#
# """, unsafe_allow_html=True)
# else:
# st.header(page or chapter)
# st.write("Content for this page will go here.")
# ----------------------------------------------------------------------------------------------------------------------------------------------------------
# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests
# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
# """Fetch Lottie animation JSON from a URL."""
# try:
# response = requests.get(url)
# if response.status_code == 200:
# return response.json()
# except requests.exceptions.RequestException:
# return None
# # CSS Styling for light and dark modes with adaptive theme support
# st.markdown("""
#
# """, unsafe_allow_html=True)
# # Sidebar Navigation
# chapters = [
# "Foundation",
# "ML Project Lifecycle",
# "Core Algorithms",
# "Model Evaluation",
# "Data Handling",
# "Computer Vision Basics",
# "Natural Language Processing (NLP)",
# "Deployment & Tools"
# ]
# selected_chapter = st.sidebar.radio("Select Chapter", chapters)
# # Nested page selection based on chapter
# if selected_chapter == "Foundation":
# page = st.sidebar.radio("Select Page", [
# "Home",
# "Introduction to Data Science",
# "Machine Learning vs Deep Learning"
# ])
# elif selected_chapter == "ML Project Lifecycle":
# section = st.sidebar.radio("Select Section", ["Life Cycle of ML Project"])
# if section:
# page = st.sidebar.radio("Select Subtopic", [
# "Problem Statement",
# "Data Collection",
# "Data Preprocessing",
# "Exploratory Data Analysis (EDA)",
# "Feature Engineering",
# "Model Selection",
# "Model Training",
# "Model Evaluation & Tuning",
# "Model Deployment",
# "Monitoring"
# ])
# elif selected_chapter == "Core Algorithms":
# page = st.sidebar.radio("Select Page", [
# "Linear Regression",
# "Logistic Regression",
# "k-Nearest Neighbors (kNN)",
# "Decision Trees",
# "Support Vector Machines (SVM)",
# "Ensemble Techniques"
# ])
# elif selected_chapter == "Model Evaluation":
# page = st.sidebar.radio("Select Page", ["Performance Metrics"])
# if page == "Performance Metrics":
# metric = st.sidebar.radio("Choose Metric", [
# "Accuracy, Precision, Recall",
# "Confusion Matrix",
# "ROC-AUC"
# ])
# elif selected_chapter == "Data Handling":
# page = st.sidebar.radio("Select Page", [
# "Data Types",
# "Data Cleaning",
# "Feature Engineering"
# ])
# if page == "Data Types":
# dtype = st.sidebar.radio("Select Type", [
# "Structured Data (SQL, Excel)",
# "Semi-Structured Data (JSON, XML)",
# "Unstructured Data (Images, Text)"
# ])
# elif selected_chapter == "Computer Vision Basics":
# page = st.sidebar.radio("Select Page", ["Image Processing", "OpenCV Basics"])
# if page == "Image Processing":
# iproc = st.sidebar.radio("Select Topic", [
# "Color Spaces",
# "Image Augmentation",
# "Splitting/Merging Images"
# ])
# elif selected_chapter == "Natural Language Processing (NLP)":
# page = st.sidebar.radio("Select Page", [
# "NLP Introduction",
# "Text Preprocessing"
# ])
# elif selected_chapter == "Deployment & Tools":
# page = st.sidebar.radio("Select Page", [
# "Model Deployment",
# "Working with Excel/CSV",
# "SQL for Data Science"
# ])
# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")
# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
# st_lottie(lottie_animation, height=200, key="animation")
# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.
# - **Model Development**: Build and optimize machine learning models.
# - **Model Deployment**: Deliver real-world solutions and monitor performance.
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)
# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.
# """)
# # Author Section
# st.markdown("""
#
#
About the Author
#
# Hello! I'm Yash Harish Gupta, an aspiring data scientist deeply passionate about machine learning.
# My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.
# Currently, I am learning and preparing to embark on my professional career in this exciting field.
#
# """, unsafe_allow_html=True)
# # Footer Section
# st.markdown("""
#
# """, unsafe_allow_html=True)
# -----------------------------------------------------------------------------------------------------------------------------------------------------
# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests
# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
# """Fetch Lottie animation JSON from a URL."""
# try:
# response = requests.get(url)
# if response.status_code == 200:
# return response.json()
# except requests.exceptions.RequestException:
# return None
# # CSS Styling for light and dark modes
# st.markdown("""
#
# """, unsafe_allow_html=True)
# # Sidebar Navigation
# chapters = [
# "Foundation",
# "ML Project Lifecycle",
# "Core Algorithms",
# "Model Evaluation",
# "Data Handling",
# "Computer Vision Basics",
# "Natural Language Processing (NLP)",
# "Deployment & Tools"
# ]
# selected_chapter = st.sidebar.radio("Select Chapter", chapters)
# # Nested page selection based on chapter
# if selected_chapter == "Foundation":
# page = st.sidebar.radio("Select Page", [
# "Home",
# "Introduction to Data Science",
# "Machine Learning vs Deep Learning"
# ])
# elif selected_chapter == "ML Project Lifecycle":
# section = st.sidebar.radio("Select Section", ["Life Cycle of ML Project"])
# if section:
# page = st.sidebar.radio("Select Subtopic", [
# "Problem Statement",
# "Data Collection",
# "Data Preprocessing",
# "Exploratory Data Analysis (EDA)",
# "Feature Engineering",
# "Model Selection",
# "Model Training",
# "Model Evaluation & Tuning",
# "Model Deployment",
# "Monitoring"
# ])
# elif selected_chapter == "Core Algorithms":
# page = st.sidebar.radio("Select Page", [
# "Linear Regression",
# "Logistic Regression",
# "k-Nearest Neighbors (kNN)",
# "Decision Trees",
# "Support Vector Machines (SVM)",
# "Ensemble Techniques"
# ])
# elif selected_chapter == "Model Evaluation":
# page = st.sidebar.radio("Select Page", ["Performance Metrics"])
# if page == "Performance Metrics":
# metric = st.sidebar.radio("Choose Metric", [
# "Accuracy, Precision, Recall",
# "Confusion Matrix",
# "ROC-AUC"
# ])
# elif selected_chapter == "Data Handling":
# page = st.sidebar.radio("Select Page", [
# "Data Types",
# "Data Cleaning",
# "Feature Engineering"
# ])
# if page == "Data Types":
# dtype = st.sidebar.radio("Select Type", [
# "Structured Data (SQL, Excel)",
# "Semi-Structured Data (JSON, XML)",
# "Unstructured Data (Images, Text)"
# ])
# elif selected_chapter == "Computer Vision Basics":
# page = st.sidebar.radio("Select Page", ["Image Processing", "OpenCV Basics"])
# if page == "Image Processing":
# iproc = st.sidebar.radio("Select Topic", [
# "Color Spaces",
# "Image Augmentation",
# "Splitting/Merging Images"
# ])
# elif selected_chapter == "Natural Language Processing (NLP)":
# page = st.sidebar.radio("Select Page", [
# "NLP Introduction",
# "Text Preprocessing"
# ])
# elif selected_chapter == "Deployment & Tools":
# page = st.sidebar.radio("Select Page", [
# "Model Deployment",
# "Working with Excel/CSV",
# "SQL for Data Science"
# ])
# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")
# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
# st_lottie(lottie_animation, height=200, key="animation")
# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.
# - **Model Development**: Build and optimize machine learning models.
# - **Model Deployment**: Deliver real-world solutions and monitor performance.
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)
# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.
# """)
# # Author Section
# st.markdown("""
#
#
About the Author
#
# Hello! I'm Yash Harish Gupta, an aspiring data scientist deeply passionate about machine learning.
# My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.
# Currently, I am learning and preparing to embark on my professional career in this exciting field.
#
# """, unsafe_allow_html=True)
# # Footer Section
# st.markdown("""
#
# """, unsafe_allow_html=True)
# -------------------------------------------------------------------------------------------------------------------------------------------
# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests
# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
# """Fetch Lottie animation JSON from a URL."""
# try:
# response = requests.get(url)
# if response.status_code == 200:
# return response.json()
# except requests.exceptions.RequestException:
# return None
# # CSS Styling for light and dark modes
# st.markdown("""
#
# """, unsafe_allow_html=True)
# # Navigation Bar (Sidebar) - Inserted with the exact list as requested
# st.sidebar.markdown(
# """
# # Navigation
# 1. **Foundation**
# - Home
# - Introduction to Data Science
# - Machine Learning vs Deep Learning
# 2. **ML Project Lifecycle**
# - Life Cycle of ML Project
# - Problem Statement
# - Data Collection
# - Data Preprocessing
# - Exploratory Data Analysis (EDA)
# - Feature Engineering
# - Model Selection
# - Model Training
# - Model Evaluation & Tuning
# - Model Deployment
# - Monitoring
# 3. **Core Algorithms**
# - Linear Regression
# - Logistic Regression
# - k-Nearest Neighbors (kNN)
# - Decision Trees
# - Support Vector Machines (SVM)
# - Ensemble Techniques
# 4. **Model Evaluation**
# - Performance Metrics
# - Accuracy, Precision, Recall
# - Confusion Matrix
# - ROC-AUC
# 5. **Data Handling**
# - Data Types:
# - Structured Data (SQL, Excel)
# - Semi-Structured Data (JSON, XML)
# - Unstructured Data (Images, Text)
# - Data Cleaning
# - Feature Engineering
# 6. **Computer Vision Basics**
# - Image Processing
# - Color Spaces
# - Image Augmentation
# - Splitting/Merging Images
# - OpenCV Basics
# 7. **Natural Language Processing (NLP)**
# - NLP Introduction
# - Text Preprocessing
# 8. **Deployment & Tools**
# - Model Deployment
# - Working with Excel/CSV
# - SQL for Data Science
# """, unsafe_allow_html=True)
# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")
# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
# st_lottie(lottie_animation, height=200, key="animation")
# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.
# - **Model Development**: Build and optimize machine learning models.
# - **Model Deployment**: Deliver real-world solutions and monitor performance.
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)
# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.
# """)
# # Author Section
# st.markdown("""
#
#
About the Author
#
# Hello! I'm Yash Harish Gupta, an aspiring data scientist deeply passionate about machine learning.
# My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.
# Currently, I am learning and preparing to embark on my professional career in this exciting field.
#
# """, unsafe_allow_html=True)
# # Footer Section
# st.markdown("""
#
# """, unsafe_allow_html=True)
# -------------------------------------------------------------------------------------------------------------------------------------------------------
# import streamlit as st
# from streamlit_lottie import st_lottie
# import requests
# # Function to load Lottie animation from a URL
# def load_lottieurl(url: str):
# """Fetch Lottie animation JSON from a URL."""
# try:
# response = requests.get(url)
# if response.status_code == 200:
# return response.json()
# except requests.exceptions.RequestException:
# return None
# # CSS Styling for light and dark modes
# st.markdown("""
#
# """, unsafe_allow_html=True)
# # Title and Tagline
# st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖")
# st.markdown("## Your Gateway To Become Master In Data Science")
# # Display Lottie animation
# animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json"
# lottie_animation = load_lottieurl(animation_url)
# if lottie_animation:
# st_lottie(lottie_animation, height=200, key="animation")
# # About the App Section
# st.subheader("About This Application")
# st.markdown("""
# This platform serves as a **comprehensive guide to Machine Learning and Data Science**.
# From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle:
# - **Problem Definition**: Understand the business context and set clear objectives.
# - **Data Handling**: Collect, clean, and explore datasets to uncover insights.
# - **Model Development**: Build and optimize machine learning models.
# - **Model Deployment**: Deliver real-world solutions and monitor performance.
# Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples.
# """)
# # Key Takeaways Section
# st.subheader("What You'll Learn Here")
# st.markdown("""
# 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges.
# 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning.
# 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts.
# 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls.
# """)
# # Author Section
# st.markdown("""
#
#
About the Author
#
# Hello! I'm Yash Harish Gupta, an aspiring data scientist deeply passionate about machine learning.
# My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions.
# Currently, I am learning and preparing to embark on my professional career in this exciting field.
#
