("Model Evaluation")
st.write("Evaluating the model on the test data.")
data = load_iris(as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
st.write(f"Accuracy: {accuracy:.2f}")
elif st.button("5. Model Deployment"):
st.header("Model Deployment")
st.write("This step involves deploying the model for usage.")
st.write("You can expose the model via APIs or integrate it into an application.")
else:
st.write("Use the buttons above to navigate through the Machine Learning life cycle.")
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