Rename pages/page1.py to pages/Introduction.py

pages/Introduction.py

@@ -0,0 +1,81 @@
+import streamlit as st
+
+# Function to add page content for introduction
+def introduction_page():
+    st.title("Introduction to Data Science and AI")
+
+    st.header("📊 What is Data?")
+    st.write("""
+        Data refers to raw facts, figures, or information that can be analyzed or processed to derive insights. 
+        It comes in various forms such as numbers, text, images, or even sounds. Data is everywhere around us - 
+        from the text messages we send to the clicks on websites. Data can be structured (like tables and spreadsheets) 
+        or unstructured (like social media posts, emails, etc.). At its core, data represents the "raw material" 
+        that helps us understand the world and make informed decisions.
+    """)
+
+    # What is Science?
+    st.header("🔬 What is Science?")
+    st.write("""
+        Science is the systematic study of the structure and behavior of the physical and natural world 
+        through observation, experimentation, and analysis. Scientists use the scientific method to hypothesize, 
+        test, and analyze data to understand how the world works. The goal of science is to uncover patterns and 
+        relationships that can explain phenomena and predict future outcomes.
+    """)
+
+    # Combining Data and Science: Data Science
+    st.header("🔍 How Data and Science Come Together: Data Science")
+    st.write("""
+        When we combine data and science, we get **Data Science**. Data Science is the field that uses scientific 
+        methods, algorithms, and systems to extract knowledge and insights from data. It applies techniques from 
+        statistics, computer science, and domain expertise to interpret large datasets and uncover valuable insights. 
+        The process typically involves collecting and cleaning data, performing exploratory data analysis, building 
+        models, and communicating results to make decisions or predictions.
+
+        In simple terms:
+        - **Data** is the raw input.
+        - **Science** provides the methods and tools for analyzing this data.
+        - **Data Science** is the application of science to data, turning it into actionable insights.
+
+        The beauty of Data Science is that it allows businesses, organizations, and individuals to make smarter decisions based on data. 
+        Whether it’s understanding customer behavior, predicting future trends, or solving complex problems, data science empowers us 
+        to uncover hidden patterns in the data that would be difficult to see otherwise.
+    """)
+    # Understanding Artificial Intelligence (AI)
+    st.header("🤖 What is Artificial Intelligence (AI)?")
+    st.write("""
+        Artificial Intelligence, or AI, refers to the simulation of natural intelligence in machines. AI allows machines to 
+        think, learn, and perform tasks that would typically require natural intelligence. 
+       \n These tasks include decision-making, speech recognition, visual perception, and language translation. 
+        AI is the broader concept, and it includes several subfields, like machine learning and deep learning.
+    """)
+
+    # Explaining Machine Learning
+    st.header("💻 What is Machine Learning?")
+    st.write("""
+        Machine Learning (ML) is a subset of AI. Machine Learning is a tool which has ability to mimic the Natural Intelligence.
+       \n ML requires the data and algorithm in which it uses relationship function between data and algorithm.
+        \nFor example, email services use ML to filter spam emails based on patterns learned from previous emails.
+    """)
+
+    # Deep Learning Explained
+    st.header("🧠 What is Deep Learning?")
+    st.write("""
+        Deep Learning is a specialized branch of machine learning that focuses on using neural networks with many layers 
+        (hence the term "deep") to analyze complex patterns in large datasets. These neural networks are inspired by the structure 
+        and function of the human brain. 
+             \nDeep learning has led to breakthroughs in areas like image recognition, natural language processing, 
+        and speech recognition. 
+             \nFor example, deep learning is the reason we have virtual assistants like Siri or Alexa.
+    """)
+
+    # Introduction to Generative AI
+    st.header("🎨 What is Generative AI?")
+    st.write("""
+        Generative AI refers to algorithms that can generate new content such as text, images, and videos. 
+        Unlike traditional AI systems that focus on analyzing data and making decisions, generative AI focuses on creating new, original data. 
+        \nThis can include generating realistic images from text descriptions or writing new music based on patterns learned from existing songs. 
+        \nExamples of generative AI are models like GPT-3, which generates human-like text, and DALL-E, which generates images from textual descriptions.
+    """)
+
+# Display the introduction page content
+introduction_page()