Update pages/1_Introductio to Data Science.py

pages/1_Introductio to Data Science.py

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+import streamlit as st
+from streamlit_lottie import st_lottie
+import requests
+
+# Function to load Lottie animations
+def load_lottieurl(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+# Load Lottie animations
+ml_animation = load_lottieurl("https://assets2.lottiefiles.com/packages/lf20_i9mtrven.json")  # General ML animation
+supervised_animation = load_lottieurl("https://assets10.lottiefiles.com/packages/lf20_wykv43rd.json")  # Supervised learning animation
+unsupervised_animation = load_lottieurl("https://assets9.lottiefiles.com/packages/lf20_gnhb5bgb.json")  # Unsupervised learning animation
+reinforcement_animation = load_lottieurl("https://assets2.lottiefiles.com/packages/lf20_xldzoar9.json")  # Reinforcement learning animation
+
+# Sidebar for navigation
+page = st.sidebar.selectbox("Navigate", ["Home", "Machine Learning Overview"])
+
+# Home Page
+if page == "Home":
+    st.title("Zero to Hero in Machine Learning 🚀")
+    st.write("Welcome to the journey of mastering Machine Learning!")
+    # Add any existing content or Lottie animations for the home page.
+
+# Machine Learning Overview Page
+elif page == "Machine Learning Overview":
+    st.title("Understanding Machine Learning 🤖")
+    st.write("""
+    Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed.
+    It involves the development of algorithms that can identify patterns in data and make predictions or decisions.
+
+    ### Why Machine Learning?
+    - Automates tasks
+    - Enables predictive modeling
+    - Helps in analyzing vast datasets
+    """)
+    st_lottie(ml_animation, height=300, key="ml")
+
+    # Types of Machine Learning
+    st.header("Types of Machine Learning")
+    st.subheader("1. Supervised Learning")
+    st.write("""
+    Supervised Learning involves training a model on labeled data. The algorithm learns to map input data to known output labels.
+    **Examples:**
+    - Predicting house prices
+    - Spam detection in emails
+    """)
+    st_lottie(supervised_animation, height=300, key="supervised")
+
+    st.subheader("2. Unsupervised Learning")
+    st.write("""
+    Unsupervised Learning is used when the data is not labeled. The algorithm identifies hidden patterns or structures in the data.
+    **Examples:**
+    - Customer segmentation
+    - Anomaly detection
+    """)
+    st_lottie(unsupervised_animation, height=300, key="unsupervised")
+
+    st.subheader("3. Reinforcement Learning")
+    st.write("""
+    Reinforcement Learning involves training an agent to make decisions by rewarding it for desirable actions and penalizing it for undesirable ones.
+    **Examples:**
+    - Self-driving cars
+    - Game playing (e.g., AlphaGo)
+    """)
+    st_lottie(reinforcement_animation, height=300, key="reinforcement")
+
+    # Conclusion
+    st.header("Conclusion")
+    st.write("""
+    Machine Learning is revolutionizing industries and enabling new possibilities. 
+    Understanding its types—Supervised, Unsupervised, and Reinforcement Learning—forms the foundation for exploring this exciting field.
+    """)
+