Update pages/8Model Training.py

pages/8Model Training.py

@@ -89,4 +89,78 @@ st.markdown("""
 
 <p>Common ways to split the data include: 80% training & 20% testing, 70% training & 30% testing, or 60% training & 40% testing.</p>
 <p>The split should be random so that every data point has a fair chance. A data point should appear in only one of the two sets.</p>
-""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)
+
+st.markdown("<h3 style='color:#2a52be;'>Types of Machine Learning Algorithms</h3>", unsafe_allow_html=True)
+
+# 4-column layout
+col1, col2, col3, col4 = st.columns(4)
+
+with col1:
+    st.markdown("""
+        <div style="background-color: #f9f9f9; padding: 15px; border-radius: 10px; height: 100%;">
+            <h4 style='color:#BB3385; text-align:center;'>Supervised</h4>
+            <p>- Linear Regression</p>
+            <p>- Logistic Regression</p>
+            <p>- Decision Tree</p>
+            <p>- Random Forest</p>
+            <p>- Extra Trees</p>
+            <p>- K-Nearest Neighbors (KNN)</p>
+            <p>- Support Vector Machine (SVM)</p>
+            <p>- Naive Bayes</p>
+            <p>- Gradient Boosting</p>
+            <p>- XGBoost</p>
+            <p>- AdaBoost</p>
+            <p>- CatBoost</p>
+            <p>- LightGBM</p>
+        </div>
+    """, unsafe_allow_html=True)
+
+with col2:
+    st.markdown("""
+        <div style="background-color: #f9f9f9; padding: 15px; border-radius: 10px; height: 100%;">
+            <h4 style='color:#BB3385; text-align:center;'>Unsupervised</h4>
+            <p>- K-Means Clustering</p>
+            <p>- Hierarchical Clustering</p>
+            <p>- DBSCAN</p>
+            <p>- OPTICS</p>
+            <p>- Mean Shift</p>
+            <p>- Gaussian Mixture Models</p>
+            <p>- Principal Component Analysis (PCA)</p>
+            <p>- t-SNE</p>
+            <p>- Autoencoders (unsupervised use)</p>
+            <p>- Apriori Algorithm</p>
+            <p>- Eclat Algorithm</p>
+        </div>
+    """, unsafe_allow_html=True)
+
+with col3:
+    st.markdown("""
+        <div style="background-color: #f9f9f9; padding: 15px; border-radius: 10px; height: 100%;">
+            <h4 style='color:#BB3385; text-align:center;'>Semi-Supervised</h4>
+            <p>- Self-Training</p>
+            <p>- Label Propagation</p>
+            <p>- Label Spreading</p>
+            <p>- Semi-Supervised Support Vector Machine (S3VM)</p>
+            <p>- Graph-Based Methods</p>
+            <p>- Semi-Supervised K-Means</p>
+        </div>
+    """, unsafe_allow_html=True)
+
+with col4:
+    st.markdown("""
+        <div style="background-color: #f9f9f9; padding: 15px; border-radius: 10px; height: 100%;">
+            <h4 style='color:#BB3385; text-align:center;'>Reinforcement</h4>
+            <p>- Q-Learning</p>
+            <p>- SARSA</p>
+            <p>- Deep Q-Network (DQN)</p>
+            <p>- Double DQN</p>
+            <p>- Dueling DQN</p>
+            <p>- REINFORCE Algorithm</p>
+            <p>- Actor-Critic Methods</p>
+            <p>- Proximal Policy Optimization (PPO)</p>
+            <p>- Deep Deterministic Policy Gradient (DDPG)</p>
+            <p>- Advantage Actor Critic (A2C)</p>
+            <p>- Soft Actor Critic (SAC)</p>
+        </div>
+    """, unsafe_allow_html=True)