Update pages/Lifecycle of Machine Learning.py

pages/Lifecycle of Machine Learning.py

@@ -1,46 +1,54 @@
 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.")
+
+
+streamlit run ml_lifecycle.py
 
-# HTML and CSS code for a simple mind map
-html_code = """
-<div style="display: flex; justify-content: center; margin-top: 20px;">
-<div style="text-align: center;">
- <!-- Root Node -->
- <div style="background-color: #4CAF50; color: white; padding: 10px 20px; border-radius: 10px; margin-bottom: 20px;">
-Data Science
-</div>
-<div style="display: flex; justify-content: space-around;">
-<!-- First Branch -->
-<div style="text-align: center; margin-right: 50px;">
-<div style="background-color: #f44336; color: white; padding: 10px 20px; border-radius: 10px;">
-Machine Learning
-</div>
-<div style="margin-top: 20px;">
-<div style="background-color: #e91e63; color: white; padding: 10px 20px; border-radius: 10px; margin-top: 10px;">
-Supervised
-</div>
-<div style="background-color: #e91e63; color: white; padding: 10px 20px; border-radius: 10px; margin-top: 10px;">
-Unsupervised
-</div>
-</div>
-</div>
-<!-- Second Branch -->
-<div style="text-align: center; margin-left: 50px;">
-<div style="background-color: #2196F3; color: white; padding: 10px 20px; border-radius: 10px;">
-Deep Learning
-</div>
-<div style="margin-top: 20px;">
-<div style="background-color: #03A9F4; color: white; padding: 10px 20px; border-radius: 10px; margin-top: 10px;">
-CNN
-</div>
-<div style="background-color: #03A9F4; color: white; padding: 10px 20px; border-radius: 10px; margin-top: 10px;">
-RNN
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-"""
-
-# Display the mind map in Streamlit
-st.markdown(html_code, unsafe_allow_html=True)