Update app.py

app.py

@@ -1,80 +1,74 @@
 import streamlit as st
 
-def generate_ml_blog():
-    ml_content = '''
-    ## What is Machine Learning (ML) :
-    🤖 **Machine Learning (ML)** is a branch of **Artificial Intelligence (AI)** that focuses on developing systems that can learn from and make decisions or predictions based on data. 📊 Instead of being explicitly programmed to perform specific tasks, machine learning algorithms use patterns and insights derived from data to improve their performance over time. 📈
-
-    At its core, **machine learning** enables computers to act autonomously in situations where explicit instructions are impractical or impossible, making it an essential technology in today’s data-driven world. 🌍
-
-    '''
-    return ml_content
-    
+# Introduction to Machine Learning
 def introduction_to_ml():
     introduction_blog = '''
-    ## Introduction to Machine Learning (ML)
-     🤖Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It has revolutionized many industries and plays a crucial role in technologies such as self-driving cars, recommendation systems, and facial recognition.
+    ## 🤖 Introduction to Machine Learning (ML)
+    Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It has revolutionized many industries and plays a crucial role in technologies such as self-driving cars 🚗, recommendation systems 📱, and facial recognition 👁️.
 
-    ### Types of Machine Learning
+    ### 🌍 Types of Machine Learning
     There are three main types of machine learning:
 
-    1. **Supervised Learning**:
-        Supervised learning algorithms learn from labeled data. The model is trained using a dataset where the input data and the correct output are both provided. The goal is to learn a mapping from inputs to outputs. Examples include linear regression, logistic regression, and decision trees.
+    1. **🔖 Supervised Learning**: 
+        Supervised learning algorithms learn from labeled data. The model is trained using a dataset where the input data and the correct output are both provided. The goal is to learn a mapping from inputs to outputs. Examples include linear regression 📈, logistic regression 🧑‍💻, and decision trees 🌳.
 
-    2. **Unsupervised Learning**:
-        In unsupervised learning, the algorithm is given data without any labeled outputs. The goal is to find hidden patterns or groupings in the data. Examples include clustering (e.g., K-means) and dimensionality reduction techniques (e.g., PCA).
+    2. **🌌 Unsupervised Learning**: 
+        In unsupervised learning, the algorithm is given data without any labeled outputs. The goal is to find hidden patterns or groupings in the data. Examples include clustering 🧠 (e.g., K-means) and dimensionality reduction techniques 🏗️ (e.g., PCA).
 
-    3. **Reinforcement Learning**:
-        Reinforcement learning involves an agent that learns to make decisions by interacting with an environment to maximize a cumulative reward. It is widely used in robotics, game AI, and real-time decision-making systems.
+    3. **🏅 Reinforcement Learning**: 
+        Reinforcement learning involves an agent that learns to make decisions by interacting with an environment to maximize a cumulative reward. It is widely used in robotics 🤖, game AI 🎮, and real-time decision-making systems.
 
-    ### Popular Machine Learning Algorithms
+    ### 🚀 Popular Machine Learning Algorithms
     Some of the most commonly used ML algorithms include:
 
-    - **Linear Regression**: A simple algorithm used for predicting continuous values.
-    - **Logistic Regression**: Used for binary classification problems.
-    - **Decision Trees**: A tree-like model used for both classification and regression tasks.
-    - **K-Nearest Neighbors (KNN)**: A non-parametric method used for classification and regression.
-    - **Support Vector Machines (SVM)**: A powerful classifier that works well for high-dimensional spaces.
-    - **Neural Networks**: A set of algorithms, modeled after the human brain, that are used for complex tasks like image and speech recognition.
+    - **📉 Linear Regression**: A simple algorithm used for predicting continuous values.
+    - **🔐 Logistic Regression**: Used for binary classification problems.
+    - **🌳 Decision Trees**: A tree-like model used for both classification and regression tasks.
+    - **🔍 K-Nearest Neighbors (KNN)**: A non-parametric method used for classification and regression.
+    - **⚡ Support Vector Machines (SVM)**: A powerful classifier that works well for high-dimensional spaces.
+    - **🧠 Neural Networks**: A set of algorithms, modeled after the human brain, that are used for complex tasks like image and speech recognition.
 
-    #### Applications of Machine Learning
+    #### 🌍 Applications of Machine Learning
     Machine learning is used in a wide variety of fields, including:
 
-    - **Healthcare**: ML is used for predicting diseases, recommending treatments, and analyzing medical data.
-    - **Finance**: Used for fraud detection, algorithmic trading, and risk analysis.
-    - **E-commerce**: ML powers recommendation systems, personalized marketing, and customer support chatbots.
-    - **Self-driving Cars**: ML algorithms help autonomous vehicles navigate and make real-time decisions.
+    - **🏥 Healthcare**: ML is used for predicting diseases, recommending treatments, and analyzing medical data.
+    - **💰 Finance**: Used for fraud detection, algorithmic trading, and risk analysis.
+    - **🛍️ E-commerce**: ML powers recommendation systems, personalized marketing, and customer support chatbots.
+    - **🚘 Self-driving Cars**: ML algorithms help autonomous vehicles navigate and make real-time decisions.
 
-    ### Conclusion
-    Machine learning continues to evolve, with new algorithms, techniques, and applications emerging regularly. As the amount of data grows and computational power increases, the potential of ML to impact industries and improve our daily lives is limitless.
+    ### 🔚 Conclusion
+    Machine learning continues to evolve, with new algorithms, techniques, and applications emerging regularly. As the amount of data grows 📊 and computational power increases ⚡, the potential of ML to impact industries and improve our daily lives is limitless.
     '''
     
     return introduction_blog
+
+# Supervised Learning
 def supervised_learning():
     supervised = '''
-    ### Supervised Learning
+    ### 🔖 Supervised Learning
     Supervised learning algorithms learn from labeled data. The model is trained using a dataset where the input data and the correct output are both provided. The goal is to learn a mapping from inputs to outputs.
     
-    **Example**: 
-    - **Linear Regression**: Used to predict a continuous value, such as predicting house prices.
+    **📚 Example**: 
+    - **📈 Linear Regression**: Used to predict a continuous value, such as predicting house prices 🏠.
     ```python
     from sklearn.linear_model import LinearRegression
     X = [[1], [2], [3], [4], [5]]  # Features
     y = [1, 2, 2.5, 4, 5]  # Target
     model = LinearRegression()
     model.fit(X, y)
-    predictions = model.predict([[6]])  # Predict for 6 hours of study
+    predictions = model.predict([[6]])  # Predict for 6 hours of study 📚
     ```
     '''
     return supervised
 
+# Unsupervised Learning
 def unsupervised_learning():
     unsupervised = '''
-    ### Unsupervised Learning
-    In unsupervised learning, the algorithm is given data without any labeled outputs. The goal is to find hidden patterns or groupings in the data. Examples include clustering (e.g., K-means) and dimensionality reduction techniques (e.g., PCA).
+    ### 🌌 Unsupervised Learning
+    In unsupervised learning, the algorithm is given data without any labeled outputs. The goal is to find hidden patterns or groupings in the data. Examples include clustering 🧠 (e.g., K-means) and dimensionality reduction techniques 🏗️ (e.g., PCA).
     
-    **Example**:
-    - **K-Means Clustering**: Grouping data points into clusters based on similarity.
+    **📚 Example**:
+    - **🔄 K-Means Clustering**: Grouping data points into clusters based on similarity.
     ```python
     from sklearn.cluster import KMeans
     X = [[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]]
@@ -85,13 +79,14 @@ def unsupervised_learning():
     '''
     return unsupervised
 
+# Reinforcement Learning
 def reinforcement_learning():
     reinforcement = '''
-    ### Reinforcement Learning
-    Reinforcement learning involves an agent that learns to make decisions by interacting with an environment to maximize a cumulative reward. It is widely used in robotics, game AI, and real-time decision-making systems.
+    ### 🏅 Reinforcement Learning
+    Reinforcement learning involves an agent that learns to make decisions by interacting with an environment to maximize a cumulative reward. It is widely used in robotics 🤖, game AI 🎮, and real-time decision-making systems.
     
-    **Example**: 
-    - **Q-Learning**: A reinforcement learning algorithm where an agent learns to maximize rewards by updating Q-values.
+    **📚 Example**: 
+    - **🔄 Q-Learning**: A reinforcement learning algorithm where an agent learns to maximize rewards by updating Q-values.
     ```python
     import numpy as np
     Q = np.zeros((5, 5))  # Example Q-table for 5 states and 5 actions
@@ -105,13 +100,15 @@ def reinforcement_learning():
     ```
     '''
     return reinforcement
+
+# Linear Regression
 def linear_regression():
     linear = '''
-    ### Linear Regression
+    ### 📉 Linear Regression
     Linear regression is used to predict a continuous value based on one or more input features. It finds the best-fit line to minimize the error between the predicted and actual values.
 
-    **Example**: 
-    - **Predicting House Prices**: Predict the price of a house based on its features such as size and location.
+    **📚 Example**: 
+    - **🏠 Predicting House Prices**: Predict the price of a house based on its features such as size and location.
     ```python
     from sklearn.linear_model import LinearRegression
     X = [[1], [2], [3], [4], [5]]  # Features (e.g., years of experience)
@@ -123,13 +120,14 @@ def linear_regression():
     '''
     return linear
 
+# Logistic Regression
 def logistic_regression():
     logistic = '''
-    ### Logistic Regression
+    ### 🔐 Logistic Regression
     Logistic regression is used for binary classification tasks, where the goal is to predict one of two outcomes, such as pass/fail or spam/not spam.
 
-    **Example**:
-    - **Predicting Spam Emails**: Classifying emails as spam or not spam.
+    **📚 Example**:
+    - **📧 Predicting Spam Emails**: Classifying emails as spam or not spam.
     ```python
     from sklearn.linear_model import LogisticRegression
     from sklearn.datasets import load_iris
@@ -143,13 +141,14 @@ def logistic_regression():
     '''
     return logistic
 
+# Decision Trees
 def decision_trees():
     decision = '''
-    ### Decision Trees
+    ### 🌳 Decision Trees
     Decision trees split the data into subsets based on feature values, creating a tree-like model. It is used for both classification and regression tasks.
 
-    **Example**:
-    - **Classifying Iris Species**: A decision tree can be used to classify different species of Iris flowers.
+    **📚 Example**:
+    - **🌸 Classifying Iris Species**: A decision tree can be used to classify different species of Iris flowers.
     ```python
     from sklearn.tree import DecisionTreeClassifier
     from sklearn.datasets import load_iris
@@ -163,13 +162,14 @@ def decision_trees():
     '''
     return decision
 
+# K-Nearest Neighbors (KNN)
 def knn():
     knn = '''
-    ### K-Nearest Neighbors (KNN)
+    ### 🔍 K-Nearest Neighbors (KNN)
     KNN is a simple, non-parametric algorithm that classifies data based on the majority vote of its nearest neighbors.
 
-    **Example**:
-    - **Classifying a Data Point**: Predict the class of a data point based on its nearest neighbors.
+    **📚 Example**:
+    - **📊 Classifying a Data Point**: Predict the class of a data point based on its nearest neighbors.
     ```python
     from sklearn.neighbors import KNeighborsClassifier
     from sklearn.datasets import load_iris
@@ -183,65 +183,31 @@ def knn():
     '''
     return knn
 
+# Support Vector Machines (SVM)
 def svm():
     svm = '''
-    ### Support Vector Machines (SVM)
-    SVM is a powerful classifier that works well for high-dimensional data. It tries to find the hyperplane that best separates the data points of different classes.
-
-    **Example**:
-    - **Classifying Iris Flowers**: An SVM can be used to classify Iris flowers into different species.
-    ```python
-    from sklearn.svm import SVC
-    from sklearn.datasets import load_iris
-    data = load_iris()
-    X = data.data
-    y = data.target
-    model = SVC(kernel='linear')
-    model.fit(X, y)
-    predictions = model.predict(X)
-    ```
+    ### ⚡ Support Vector Machines (SVM)
+    SVM is a powerful classifier that works well for high-dimensional data. It tries to find the hyperplane that best separates the data points of different
     '''
     return svm
-
-def neural_networks():
-    neural = '''
-    ### Neural Networks
-    Neural networks are modeled after the human brain, with layers of interconnected nodes (neurons) used for tasks like image and speech recognition.
-
-    **Example**:
-    - **Classifying Handwritten Digits**: A simple neural network can be used to classify digits from the MNIST dataset.
-    ```python
-    from sklearn.neural_network import MLPClassifier
-    from sklearn.datasets import load_iris
-    data = load_iris()
-    X = data.data
-    y = data.target
-    model = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)
-    model.fit(X, y)
-    predictions = model.predict(X)
-    ```
-    '''
-    return neural
-
-# Sidebar for content navigation
-# Sidebar for content navigation
+# Sidebar for content navigation with emojis
 st.sidebar.header("📚 Contents")
 
 # Show Introduction first in the sidebar
-page = st.sidebar.radio("📚 Select a Topic", 
+page = st.sidebar.radio("📖 Select a Topic", 
                         ["Introduction", "Types of Machine Learning", "Popular Algorithms"])
 
 # Conditional options based on sidebar selection
 if page == "Types of Machine Learning":
     types_of_ml = st.sidebar.radio("📊 Types of Machine Learning", 
-                                   ["Supervised Learning", "Unsupervised Learning", "Reinforcement Learning"])
+                                   ["🔸 Supervised Learning", "🔸 Unsupervised Learning", "🔸 Reinforcement Learning"])
 else:
     types_of_ml = None
 
 if page == "Popular Algorithms":
     popular_algorithms = st.sidebar.radio("🚀 Popular Algorithms", 
-                                          ["Linear Regression", "Logistic Regression", "Decision Trees", 
-                                           "K-Nearest Neighbors (KNN)", "Support Vector Machines (SVM)", "Neural Networks"])
+                                          ["🔗 Linear Regression", "📈 Logistic Regression", "🌳 Decision Trees", 
+                                           "🔍 K-Nearest Neighbors (KNN)", "⚡ Support Vector Machines (SVM)", "🧠 Neural Networks"])
 else:
     popular_algorithms = None
 
@@ -252,22 +218,22 @@ st.markdown("<h1 style='text-align: center; color: orange;'>Machine Learning (ML
 if page == "Introduction":
     st.markdown(introduction_to_ml())
 
-elif types_of_ml == "Supervised Learning":
+elif types_of_ml == "🔸 Supervised Learning":
     st.markdown(supervised_learning())
-elif types_of_ml == "Unsupervised Learning":
+elif types_of_ml == "🔸 Unsupervised Learning":
     st.markdown(unsupervised_learning())
-elif types_of_ml == "Reinforcement Learning":
+elif types_of_ml == "🔸 Reinforcement Learning":
     st.markdown(reinforcement_learning())
 
-elif popular_algorithms == "Linear Regression":
+elif popular_algorithms == "🔗 Linear Regression":
     st.markdown(linear_regression())
-elif popular_algorithms == "Logistic Regression":
+elif popular_algorithms == "📈 Logistic Regression":
     st.markdown(logistic_regression())
-elif popular_algorithms == "Decision Trees":
+elif popular_algorithms == "🌳 Decision Trees":
     st.markdown(decision_trees())
-elif popular_algorithms == "K-Nearest Neighbors (KNN)":
+elif popular_algorithms == "🔍 K-Nearest Neighbors (KNN)":
     st.markdown(knn())
-elif popular_algorithms == "Support Vector Machines (SVM)":
+elif popular_algorithms == "⚡ Support Vector Machines (SVM)":
     st.markdown(svm())
-elif popular_algorithms == "Neural Networks":
-    st.markdown(neural_networks())
+elif popular_algorithms == "🧠 Neural Networks":
+    st.markdown(neural_networks())