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| import streamlit as st | |
| from streamlit_lottie import st_lottie | |
| import requests | |
| import importlib.util | |
| import os | |
| # 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 adaptive light/dark modes | |
| st.markdown(""" | |
| <style> | |
| :root { | |
| --background-color: #f8f9fa; | |
| --text-color: #212529; | |
| --primary-color: #007acc; | |
| --secondary-color: #005b96; | |
| --card-bg: #ffffff; | |
| --text-muted: #6c757d; | |
| } | |
| @media (prefers-color-scheme: dark) { | |
| :root { | |
| --background-color: #0e1117; | |
| --text-color: #e5e5e5; | |
| --primary-color: #29b6f6; | |
| --secondary-color: #90caf9; | |
| --card-bg: #1f2937; | |
| --text-muted: #a3a3a3; | |
| } | |
| } | |
| body { margin: 0; padding: 0; font-family: 'Roboto', sans-serif; background-color: var(--background-color) !important; color: var(--text-color) !important; } | |
| h1 { font-size: 3rem; color: var(--primary-color) !important; text-align: center; margin-bottom: 15px; } | |
| h2, h3 { font-size: 1.5rem; color: var(--secondary-color) !important; text-align: center; margin-top: 20px; } | |
| p { font-family: 'Georgia', serif; color: var(--text-color) !important; line-height: 1.6; text-align: justify; } | |
| .about-author { background-color: var(--card-bg) !important; border-radius: 10px; padding: 25px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); margin: 20px auto; max-width: 700px; text-align: center; color: var(--text-color) !important; } | |
| .social-icons { display: flex; justify-content: center; gap: 20px; margin-top: 15px; } | |
| .social-icons a img { width: 40px; height: 40px; transition: transform 0.3s ease-in-out; } | |
| .social-icons a img:hover { transform: scale(1.2); } | |
| footer { margin-top: 50px; text-align: center; font-family: 'Georgia', serif; color: var(--text-muted) !important; } | |
| .css-1d391kg, .css-1d391kg * { background-color: var(--card-bg) !important; color: var(--text-color) !important; } | |
| </style> | |
| """, 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" | |
| ] | |
| chapter = st.sidebar.radio("Chapter", chapters) | |
| # Nested page selection based on chapter | |
| page = None | |
| if chapter == "Foundation": | |
| page = st.sidebar.radio("Page", ["Home", "Introduction to Data Science", "Machine Learning vs Deep Learning"]) | |
| elif chapter == "ML Project Lifecycle": | |
| section = st.sidebar.radio("Section", ["Life Cycle of ML Project"]) | |
| if section: | |
| page = st.sidebar.radio("Subtopic", [ | |
| "Problem Statement", "Data Collection", "Data Preprocessing", | |
| "Exploratory Data Analysis (EDA)", "Feature Engineering", "Model Selection", | |
| "Model Training", "Model Evaluation & Tuning", "Model Deployment", "Monitoring" | |
| ]) | |
| elif chapter == "Core Algorithms": | |
| page = st.sidebar.radio("Page", [ | |
| "Linear Regression", "Logistic Regression", "k-Nearest Neighbors (kNN)", | |
| "Decision Trees", "Support Vector Machines (SVM)", "Ensemble Techniques" | |
| ]) | |
| elif chapter == "Model Evaluation": | |
| page = st.sidebar.radio("Page", ["Performance Metrics"]) | |
| if page == "Performance Metrics": | |
| page = st.sidebar.radio("Metric", ["Accuracy, Precision, Recall", "Confusion Matrix", "ROC-AUC"]) | |
| elif chapter == "Data Handling": | |
| page = st.sidebar.radio("Page", ["Data Types", "Data Cleaning", "Feature Engineering"]) | |
| if page == "Data Types": | |
| page = st.sidebar.radio("Type", ["Structured Data (SQL, Excel)", "Semi-Structured Data (JSON, XML)", "Unstructured Data (Images, Text)"]) | |
| elif chapter == "Computer Vision Basics": | |
| page = st.sidebar.radio("Page", ["Image Processing", "OpenCV Basics"]) | |
| if page == "Image Processing": | |
| page = st.sidebar.radio("Topic", ["Color Spaces", "Image Augmentation", "Splitting/Merging Images"]) | |
| elif chapter == "Natural Language Processing (NLP)": | |
| page = st.sidebar.radio("Page", ["NLP Introduction", "Text Preprocessing"]) | |
| elif chapter == "Deployment & Tools": | |
| page = st.sidebar.radio("Page", ["Model Deployment", "Working with Excel/CSV", "SQL for Data Science"]) | |
| # Map pages to external scripts | |
| page_to_script = { | |
| "Introduction to Data Science": "01_introduction.py", | |
| "Machine Learning vs Deep Learning": "02_ml_vs_dl.py", | |
| "Life Cycle of ML Project": "03_life_cycle_of_ml_project.py", | |
| "Data Handling": "04_data.py" | |
| } | |
| # Helper to load and execute a module from path | |
| def run_script(path, func_name=None, *args): | |
| full_path = os.path.join(os.path.dirname(__file__), path) | |
| spec = importlib.util.spec_from_file_location(path, full_path) | |
| module = importlib.util.module_from_spec(spec) | |
| spec.loader.exec_module(module) | |
| if func_name: | |
| getattr(module, func_name)(*args) | |
| # Content rendering | |
| if page == "Home": | |
| # existing Home content | |
| st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖") | |
| st.markdown("## Your Gateway To Become Master In Data Science") | |
| anim = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json") | |
| if anim: | |
| st_lottie(anim, height=200) | |
| 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 context and objectives. | |
| - **Data Handling**: Collect, clean, explore. | |
| - **Model Development**: Build and optimize. | |
| - **Model Deployment**: Deliver and monitor solutions. | |
| """) | |
| st.subheader("What You'll Learn Here") | |
| st.markdown(""" | |
| 1. **Roadmaps**: Navigate challenges step-by-step. | |
| 2. **Hands-on Projects**: Applied examples. | |
| 3. **Visualizations**: Intuitive graphs. | |
| 4. **Insights**: Lessons from experience. | |
| """) | |
| st.markdown(""" | |
| <div class='about-author'> | |
| <h2>About the Author</h2> | |
| <p>Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist passionate about ML.</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown(""" | |
| <div class='social-icons'> | |
| <a href='https://www.linkedin.com/in/yash-harish-gupta-71b011189/' target='_blank'> | |
| <img src='https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png' alt='LinkedIn'> | |
| </a> | |
| <a href='https://github.com/YashGupta018' target='_blank'> | |
| <img src='https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png' alt='GitHub'> | |
| </a> | |
| </div> | |
| <footer>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</footer> | |
| """, unsafe_allow_html=True) | |
| elif page in page_to_script: | |
| script = page_to_script[page] | |
| # call appropriate function if needed | |
| if page == "Introduction to Data Science": | |
| run_script(script, "show_content", "Introduction") | |
| elif page == "Machine Learning vs Deep Learning": | |
| run_script(script) # top-level execution handles its own nav | |
| elif page == "Life Cycle of ML Project": | |
| run_script(script, "draw_lifecycle_diagram") | |
| elif page == "Data Handling": | |
| run_script(script, "main") | |
| else: | |
| # default placeholder for other pages | |
| 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 adaptive light/dark modes | |
| # st.markdown(""" | |
| # <style> | |
| # :root { | |
| # --background-color: #f8f9fa; | |
| # --text-color: #212529; | |
| # --primary-color: #007acc; | |
| # --secondary-color: #005b96; | |
| # --card-bg: #ffffff; | |
| # --text-muted: #6c757d; | |
| # } | |
| # @media (prefers-color-scheme: dark) { | |
| # :root { | |
| # --background-color: #0e1117; | |
| # --text-color: #e5e5e5; | |
| # --primary-color: #29b6f6; | |
| # --secondary-color: #90caf9; | |
| # --card-bg: #1f2937; | |
| # --text-muted: #a3a3a3; | |
| # } | |
| # } | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # h1 { font-size: 3rem; color: var(--primary-color) !important; text-align: center; margin-bottom: 15px; } | |
| # h2, h3 { font-size: 1.5rem; color: var(--secondary-color) !important; text-align: center; margin-top: 20px; } | |
| # p { font-family: 'Georgia', serif; color: var(--text-color) !important; line-height: 1.6; text-align: justify; } | |
| # .about-author { background-color: var(--card-bg) !important; border-radius: 10px; padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); margin: 20px auto; max-width: 700px; text-align: center; | |
| # color: var(--text-color) !important; } | |
| # .social-icons { display: flex; justify-content: center; gap: 20px; margin-top: 15px; } | |
| # .social-icons a img { width: 40px; height: 40px; transition: transform 0.3s ease-in-out; } | |
| # .social-icons a img:hover { transform: scale(1.2); } | |
| # footer { margin-top: 50px; text-align: center; font-family: 'Georgia', serif; color: var(--text-muted) !important; } | |
| # .css-1d391kg, .css-1d391kg * { background-color: var(--card-bg) !important; color: var(--text-color) !important; } | |
| # </style> | |
| # """, unsafe_allow_html=True) | |
| # # Sidebar Navigation | |
| # episodes = [ | |
| # "Foundation", | |
| # "ML Project Lifecycle", | |
| # "Core Algorithms", | |
| # "Model Evaluation", | |
| # "Data Handling", | |
| # "Computer Vision Basics", | |
| # "Natural Language Processing (NLP)", | |
| # "Deployment & Tools" | |
| # ] | |
| # chapter = st.sidebar.radio("Chapter", episodes) | |
| # # Initialize page variables | |
| # page = None | |
| # if chapter == "Foundation": | |
| # page = st.sidebar.radio("Page", ["Home", "Introduction to Data Science", "Machine Learning vs Deep Learning"]) | |
| # elif chapter == "ML Project Lifecycle": | |
| # section = st.sidebar.radio("Section", ["Life Cycle of ML Project"]) | |
| # if section: | |
| # page = st.sidebar.radio("Subtopic", [ | |
| # "Problem Statement", "Data Collection", "Data Preprocessing", | |
| # "Exploratory Data Analysis (EDA)", "Feature Engineering", "Model Selection", | |
| # "Model Training", "Model Evaluation & Tuning", "Model Deployment", "Monitoring" | |
| # ]) | |
| # elif chapter == "Core Algorithms": | |
| # page = st.sidebar.radio("Page", [ | |
| # "Linear Regression", "Logistic Regression", "k-Nearest Neighbors (kNN)", | |
| # "Decision Trees", "Support Vector Machines (SVM)", "Ensemble Techniques" | |
| # ]) | |
| # elif chapter == "Model Evaluation": | |
| # page = st.sidebar.radio("Page", ["Performance Metrics"]) | |
| # if page == "Performance Metrics": | |
| # page = st.sidebar.radio("Metric", ["Accuracy, Precision, Recall", "Confusion Matrix", "ROC-AUC"]) | |
| # elif chapter == "Data Handling": | |
| # page = st.sidebar.radio("Page", ["Data Types", "Data Cleaning", "Feature Engineering"]) | |
| # if page == "Data Types": | |
| # page = st.sidebar.radio("Type", ["Structured Data (SQL, Excel)", "Semi-Structured Data (JSON, XML)", "Unstructured Data (Images, Text)"]) | |
| # elif chapter == "Computer Vision Basics": | |
| # page = st.sidebar.radio("Page", ["Image Processing", "OpenCV Basics"]) | |
| # if page == "Image Processing": | |
| # page = st.sidebar.radio("Topic", ["Color Spaces", "Image Augmentation", "Splitting/Merging Images"]) | |
| # elif chapter == "Natural Language Processing (NLP)": | |
| # page = st.sidebar.radio("Page", ["NLP Introduction", "Text Preprocessing"]) | |
| # elif chapter == "Deployment & Tools": | |
| # page = st.sidebar.radio("Page", ["Model Deployment", "Working with Excel/CSV", "SQL for Data Science"]) | |
| # # Content rendering based on selection | |
| # if page == "Home": | |
| # st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖") | |
| # st.markdown("## Your Gateway To Become Master In Data Science") | |
| # anim = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json") | |
| # if anim: | |
| # st_lottie(anim, height=200) | |
| # 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 context and objectives. | |
| # - **Data Handling**: Collect, clean, explore. | |
| # - **Model Development**: Build and optimize. | |
| # - **Model Deployment**: Deliver and monitor solutions. | |
| # """) | |
| # st.subheader("What You'll Learn Here") | |
| # st.markdown(""" | |
| # 1. **Roadmaps**: Navigate challenges step-by-step. | |
| # 2. **Hands-on Projects**: Applied examples. | |
| # 3. **Visualizations**: Intuitive graphs. | |
| # 4. **Insights**: Lessons from experience. | |
| # """) | |
| # st.markdown(""" | |
| # <div class='about-author'> | |
| # <h2>About the Author</h2> | |
| # <p>Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist passionate about ML.</p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # st.markdown(""" | |
| # <div class='social-icons'> | |
| # <a href='https://www.linkedin.com/in/yash-harish-gupta-71b011189/' target='_blank'> | |
| # <img src='https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png' alt='LinkedIn'> | |
| # </a> | |
| # <a href='https://github.com/YashGupta018' target='_blank'> | |
| # <img src='https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png' alt='GitHub'> | |
| # </a> | |
| # </div> | |
| # <footer>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</footer> | |
| # """, 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(""" | |
| # <style> | |
| # :root { | |
| # --background-color: #f8f9fa; | |
| # --text-color: #212529; | |
| # --primary-color: #007acc; | |
| # --secondary-color: #005b96; | |
| # --card-bg: #ffffff; | |
| # --text-muted: #6c757d; | |
| # } | |
| # @media (prefers-color-scheme: dark) { | |
| # :root { | |
| # --background-color: #0e1117; | |
| # --text-color: #e5e5e5; | |
| # --primary-color: #29b6f6; | |
| # --secondary-color: #90caf9; | |
| # --card-bg: #1f2937; | |
| # --text-muted: #a3a3a3; | |
| # } | |
| # } | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color) !important; | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color) !important; | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color) !important; | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg) !important; | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color) !important; | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted) !important; | |
| # } | |
| # /* Sidebar adjustments */ | |
| # .css-1d391kg, .css-1d391kg * { | |
| # background-color: var(--card-bg) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # </style> | |
| # """, 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(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, 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. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, 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(""" | |
| # <style> | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color, #f8f9fa); | |
| # color: var(--text-color, #212529); | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color, #007acc); | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color, #005b96); | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color, #212529); | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg, #ffffff); | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color, #212529); | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted, #6c757d); | |
| # } | |
| # </style> | |
| # """, 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(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, 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. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, 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(""" | |
| # <style> | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color, #f8f9fa); | |
| # color: var(--text-color, #212529); | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color, #007acc); | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color, #005b96); | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color, #212529); | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg, #ffffff); | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color, #212529); | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted, #6c757d); | |
| # } | |
| # </style> | |
| # """, 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(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, 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. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, 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(""" | |
| # <style> | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color, #f8f9fa); | |
| # color: var(--text-color, #212529); | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color, #007acc); | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color, #005b96); | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color, #212529); | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg, #ffffff); | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color, #212529); | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted, #6c757d); | |
| # } | |
| # </style> | |
| # """, 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(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, 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. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, unsafe_allow_html=True) |