{"id":109367,"date":"2025-07-03T11:18:24","date_gmt":"2025-07-03T05:48:24","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/?page_id=109367"},"modified":"2025-07-02T15:12:13","modified_gmt":"2025-07-02T09:42:13","slug":"machine-learning-quiz","status":"publish","type":"page","link":"https:\/\/www.mygreatlearning.com\/blog\/machine-learning-quiz\/","title":{"rendered":"Machine Learning Quiz"},"content":{"rendered":"\n<div id=\"ml-quiz-app-wrapper\">\n    <style>\n        \/* --- General Reset & Variables --- *\/\n        html { scroll-behavior: smooth; }\n        #ml-quiz-app-wrapper {\n            --primary-blue: #1A73E8; --hover-blue: #1865c9; --background-grey: #F8F9FA; --border-grey: #DADCE0; --text-primary: #202124; --text-secondary: #5F6368; --option-bg: #FFFFFF; --option-hover-bg: #F1F3F4; --color-success: #1E8E3E; --correct-bg: #E6F4EA; --correct-text: #117233; --color-danger: #D93025; --incorrect-bg: #FCE8E6; --incorrect-text: #A50E0E; --color-warning: #f59e0b; --whatsapp-green: #25D366; 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margin-top: 1rem; text-align: left; }\n        #ml-quiz-app-wrapper .question-breakdown strong { color: var(--correct-text); }\n        #ml-quiz-app-wrapper .question-breakdown span { color: var(--incorrect-text); }\n        #ml-quiz-app-wrapper .hidden { display: none; }\n\n        \/* --- START: New Results View Styles --- *\/\n        #results-view .container { max-width: 1100px; margin: 0 auto; padding: 2rem 1rem; color: #1A202C;}\n        #results-view .page-header { display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 2rem; flex-wrap: wrap; }\n        #results-view .header-title h1 { font-size: 2rem; font-weight: 700; margin: 0; }\n        #results-view .header-title p { color: #4A5568; margin-top: 0.25rem; }\n        #results-view .restart-quiz-btn { display: flex; align-items: center; gap: 0.5rem; padding: 0.6rem 1.2rem; background-color: #4353FF; border: 1px solid #E2E8F0; border-radius: 0.5rem; font-size: 0.875rem; font-weight: 600; color: white; cursor: pointer; transition: background-color 0.2s; margin-top: 1rem; }\n        #results-view .restart-quiz-btn:hover { background-color: #4353FF; }\n        #results-view .main-content { display: grid; grid-template-columns: 1fr; gap: 2rem; }\n        #results-view .card { background-color: #ffffff; padding: 1.5rem; }\n        #results-view .score-card { text-align: center; color:gray;}\n        #results-view .score-card h2 { font-size: 1.125rem; font-weight: 600; margin-top: 0; margin-bottom: 1rem; }\n        #results-view .score-visual { position: relative; width: 10rem; height: 10rem; margin: 0 auto; }\n        #results-view .score-visual svg { width: 100%; height: 100%; }\n        #results-view .progress-ring-circle { transition: stroke-dashoffset 0.8s ease-out; transform: rotate(-90deg); transform-origin: 50% 50%; }\n        #results-view .score-text { position: absolute; top: 0; left: 0; right: 0; bottom: 0; display: flex; flex-direction: column; justify-content: center; align-items: center; }\n        #results-view .score-text .score-value { font-size: 2.25rem; font-weight: 700; }\n        #results-view .score-text .score-value span { font-size: 1.5rem; color: #4A5568; }\n        #results-view .score-text .score-label { font-size: 0.875rem; color: #4A5568; margin-top: 0.25rem; }\n        #results-view .score-breakdown { margin-top: 1.5rem; display: grid; grid-template-columns: repeat(3, 1fr); gap: 1rem; }\n        #results-view .score-breakdown div span { font-weight: 700; font-size: 1.125rem; }\n        #results-view .score-breakdown div p { font-size: 0.75rem; color: #4A5568; margin: 0; }\n        #results-view .text-correct { color: #22c55e; }\n        #results-view .text-incorrect { color: #ef4444; }\n        #results-view .text-skipped { color: #6b7280; }\n        #results-view .analysis-card { display: flex; align-items: flex-start; gap: 1rem; justify-content: center; color:gray; }\n        #results-view .analysis-icon { flex-shrink: 0; width: 2.5rem; height: 2.5rem; background-color: #F0F1FF; border-radius: 0.5rem; display: flex; align-items: center; justify-content: center; }\n        #results-view .analysis-content h3 { font-weight: 600; margin: 0; font-size: 1.2rem; }\n        #results-view .analysis-content p { font-size: 0.875rem; color: #4A5568; margin: 0.25rem 0 1rem 0; }\n        #results-view .primary-button { width: 100%; padding: 0.6rem 1.2rem; background-color: #4353FF; border-radius: 0.5rem; font-size: 0.875rem; font-weight: 600; color: #ffffff; border: none; cursor: pointer; transition: background-color 0.2s; }\n        #results-view .primary-button:hover { background-color: #2539cc; }\n        #results-view .learning-journey-card h2 { font-size: 1.5rem; font-weight: 700; margin-top: 0; }\n        #results-view .learning-journey-card > p { color: #4A5568; margin: 0.25rem 0 2rem 0; }\n        #results-view .course-list { display: flex; flex-direction: column; gap: 1rem; }\n        #results-view .course-card { padding: 1.25rem; border: 1px solid #E2E8F0; border-radius: 0.75rem; transition: box-shadow 0.2s, border-color 0.2s; }\n        #results-view .course-card:hover { border-color: #4353FF; box-shadow: 0 4px 6px -1px rgb(0 0 0 \/ 0.1), 0 2px 4px -2px rgb(0 0 0 \/ 0.1); }\n        #results-view .course-card-content { display: flex; justify-content: space-between; align-items: center; flex-wrap: wrap; gap: 3px; }\n        #results-view .course-details h3 { font-weight: 700; margin: 0; font-size: 1rem; }\n        #results-view .course-details p { font-size: 0.875rem; color: #4A5568; margin-top: 0.25rem; max-width: 36rem; }\n        #results-view .course-button { margin-top: 1rem; flex-shrink: 0; padding: 0.3rem 1rem; border-radius: 0.5rem; font-size: 0.875rem; font-weight: 600; cursor: pointer; border: 1px solid #4353FF; transition: background-color 0.2s; text-decoration: none; display: inline-block; }\n        #results-view .btn-primary { background-color: #4353FF; color: #ffffff; }\n        #results-view .btn-primary:hover { background-color: #2539cc; }\n        #results-view .page-footer { margin-top: 2rem; padding: 2rem; }\n        #results-view .footer-content { display: grid; grid-template-columns: 1fr; gap: 2rem; }\n        #results-view .footer-column h3 { font-size: 1.125rem; font-weight: 700; margin: 0; }\n        #results-view .footer-column p { font-size: 0.875rem; color: #4A5568; margin-top: 0.25rem; margin-bottom: 1rem; }\n        #results-view .resource-links { display: flex; flex-wrap: wrap; gap: 0.5rem; }\n        #results-view .resource-link { padding: 0.5rem 1rem; background-color: #f3f4f6; color: #4A5568; font-size: 0.875rem; font-weight: 500; border-radius: 9999px; text-decoration: none; transition: background-color 0.2s, color 0.2s; }\n        #results-view .resource-link:hover { background-color: #F0F1FF; color: #4353FF; }\n        #results-view .share-icons { display: flex; align-items: center; gap: 0.75rem; }\n        #results-view .share-icon { width: 2.5rem; height: 2.5rem; display: flex; align-items: center; justify-content: center; background-color: #f3f4f6; border-radius: 9999px; color: #4A5568; transition: background-color 0.2s; }\n        #results-view .share-icon:hover { background-color: #e5e7eb; }\n        #results-view .share-icon svg { width: 1.25rem; height: 1.25rem; }\n        \/* --- END: New Results View Styles --- *\/\n\n        \/* --- Media Queries (Responsiveness) --- *\/\n        @media (max-width: 768px) {\n            #main-menu-view, #quiz-body, #user-details-view, #analysis-view, #results-view .container { padding: 1.5rem; }\n            #ml-quiz-app-wrapper .quiz-header h1 { font-size: 2rem; }\n            #ml-quiz-app-wrapper .difficulty-grid { grid-template-columns: 1fr; }\n            #progress-stepper { display: none; }\n            #quiz-header h1 { font-size: 1rem; }\n            #quiz-footer { flex-direction: column-reverse; gap: 5rem; margin-bottom: -20px; }\n            #quiz-footer>div { display: flex; justify-content: flex-end; width: 100%; gap: 0.5rem; }\n        }\n        @media (min-width: 640px) {\n            #results-view .page-header { flex-wrap: nowrap; }\n            #results-view .restart-quiz-btn { margin-top: 0; }\n            #results-view .course-button { margin-top: 0; width: auto; }\n            #results-view .course-card-content { flex-wrap: nowrap; }\n        }\n        @media (min-width: 768px) {\n            #results-view .footer-content { grid-template-columns: 2fr 1fr; }\n        }\n        @media (min-width: 1024px) {\n            #results-view .main-content { grid-template-columns: 1fr 2fr; }\n        }\n    <\/style>\n    <div id=\"quiz-app-container\">\n        <div id=\"main-menu-view\" class=\"quiz-screen active\">\n            <div class=\"quiz-header\"><h1 id=\"machine-learning-quiz-challenge\">Machine Learning Quiz Challenge<\/h1><p>Test your ML knowledge with our comprehensive MCQs. Choose your difficulty and share your score with friends!<\/p><\/div>\n            <div class=\"difficulty-grid\">\n                <div class=\"difficulty-card beginner\" onclick=\"showUserDetailsView('beginner')\">\n                    <div class=\"icon\">\u2b50<\/div><h2 id=\"beginner\">Beginner<\/h2><p>Perfect for ML newcomers<\/p>\n                    <div class=\"features\"><p>\u2022 Fundamental concepts<\/p><p>\u2022 Supervised vs. Unsupervised<\/p><p>\u2022 Basic algorithms<\/p><p>\u2022 10 Questions<\/p><\/div>\n                    <button class=\"quiz-btn primary start-btn\">Start Beginner Quiz<\/button>\n                <\/div>\n                <div class=\"difficulty-card intermediate\" onclick=\"showUserDetailsView('intermediate')\">\n                    <div class=\"icon\">\ud83c\udfc6<\/div><h2 id=\"intermediate\">Intermediate<\/h2><p>For those with some experience<\/p>\n                    <div class=\"features\"><p>\u2022 Classification & Regression<\/p><p>\u2022 Model evaluation<\/p><p>\u2022 Feature engineering<\/p><p>\u2022 15 Questions<\/p><\/div>\n                    <button class=\"quiz-btn primary start-btn\">Start Intermediate Quiz<\/button>\n                <\/div>\n                <div class=\"difficulty-card advanced\" onclick=\"showUserDetailsView('advanced')\">\n                    <div class=\"icon\">\ud83e\udd47<\/div><h2 id=\"advanced\">Advanced<\/h2><p>A challenge for ML experts<\/p>\n                    <div class=\"features\"><p>\u2022 Deep Learning & Networks<\/p><p>\u2022 Ensemble methods<\/p><p>\u2022 Dimensionality Reduction<\/p><p>\u2022 15 Questions<\/p><\/div>\n                    <button class=\"quiz-btn primary start-btn\">Start Advanced Quiz<\/button>\n                <\/div>\n            <\/div>\n            <div class=\"quiz-features-section\">\n                <h2 id=\"quiz-features\">Quiz Features<\/h2>\n                <div class=\"features-grid\">\n                    <div class=\"feature-item\"><div class=\"icon\">\ud83e\udde0<\/div><p>Detailed Feedback<\/p><\/div>\n                    <div class=\"feature-item\"><div class=\"icon\">\ud83d\udcca<\/div><p>Performance Analysis<\/p><\/div>\n                    <div class=\"feature-item\"><div class=\"icon\">\ud83d\udca1<\/div><p>Helpful Hints<\/p><\/div>\n                <\/div>\n            <\/div>\n        <\/div>\n        <div id=\"user-details-view\" class=\"quiz-screen\">\n            <div class=\"quiz-header\">\n                <h1 id=\"almost-there\">Almost There!<\/h1>\n                <p>Just a few details before you start the quiz.<\/p>\n            <\/div>\n            <div id=\"user-details-form-container\">\n               <form id=\"user-details-form\">\n                    <div class=\"form-group\"><label for=\"user-name\">Your Name<\/label><input type=\"text\" id=\"user-name\" name=\"user-name\" required><\/div>\n                    <div class=\"form-group\"><label for=\"user-email\">Your Email<\/label><input type=\"email\" id=\"user-email\" name=\"user-email\" required><\/div>\n                    <small>You will also get free access to exclusive content through email.<\/small>\n                    <div class=\"form-actions\">\n                        <button type=\"button\" id=\"back-to-menu-btn\" class=\"quiz-btn\">Back to Menu<\/button>\n                        <button type=\"submit\" class=\"quiz-btn primary\">Let's Go!<\/button>\n                    <\/div>\n                <\/form>\n            <\/div>\n        <\/div>\n        <div id=\"quiz-view\" class=\"quiz-screen\">\n            <header id=\"quiz-header\">\n                <h1 id=\"machine-learning-quiz\">Machine Learning Quiz<\/h1>\n                <div id=\"progress-stepper\"><\/div>\n                <div class=\"header-actions\"><div id=\"timer\">00:00<\/div><div id=\"question-count\">1\/10<\/div><\/div>\n            <\/header>\n            <main id=\"quiz-body\">\n                <h2 class=\"ml-quiz-question-text\" style=\"font-size: 22px;\" class=\"ml-quiz-question-text\" style=\"font-size: 22px;\" id=\"question-will-appear-here\">Question will appear here.<\/h2>\n                <div id=\"options-container\"><\/div>\n                <div id=\"hint-container\">\n                    <button id=\"hint-toggle\">Show hint <svg viewBox=\"0 0 24 24\"><path fill=\"currentColor\" d=\"M7.41,8.58L12,13.17L16.59,8.58L18,10L12,16L6,10L7.41,8.58Z\"><\/path><\/svg><\/button>\n                    <div id=\"hint-text\" class=\"hidden\">Hint text goes here.<\/div>\n                <\/div>\n            <\/main>\n            <footer id=\"quiz-footer\">\n                <button class=\"quiz-btn\" onclick=\"showMainMenu(true)\">Back to Menu<\/button>\n                <div><button id=\"back-btn\" class=\"quiz-btn\">Back<\/button><button id=\"next-btn\" class=\"quiz-btn primary\">Next<\/button><\/div>\n            <\/footer>\n        <\/div>\n        \n        <div id=\"results-view\" class=\"quiz-screen\">\n            <div class=\"container\">\n                <header class=\"page-header\">\n                    <div class=\"header-title\">\n                        <h1 id=\"quiz-completed\">Quiz Completed!<\/h1>\n                        <p>Great effort! 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to Results<\/button>\n            <\/footer>\n        <\/div>\n    <\/div>\n    <script>\n    document.addEventListener('DOMContentLoaded', function() {\n        \/\/ --- DATA ---\n        const quizData = {\n            beginner: [\n                { category: \"Fundamentals\", question: \"What is the primary goal of Supervised Learning?\", options: { A: \"To find hidden patterns in unlabeled data\", B: \"To learn a mapping from inputs to outputs\", C: \"To learn through trial and error\", D: \"To group similar data points together\" }, answer: \"B\", hint: \"Think about having a 'supervisor' or 'teacher' who provides the correct answers (labels).\", feedback: { B: \"Correct! Supervised learning uses labeled data to learn a function that maps inputs to outputs.\", A: \"This describes Unsupervised Learning.\", C: \"This is the core idea of Reinforcement Learning.\" } },\n                { category: \"Problem Types\", question: \"Which of the following is a classification problem?\", options: { A: \"Predicting the price of a house\", B: \"Predicting whether an email is spam or not\", C: \"Predicting the temperature for tomorrow\", D: \"Grouping customers by purchasing behavior\" }, answer: \"B\", hint: \"Classification is about predicting a category or class.\", feedback: { B: \"Correct! Predicting a discrete category like 'spam' or 'not spam' is a classic classification task.\", A: \"Predicting a continuous value like price is a regression problem.\", D: \"Grouping is a clustering (unsupervised) problem.\" } },\n                { category: \"Fundamentals\", question: \"What is Unsupervised Learning?\", options: { A: \"Learning from data that has labels\", B: \"Learning from data without any labels\", C: \"Learning with a human providing feedback\", D: \"Learning to play a game\" }, answer: \"B\", hint: \"The 'un' in unsupervised means 'without' labels or correct answers.\", feedback: { B: \"Correct! Unsupervised learning deals with unlabeled data to find inherent structures or patterns.\" } },\n                { category: \"Terminology\", question: \"In machine learning, what is a 'feature'?\", options: { A: \"The outcome or prediction\", B: \"An individual measurable property of a phenomenon being observed\", C: \"The algorithm used for training\", D: \"A row in the dataset\" }, answer: \"B\", hint: \"Features are the inputs to your model, like the height, weight, or age of a person.\", feedback: { B: \"Correct! A feature is an input variable used by the model to make a prediction. For example, the size of a house is a feature when predicting its price.\" } },\n                { category: \"Algorithms\", question: \"Which algorithm is suitable for predicting a continuous value, like a house price?\", options: { A: \"K-Means\", B: \"Logistic Regression\", C: \"Linear Regression\", D: \"K-Nearest Neighbors (Classification)\" }, answer: \"C\", hint: \"The name of this algorithm contains the type of problem it solves.\", feedback: { C: \"Correct! Linear Regression is the fundamental algorithm for regression tasks, which involve predicting continuous outcomes.\", B: \"Logistic Regression is used for classification, not regression.\" } },\n                { category: \"Model Training\", question: \"What is the purpose of a 'training set'?\", options: { A: \"To evaluate the final performance of a model\", B: \"To provide the model with unseen data\", C: \"To fit the parameters of the model\", D: \"To fine-tune hyperparameters\" }, answer: \"C\", hint: \"The model 'learns' from this specific set of data.\", feedback: { C: \"Correct! The training set is the data the model uses to learn the relationships between features and the target variable.\" } },\n                { category: \"Model Evaluation\", question: \"What does a 'test set' evaluate?\", options: { A: \"The model's ability to learn from data\", B: \"The model's generalization to new, unseen data\", C: \"The speed of the training process\", D: \"The number of features in the data\" }, answer: \"B\", hint: \"This set is kept separate and is used only after the model is trained.\", feedback: { B: \"Correct! The test set assesses how well the model performs on data it has never encountered before, which is a crucial measure of its real-world performance.\" } },\n                { category: \"Model Issues\", question: \"A model that performs very well on training data but poorly on test data is likely...?\", options: { A: \"Underfitting\", B: \"Overfitting\", C: \"A good model\", D: \"Biased\" }, answer: \"B\", hint: \"The model has learned the training data 'too well', including its noise.\", feedback: { B: \"Correct! Overfitting occurs when a model learns the training data so closely that it fails to generalize to new data.\", A: \"Underfitting is when the model is too simple and performs poorly on both training and test data.\" } },\n                { category: \"Problem Types\", question: \"Which of these is a common task in Unsupervised Learning?\", options: { A: \"Classification\", B: \"Regression\", C: \"Clustering\", D: \"Reinforcement\" }, answer: \"C\", hint: \"This task involves grouping similar data points together without predefined labels.\", feedback: { C: \"Correct! Clustering algorithms like K-Means group data into clusters based on similarity, a core task of unsupervised learning.\" } },\n                { category: \"Terminology\", question: \"What is the 'target' or 'label' in a dataset?\", options: { A: \"The input variables of the model\", B: \"The value or category you are trying to predict\", C: \"The number of samples in the dataset\", D: \"A random column in the data\" }, answer: \"B\", hint: \"This is the 'answer' that the model is trying to learn to predict.\", feedback: { B: \"Correct! The target (or label) is the output variable that you want your model to predict, such as 'price' in a housing dataset.\" } }\n            ],\n            intermediate: [\n                { category: \"Core Concepts\", question: \"What is the bias-variance tradeoff?\", options: { A: \"A tradeoff between model speed and accuracy\", B: \"A tradeoff between a model's ability to fit data and generalize to new data\", C: \"A tradeoff between using more data and less data\", D: \"A tradeoff between L1 and L2 regularization\" }, answer: \"B\", hint: \"Simple models have high bias; complex models have high variance. The goal is to find a balance.\", feedback: { B: \"Correct! It's a fundamental concept where decreasing one (e.g., bias) tends to increase the other (variance). An ideal model balances both to generalize well.\" } },\n                { category: \"Evaluation Metrics\", question: \"In a confusion matrix, what does the diagonal represent?\", options: { A: \"False Positives\", B: \"False Negatives\", C: \"Total Predictions\", D: \"Correctly Classified Instances\" }, answer: \"D\", hint: \"The diagonal from top-left to bottom-right shows where the predicted class matches the actual class.\", feedback: { D: \"Correct! Each cell on the main diagonal represents the count of instances that were correctly classified for that specific class.\" } },\n                { category: \"Evaluation Metrics\", question: \"What is 'Precision' in the context of binary classification?\", options: { A: \"TP \/ (TP + FN)\", B: \"TP \/ (TP + FP)\", C: \"TN \/ (TN + FP)\", D: \"(TP + TN) \/ Total\" }, answer: \"B\", hint: \"Precision answers the question: Of all the positive predictions, how many were actually correct?\", feedback: { B: \"Correct! Precision measures the accuracy of the positive predictions. TP = True Positives, FP = False Positives.\" } },\n                { category: \"Evaluation Metrics\", question: \"What is 'Recall' (or Sensitivity)?\", options: { A: \"TP \/ (TP + FP)\", B: \"TN \/ (TN + FP)\", C: \"TP \/ (TP + FN)\", D: \"FP \/ (FP + TN)\" }, answer: \"C\", hint: \"Recall answers the question: Of all the actual positives, how many did the model correctly identify?\", feedback: { C: \"Correct! Recall measures the model's ability to find all the positive instances. TP = True Positives, FN = False Negatives.\" } },\n                { category: \"Evaluation Metrics\", question: \"The F1-Score is the harmonic mean of which two metrics?\", options: { A: \"Accuracy and Error\", B: \"Bias and Variance\", C: \"Precision and Recall\", D: \"True Positives and True Negatives\" }, answer: \"C\", hint: \"It provides a single score that balances both of these important metrics.\", feedback: { C: \"Correct! The F1-Score is a way to balance Precision and Recall, especially useful when you have an imbalanced class distribution.\" } },\n                { category: \"Evaluation Metrics\", question: \"What does a Receiver Operating Characteristic (ROC) curve plot?\", options: { A: \"Precision vs. Recall\", B: \"True Positive Rate vs. False Positive Rate\", C: \"Accuracy vs. Number of Epochs\", D: \"Loss vs. Iterations\" }, answer: \"B\", hint: \"This curve helps visualize the performance of a classifier across all classification thresholds.\", feedback: { B: \"Correct! The ROC curve shows the trade-off between the True Positive Rate (Recall) and the False Positive Rate at various threshold settings.\" } },\n                { category: \"Algorithms\", question: \"How does a Decision Tree make predictions?\", options: { A: \"By finding a hyperplane\", B: \"By calculating distance to neighbors\", C: \"By asking a series of if\/else questions about features\", D: \"By using a linear equation\" }, answer: \"C\", hint: \"It creates a tree-like model of decisions, where each internal node represents a 'test' on an attribute.\", feedback: { C: \"Correct! A decision tree splits the data into branches based on feature values, creating a flowchart-like structure to arrive at a decision.\" } },\n                { category: \"Algorithms\", question: \"What is the purpose of 'pruning' a decision tree?\", options: { A: \"To make it more complex\", B: \"To reduce overfitting by removing branches\", C: \"To increase the number of leaves\", D: \"To convert it to a linear model\" }, answer: \"B\", hint: \"This process is like trimming a real tree to make it healthier and less overgrown.\", feedback: { B: \"Correct! Pruning removes parts of the tree (like specific branches) that are non-critical and may be based on noise, which helps the model generalize better.\" } },\n                { category: \"Regularization\", question: \"What is the primary difference between L1 and L2 regularization?\", options: { A: \"L1 can lead to sparse models (some weights are zero)\", B: \"L2 can lead to sparse models\", C: \"L1 is only for regression\", D: \"L2 is faster to compute\" }, answer: \"A\", hint: \"L1 (Lasso) can be used for feature selection because it can eliminate the impact of some features entirely.\", feedback: { A: \"Correct! L1 regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some to zero. L2 (Ridge) adds a squared magnitude penalty, which shrinks weights but rarely to absolute zero.\" } },\n                { category: \"Algorithms\", question: \"Which of the following is an assumption of the Naive Bayes algorithm?\", options: { A: \"Features are perfectly correlated\", B: \"Features are conditionally independent given the class\", C: \"The data follows a normal distribution\", D: \"The data is linearly separable\" }, answer: \"B\", hint: \"This 'naive' assumption is what makes the algorithm computationally efficient.\", feedback: { B: \"Correct! The core (and naive) assumption is that the presence of a particular feature in a class is unrelated to the presence of any other feature.\" } },\n                { category: \"Feature Engineering\", question: \"What is feature scaling?\", options: { A: \"Removing features from a dataset\", B: \"The process of normalizing the range of independent variables\", C: \"Creating new features\", D: \"Selecting the best features\" }, answer: \"B\", hint: \"It ensures that features with large ranges don't dominate those with small ranges in certain algorithms.\", feedback: { B: \"Correct! Techniques like Standardization or Normalization are used to bring all features onto a similar scale, which is important for algorithms like SVMs and Gradient Descent.\" } },\n                { category: \"Feature Engineering\", question: \"One-Hot Encoding is used for what type of data?\", options: { A: \"Numerical Data\", B: \"Categorical Data\", C: \"Text Data\", D: \"Time Series Data\" }, answer: \"B\", hint: \"It converts categories like 'Red', 'Green', 'Blue' into a numerical format.\", feedback: { B: \"Correct! One-Hot Encoding transforms categorical variables into a binary vector format that machine learning models can understand.\" } },\n                { category: \"Model Training\", question: \"What is the purpose of a validation set in model training?\", options: { A: \"To train the final model\", B: \"To perform final evaluation\", C: \"To tune the model's hyperparameters\", D: \"To replace the test set\" }, answer: \"C\", hint: \"It acts as a proxy for the test set during the training process to guide model choices.\", feedback: { C: \"Correct! The validation set is used to evaluate the model during training to find the best hyperparameters (like learning rate or tree depth) without 'peeking' at the test set.\" } },\n                { category: \"Model Evaluation\", question: \"K-Fold Cross-Validation is a technique used to...?\", options: { A: \"Speed up model training\", B: \"Get a more robust estimate of model performance\", C: \"Simplify a model\", D: \"Increase the number of features\" }, answer: \"B\", hint: \"It involves splitting the data into multiple 'folds' and training\/testing the model several times.\", feedback: { B: \"Correct! By averaging the performance across K different folds, it provides a more stable and reliable estimate of the model's performance on unseen data.\" } },\n                { category: \"Ensemble Methods\", question: \"What is an 'ensemble method'?\", options: { A: \"A single, highly complex model\", B: \"A method that combines multiple models to improve performance\", C: \"A technique for data visualization\", D: \"A method for feature selection\" }, answer: \"B\", hint: \"Think of it as 'the wisdom of the crowd' for machine learning models.\", feedback: { B: \"Correct! Ensemble methods, like Random Forests and Gradient Boosting, combine the predictions of several base estimators to improve generalizability and robustness.\" } }\n            ],\n            advanced: [\n                { category: \"Dimensionality Reduction\", question: \"What is the main purpose of Principal Component Analysis (PCA)?\", options: { A: \"Classification\", B: \"Clustering\", C: \"Dimensionality reduction\", D: \"Regression\" }, answer: \"C\", hint: \"It transforms data into a new coordinate system to reduce the number of variables (features).\", feedback: { C: \"Correct! PCA is an unsupervised technique used to reduce the dimensionality of a dataset while preserving as much of the original variance as possible.\" } },\n                { category: \"Deep Learning\", question: \"In a neural network, what is the role of an activation function?\", options: { A: \"To initialize the weights\", B: \"To calculate the loss\", C: \"To introduce non-linearity into the model\", D: \"To normalize the input data\" }, answer: \"C\", hint: \"Without this, a deep neural network would just be a complex linear regression model.\", feedback: { C: \"Correct! Activation functions like ReLU or Sigmoid introduce non-linear properties, allowing the network to learn complex patterns that a simple linear model cannot.\" } },\n                { category: \"Deep Learning\", question: \"What is backpropagation?\", options: { A: \"A method for initializing weights\", B: \"An algorithm for efficiently calculating gradients in a neural network\", C: \"A technique to prevent overfitting\", D: \"A type of network layer\" }, answer: \"B\", hint: \"It works backward from the output layer to the input layer to update the model's weights.\", feedback: { B: \"Correct! Backpropagation is the core algorithm for training neural networks, used to calculate the gradients of the loss function with respect to the network's weights.\" } },\n                { category: \"Deep Learning\", question: \"What is the 'vanishing gradient problem' in deep networks?\", options: { A: \"When gradients become too large, causing instability\", B: \"When gradients become extremely small, preventing weights from changing\", C: \"When the model overfits the data\", D: \"When the model runs out of memory\" }, answer: \"B\", hint: \"This problem is particularly common in deep networks with many layers using certain activation functions like sigmoid.\", feedback: { B: \"Correct! During backpropagation, gradients can shrink exponentially as they pass through layers, becoming so small that the weights of the initial layers barely update.\" } },\n                { category: \"Deep Learning\", question: \"What is 'dropout' in the context of neural networks?\", options: { A: \"A method to speed up training\", B: \"A regularization technique where random neurons are ignored during training\", C: \"A way to decrease the learning rate over time\", D: \"A type of activation function\" }, answer: \"B\", hint: \"This technique prevents neurons from co-adapting too much with each other.\", feedback: { B: \"Correct! Dropout is a powerful regularization technique that helps prevent overfitting by randomly setting the output of some neurons to zero during each training step.\" } },\n                { category: \"Deep Learning\", question: \"What is Transfer Learning?\", options: { A: \"Training a model from scratch\", B: \"Using a pre-trained model on a new, related problem\", C: \"Combining multiple models into one\", D: \"A data preprocessing technique\" }, answer: \"B\", hint: \"It leverages knowledge gained from solving one problem and applies it to a different but related problem.\", feedback: { B: \"Correct! Transfer learning involves taking a model trained on a large dataset (like ImageNet) and fine-tuning it for a new, specific task, which can save significant training time and improve performance.\" } },\n                { category: \"Ensemble Methods\", question: \"What is the primary difference between Bagging and Boosting ensemble methods?\", options: { A: \"Bagging trains models in parallel, Boosting trains sequentially\", B: \"Boosting trains models in parallel, Bagging trains sequentially\", C: \"Bagging is for regression, Boosting is for classification\", D: \"There is no difference\" }, answer: \"A\", hint: \"Boosting focuses on mistakes of previous models, which requires a sequential process.\", feedback: { A: \"Correct! Bagging (like Random Forest) builds independent models on bootstrap samples in parallel. Boosting (like AdaBoost, XGBoost) builds models sequentially, with each new model trying to correct the errors of its predecessor.\" } },\n                { category: \"Algorithms\", question: \"t-SNE (t-Distributed Stochastic Neighbor Embedding) is primarily used for...?\", options: { A: \"Dimensionality reduction for visualization\", B: \"High-dimensional classification\", C: \"Training deep neural networks\", D: \"Feature engineering\" }, answer: \"A\", hint: \"It's excellent at revealing the underlying structure of high-dimensional data in 2D or 3D.\", feedback: { A: \"Correct! t-SNE is a popular non-linear technique for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.\" } },\n                { category: \"Deep Learning\", question: \"What is a Generative Adversarial Network (GAN)?\", options: { A: \"A type of reinforcement learning algorithm\", B: \"A framework where two neural networks (Generator and Discriminator) compete\", C: \"A model used for anomaly detection\", D: \"A supervised learning algorithm\" }, answer: \"B\", hint: \"One network tries to create realistic data, while the other tries to tell if it's real or fake.\", feedback: { B: \"Correct! GANs consist of a Generator that creates new data instances and a Discriminator that evaluates them for authenticity, leading to the generation of highly realistic data.\" } },\n                { category: \"NLP\", question: \"What is the key innovation of the Transformer architecture in NLP?\", options: { A: \"Recurrent connections\", B: \"Convolutional layers\", C: \"The self-attention mechanism\", D: \"A deeper network structure\" }, answer: \"C\", hint: \"This mechanism allows the model to weigh the importance of different words in the input when processing a specific word.\", feedback: { C: \"Correct! The self-attention mechanism allows Transformers to process all words in a sequence simultaneously and draw context from the entire sequence, revolutionizing NLP tasks.\" } },\n                { category: \"Algorithms\", question: \"Which of these is a density-based clustering algorithm, good for non-spherical clusters?\", options: { A: \"K-Means\", B: \"Hierarchical Clustering\", C: \"DBSCAN\", D: \"Gaussian Mixture Model\" }, answer: \"C\", hint: \"This algorithm groups together points that are closely packed together, marking outliers as noise.\", feedback: { C: \"Correct! DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is effective at finding arbitrarily shaped clusters and identifying noise points, unlike K-Means which assumes spherical clusters.\" } },\n                { category: \"Deep Learning\", question: \"What is Batch Normalization used for in neural networks?\", options: { A: \"To normalize the input data only\", B: \"To normalize the output of a previous activation layer to stabilize and speed up training\", C: \"To regularize the model by adding noise\", D: \"To select the batch size\" }, answer: \"B\", hint: \"It addresses the problem of 'internal covariate shift' during training.\", feedback: { B: \"Correct! By normalizing the inputs to each layer, Batch Normalization helps stabilize the learning process, often allowing for faster training and better performance.\" } },\n                { category: \"Ensemble Methods\", question: \"XGBoost is an implementation of which algorithm?\", options: { A: \"Random Forest\", B: \"Gradient Boosting\", C: \"Support Vector Machine\", D: \"AdaBoost\" }, answer: \"B\", hint: \"It is a highly efficient and popular implementation of this sequential ensemble method.\", feedback: { B: \"Correct! XGBoost (eXtreme Gradient Boosting) is a powerful and widely-used library that provides an optimized implementation of the gradient boosting framework.\" } },\n                { category: \"Algorithms\", question: \"What is an Autoencoder?\", options: { A: \"A supervised model for classification\", B: \"An unsupervised neural network for learning efficient data codings (encodings)\", C: \"A type of GAN\", D: \"A reinforcement learning agent\" }, answer: \"B\", hint: \"It consists of an encoder that compresses the data and a decoder that tries to reconstruct it.\", feedback: { B: \"Correct! Autoencoders are used for unsupervised tasks like dimensionality reduction and feature learning by learning to reconstruct their own input.\" } },\n                { category: \"Algorithms\", question: \"What is a 'kernel trick' in the context of SVMs?\", options: { A: \"A method to speed up SVM training\", B: \"A way to map data to a higher dimension to find a non-linear decision boundary\", C: \"A technique for choosing the C parameter\", D: \"A method for handling missing data\" }, answer: \"B\", hint: \"It allows SVMs to operate in a high-dimensional feature space without explicitly computing the coordinates of the data in that space.\", feedback: { B: \"Correct! The kernel trick enables Support Vector Machines to efficiently find non-linear separators for classification tasks.\" } }\n            ]\n        };\n        \/\/ --- STATE ---\n        let quizState = {\n            difficulty: null,\n            questions: [],\n            currentIndex: 0,\n            userAnswers: [],\n            timerInterval: null,\n            totalSeconds: 0,\n        };\n        let selectedDifficulty = null;\n        \/\/ --- CORE FUNCTIONS ---\n        function showScreen(screenId) {\n            document.querySelectorAll('#ml-quiz-app-wrapper .quiz-screen').forEach(screen => {\n                screen.classList.remove('active');\n            });\n            const targetScreen = document.getElementById(screenId);\n            if (targetScreen) {\n                targetScreen.classList.add('active');\n            }\n        }\n        function startTimer() {\n            if (quizState.timerInterval) clearInterval(quizState.timerInterval);\n            quizState.timerInterval = setInterval(() => {\n                quizState.totalSeconds++;\n                const mins = Math.floor(quizState.totalSeconds \/ 60).toString().padStart(2, '0');\n                const secs = (quizState.totalSeconds % 60).toString().padStart(2, '0');\n                const timerEl = document.querySelector('#quiz-view #timer');\n                if(timerEl) timerEl.textContent = `${mins}:${secs}`;\n            }, 1000);\n        }\n        function stopTimer() {\n            clearInterval(quizState.timerInterval);\n        }\n        function resetQuizState() {\n            stopTimer();\n            quizState = {\n                difficulty: null, questions: [], currentIndex: 0, userAnswers: [], timerInterval: null, totalSeconds: 0,\n            };\n        }\n        window.showMainMenu = function(confirmFirst = false) {\n            if (confirmFirst) {\n                if (confirm('Are you sure you want to exit? Your progress will be lost.')) {\n                    resetQuizState();\n                    sessionStorage.removeItem('mlQuizResultState'); \/\/ Clear saved state\n                    showScreen('main-menu-view');\n                }\n            } else {\n                resetQuizState();\n                sessionStorage.removeItem('mlQuizResultState'); \/\/ Clear saved state\n                showScreen('main-menu-view');\n            }\n        }\n        window.showUserDetailsView = function(difficulty) {\n            const userInfoJSON = localStorage.getItem('quizUserInfo');\n            if (userInfoJSON) {\n                try {\n                    const userInfo = JSON.parse(userInfoJSON);\n                    if (new Date().getTime() < userInfo.expires) {\n                        startQuiz(difficulty);\n                        return;\n                    }\n                } catch(e) { \/* Invalid JSON *\/ }\n            }\n            selectedDifficulty = difficulty;\n            showScreen('user-details-view');\n            document.getElementById('user-name').focus();\n        }\n        function startQuiz(difficulty) {\n            quizState = {\n                difficulty,\n                questions: quizData[difficulty] || [],\n                currentIndex: 0,\n                userAnswers: new Array((quizData[difficulty] || []).length).fill(null),\n                timerInterval: null,\n                totalSeconds: 0,\n            };\n            showScreen('quiz-view');\n            setupQuizUI();\n            loadQuestion();\n            startTimer();\n        }\n        function setupQuizUI() {\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            const quizTitle = quizScreenEl.querySelector('#quiz-title');\n            const questionCount = quizScreenEl.querySelector('#question-count');\n            const stepper = quizScreenEl.querySelector('#progress-stepper');\n            const timer = quizScreenEl.querySelector('#timer');\n            if (quizTitle) quizTitle.textContent = `${quizState.difficulty.charAt(0).toUpperCase() + quizState.difficulty.slice(1)} ML Quiz`;\n            if (questionCount) questionCount.textContent = `${quizState.currentIndex + 1}\/${quizState.questions.length}`;\n            if (timer) timer.textContent = '00:00';\n            if (stepper) stepper.innerHTML = quizState.questions.map(() => `<div class=\"step\"><\/div>`).join('');\n        }\n        function loadQuestion() {\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            const question = quizState.questions[quizState.currentIndex];\n            if (!question) return;\n            const questionText = quizScreenEl.querySelector('.ml-quiz-question-text');\n            const optionsContainer = quizScreenEl.querySelector('#options-container');\n            const hintText = quizScreenEl.querySelector('#hint-text');\n            const hintToggle = quizScreenEl.querySelector('#hint-toggle');\n            const questionCountEl = quizScreenEl.querySelector('#question-count');\n            if (questionCountEl) questionCountEl.textContent = `${quizState.currentIndex + 1}\/${quizState.questions.length}`;\n            if (questionText) questionText.innerHTML = `${quizState.currentIndex + 1}. ${question.question}`;\n            if (optionsContainer) {\n                optionsContainer.innerHTML = '';\n                Object.entries(question.options).forEach(([key, value]) => {\n                    const optionDiv = document.createElement('div');\n                    optionDiv.className = 'option';\n                    optionDiv.dataset.key = key;\n                    optionDiv.innerHTML = `<label class=\"option-label\"><span class=\"option-letter\">${key}.<\/span> ${value}<\/label>`;\n                    optionDiv.addEventListener('click', () => handleOptionClick(key));\n                    optionsContainer.appendChild(optionDiv);\n                });\n            }\n            if (hintText) {\n                hintText.textContent = question.hint;\n                hintText.classList.add('hidden');\n            }\n            if (hintToggle) {\n                hintToggle.classList.remove('open');\n                hintToggle.disabled = false;\n            }\n            updateProgress();\n            restoreAnswerState();\n        }\n        function handleOptionClick(selectedKey) {\n            if (quizState.userAnswers[quizState.currentIndex] !== null) return;\n            const question = quizState.questions[quizState.currentIndex];\n            const isCorrect = selectedKey === question.answer;\n            quizState.userAnswers[quizState.currentIndex] = { selected: selectedKey, isCorrect };\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            quizScreenEl.querySelectorAll('.option').forEach(opt => {\n                opt.classList.add('disabled');\n                if (opt.dataset.key === question.answer) {\n                    opt.classList.add('correct');\n                    addFeedback(opt, true);\n                } else if (opt.dataset.key === selectedKey) {\n                    opt.classList.add('incorrect');\n                    addFeedback(opt, false);\n                }\n            });\n            const hintToggle = quizScreenEl.querySelector('#hint-toggle');\n            if (hintToggle) hintToggle.disabled = true;\n            updateProgress();\n        }\n        function addFeedback(optionDiv, isCorrect) {\n            const question = quizState.questions[quizState.currentIndex];\n            const selectedKey = optionDiv.dataset.key;\n            const feedbackText = question.feedback[selectedKey] || (isCorrect ? question.feedback[question.answer] : \"That's not the correct choice.\");\n            if (!optionDiv.querySelector('.feedback')) {\n                optionDiv.querySelector('.option-label').insertAdjacentHTML('afterend', `\n                <div class=\"feedback\">\n                    <div class=\"feedback-title ${isCorrect ? 'correct' : 'incorrect'}\">${isCorrect ? '\u2713 Correct' : '\u2717 Incorrect'}<\/div>\n                    <div class=\"feedback-text\">${feedbackText}<\/div>\n                <\/div>`);\n            }\n        }\n        function restoreAnswerState() {\n            const answer = quizState.userAnswers[quizState.currentIndex];\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            if (answer) {\n                quizScreenEl.querySelectorAll('.option').forEach(opt => {\n                    opt.classList.add('disabled');\n                    if (opt.dataset.key === quizState.questions[quizState.currentIndex].answer) {\n                        opt.classList.add('correct');\n                        if (opt.dataset.key === answer.selected) addFeedback(opt, true);\n                    } else if (opt.dataset.key === answer.selected) {\n                        opt.classList.add('incorrect');\n                        addFeedback(opt, false);\n                    }\n                });\n            }\n        }\n        function updateProgress() {\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            const backBtn = quizScreenEl.querySelector('#back-btn');\n            const nextBtn = quizScreenEl.querySelector('#next-btn');\n            const stepper = quizScreenEl.querySelector('#progress-stepper');\n            if (backBtn) backBtn.disabled = quizState.currentIndex === 0;\n            if (nextBtn) nextBtn.textContent = (quizState.currentIndex === quizState.questions.length - 1) ? 'Finish' : 'Next';\n            if(stepper) {\n                [...stepper.children].forEach((step, i) => {\n                    step.className = 'step';\n                    if (i === quizState.currentIndex) step.classList.add('active');\n                    const answer = quizState.userAnswers[i];\n                    if (answer) step.classList.add(answer.isCorrect ? 'correct' : 'incorrect');\n                });\n            }\n        }\n        function showResults() {\n            stopTimer();\n            const correctCount = quizState.userAnswers.filter(a => a?.isCorrect).length;\n            const totalQuestions = quizState.questions.length;\n            const answeredCount = quizState.userAnswers.filter(a => a !== null).length;\n            const wrongCount = answeredCount - correctCount;\n            const skippedCount = totalQuestions - answeredCount;\n            const accuracy = answeredCount > 0 ? Math.round((correctCount \/ answeredCount) * 100) : 0;\n            const scorePercentage = totalQuestions > 0 ? Math.round((correctCount \/ totalQuestions) * 100) : 0;\n            \n            \/\/ Save quiz state to sessionStorage to persist results\n            sessionStorage.setItem('mlQuizResultState', JSON.stringify(quizState));\n\n            \/\/ Populate new results view elements\n            document.querySelector('#new-score-value').innerHTML = `${correctCount}<span>\/${totalQuestions}<\/span>`;\n            document.querySelector('#new-accuracy-label').textContent = `${accuracy}% Accuracy`;\n            document.querySelector('#new-right-value').textContent = correctCount;\n            document.querySelector('#new-wrong-value').textContent = wrongCount;\n            document.querySelector('#new-skipped-value').textContent = skippedCount;\n            \n            \/\/ Animate progress circle\n            const circle = document.getElementById('score-circle');\n            const radius = circle.r.baseVal.value;\n            const circumference = radius * 2 * Math.PI;\n            circle.style.strokeDasharray = `${circumference} ${circumference}`;\n            setTimeout(() => {\n                const offset = circumference - scorePercentage \/ 100 * circumference;\n                circle.style.strokeDashoffset = offset;\n            }, 100);\n            \n            setupShareLinks(correctCount, accuracy);\n            showScreen('results-view');\n        }\n        function setupShareLinks(score, accuracy) {\n            const url = window.location.href;\n            const text = `I scored ${score}\/${quizState.questions.length} on the ${quizState.difficulty} Machine Learning Quiz! Can you beat my score?`;\n            const title = 'Machine Learning Quiz Challenge';\n            \n            \/\/ Update to new share link IDs\n            document.getElementById('new-share-whatsapp').onclick = (e) => { e.preventDefault(); window.open(`https:\/\/api.whatsapp.com\/send?text=${encodeURIComponent(text + '\\n\\n' + url)}`, '_blank'); };\n            document.getElementById('new-share-twitter').onclick = (e) => { e.preventDefault(); window.open(`https:\/\/twitter.com\/intent\/tweet?url=${encodeURIComponent(url)}&text=${encodeURIComponent(text)}`, '_blank'); };\n            document.getElementById('new-share-linkedin').onclick = (e) => { e.preventDefault(); window.open(`https:\/\/www.linkedin.com\/shareArticle?mini=true&url=${encodeURIComponent(url)}&title=${encodeURIComponent(title)}&summary=${encodeURIComponent(text)}`, '_blank'); };\n            document.getElementById('new-share-telegram').onclick = (e) => { e.preventDefault(); window.open(`https:\/\/t.me\/share\/url?url=${encodeURIComponent(url)}&text=${encodeURIComponent(text)}`, '_blank'); };\n        }\n        function handleUserDetailsSubmit(event) {\n            event.preventDefault();\n            const form = document.getElementById('user-details-form');\n            const name = form.querySelector('#user-name').value.trim();\n            const email = form.querySelector('#user-email').value.trim();\n            if (!name || !email) { alert('Please fill in both your name and email.'); return; }\n            const expirationTime = new Date().getTime() + (60 * 24 * 60 * 60 * 1000); \/\/ 60 days\n            localStorage.setItem('quizUserInfo', JSON.stringify({ expires: expirationTime }));\n            \n            \/\/ Submit to Google Form\n            const formURL = \"https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLScAVF17oENbZXTwsTuQ7VzwLPeT6IG5iilch2LoEoYsbzik5A\/formResponse\";\n            const formData = new FormData();\n            formData.append(\"entry.1956958717\", name); \/\/ Name field\n            formData.append(\"entry.1149718564\", email); \/\/ Email field\nformData.append(\"entry.1658362758\", window.location.href);\n            fetch(formURL, { method: \"POST\", body: formData, mode: \"no-cors\" }).catch(console.error);\n            form.reset();\n            startQuiz(selectedDifficulty);\n        }\n        \/\/ --- EVENT LISTENERS ---\n        document.getElementById('user-details-form').addEventListener('submit', handleUserDetailsSubmit);\n        document.getElementById('back-to-menu-btn').addEventListener('click', () => showMainMenu(false));\n        document.getElementById('back-to-results-btn').addEventListener('click', () => showScreen('results-view'));\n        document.getElementById('next-btn').addEventListener('click', () => {\n             if (quizState.currentIndex < quizState.questions.length - 1) {\n                 quizState.currentIndex++;\n                 loadQuestion();\n             } else {\n                 showResults();\n             }\n         });\n        document.getElementById('back-btn').addEventListener('click', () => {\n             if (quizState.currentIndex > 0) {\n                 quizState.currentIndex--;\n                 loadQuestion();\n             }\n         });\n        document.getElementById('hint-toggle').addEventListener('click', () => {\n             document.getElementById('hint-text').classList.toggle('hidden');\n             document.getElementById('hint-toggle').classList.toggle('open');\n         });\n        document.getElementById('analyse-btn').addEventListener('click', () => {\n            const analysisByCat = quizState.questions.reduce((acc, { category }, i) => {\n                if (!acc[category]) acc[category] = { total: 0, correct: 0, correctQs: [], incorrectQs: [] };\n                acc[category].total++;\n                const answer = quizState.userAnswers[i];\n                if (answer?.isCorrect) {\n                    acc[category].correct++;\n                    acc[category].correctQs.push(i + 1);\n                } else if (answer) {\n                    acc[category].incorrectQs.push(i + 1);\n                }\n                return acc;\n            }, {});\n            const analysisModalBody = document.getElementById('analysis-body');\n            analysisModalBody.innerHTML = Object.entries(analysisByCat).map(([category, data]) => {\n                const percentage = data.total > 0 ? 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