Update app.py

app.py

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+import streamlit as st
+
+st.set_page_config(page_title="ML Algorithms Overview", page_icon="📘", layout="wide")
+
+st.markdown("<h1 style='color:#4CAF50;'>📘 Machine Learning Algorithms - Overview</h1>", unsafe_allow_html=True)
+
+st.markdown("""
+Welcome to the **Machine Learning Algorithms Explorer**!
+
+This app provides detailed, beginner-friendly explanations of popular ML algorithms.  
+Each page will walk you through how the algorithm works, its use cases, formulas, evaluation metrics, and more. 🚀
+
+Here’s a quick snapshot of what's inside:
+""")
+
+# Overview list
+st.markdown("### 🧠 Algorithms Covered")
+
+st.markdown("""
+#### 🔹 Linear Regression
+- Used to predict continuous numeric values (e.g., house prices).
+- It draws a best-fit line based on the relationship between independent and dependent variables.
+
+#### 🔹 Logistic Regression
+- Used for binary/multi-class classification (e.g., spam vs. not spam).
+- Uses the sigmoid function to model probabilities.
+
+#### 🔹 Decision Tree
+- Tree-like structure that splits data using feature values.
+- Easy to visualize and interpret.
+
+#### 🔹 Random Forest
+- An ensemble of decision trees.
+- Improves accuracy and reduces overfitting through voting/averaging.
+
+#### 🔹 K-Nearest Neighbors (KNN)
+- Lazy learner that classifies based on the 'K' nearest data points.
+- No training phase; works well with low-dimensional data.
+
+#### 🔹 Support Vector Machine (SVM)
+- Finds the optimal hyperplane to separate classes.
+- Works well in high-dimensional and complex data using kernel tricks.
+
+---
+
+Each page contains:
+- ✨ **Intuitive explanations**
+- 🧪 **Mathematical equations**
+- 📊 **Visual insights**
+- ⚙️ **Hyperparameter info**
+- 📏 **Evaluation metrics**
+
+
+""")