app.py

import streamlit as st

# ---------- Page Config ----------
st.set_page_config(
    page_title="📘 Machine Learning Notes Hub",
    layout="wide",
    initial_sidebar_state="expanded"
)

# ---------- Custom CSS ----------
st.markdown("""
    <style>
    .main-title {
        font-size: 3rem;
        color: #004080;
        font-weight: 700;
        text-align: center;
        margin-bottom: 0.5rem;
    }
    .subtitle {
        font-size: 1.2rem;
        color: #555;
        text-align: center;
        margin-bottom: 2rem;
    }
    .note-box {
        background-color: #f9f9f9;
        padding: 1rem;
        border-radius: 10px;
        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
        margin-bottom: 1rem;
    }
    </style>
""", unsafe_allow_html=True)

# ---------- Sidebar Navigation ----------
st.sidebar.title("📚 ML Notebook")
page = st.sidebar.radio("Go to", [
    "Home",
    "Supervised Learning",
    "Unsupervised Learning",
    "Evaluation Metrics",
    "Data Preprocessing",
    "Interview Tips",
    "Resources"
])

# ---------- Home Page ----------
if page == "Home":
    st.markdown('<div class="main-title">📘 Machine Learning Notes</div>', unsafe_allow_html=True)
    st.markdown('<div class="subtitle">Your personal guide to mastering ML concepts!</div>', unsafe_allow_html=True)

    st.markdown("### 🚀 Why Use This App?")
    st.markdown("""
    - Concise, organized ML notes
    - Beginner-friendly explanations with examples
    - Great for revision, projects, and interviews
    """)

    st.markdown('<div class="note-box">', unsafe_allow_html=True)
    st.markdown("### 📌 Topics Covered:")
    st.markdown("""
    - 🔹 Supervised Learning  
    - 🔹 Unsupervised Learning  
    - 🔹 Model Evaluation Metrics  
    - 🔹 Data Preprocessing  
    - 🔹 Interview Tips  
    - 🔹 Useful Resources  
    """)
    st.markdown('</div>', unsafe_allow_html=True)

    st.info("🛠 More features coming soon: interactive examples, quizzes, and visual diagrams!")

# ---------- Supervised Learning ----------
elif page == "Supervised Learning":
    st.title("🔍 Supervised Learning")
    st.write("""
    Supervised Learning is a type of machine learning where the model learns from labeled data 
    — meaning the input comes with the correct output.
    """)

    with st.expander("📘 Key Concepts"):
        st.markdown("""
        - **Goal:** Predict an output based on given input.
        - **Types:**
            - **Regression:** Predicts continuous values (e.g., house price prediction)
            - **Classification:** Predicts categories (e.g., spam or not spam)
        - **Examples:**
            - Predicting a student's exam score based on study hours (Regression)
            - Classifying emails as spam or not spam (Classification)
        """)

    with st.expander("💡 Real-life Analogy"):
        st.write("""
        Imagine you are teaching a child to recognize fruits.
        You show an apple and say, "This is an apple."  
        You show a banana and say, "This is a banana."  
        After many examples, the child can identify fruits on their own.
        """)

# ---------- Unsupervised Learning ----------
elif page == "Unsupervised Learning":
    st.title("🧩 Unsupervised Learning")
    st.write("""
    Unsupervised Learning works with **unlabeled data** — meaning we only have inputs and no corresponding outputs.
    The algorithm tries to find patterns or groupings in the data.
    """)

    with st.expander("📘 Key Concepts"):
        st.markdown("""
        - **Goal:** Discover hidden structure in data.
        - **Types:**
            - **Clustering:** Grouping similar items together (e.g., customer segmentation)
            - **Dimensionality Reduction:** Reducing features while keeping important info (e.g., PCA)
        - **Examples:**
            - Grouping customers by purchasing behavior
            - Compressing images while keeping quality
        """)

    with st.expander("💡 Real-life Analogy"):
        st.write("""
        Think of visiting a supermarket where items are grouped by similarity:  
        fruits in one section, vegetables in another.  
        Nobody told the store which items belong together — they just grouped them by observation.
        """)

# ---------- Evaluation Metrics ----------
elif page == "Evaluation Metrics":
    st.title("📏 Model Evaluation Metrics")
    st.write("Evaluation metrics tell us how well our machine learning model is performing.")

    with st.expander("📊 For Classification"):
        st.markdown("""
        - **Accuracy** = Correct predictions / Total predictions
        - **Precision** = Of all predicted positives, how many are actually positive?
        - **Recall** = Of all actual positives, how many did we find?
        - **F1-Score** = Harmonic mean of precision & recall
        - **Confusion Matrix** = Table showing TP, FP, TN, FN
        """)

    with st.expander("📈 For Regression"):
        st.markdown("""
        - **MAE (Mean Absolute Error)** = Average absolute difference between predictions and actual values
        - **MSE (Mean Squared Error)** = Average of squared differences
        - **RMSE** = Square root of MSE
        - **R² Score** = Proportion of variance explained by the model
        """)

# ---------- Data Preprocessing ----------
elif page == "Data Preprocessing":
    st.title("⚙ Data Preprocessing")
    st.write("""
    Preprocessing prepares raw data so that a machine learning model can use it effectively.
    """)

    with st.expander("📘 Steps"):
        st.markdown("""
        1. **Handling Missing Values** — Remove or fill in missing data
        2. **Encoding Categorical Data** — Convert text labels to numbers
        3. **Feature Scaling** — Normalize or standardize values
        4. **Removing Outliers** — Detect and handle extreme values
        """)

# ---------- Interview Tips ----------
elif page == "Interview Tips":
    st.title("💼 ML Interview Tips")
    st.markdown("""
    - Be clear with definitions and examples
    - Explain algorithms with analogies
    - Understand trade-offs between models
    - Practice with real datasets
    """)

# ---------- Resources ----------
elif page == "Resources":
    st.title("📚 Useful Resources")
    st.markdown("""
    - [Scikit-learn Docs](https://scikit-learn.org/)
    - [Kaggle Courses](https://www.kaggle.com/learn)
    - [Google ML Crash Course](https://developers.google.com/machine-learning/crash-course)
    """)