pages/5_ML Algoriths.py

import streamlit as st

st.set_page_config(page_title="ML Algorithms", layout="centered")

st.title("🤖 Machine Learning Algorithms Overview")

st.markdown("""
This app gives a quick overview of **core Machine Learning algorithms** and their types.
Explore **Supervised** and **Unsupervised** learning with common algorithms like **KNN**, **SVM**, **Decision Trees**, etc.
""")

ml_type = st.selectbox("Select Type of Machine Learning", ["Supervised Learning", "Unsupervised Learning"])

if ml_type == "Supervised Learning":
    st.header("📘 Supervised Learning")
    st.write("Supervised learning works with **labeled data**, where the goal is to predict a target (output) from input features.")

    st.subheader("🔹 Common Algorithms:")
    st.markdown("""
    - **Linear Regression** → Predicts continuous values  
    - **Logistic Regression** → Binary or multiclass classification  
    - **K-Nearest Neighbors (KNN)** → Classifies based on closest data points  
    - **Support Vector Machine (SVM)** → Classifies using hyperplanes  
    - **Decision Tree** → Uses tree-based decision rules  
    - **Random Forest** → Ensemble of decision trees  
    """)

    st.subheader("🛠️ Use Cases:")
    st.markdown("""
    - Email spam detection  
    - House price prediction  
    - Medical diagnosis  
    - Loan approval  
    """)

elif ml_type == "Unsupervised Learning":
    st.header("📙 Unsupervised Learning")
    st.write("Unsupervised learning deals with **unlabeled data**. It finds hidden patterns, groupings, or structures in the data.")

    st.subheader("🔹 Common Algorithms:")
    st.markdown("""
    - **K-Means Clustering** → Groups data into clusters  
    - **Hierarchical Clustering** → Builds a tree of clusters  
    - **DBSCAN** → Density-based clustering  
    - **PCA (Principal Component Analysis)** → Reduces dimensionality  
    """)

    st.subheader("🛠️ Use Cases:")
    st.markdown("""
    - Customer segmentation  
    - Market basket analysis  
    - Anomaly detection  
    """)

st.markdown("---")
st.caption("Made with ❤️ using Streamlit — for educational ML projects")