Update pages/Random_Forest.py

pages/Random_Forest.py

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+import streamlit as st
+
+st.set_page_config(page_title="Random Forest", page_icon="🌲", layout="wide")
+
+# Title
+st.markdown("<h1 style='text-align:center;'>🌲 Random Forest Algorithm</h1>", unsafe_allow_html=True)
+
+# Introduction
+st.header("📚 What is Random Forest?")
+st.markdown("""
+Random Forest is a **supervised learning algorithm** used for both **classification** and **regression** tasks.
+
+It works by building a large number of **decision trees** and combining their results to make a final prediction.  
+Think of it like a group of people (trees) voting on an answer – this **reduces overfitting** and improves accuracy.
+""")
+
+# Real-world Uses
+st.header("🎯 Where is Random Forest Used?")
+st.markdown("""
+- 🌾 Crop disease detection  
+- 📊 Stock market predictions  
+- 🏥 Medical diagnosis  
+- 💳 Fraud detection  
+- 👥 Customer churn prediction  
+""")
+
+# How it works
+st.header("⚙️ How Random Forest Works")
+
+with st.expander("Step 1: Bootstrapping (Sampling with Replacement)"):
+    st.markdown("""
+    - Randomly select multiple **subsets** of the dataset (with replacement).
+    - Each subset is used to train a **separate decision tree**.
+    """)
+
+with st.expander("Step 2: Tree Building"):
+    st.markdown("""
+    - For each decision tree, choose a **random subset of features** at every split.
+    - This randomness helps in creating **diverse trees**, reducing correlation.
+    """)
+
+with st.expander("Step 3: Aggregating Results"):
+    st.markdown("""
+    - **Classification**: Majority Voting (most common class wins)  
+    - **Regression**: Average of all tree predictions  
+    """)
+
+# Visual Illustration
+st.header("🌳 Visual Intuition")
+st.image("https://upload.wikimedia.org/wikipedia/commons/7/76/Random_forest_diagram_complete.png", caption="Random Forest Structure", use_column_width=True)
+
+# Advantages
+st.header("✅ Why Use Random Forest?")
+st.markdown("""
+- Handles both classification and regression  
+- Reduces overfitting compared to a single decision tree  
+- Works well with large datasets and high-dimensional data  
+- Robust to outliers and missing data  
+""")
+
+# Hyperparameters
+st.header("🛠️ Key Hyperparameters")
+
+with st.expander("🌲 n_estimators"):
+    st.markdown("Number of trees in the forest. More trees = better performance, but more computation.")
+
+with st.expander("🧮 max_depth"):
+    st.markdown("Maximum depth of each tree. Controls overfitting.")
+
+with st.expander("🔀 max_features"):
+    st.markdown("Number of features to consider at each split (auto, sqrt, log2).")
+
+with st.expander("🪓 min_samples_split"):
+    st.markdown("Minimum samples required to split a node.")
+
+with st.expander("🎯 criterion"):
+    st.markdown("Function to measure the quality of a split (`gini` or `entropy` for classification).")
+
+# Evaluation Metrics
+st.header("📏 Evaluation Metrics")
+
+with st.expander("✔️ Accuracy"):
+    st.latex(r"Accuracy = \frac{TP + TN}{TP + TN + FP + FN}")
+    st.markdown("Overall how often the model was correct.")
+
+with st.expander("🎯 Precision"):
+    st.latex(r"Precision = \frac{TP}{TP + FP}")
+    st.markdown("Out of all predicted positives, how many were actually positive?")
+
+with st.expander("🔍 Recall"):
+    st.latex(r"Recall = \frac{TP}{TP + FN}")
+    st.markdown("Out of all actual positives, how many did we correctly identify?")
+
+with st.expander("⚖️ F1 Score"):
+    st.latex(r"F1 = 2 \cdot \frac{Precision \cdot Recall}{Precision + Recall}")
+    st.markdown("Harmonic mean of precision and recall.")
+
+with st.expander("📉 ROC-AUC"):
+    st.markdown("Measures the tradeoff between true positive rate and false positive rate.")
+
+# Summary
+st.header("📌 Summary")
+st.markdown("""
+- Random Forest is an **ensemble** of decision trees  
+- Uses **bagging** and **feature randomness** to create a robust model  
+- Great for **accuracy**, **stability**, and **generalization**  
+- Handles missing data and avoids overfitting well  
+- Best when you need strong baseline performance with minimal tuning  
+""")
+
+st.success("🎉 Now you’ve got a solid grip on Random Forest!")