Update app.py

app.py

@@ -1,13 +1,13 @@
 import streamlit as st
 
-# Page settings
+# ---------- Page Config ----------
 st.set_page_config(
-    page_title="📘 ML Notes Hub",
+    page_title="📘 Machine Learning Notes Hub",
     layout="wide",
     initial_sidebar_state="expanded"
 )
 
-# --- Custom CSS ---
+# ---------- Custom CSS ----------
 st.markdown("""
     <style>
     .main-title {
@@ -19,13 +19,13 @@ st.markdown("""
     }
     .subtitle {
         font-size: 1.2rem;
-        color: #333333;
+        color: #555;
         text-align: center;
         margin-bottom: 2rem;
     }
     .note-box {
         background-color: #f9f9f9;
-        padding: 1.5rem;
+        padding: 1rem;
         border-radius: 10px;
         box-shadow: 0 2px 8px rgba(0,0,0,0.05);
         margin-bottom: 1rem;
@@ -33,11 +33,19 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# --- Sidebar Navigation ---
+# ---------- Sidebar Navigation ----------
 st.sidebar.title("📚 ML Notebook")
-page = st.sidebar.radio("Go to", ["Home", "Supervised Learning", "Unsupervised Learning", "Evaluation Metrics", "Resources"])
+page = st.sidebar.radio("Go to", [
+    "Home",
+    "Supervised Learning",
+    "Unsupervised Learning",
+    "Evaluation Metrics",
+    "Data Preprocessing",
+    "Interview Tips",
+    "Resources"
+])
 
-# --- Home Page ---
+# ---------- 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)
@@ -45,8 +53,7 @@ if page == "Home":
     st.markdown("### 🚀 Why Use This App?")
     st.markdown("""
     - Concise, organized ML notes
-    - Easy to navigate concepts
-    - Interactive widgets (coming soon)
+    - Beginner-friendly explanations with examples
     - Great for revision, projects, and interviews
     """)
 
@@ -57,31 +64,118 @@ if page == "Home":
     - 🔹 Unsupervised Learning  
     - 🔹 Model Evaluation Metrics  
     - 🔹 Data Preprocessing  
-    - 🔹 Tips for Interviews  
+    - 🔹 Interview Tips  
     - 🔹 Useful Resources  
     """)
     st.markdown('</div>', unsafe_allow_html=True)
 
-    st.markdown("🛠 *More features coming soon: interactive examples, quizzes, and more!*")
+    st.info("🛠 More features coming soon: interactive examples, quizzes, and visual diagrams!")
 
-# --- Additional Pages Placeholder ---
+# ---------- Supervised Learning ----------
 elif page == "Supervised Learning":
     st.title("🔍 Supervised Learning")
-    st.write("This section will contain detailed notes on regression, classification, and more.")
+    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("Learn about clustering, dimensionality reduction, and pattern discovery.")
+    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("Accuracy, precision, recall, F1-score, confusion matrix and more.")
+    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("📚 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)
     """)
-