index.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>ML-Scope Home</title>
    <link rel="icon" type="image/x-icon" href="aida-logo.jpeg" />
    <style>
      .card {
        width: 300px;
        border-radius: 10px;
        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
        background-color: white;
        overflow: hidden;
        transition: transform 0.3s ease, box-shadow 0.3s ease;
      }

      .card-img {
        width: 100%;
        height: 200px;
        object-fit: cover;
      }

      .card-content {
        padding: 20px;
      }

      .card-title {
        font-size: 1.5rem;
        font-weight: bold;
        margin-bottom: 10px;
        color: #333;
      }

      .card-description {
        font-size: 1rem;
        color: #777;
        margin-bottom: 20px;
      }

      .card-button {
        display: inline-block;
        padding: 10px 20px;
        background-color: #3498db;
        color: white;
        text-decoration: none;
        border-radius: 5px;
        text-align: center;
        transition: background-color 0.3s ease;
      }

      .card-button:hover {
        background-color: #2980b9;
      }

      .card:hover {
        transform: translateY(-10px);
        box-shadow: 0 6px 12px rgba(0, 0, 0, 0.2);
      }
      :root {
        --dark-blue: #1a365d;
        --light-blue: #7db0e8;
        --orange: #ff6b35;
      }
      body {
        font-family: Arial, sans-serif;
        line-height: 1.6;
        color: var(--dark-blue);
        margin: 0;
        padding: 0;
        background-color: #f0f8ff;
      }
      .container {
        max-width: 800px;
        margin: 0 auto;
        padding: 20px;
      }
      nav {
        background-color: var(--dark-blue);
        padding: 10px 0;
        width: 100%;
      }
      nav .container {
        display: flex;
        justify-content: space-between;
        align-items: center;
      }
      .logo {
        width: 50px;
        height: 50px;
      }
      .nav-links a {
        color: white;
        text-decoration: none;
        margin-left: 20px;
      }
      .nav-links a:hover {
        color: var(--light-blue);
      }
      h1 {
        text-align: center;
        color: var(--dark-blue);
      }
      .project-container {
        border: 1px solid var(--light-blue);
        margin-bottom: 20px;
        border-radius: 5px;
        overflow: hidden;
        background-color: white;
      }
      .project-header {
        background-color: var(--light-blue);
        padding: 10px;
        cursor: pointer;
        display: flex;
        justify-content: space-between;
        align-items: center;
        transition: background-color 0.3s ease;
      }
      .project-header:hover {
        background-color: #6a9fd4;
      }
      .project-header h2 {
        margin: 0;
        color: var(--dark-blue);
      }
      .project-content {
        display: none;
        padding: 20px;
      }
      .project-content.active {
        display: block;
      }
      .toggle-icon::after {
        content: "\25BC";
        color: var(--dark-blue);
      }
      .project-header.active .toggle-icon::after {
        content: "\25B2";
      }
      a {
        color: var(--orange);
        text-decoration: none;
        text-decoration: underline;
      }
      a:hover {
        text-decoration: underline;
      }

      .btn {
        cursor: pointer;
        background-color: var(--orange);
        color: var(--dark-blue);
        font-weight: 700;
        border: none;
        width: 200px;
        height: 50px;
        padding: 5px;
        margin: 20px;
        transition: 0.8s;
      }
      .btn:hover {
        color: var(--orange);
        background-color: var(--dark-blue);
      }
    </style>
  </head>
  <body>
    <nav>
      <div class="container">
        <a href="index.html">
          <img src="aida-logo.jpeg" alt="Logo" class="logo" />
        </a>
        <div class="nav-links">
          <a href="index.html">Problems</a>
          <a href="solutions.html">Solutions</a>
          <a href="leaderboard.html">Leaderboard</a>
          <a href="resources.html">Resources</a>
        </div>
      </div>
    </nav>

    <div class="container">
      <h1>MLScope</h1>
      <center>
        <a target="_blank" href="https://forms.gle/f6KSeTXnQsAuopAw9"
          ><button class="btn">Challenge 3 Submission</button></a
        >
      </center>
      <div id="content"></div>
    </div>
    <!-- <center>
      <div class="card">
        <img src="aida-logo.jpeg" alt="Image" class="card-img" />
        <div class="card-content">
          <h3 class="card-title">Welcome to SigAida for Spring 2025!</h3>
          <p class="card-description">Problems to be released soon!</p>
          <a
            target="_blank"
            href="https://docs.google.com/presentation/d/12lF7_MIpwJHRxNBcHVJDx2PAkXy9FHm-A2v9gMwLUwo/edit?usp=sharing"
            class="card-button"
            >Learn More</a
          >
        </div>
      </div>
    </center> -->
    <script>
      // const problemsData = [
      //   {
      //     project: 1,
      //     title: "House Price Prediction",
      //     content: `
      //               <p>In this task, the goal is to predict house prices in King County using a regression model. Given features like square footage, number of bedrooms, and location, you will utilize models to accurately estimate house prices. This involves data cleaning, model training/utilization, and model performance evaluation.</p>

      //               <h4>Dataset:</h4>
      //               <p><a href="https://www.kaggle.com/datasets/harlfoxem/housesalesprediction" target="_blank" rel="noopener noreferrer">House Sales Prediction Dataset</a></p>
      //               <p>The dataset contains house sales data from King County, Washington. It has 21,613 rows and 21 columns, covering features like price, sqft_living, bedrooms, bathrooms, waterfront, and zip code, among others. The price column is the target variable, and the other features are used to predict house prices.</p>

      //               <h4>Starter Notebook (not required to use):</h4>
      //               <p><a href="https://colab.research.google.com/drive/1yhrpIwnvJ7ExjYacx-ixmRcW09JPn2rX?usp=sharing" target="_blank" rel="noopener noreferrer">Make a Copy to Use:</a></p>

      //               <h4>Goals/tasks:</h4>
      //               <ul>
      //                   <li>Clean dataset (remove unnecessary columns from the dataset to use it for regression)</li>
      //                   <li>Train/test split</li>
      //                   <li>Set up code for training model (using library of choice)</li>
      //                   <li>Train model with hyperparameters</li>
      //                   <li>Test model against test data</li>
      //               </ul>

      //               <h4>Potential Skills:</h4>
      //               <h5>Starter</h5>
      //               <ul>
      //                   <li>Linear Regression</li>
      //                   <li>scikit-learn</li>
      //               </ul>
      //               <h5>Bonus</h5>
      //               <ul>
      //                   <li>Simple Decision Tree</li>
      //                   <li>Random Forest</li>
      //                   <li>PyTorch</li>
      //               </ul>

      //               <h4>Useful Package Documentation:</h4>
      //               <ol>
      //                   <li><strong>NumPy</strong> (for numerical computations): <a href="https://numpy.org/doc/" target="_blank" rel="noopener noreferrer">NumPy Documentation</a></li>
      //                   <li><strong>pandas</strong> (for data manipulation): <a href="https://pandas.pydata.org/docs/" target="_blank" rel="noopener noreferrer">pandas Documentation</a></li>
      //                   <li><strong>scikit-learn</strong> (for model training and evaluation): <a href="https://scikit-learn.org/stable/documentation.html" target="_blank" rel="noopener noreferrer">scikit-learn Documentation</a></li>
      //                   <li><strong>matplotlib</strong> (for data visualization): <a href="https://matplotlib.org/stable/contents.html" target="_blank" rel="noopener noreferrer">matplotlib Documentation</a></li>
      //                   <li><strong>PyTorch</strong> (for deep learning-based models): <a href="https://pytorch.org/docs/stable/index.html" target="_blank" rel="noopener noreferrer">PyTorch Documentation</a></li>
      //               </ol>
      //           `,
      //   },
      //   {
      //     project: 2,
      //     title: "Heart Disease Classifier",
      //     content: `
      //               <p>In this task, the goal is to classify whether a given patient has heart disease using a classifier model. Given features like age, sex, chest pain type, blood pressure, etc. you will utilize models to accurately predict whether or not a patient has heart disease. This involves data cleaning, model training/utilization, and model performance evaluation.</p>

      //               <h4>Dataset:</h4>
      //               <p><a href="https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset" target="_blank" rel="noopener noreferrer">Heart Disease Classifier Dataset</a></p>
      //               <p>The dataset contains heart data attributes from four medical databases (Cleveland, Hungary, Switzerland, Long Beach). It has 1026 rows and 15 columns, covering features like age, sex, chest pain type, blood pressure, among others. The target column is the target variable, where 1 = heart disease and 0 = no heart disease. For a full breakdown, you can view the kaggle description of the dataset.</p>

      //               <h4>Starter Notebook (not required to use):</h4>
      //               <p><a href="https://colab.research.google.com/drive/1oYpGxTEJN3fo4RInWTGrQIvfUrXqW67I?usp=sharing" target="_blank" rel="noopener noreferrer">Make a Copy to Use</a></p>

      //               <h4>Goals/tasks:</h4>
      //               <ul>
      //                   <li>Clean dataset (remove unnecessary columns from the dataset to use it for classification)</li>
      //                   <li>Train/test split</li>
      //                   <li>Set up code for training model (using library of choice)</li>
      //                   <li>Train model with hyperparameters</li>
      //                   <li>Test model against test data</li>
      //               </ul>

      //               <h4>Potential Skills:</h4>
      //               <h5>Starter</h5>
      //               <ul>
      //                   <li>Logistic Regression</li>
      //                   <li>SVM</li>
      //                   <li>scikit-learn</li>
      //               </ul>
      //               <h5>Bonus</h5>
      //               <ul>
      //                   <li>Decision Tree</li>
      //                   <li>Random Forest Classifier</li>
      //                   <li>PyTorch</li>
      //                   <li>Neural Networks</li>
      //               </ul>

      //               <h4>Useful Package Documentation:</h4>
      //               <ol>
      //                   <li><strong>NumPy</strong> (for numerical computations): <a href="https://numpy.org/doc/" target="_blank" rel="noopener noreferrer">NumPy Documentation</a></li>
      //                   <li><strong>pandas</strong> (for data manipulation): <a href="https://pandas.pydata.org/docs/" target="_blank" rel="noopener noreferrer">pandas Documentation</a></li>
      //                   <li><strong>scikit-learn</strong> (for model training and evaluation): <a href="https://scikit-learn.org/stable/documentation.html" target="_blank" rel="noopener noreferrer">scikit-learn Documentation</a></li>
      //                   <li><strong>matplotlib</strong> (for data visualization): <a href="https://matplotlib.org/stable/contents.html" target="_blank" rel="noopener noreferrer">matplotlib Documentation</a></li>
      //                   <li><strong>PyTorch</strong> (for deep learning-based models): <a href="https://pytorch.org/docs/stable/index.html" target="_blank" rel="noopener noreferrer">PyTorch Documentation</a></li>
      //               </ol>
      //           `,
      //   },
      //   {
      //     project: 3,
      //     title: "Benchmarking LLMs Against a Baseline Dataset",
      //     content: `
      //               <p>This project explores the crucial task of benchmarking Large Language Models (LLMs) against a baseline dataset. The goal is to evaluate LLM performance and analyze the impact of design choices and parameter tuning on model accuracy. By systematically comparing LLMs to a baseline, participants can gain insights into their strengths, weaknesses, and overall capabilities, aiding in selecting the most suitable LLM for specific applications.</p>

      //               <h4>Dataset:</h4>
      //               <p><a href="https://huggingface.co/datasets/gsm8k" target="_blank" rel="noopener noreferrer">GSM8K Dataset</a></p>
      //               <p>The GSM8K dataset consists of 8,000 high-quality grade school math problems specifically designed to test LLM reasoning capabilities. The "main" version of the dataset is divided into train and test splits, with the test split containing 1,319 problems. Each problem contains a question and an answer in text format, and the challenge is to produce the correct numerical answer. The test split will be used for evaluating LLM performance.</p>

      //               <h4>Starter Notebook:</h4>
      //               <p><a href="https://colab.research.google.com/drive/1xWtiFpSRIUGO1a_24T1e_PtDQCEf-haF?usp=sharing" target="_blank" rel="noopener noreferrer">Make a Copy to Use</a></p>

      //               <h4>Goals/tasks:</h4>
      //               <ul>
      //                   <li>Set up API calls to interact with a given LLM model (LLaMa-3-8B)</li>
      //                   <li>Explore different prompt engineering techniques</li>
      //                   <li>Adjust model parameters and settings to optimize performance</li>
      //                   <li>Evaluate the model against the test dataset</li>
      //                   <li>Calculate the final accuracy and compare against the baseline</li>
      //               </ul>

      //               <h4>Potential Skills:</h4>
      //               <h5>Starter</h5>
      //               <ul>
      //                   <li>Prompt Engineering</li>
      //                   <li>API Integration</li>
      //                   <li>Text Normalization and Cleaning</li>
      //               </ul>
      //               <h5>Bonus</h5>
      //               <ul>
      //                   <li>Advanced Parameter Tuning</li>
      //                   <li>Exploring Different LLM Architectures</li>
      //                   <li>Evaluation and Analysis Techniques</li>
      //               </ul>

      //               <h4>Useful Package Documentation:</h4>
      //               <ol>
      //                   <li><strong>transformers</strong> (for LLM model interactions): <a href="https://huggingface.co/docs/transformers/index" target="_blank" rel="noopener noreferrer">Transformers Documentation</a></li>
      //                   <li><strong>datasets</strong> (for dataset loading and management): <a href="https://huggingface.co/docs/datasets/index" target="_blank" rel="noopener noreferrer">Datasets Documentation</a></li>
      //                   <li><strong>Replicate</strong> (for model hosting and API calls): <a href="https://replicate.com/docs/reference/http" target="_blank" rel="noopener noreferrer">Replicate Documentation</a></li>
      //                   <li><strong>re</strong> (for text processing): <a href="https://docs.python.org/3/library/re.html" target="_blank" rel="noopener noreferrer">Python re Documentation</a></li>
      //               </ol>
      //           `,
      //   },
      //   {
      //     project: 4,
      //     title: "MNIST Digit Classification",
      //     content: `
      //       <p>In this challenge, you'll work with the classic MNIST dataset to create a digit classification model. Beyond basic classification, you can tackle additional advanced challenges to enhance your model's capabilities and earn bonus points.</p>

      //       <h4>Dataset:</h4>
      //       <p><a href="https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data" target="_blank" rel="noopener noreferrer">MNIST Dataset</a> (no need to specifiy 'path' parameter)</p>
      //       <p>The MNIST database contains 60,000 training images and 10,000 testing images of handwritten digits. Each image is a 28x28 grayscale image of a single digit (0-9). This is a great dataset for learning the basics of neural networks and image classification.</p>

      //       <h4>Starter Notebook (not required to use):</h4>
      //       <p><a href="https://colab.research.google.com/drive/1zm01NpRwq4j2tQuqCDW70sUFzp5r6XKW?usp=sharing" target="_blank" rel="noopener noreferrer">Make a Copy to Use</a></p>

      //       <h4>Basic Goals/tasks:</h4>
      //       <ul>
      //           <li>Load and preprocess the MNIST dataset</li>
      //           <li>Design and implement a neural network architecture</li>
      //           <li>Train the model with appropriate hyperparameters</li>
      //           <li>Evaluate model performance on the test set</li>
      //           <li>Visualize results and confusion matrix</li>
      //       </ul>

      //       <h4>[Optional]: Bonus Challenges (Choose any or do your own):</h4>
      //       <h5>1. Confidence Scoring and Uncertainty</h5>
      //       <ul>
      //           <li>Implement confidence scoring for predictions</li>
      //           <li>Visualize prediction uncertainty</li>
      //           <li>Analyze cases with low confidence scores</li>
      //       </ul>

      //       <h5>2. Sequential Digits Recognition</h5>
      //       <ul>
      //           <li>Create synthetic sequences from MNIST digits (for example: 123 or 38183)</li>
      //           <li>Implement RNN/LSTM architecture for sequence recognition</li>
      //           <li>Train and evaluate on multi-digit sequences</li>
      //       </ul>

      //       <h5>3. Image-to-Equation Conversion</h5>
      //       <ul>
      //           <li>Generate simple mathematical equations using MNIST digits</li>
      //           <li>Develop a pipeline to recognize and parse equations</li>
      //           <li>Implement equation solving functionality</li>
      //       </ul>

      //       <h5>4. Stylized MNIST Digits</h5>
      //       <ul>
      //           <li>Apply various transformations to MNIST digits (rotations, noise addition, etc.)</li>
      //           <li>Train model on augmented dataset</li>
      //           <li>Evaluate robustness to different styles and transformations</li>
      //       </ul>

      //       <h4>Potential Skills:</h4>
      //       <h5>Starter</h5>
      //       <ul>
      //           <li>Convolutional Neural Networks (CNNs)</li>
      //           <li>PyTorch or TensorFlow</li>
      //           <li>Basic Image Processing</li>
      //       </ul>
      //       <h5>Bonus</h5>
      //       <ul>
      //           <li>RNNs/LSTMs</li>
      //           <li>Data Augmentation</li>
      //           <li>Uncertainty Estimation</li>
      //           <li>Advanced Architecture Design</li>
      //       </ul>

      //       <h4>Useful Package Documentation:</h4>
      //       <ol>
      //           <li><strong>PyTorch</strong>: <a href="https://pytorch.org/docs/stable/index.html" target="_blank" rel="noopener noreferrer">PyTorch Documentation</a></li>
      //           <li><strong>TensorFlow</strong>: <a href="https://www.tensorflow.org/api_docs" target="_blank" rel="noopener noreferrer">TensorFlow Documentation</a></li>
      //           <li><strong>OpenCV</strong>: <a href="https://docs.opencv.org/4.x/" target="_blank" rel="noopener noreferrer">OpenCV Documentation</a></li>
      //           <li><strong>scikit-learn</strong>: <a href="https://scikit-learn.org/stable/documentation.html" target="_blank" rel="noopener noreferrer">scikit-learn Documentation</a></li>
      //       </ol>
      //     `,
      //   },
      // ];
      const problemsData = [
        {
          project: 1,
          title: "Multi-City Housing Market Analysis & Prediction",
          content: `
        <p>In this comprehensive project, you'll analyze and predict housing prices across multiple major US metropolitan areas using advanced regression techniques. The goal is to not only predict prices but also understand market trends, identify key value drivers, and develop insights for real estate investment decisions.</p>

        <h4>Datasets:</h4>
        <p><strong>Primary Dataset:</strong> <a href="https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset" target="_blank" rel="noopener noreferrer">California Housing Dataset</a></p>
        <p>Contains housing data from California districts with features including median income, housing median age, average rooms, population, etc.</p>
        
        <p><strong>Supplementary Datasets (optional):</strong></p>
        <ul>
            <li><a href="https://www.zillow.com/research/data/" target="_blank" rel="noopener noreferrer">Zillow Research Data</a></li>
            <li><a href="https://www.census.gov/housing/hvs/data/index.html" target="_blank" rel="noopener noreferrer">US Census Housing Data</a></li>
        </ul>

        <h4>Project Components (% of points):</h4>
        <h5>1. Data Preparation & Analysis (25%)</h5>
        <ul>
            <li>Clean and preprocess multiple datasets</li>
            <li>Handle missing values and outliers</li>
            <li>Feature engineering:
                <ul>
                    <li>Create new features (price per sq ft, age of house categories)</li>
                    <li>Generate location-based features (distance to amenities)</li>
                    <li>Develop economic indicators (price-to-rent ratio)</li>
                </ul>
            </li>
            <li>Perform exploratory data analysis with visualizations</li>
        </ul>

        <h5>2. Model Development (45%)</h5>
        <ul>
            <li>Options for implementing models:
                <ul>
                    <li>Linear Regression (baseline)</li>
                    <li>Random Forest</li>
                    <li>Gradient Boosting (XGBoost/LightGBM)</li>
                    <li>Neural Network (optional)</li>
                </ul>
            </li>
            <li>Feature selection and importance analysis</li>
            <li>Cross-validation strategy</li>
            <li>Hyperparameter optimization</li>
        </ul>

        <h5>3. Market Analysis/Data Visualization (30%)</h5>
        <ul>
            <li>Segment markets by geographic regions</li>
            <li>Analyze price trends over time</li>
            <li>Identify key value drivers by region</li>
            <li>Create market health indicators</li>
            <li>Build interactive visualizations</li>
            <h5>*Note: This part is subjective - visualize data, trends, make some cool graphs!, etc. </h5>

        </ul>

        

        <h4>Required Libraries:</h4>
        <pre><code>
import pandas as pd
import numpy as np
import sklearn
import matplotlib.pyplot as plt
        </code></pre>

        <h4>Skills:</h4>
        <h5>Required Skills</h5>
        <ul>
            <li>Python programming</li>
            <li>Data cleaning and preprocessing</li>
            <li>Statistical analysis</li>
            <li>Machine learning fundamentals</li>
            <li>Data visualization</li>
        </ul>

        <h5>Advanced Skills (Bonus)</h5>
        <ul>
            <li>Advanced regression models</li>
            <li>Time series analysis</li>
            <li>Deep learning</li>
        </ul>

        

        <h4>Useful Package Documentation:</h4>
        <ol>
            <li><strong>pandas</strong> (for data manipulation): <a href="https://pandas.pydata.org/docs/" target="_blank" rel="noopener noreferrer">pandas Documentation</a></li>
            <li><strong>scikit-learn</strong> (for model training): <a href="https://scikit-learn.org/stable/documentation.html" target="_blank" rel="noopener noreferrer">scikit-learn Documentation</a></li>
            <li><strong>XGBoost</strong> (for gradient boosting): <a href="https://xgboost.readthedocs.io/" target="_blank" rel="noopener noreferrer">XGBoost Documentation</a></li>
        </ol>
    `,
        },
        {
          project: 2,
          title: "Heart Disease Binary Classifier",
          content: `
         <p>In this comprehensive project, you'll build an advanced heart disease prediction system using classification algorithms. The goal is to accurately predict the presence of heart disease while understanding key risk factors, developing interpretable models, and creating an interactive system that could potentially aid medical professionals.</p>

<p><strong>Primary Dataset:</strong> <a href="https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset" target="_blank" rel="noopener noreferrer">Heart Disease Classifier Dataset</a></p>
<p>The dataset contains heart data attributes from four medical databases (Cleveland, Hungary, Switzerland, Long Beach). It has 1026 rows and 15 columns, covering features like age, sex, chest pain type, blood pressure, among others. The target column is the target variable, where 1 = heart disease and 0 = no heart disease.</p>

<p><strong>Supplementary Datasets (optional):</strong></p>
<ul>
    <li><a href="https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction" target="_blank" rel="noopener noreferrer">Heart Failure Prediction Dataset</a></li>
    <li><a href="https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset" target="_blank" rel="noopener noreferrer">Cardiovascular Disease Dataset</a></li>
    <li><a href="https://www.cdc.gov/nchs/fastats/heart-disease.htm" target="_blank" rel="noopener noreferrer">CDC Heart Disease Statistics</a></li>
</ul>

<h4>Project Components (% of points):</h4>
<h5>1. Data Preparation & Analysis (25%)</h5>
<ul>
    <li>Clean and preprocess the dataset</li>
    <li>Handle missing values and outliers using medical domain knowledge</li>
    <li>Feature engineering (optional):
        <ul>
            <li>Create new features (BMI, combined risk factors)</li>
            <li>Develop risk index scores</li>
            <li>Normalize/standardize features appropriately</li>
        </ul>
    </li>
    <li>Perform exploratory data analysis with medical context</li>
    <li>Analyze feature correlations</li>
    <li>Apply feature selection methods</li>
    <li>Address class imbalance if present (SMOTE, class weights) (optional)</li>
</ul>

<h5>2. Model Development (45%)</h5>
<ul>
    <li>Options for implementing models:
        <ul>
            <li>Logistic Regression (baseline with regularization)</li>
            <li>Decision Trees</li>
            <li>Random Fores</li>
            <li>Support Vector Machines</li>
            <li>Gradient Boosting (XGBoost/LightGBM)</li>
            <li>Neural Networks</li>
        </ul>
    </li>
    <li>Cross-validation strategy (stratified k-fold)</li>
    <li>Hyperparameter optimization using grid/random/Bayesian search (optional)</li>
    <li>ROC curve analysis and optimal threshold selectionn (optional)</li>
    <li>Evaluation metrics other than accuracy (sensitivity, specificity, F1-score)</li>
</ul>

<h5>3. Clinical Relevance (20%)</h5>
<ul>
    <li>Analyze model performance across different demographic groups</li>
    <li>Create visualizations showing how feature values impact risk</li>
    <li>Analyze false positives and false negatives from a clinical perspective</li>
    <li><h5>*Note: This part is subjective - visualize data, patient profiles, make some cool graphs!, etc.</h5></li>
</ul>

<h5>4. Advanced Implementation (10%) (optional)</h5>
<ul>
    <li>Develop a simple web application for model deployment</li>
    <li>Add confidence intervals for predictions</li>
    <li>Compare model performance with published medical studies</li>
</ul>

<h4>Useful Libraries:</h4>
<pre><code>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report
import xgboost as xgb
import shap
import plotly.express as px
</code></pre>

<h4>Skills:</h4>
<h5>Required Skills</h5>
<ul>
    <li>Python programming</li>
    <li>Data cleaning and preprocessing</li>
    <li>Statistical analysis</li>
    <li>Machine learning fundamentals</li>
    <li>Classification algorithms</li>
    <li>Model evaluation techniques</li>
</ul>

<h5>Advanced Skills (Bonus)</h5>
<ul>
    <li>Advanced classification models</li>
    <li>Model interpretability techniques (eg. Identify what 3 features that lead to the classification decision)</li>
    <li>Healthcare analytics</li>
    <li>Deep learning for tabular data</li>
    <li>Web application development (Flask/Streamlit)</li>
    <li>Fairness and bias analysis in healthcare models</li>
    <li>Deployment strategies for healthcare applications</li>
    <li>Feature importance visualization</li>
</ul>

<h4>Useful Package Documentation:</h4>
<ol>
    <li><strong>NumPy</strong> (for numerical computations): <a href="https://numpy.org/doc/" target="_blank" rel="noopener noreferrer">NumPy Documentation</a></li>
    <li><strong>pandas</strong> (for data manipulation): <a href="https://pandas.pydata.org/docs/" target="_blank" rel="noopener noreferrer">pandas Documentation</a></li>
    <li><strong>scikit-learn</strong> (for model training and evaluation): <a href="https://scikit-learn.org/stable/documentation.html" target="_blank" rel="noopener noreferrer">scikit-learn Documentation</a></li>
    <li><strong>matplotlib</strong> (for data visualization): <a href="https://matplotlib.org/stable/contents.html" target="_blank" rel="noopener noreferrer">matplotlib Documentation</a></li>
    <li><strong>XGBoost</strong> (for gradient boosting): <a href="https://xgboost.readthedocs.io/" target="_blank" rel="noopener noreferrer">XGBoost Documentation</a></li>
    <li><strong>SHAP</strong> (for model interpretability): <a href="https://shap.readthedocs.io/" target="_blank" rel="noopener noreferrer">SHAP Documentation</a></li>
    <li><strong>Streamlit</strong> (for creating dashboards): <a href="https://docs.streamlit.io/" target="_blank" rel="noopener noreferrer">Streamlit Documentation</a></li>
    <li><strong>Plotly</strong> (for interactive visualizations): <a href="https://plotly.com/python/" target="_blank" rel="noopener noreferrer">Plotly Documentation</a></li>
    <li><strong>Flask</strong> (for web applications): <a href="https://flask.palletsprojects.com/" target="_blank" rel="noopener noreferrer">Flask Documentation</a></li>
</ol>
         `,
        },
        {
          project: 3,
          title: "Stock Market Prediction with Time Series Methods",
          content: `
    <p>In this challenge, you'll develop an advanced stock market prediction model using deep learning techniques like RNNs, LSTMs, while comparing them to traditional time series models like ARIMA. You'll explore feature engineering, interpretability, and the effectiveness of different approaches in predicting stock prices.</p>

    <h4>Datasets:</h4>
    <p><strong>Primary Dataset:</strong> Publicly available stock market data from:</p>
    <ul>
      <li><a href="https://finance.yahoo.com/" target="_blank" rel="noopener noreferrer">Yahoo Finance</a></li>
      <li><a href="https://www.quandl.com/" target="_blank" rel="noopener noreferrer">Quandl</a></li>
      <li><a href="https://www.alphavantage.co/" target="_blank" rel="noopener noreferrer">Alpha Vantage</a></li>
      <li><a href="https://www.kaggle.com/" target="_blank" rel="noopener noreferrer">Kaggle Stock Market Datasets</a></li>
    </ul>

    <h4>Supplementary Datasets (optional):</h4>
    <ul>
      <li>Public sentiment data (Twitter, Reddit, news sources)</li>
      <li>Economic indicators (inflation rate, interest rates, unemployment)</li>
      <li>Alternative data (climate effects, holiday trends)</li>
    </ul>

    <h4>Project Components (% of points):</h4>
    
    <h5>1. Data Preparation & Feature Engineering (25%)</h5>
    <ul>
      <li>Clean and preprocess stock market data</li>
      <li>Handle missing values, outliers, and normalize appropriately</li>
      <li>Feature engineering (choose at least three):
        <ul>
          <li>Volatility, moving averages, Bollinger Bands</li>
          <li>Fourier or Wavelet Transform of time series data</li>
          <li>Sentiment scores from news/social media</li>
          <li>Time-based features (day of the week, market open/close trends)</li>
          <li>Macroeconomic indicators (inflation rate, interest rates, unemployment)</li>
        </ul>
      </li>
      <li>Perform exploratory data analysis (EDA) with stock market insights</li>
      <li>Stationarity testing and transformations for time series models (optional)</li>
    </ul>

    <h5>2. Model Development & Comparison (45%)</h5>
    <ul>
      <li>Baseline Models:
        <ul>
          <li>ARIMA (or SARIMA)</li>
          <li>Simple Moving Average & Exponential Smoothing</li>
        </ul>
      </li>
      <li>Deep Learning Approaches:
        <ul>
          <li>RNN, LSTM, or GRU for stock price forecasting</li>
        </ul>
      </li>
      <li>Model Comparison:
        <ul>
          <li>Compare deep learning models vs. traditional models using RMSE, MAE, MAPE</li>
          <li>Backtesting strategy to evaluate performance on real data</li>
          <li>Hyperparameter tuning (Bayesian search, Grid search, etc.)</li>
        </ul>
      </li>
    </ul>

    <h5>3. Explainability & Feature Importance (20%)</h5>
    <ul>
      <li>Use SHAP, LIME, or attention mechanisms to explain model predictions</li>
      <li>Identify key indicators that influence stock price movements</li>
      <li>Analyze why simpler models may/may not outperform deep learning methods</li>
      <li>Visualize feature impact using SHAP plots or correlation heatmaps</li>
    </ul>

    <h5>4. Advanced Implementation (10%) (Optional)</h5>
    <ul>
      <li>Deploy your model using a simple Streamlit/Flask dashboard</li>
      <li>Implement a real-time data pipeline (fetch live stock data and predict)</li>
      <li>Develop a basic trading strategy using reinforcement learning</li>
      <li>Compare performance across different stock types (e.g., tech vs. commodities)</li>
    </ul>

    <h4>Required Libraries:</h4>
    <pre><code>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import yfinance as yf
from statsmodels.tsa.arima.model import ARIMA
import torch
import torch.nn as nn
import torch.optim as optim
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
import shap
import plotly.express as px
    </code></pre>

    <h4>Skills:</h4>
    <h5>Required Skills</h5>
    <ul>
      <li>Python programming</li>
      <li>Time series analysis</li>
      <li>Data preprocessing</li>
      <li>Machine learning & deep learning fundamentals</li>
      <li>Model evaluation techniques</li>
    </ul>

    <h5>Advanced Skills (Bonus)</h5>
    <ul>
      <li>Stock market analysis & financial metrics</li>
      <li>Time series forecasting techniques (Fourier Transform, Wavelets)</li>
      <li>Model interpretability (SHAP, LIME, Attention)</li>
      <li>Reinforcement Learning for trading bots</li>
      <li>Web deployment (Flask/Streamlit)</li>
    </ul>

    <h4>Useful Package Documentation:</h4>
    <ol>
      <li><strong>pandas</strong> (for data manipulation): <a href="https://pandas.pydata.org/docs/" target="_blank" rel="noopener noreferrer">pandas Documentation</a></li>
      <li><strong>scikit-learn</strong> (for model training): <a href="https://scikit-learn.org/stable/documentation.html" target="_blank" rel="noopener noreferrer">scikit-learn Documentation</a></li>
      <li><strong>TensorFlow/Keras</strong> (for deep learning): <a href="https://www.tensorflow.org/guide/keras" target="_blank" rel="noopener noreferrer">Keras Documentation</a></li>
      <li><strong>SHAP</strong> (for model interpretability): <a href="https://shap.readthedocs.io/" target="_blank" rel="noopener noreferrer">SHAP Documentation</a></li>
    </ol>
  `,
        },
      ];
      const solutionsData = [];

      // const solutionsData = [
      //   {
      //     project: 1,
      //     title: "House Price Prediction Solution",
      //     content: `
      //               <p>Solutions for Project 1 will be uploaded here after the submission deadline.</p>
      //           `,
      //   },
      //
      // ];

      function createProjectElement(projectData) {
        const projectElement = document.createElement("div");
        projectElement.className = "project-container";
        projectElement.innerHTML = `
                <div class="project-header">
                    <h2>MLScope ${projectData.project}: ${projectData.title}</h2>
                    <span class="toggle-icon"></span>
                </div>
                <div class="project-content">
                    ${projectData.content}
                </div>
            `;
        return projectElement;
      }

      function toggleProject(projectHeader) {
        projectHeader.classList.toggle("active");
        const content = projectHeader.nextElementSibling;
        content.classList.toggle("active");
      }

      function loadContent(data) {
        const contentDiv = document.getElementById("content");
        contentDiv.innerHTML = "";
        data.forEach((project, index) => {
          const projectElement = createProjectElement(project);
          contentDiv.appendChild(projectElement);

          const projectHeader = projectElement.querySelector(".project-header");
          projectHeader.addEventListener("click", () =>
            toggleProject(projectHeader)
          );

          // Open the last project by default
          if (index === data.length - 1) {
            toggleProject(projectHeader);
          }
        });
      }

      // Determine which page to load based on the current URL
      const currentPage = window.location.pathname.includes("solutions.html")
        ? "solutions"
        : "problems";
      loadContent(currentPage === "solutions" ? solutionsData : problemsData);
    </script>
  </body>
</html>