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**


""")