Delete pages/1_Introductio to Data Science.py

pages/1_Introductio to Data Science.py

@@ -1,76 +0,0 @@
-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.
-    """)
-