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import streamlit as st
st.set_page_config(page_title="ML Algorithms", layout="centered")
st.title("🤖 Machine Learning Algorithms Overview")
st.markdown("""
This 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
""")
if st.button("🔀 Go to ML Algorithms App"):
st.markdown("[Click here](https://huggingface.co/spaces/sree4411/ML_ALGORITHMS)")
st.markdown("---")
st.caption("Made with ❤️ using Streamlit — for educational ML projects")
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