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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8"/>
<meta name="viewport" content="width=device-width,initial-scale=1"/>
<title>Omkar Pallerla — Analytics Portfolio Dashboard</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.1/chart.umd.min.js"></script>
<link href="https://fonts.googleapis.com/css2?family=Bebas+Neue&family=Barlow:wght@400;500;600;700&family=DM+Mono:wght@400;500&display=swap" rel="stylesheet"/>
<style>
*{box-sizing:border-box;margin:0;padding:0}
:root{
--bg:#080b10;--bg2:#0d1117;--surface:#111620;--surface2:#161c28;
--border:rgba(255,255,255,0.07);--border2:rgba(255,255,255,0.14);
--text:#e8edf5;--muted:#7a8499;--muted2:#4a5568;
--blue:#4f9cf9;--green:#06d6a0;--purple:#7c3aed;
--amber:#f59e0b;--red:#ef4444;--pink:#ec4899;
}
body{background:var(--bg);color:var(--text);font-family:'Barlow',sans-serif;min-height:100vh;overflow-x:hidden}
/* SIDEBAR */
.sidebar{
width:220px;background:var(--bg2);border-right:1px solid var(--border);
position:fixed;top:0;left:0;bottom:0;z-index:50;
display:flex;flex-direction:column;padding:24px 0;overflow-y:auto;
}
.sidebar-logo{
padding:0 20px 24px;border-bottom:1px solid var(--border);margin-bottom:20px;
}
.sidebar-logo .mark{
width:36px;height:36px;
background:linear-gradient(135deg,var(--blue),var(--purple));
border-radius:10px;display:flex;align-items:center;justify-content:center;
font-family:'Bebas Neue';font-size:1rem;color:#fff;margin-bottom:8px;
}
.sidebar-logo .name{font-family:'Bebas Neue';font-size:1.1rem;letter-spacing:0.05em;color:var(--text)}
.sidebar-logo .sub{font-family:'DM Mono';font-size:0.65rem;color:var(--muted);margin-top:2px}
.nav-section{padding:0 12px;margin-bottom:8px}
.nav-label{font-family:'DM Mono';font-size:0.62rem;letter-spacing:0.12em;color:var(--muted2);text-transform:uppercase;padding:0 8px;margin-bottom:6px}
.nav-item{
display:flex;align-items:center;gap:10px;padding:10px 12px;
border-radius:10px;cursor:pointer;transition:all 0.2s;
font-size:0.85rem;font-weight:500;color:var(--muted);
border:1px solid transparent;margin-bottom:2px;
}
.nav-item:hover{background:var(--surface);color:var(--text)}
.nav-item.active{background:var(--surface);color:var(--blue);border-color:rgba(79,156,249,0.2)}
.nav-dot{width:8px;height:8px;border-radius:50%;flex-shrink:0}
/* MAIN */
.main{margin-left:220px;min-height:100vh;padding:0}
/* TOPBAR */
.topbar{
background:rgba(8,11,16,0.9);backdrop-filter:blur(20px);
border-bottom:1px solid var(--border);
padding:16px 32px;display:flex;align-items:center;justify-content:space-between;
position:sticky;top:0;z-index:40;
}
.topbar-title{font-family:'Bebas Neue';font-size:1.4rem;letter-spacing:0.05em}
.topbar-meta{font-family:'DM Mono';font-size:0.75rem;color:var(--muted)}
.status-pill{
display:flex;align-items:center;gap:6px;
background:rgba(6,214,160,0.1);border:1px solid rgba(6,214,160,0.2);
border-radius:100px;padding:5px 12px;
font-family:'DM Mono';font-size:0.7rem;color:var(--green);
}
.pulse{width:6px;height:6px;background:var(--green);border-radius:50%;animation:pulse 2s infinite}
@keyframes pulse{0%,100%{opacity:1;transform:scale(1)}50%{opacity:0.4;transform:scale(0.8)}}
/* PAGE */
.page{display:none;padding:32px;animation:fadeIn 0.3s ease}
.page.active{display:block}
@keyframes fadeIn{from{opacity:0;transform:translateY(8px)}to{opacity:1;transform:translateY(0)}}
/* KPI CARDS */
.kpi-row{display:grid;grid-template-columns:repeat(4,1fr);gap:16px;margin-bottom:28px}
.kpi-card{
background:var(--surface);border:1px solid var(--border);border-radius:14px;
padding:20px;transition:all 0.3s;position:relative;overflow:hidden;
}
.kpi-card::before{content:'';position:absolute;top:0;left:0;right:0;height:2px}
.kpi-card.blue::before{background:linear-gradient(90deg,var(--blue),transparent)}
.kpi-card.green::before{background:linear-gradient(90deg,var(--green),transparent)}
.kpi-card.purple::before{background:linear-gradient(90deg,var(--purple),transparent)}
.kpi-card.amber::before{background:linear-gradient(90deg,var(--amber),transparent)}
.kpi-card:hover{transform:translateY(-2px);border-color:var(--border2)}
.kpi-label{font-family:'DM Mono';font-size:0.7rem;color:var(--muted);letter-spacing:0.05em;margin-bottom:8px}
.kpi-value{font-family:'Bebas Neue';font-size:2.2rem;letter-spacing:0.02em;line-height:1}
.kpi-card.blue .kpi-value{color:var(--blue)}
.kpi-card.green .kpi-value{color:var(--green)}
.kpi-card.purple .kpi-value{color:var(--purple)}
.kpi-card.amber .kpi-value{color:var(--amber)}
.kpi-change{font-size:0.75rem;color:var(--muted);margin-top:6px}
.kpi-change.up{color:var(--green)}.kpi-change.down{color:var(--red)}
/* GRID */
.grid-2{display:grid;grid-template-columns:1fr 1fr;gap:20px;margin-bottom:20px}
.grid-3{display:grid;grid-template-columns:1fr 1fr 1fr;gap:20px;margin-bottom:20px}
.grid-23{display:grid;grid-template-columns:2fr 1fr;gap:20px;margin-bottom:20px}
.grid-32{display:grid;grid-template-columns:1fr 2fr;gap:20px;margin-bottom:20px}
/* CHART CARDS */
.chart-card{
background:var(--surface);border:1px solid var(--border);border-radius:14px;padding:24px;
}
.chart-header{display:flex;align-items:center;justify-content:space-between;margin-bottom:20px}
.chart-title{font-family:'Barlow';font-weight:700;font-size:0.95rem;color:var(--text)}
.chart-sub{font-family:'DM Mono';font-size:0.68rem;color:var(--muted);margin-top:3px}
.chart-badge{
background:var(--surface2);border:1px solid var(--border);border-radius:6px;
padding:4px 10px;font-family:'DM Mono';font-size:0.65rem;color:var(--muted);
}
.chart-wrap{position:relative;height:220px}
.chart-wrap.tall{height:280px}
.chart-wrap.short{height:160px}
/* TABLE */
.data-table{width:100%;border-collapse:collapse;font-size:0.82rem}
.data-table th{
background:var(--surface2);padding:10px 14px;text-align:left;
font-family:'DM Mono';font-size:0.68rem;letter-spacing:0.06em;text-transform:uppercase;
color:var(--muted);border-bottom:1px solid var(--border);
}
.data-table td{padding:10px 14px;border-bottom:1px solid var(--border);color:var(--text)}
.data-table tr:last-child td{border-bottom:none}
.data-table tr:hover td{background:var(--surface2)}
.badge-high{background:rgba(239,68,68,0.15);color:#ef4444;padding:2px 8px;border-radius:4px;font-family:'DM Mono';font-size:0.65rem}
.badge-med{background:rgba(245,158,11,0.15);color:#f59e0b;padding:2px 8px;border-radius:4px;font-family:'DM Mono';font-size:0.65rem}
.badge-low{background:rgba(6,214,160,0.15);color:#06d6a0;padding:2px 8px;border-radius:4px;font-family:'DM Mono';font-size:0.65rem}
/* SECTION LABEL */
.section-label{
font-family:'DM Mono';font-size:0.68rem;letter-spacing:0.12em;text-transform:uppercase;
color:var(--blue);margin-bottom:16px;display:flex;align-items:center;gap:8px;
}
.section-label::after{content:'';flex:1;height:1px;background:var(--border)}
/* FILTER BAR */
.filter-bar{display:flex;gap:8px;margin-bottom:24px;flex-wrap:wrap}
.filter-chip{
padding:6px 16px;border-radius:100px;border:1px solid var(--border);
background:transparent;color:var(--muted);font-size:0.78rem;cursor:pointer;
transition:all 0.2s;font-family:'Barlow';font-weight:500;
}
.filter-chip:hover,.filter-chip.active{background:var(--surface);border-color:var(--blue);color:var(--blue)}
/* INSIGHTS */
.insight-card{
background:linear-gradient(135deg,var(--surface) 0%,rgba(79,156,249,0.05) 100%);
border:1px solid var(--border);border-radius:14px;padding:20px;
display:flex;gap:14px;align-items:flex-start;
}
.insight-icon{
width:40px;height:40px;border-radius:10px;display:flex;align-items:center;justify-content:center;
font-size:1.1rem;flex-shrink:0;
}
.insight-title{font-weight:700;font-size:0.9rem;margin-bottom:4px}
.insight-desc{font-size:0.82rem;color:var(--muted);line-height:1.6}
/* PROGRESS BAR */
.prog-item{margin-bottom:14px}
.prog-header{display:flex;justify-content:space-between;margin-bottom:6px;font-size:0.82rem}
.prog-val{font-family:'DM Mono';font-size:0.75rem;color:var(--blue)}
.prog-bar{height:6px;background:var(--surface2);border-radius:3px;overflow:hidden}
.prog-fill{height:100%;border-radius:3px;transition:width 1s ease}
</style>
</head>
<body>
<!-- SIDEBAR -->
<div class="sidebar">
<div class="sidebar-logo">
<div class="mark">OP</div>
<div class="name">Omkar Pallerla</div>
<div class="sub">Analytics Portfolio</div>
</div>
<div class="nav-section">
<div class="nav-label">Overview</div>
<div class="nav-item active" onclick="showPage('overview',this)">
<span class="nav-dot" style="background:#4f9cf9"></span>Portfolio Summary
</div>
</div>
<div class="nav-section">
<div class="nav-label">BI & Analytics</div>
<div class="nav-item" onclick="showPage('spotify',this)">
<span class="nav-dot" style="background:#1db954"></span>Spotify Analysis
</div>
<div class="nav-item" onclick="showPage('cars',this)">
<span class="nav-dot" style="background:#f59e0b"></span>Car Price Pricing
</div>
<div class="nav-item" onclick="showPage('monte',this)">
<span class="nav-dot" style="background:#7c3aed"></span>Risk Analysis
</div>
<div class="nav-item" onclick="showPage('lss',this)">
<span class="nav-dot" style="background:#06d6a0"></span>Lean Six Sigma
</div>
</div>
<div class="nav-section">
<div class="nav-label">Machine Learning</div>
<div class="nav-item" onclick="showPage('obesity',this)">
<span class="nav-dot" style="background:#ef4444"></span>Obesity Risk
</div>
<div class="nav-item" onclick="showPage('diabetes',this)">
<span class="nav-dot" style="background:#ec4899"></span>Diabetes Prediction
</div>
<div class="nav-item" onclick="showPage('yelp',this)">
<span class="nav-dot" style="background:#f97316"></span>Fake Review NLP
</div>
<div class="nav-item" onclick="showPage('solar',this)">
<span class="nav-dot" style="background:#facc15"></span>Solar Fault CV
</div>
</div>
<div class="nav-section">
<div class="nav-label">Data Engineering</div>
<div class="nav-item" onclick="showPage('escoot',this)">
<span class="nav-dot" style="background:#38bdf8"></span>Network Optimization
</div>
<div class="nav-item" onclick="showPage('grip',this)">
<span class="nav-dot" style="background:#a78bfa"></span>Wearable AI
</div>
<div class="nav-item" onclick="showPage('doe',this)">
<span class="nav-dot" style="background:#86efac"></span>DOE Optimization
</div>
</div>
</div>
<!-- MAIN -->
<div class="main">
<!-- TOPBAR -->
<div class="topbar">
<div>
<div class="topbar-title" id="pageTitle">Portfolio Overview</div>
<div class="topbar-meta" id="pageMeta">MS Business Analytics · ASU · Azure | Databricks | GCP Certified</div>
</div>
<div class="status-pill"><span class="pulse"></span>Interactive Dashboard</div>
</div>
<!-- ═══ OVERVIEW PAGE ═══════════════════════════════════════ -->
<div class="page active" id="page-overview">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">TOTAL PROJECTS</div><div class="kpi-value">11</div><div class="kpi-change up">↑ BI, ML & DE coverage</div></div>
<div class="kpi-card green"><div class="kpi-label">DASHBOARDS BUILT</div><div class="kpi-value">30+</div><div class="kpi-change">At Onix — C-suite level</div></div>
<div class="kpi-card purple"><div class="kpi-label">DATA ACCURACY</div><div class="kpi-value">99%</div><div class="kpi-change">ETL pipeline benchmark</div></div>
<div class="kpi-card amber"><div class="kpi-label">ASU GPA</div><div class="kpi-value">4.0</div><div class="kpi-change">MS Business Analytics</div></div>
</div>
<div class="grid-23">
<div class="chart-card">
<div class="chart-header">
<div><div class="chart-title">Project Portfolio — Skill Coverage</div><div class="chart-sub">By category and tech stack depth</div></div>
<div class="chart-badge">11 projects</div>
</div>
<div class="chart-wrap tall"><canvas id="radarChart"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Projects by Category</div><div class="chart-sub">Distribution of work</div></div></div>
<div class="chart-wrap"><canvas id="catChart"></canvas></div>
</div>
</div>
<div class="grid-3">
<div class="insight-card">
<div class="insight-icon" style="background:rgba(79,156,249,0.15)">📊</div>
<div><div class="insight-title">BI-First Mindset</div><div class="insight-desc">Every ML model I build has a dashboard output. Outputs go to Power BI, Tableau, or Looker — not just a notebook.</div></div>
</div>
<div class="insight-card">
<div class="insight-icon" style="background:rgba(6,214,160,0.15)">⚙️</div>
<div><div class="insight-title">Pipeline-Aware</div><div class="insight-desc">Built streaming pipelines with GCP Dataflow, Pub/Sub, and Airflow. Can trace data quality issues back to source.</div></div>
</div>
<div class="insight-card">
<div class="insight-icon" style="background:rgba(124,58,237,0.15)">☁️</div>
<div><div class="insight-title">Multi-Cloud Certified</div><div class="insight-desc">Azure Data Engineer Associate · Databricks DE Associate · GCP Associate Cloud Engineer</div></div>
</div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Tech Stack Proficiency</div><div class="chart-sub">Self-assessed depth across tools and platforms</div></div></div>
<div style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:32px;padding-top:8px">
<div>
<div class="section-label">BI & Visualization</div>
<div class="prog-item"><div class="prog-header"><span>Power BI / DAX</span><span class="prog-val">95%</span></div><div class="prog-bar"><div class="prog-fill" style="width:95%;background:var(--blue)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>Tableau</span><span class="prog-val">90%</span></div><div class="prog-bar"><div class="prog-fill" style="width:90%;background:var(--blue)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>Looker / LookML</span><span class="prog-val">80%</span></div><div class="prog-bar"><div class="prog-fill" style="width:80%;background:var(--blue)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>MicroStrategy</span><span class="prog-val">75%</span></div><div class="prog-bar"><div class="prog-fill" style="width:75%;background:var(--blue)"></div></div></div>
</div>
<div>
<div class="section-label">Data Engineering</div>
<div class="prog-item"><div class="prog-header"><span>SQL / BigQuery / Snowflake</span><span class="prog-val">95%</span></div><div class="prog-bar"><div class="prog-fill" style="width:95%;background:var(--green)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>GCP Dataflow / Pub/Sub</span><span class="prog-val">80%</span></div><div class="prog-bar"><div class="prog-fill" style="width:80%;background:var(--green)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>Airflow / Cloud Composer</span><span class="prog-val">78%</span></div><div class="prog-bar"><div class="prog-fill" style="width:78%;background:var(--green)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>dbt / Delta Lake</span><span class="prog-val">82%</span></div><div class="prog-bar"><div class="prog-fill" style="width:82%;background:var(--green)"></div></div></div>
</div>
<div>
<div class="section-label">Languages & ML</div>
<div class="prog-item"><div class="prog-header"><span>Python</span><span class="prog-val">88%</span></div><div class="prog-bar"><div class="prog-fill" style="width:88%;background:var(--purple)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>Scikit-Learn / XGBoost</span><span class="prog-val">85%</span></div><div class="prog-bar"><div class="prog-fill" style="width:85%;background:var(--purple)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>Databricks / Spark</span><span class="prog-val">75%</span></div><div class="prog-bar"><div class="prog-fill" style="width:75%;background:var(--purple)"></div></div></div>
<div class="prog-item"><div class="prog-header"><span>TensorFlow / Keras</span><span class="prog-val">70%</span></div><div class="prog-bar"><div class="prog-fill" style="width:70%;background:var(--purple)"></div></div></div>
</div>
</div>
</div>
</div>
<!-- ═══ SPOTIFY PAGE ═══════════════════════════════════════ -->
<div class="page" id="page-spotify">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">TRACKS ANALYZED</div><div class="kpi-value">50K+</div><div class="kpi-change">Spotify dataset</div></div>
<div class="kpi-card green"><div class="kpi-label">TOP CORRELATION</div><div class="kpi-value">-0.48</div><div class="kpi-change up">Instrumentalness vs Popularity</div></div>
<div class="kpi-card purple"><div class="kpi-label">MODEL R²</div><div class="kpi-value">0.74</div><div class="kpi-change">Random Forest predictor</div></div>
<div class="kpi-card amber"><div class="kpi-label">HIT FORMULA</div><div class="kpi-value">>0.7</div><div class="kpi-change">Dance + Energy threshold</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Audio Feature Correlation with Popularity</div><div class="chart-sub">Pearson correlation coefficients</div></div></div>
<div class="chart-wrap tall"><canvas id="spotifyCorr"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Average Popularity by Genre</div><div class="chart-sub">Pop & Hip-Hop dominate</div></div></div>
<div class="chart-wrap tall"><canvas id="genreChart"></canvas></div>
</div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Popularity Trend by Year</div><div class="chart-sub">2018–2023 streaming data</div></div></div>
<div class="chart-wrap"><canvas id="yearTrend"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Track Hit Score Distribution</div><div class="chart-sub">Model-predicted tiers</div></div></div>
<div class="chart-wrap"><canvas id="hitDist"></canvas></div>
</div>
</div>
</div>
<!-- ═══ OBESITY PAGE ═══════════════════════════════════════ -->
<div class="page" id="page-obesity">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">PATIENTS ANALYZED</div><div class="kpi-value">2,111</div><div class="kpi-change">17 lifestyle features</div></div>
<div class="kpi-card green"><div class="kpi-label">BEST ACCURACY</div><div class="kpi-value">94.2%</div><div class="kpi-change up">Random Forest</div></div>
<div class="kpi-card purple"><div class="kpi-label">MACRO AUC</div><div class="kpi-value">0.97</div><div class="kpi-change up">Multi-class ROC</div></div>
<div class="kpi-card amber"><div class="kpi-label">SMOTE BOOST</div><div class="kpi-value">+28%</div><div class="kpi-change up">Minority class recall</div></div>
</div>
<div class="grid-23">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Model Accuracy Comparison</div><div class="chart-sub">4 algorithms benchmarked</div></div></div>
<div class="chart-wrap tall"><canvas id="obesityModels"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Risk Tier Distribution</div><div class="chart-sub">Power BI dashboard output</div></div></div>
<div class="chart-wrap"><canvas id="riskTier"></canvas></div>
</div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Top Feature Importances — Random Forest</div><div class="chart-sub">SHAP-validated key drivers of obesity risk</div></div></div>
<div class="chart-wrap"><canvas id="obesityFeatures"></canvas></div>
</div>
</div>
<!-- ═══ DIABETES PAGE ═══════════════════════════════════════ -->
<div class="page" id="page-diabetes">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">MODELS BENCHMARKED</div><div class="kpi-value">10+</div><div class="kpi-change">Full algorithm comparison</div></div>
<div class="kpi-card green"><div class="kpi-label">BEST AUC</div><div class="kpi-value">0.84</div><div class="kpi-change up">XGBoost (tuned)</div></div>
<div class="kpi-card purple"><div class="kpi-label">PIPELINE</div><div class="kpi-value">dbt</div><div class="kpi-change">Snowflake + daily scoring</div></div>
<div class="kpi-card amber"><div class="kpi-label">PATIENTS SCORED</div><div class="kpi-value">768</div><div class="kpi-change">Pima Indians dataset</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">AUC-ROC Benchmark — All Models</div><div class="chart-sub">Higher = better discrimination</div></div></div>
<div class="chart-wrap tall"><canvas id="diabetesAUC"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Feature Importance — XGBoost</div><div class="chart-sub">Top predictors of diabetes risk</div></div></div>
<div class="chart-wrap tall"><canvas id="diabetesFeatures"></canvas></div>
</div>
</div>
</div>
<!-- ═══ YELP PAGE ═══════════════════════════════════════════ -->
<div class="page" id="page-yelp">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">F1 SCORE</div><div class="kpi-value">0.99</div><div class="kpi-change up">XGBoost classifier</div></div>
<div class="kpi-card green"><div class="kpi-label">LATENCY</div><div class="kpi-value"><8s</div><div class="kpi-change up">Databricks streaming</div></div>
<div class="kpi-card purple"><div class="kpi-label">FAKE RATE</div><div class="kpi-value">8.3%</div><div class="kpi-change">Of reviews flagged</div></div>
<div class="kpi-card amber"><div class="kpi-label">WORKLOAD CUT</div><div class="kpi-value">90%+</div><div class="kpi-change up">Moderator reduction</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Sentiment Distribution: Real vs Fake</div><div class="chart-sub">Fake reviews cluster at ±1.0 extremes</div></div></div>
<div class="chart-wrap tall"><canvas id="sentimentDist"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Detection Pipeline Performance</div><div class="chart-sub">3-stage accuracy metrics</div></div></div>
<div class="chart-wrap tall"><canvas id="pipelinePerf"></canvas></div>
</div>
</div>
</div>
<!-- ═══ CARS PAGE ═══════════════════════════════════════════ -->
<div class="page" id="page-cars">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">BEST R²</div><div class="kpi-value">0.93</div><div class="kpi-change up">Polynomial Regression</div></div>
<div class="kpi-card green"><div class="kpi-label">DIESEL PREMIUM</div><div class="kpi-value">+18%</div><div class="kpi-change up">Higher resale value</div></div>
<div class="kpi-card purple"><div class="kpi-label">TOP PREDICTOR</div><div class="kpi-value">74%</div><div class="kpi-change">Present price variance explained</div></div>
<div class="kpi-card amber"><div class="kpi-label">MODELS RUN</div><div class="kpi-value">20+</div><div class="kpi-change">LazyPredict benchmark</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Model Performance Comparison</div><div class="chart-sub">R² and RMSE across algorithms</div></div></div>
<div class="chart-wrap tall"><canvas id="carsModels"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Inventory Pricing Status</div><div class="chart-sub">Power BI dashboard output</div></div></div>
<div class="chart-wrap tall"><canvas id="pricingStatus"></canvas></div>
</div>
</div>
</div>
<!-- ═══ MONTE CARLO PAGE ════════════════════════════════════ -->
<div class="page" id="page-monte">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">OPTIMAL CAPACITY</div><div class="kpi-value">30K</div><div class="kpi-change">EV units per year</div></div>
<div class="kpi-card green"><div class="kpi-label">EXPECTED NPV</div><div class="kpi-value">$883M</div><div class="kpi-change up">5-year profit projection</div></div>
<div class="kpi-card purple"><div class="kpi-label">SIMULATIONS</div><div class="kpi-value">10K</div><div class="kpi-change">Monte Carlo iterations</div></div>
<div class="kpi-card amber"><div class="kpi-label">P(LOSS) AT 30K</div><div class="kpi-value">8%</div><div class="kpi-change up">Acceptable risk level</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">NPV Distribution by Capacity Option</div><div class="chart-sub">Monte Carlo simulation results</div></div></div>
<div class="chart-wrap tall"><canvas id="npvDist"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Risk vs Reward — Capacity Tradeoff</div><div class="chart-sub">Probability of loss increases sharply above 35K</div></div></div>
<div class="chart-wrap tall"><canvas id="riskReward"></canvas></div>
</div>
</div>
</div>
<!-- ═══ LSS PAGE ════════════════════════════════════════════ -->
<div class="page" id="page-lss">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">CYCLE TIME BEFORE</div><div class="kpi-value">31.6d</div><div class="kpi-change">Avg proposal days</div></div>
<div class="kpi-card green"><div class="kpi-label">CYCLE TIME AFTER</div><div class="kpi-value">26.9d</div><div class="kpi-change up">-15.2% reduction ✅</div></div>
<div class="kpi-card purple"><div class="kpi-label">SIGMA LEVEL</div><div class="kpi-value">2.78</div><div class="kpi-change up">Was 2.08 (+0.70)</div></div>
<div class="kpi-card amber"><div class="kpi-label">REWORK REDUCTION</div><div class="kpi-value">-83%</div><div class="kpi-change up">12% → 2%</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">DMAIC Improvement — Before vs After</div><div class="chart-sub">Key process metrics comparison</div></div></div>
<div class="chart-wrap tall"><canvas id="lssImprove"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Root Cause Pareto Chart</div><div class="chart-sub">Defects by category (Fishbone analysis)</div></div></div>
<div class="chart-wrap tall"><canvas id="paretoChart"></canvas></div>
</div>
</div>
</div>
<!-- ═══ SOLAR PAGE ══════════════════════════════════════════ -->
<div class="page" id="page-solar">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">OVERALL F1</div><div class="kpi-value">0.94</div><div class="kpi-change up">VGG16 Transfer Learning</div></div>
<div class="kpi-card green"><div class="kpi-label">O&M COST CUT</div><div class="kpi-value">35%</div><div class="kpi-change up">Targeted maintenance</div></div>
<div class="kpi-card purple"><div class="kpi-label">FAULT CLASSES</div><div class="kpi-value">5</div><div class="kpi-change">Clean/Dusty/Crack/Bird/Damage</div></div>
<div class="kpi-card amber"><div class="kpi-label">BATCH SPEED</div><div class="kpi-value">12min</div><div class="kpi-change">500+ images via ADF</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Per-Class F1 Score</div><div class="chart-sub">VGG16 classification performance</div></div></div>
<div class="chart-wrap tall"><canvas id="solarF1"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Fault Type Distribution</div><div class="chart-sub">Detected across solar array</div></div></div>
<div class="chart-wrap tall"><canvas id="faultDist"></canvas></div>
</div>
</div>
</div>
<!-- ═══ ESCOOT PAGE ═════════════════════════════════════════ -->
<div class="page" id="page-escoot">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">OPTIMAL COST</div><div class="kpi-value">$28,350</div><div class="kpi-change">MILP solution</div></div>
<div class="kpi-card green"><div class="kpi-label">BUDGET SAVED</div><div class="kpi-value">19%</div><div class="kpi-change up">$6,650 under budget</div></div>
<div class="kpi-card purple"><div class="kpi-label">SITES SELECTED</div><div class="kpi-value">4 / 8</div><div class="kpi-change">Optimal locations</div></div>
<div class="kpi-card amber"><div class="kpi-label">DEMAND MET</div><div class="kpi-value">100%</div><div class="kpi-change up">All constraints satisfied</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Station Cost vs Coverage</div><div class="chart-sub">8 candidate locations evaluated</div></div></div>
<div class="chart-wrap tall"><canvas id="stationCost"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Budget Allocation Breakdown</div><div class="chart-sub">Fixed + variable charger costs</div></div></div>
<div class="chart-wrap tall"><canvas id="budgetBreak"></canvas></div>
</div>
</div>
</div>
<!-- ═══ GRIP LAB PAGE ═══════════════════════════════════════ -->
<div class="page" id="page-grip">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">HIDDEN FATIGUE CAUGHT</div><div class="kpi-value">86%</div><div class="kpi-change up">vs HRV-only baseline</div></div>
<div class="kpi-card green"><div class="kpi-label">MODEL ALIGNMENT</div><div class="kpi-value">89%</div><div class="kpi-change up">Matches athlete RPE</div></div>
<div class="kpi-card purple"><div class="kpi-label">EARLY WARNING</div><div class="kpi-value">12-24h</div><div class="kpi-change">Before physical symptoms</div></div>
<div class="kpi-card amber"><div class="kpi-label">TOP FEATURE</div><div class="kpi-value">Decay</div><div class="kpi-change">Grip force decay rate</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Readiness Score: HRV-Only vs Corrected</div><div class="chart-sub">Grip-adjusted algorithm comparison</div></div></div>
<div class="chart-wrap tall"><canvas id="gripScore"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Feature Importance — XGBoost Adjustment</div><div class="chart-sub">What drives the readiness correction</div></div></div>
<div class="chart-wrap tall"><canvas id="gripFeatures"></canvas></div>
</div>
</div>
</div>
<!-- ═══ DOE PAGE ════════════════════════════════════════════ -->
<div class="page" id="page-doe">
<div class="kpi-row">
<div class="kpi-card blue"><div class="kpi-label">OPTIMAL DISTANCE</div><div class="kpi-value">151cm</div><div class="kpi-change up">Max performance config</div></div>
<div class="kpi-card green"><div class="kpi-label">KEY INTERACTION</div><div class="kpi-value">+35%</div><div class="kpi-change up">Length × Width synergy</div></div>
<div class="kpi-card purple"><div class="kpi-label">EXPERIMENTS RUN</div><div class="kpi-value">48</div><div class="kpi-change">2⁴ × 3 replicates</div></div>
<div class="kpi-card amber"><div class="kpi-label">COST SAVING</div><div class="kpi-value">-$4/unit</div><div class="kpi-change up">Small tyres = same performance</div></div>
</div>
<div class="grid-2">
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Main Effects — Factor vs Response</div><div class="chart-sub">ANOVA significance (p-values)</div></div></div>
<div class="chart-wrap tall"><canvas id="mainEffects"></canvas></div>
</div>
<div class="chart-card">
<div class="chart-header"><div><div class="chart-title">Configuration Performance</div><div class="chart-sub">Distance by design combination</div></div></div>
<div class="chart-wrap tall"><canvas id="configPerf"></canvas></div>
</div>
</div>
</div>
</div><!-- /main -->
<script>
// ─── NAVIGATION ──────────────────────────────────────────────
const titles = {
overview:'Portfolio Overview', spotify:'Spotify Songs Popularity',
obesity:'Obesity Risk Classification', diabetes:'Diabetes Ensemble Benchmark',
yelp:'Yelp Fake Review Detection', cars:'Used Car Price Prediction',
monte:'Strategic Risk Analysis — Monte Carlo', lss:'Lean Six Sigma Process Analytics',
solar:'Solar Panel Fault Detection CV', escoot:'E-Scoot Network Optimization',
grip:'Grip Lab Wearable AI', doe:'Product Design Optimization DOE'
};
function showPage(id, el) {
document.querySelectorAll('.page').forEach(p => p.classList.remove('active'));
document.querySelectorAll('.nav-item').forEach(n => n.classList.remove('active'));
document.getElementById('page-' + id).classList.add('active');
el.classList.add('active');
document.getElementById('pageTitle').textContent = titles[id] || id;
initChartsForPage(id);
}
// ─── CHART.JS DEFAULTS ───────────────────────────────────────
Chart.defaults.color = '#7a8499';
Chart.defaults.borderColor = 'rgba(255,255,255,0.07)';
Chart.defaults.font.family = "'Barlow', sans-serif";
const chartInstances = {};
function createChart(id, config) {
if (chartInstances[id]) chartInstances[id].destroy();
const ctx = document.getElementById(id);
if (!ctx) return;
chartInstances[id] = new Chart(ctx, config);
}
const BLUES = ['#4f9cf9','#3b82f6','#2563eb','#1d4ed8'];
const GREENS = ['#06d6a0','#10b981','#059669','#047857'];
const MIXED = ['#4f9cf9','#06d6a0','#7c3aed','#f59e0b','#ef4444','#ec4899','#38bdf8','#a78bfa','#86efac','#fcd34d'];
// ─── CHART INITIALIZERS BY PAGE ───────────────────────────────
const pageCharts = {
overview: () => {
createChart('radarChart', {type:'radar', data:{
labels:['Power BI','SQL','Python','Snowflake','Dataflow/GCP','Tableau','Looker','dbt','ML/Scikit','Databricks'],
datasets:[{label:'Proficiency',data:[95,95,88,88,80,90,80,82,85,75],
backgroundColor:'rgba(79,156,249,0.2)',borderColor:'#4f9cf9',pointBackgroundColor:'#4f9cf9',pointRadius:4}]
}, options:{scales:{r:{ticks:{backdropColor:'transparent',stepSize:20},grid:{color:'rgba(255,255,255,0.1)'},pointLabels:{color:'#e8edf5',font:{size:11}}}}, plugins:{legend:{display:false}}}});
createChart('catChart', {type:'doughnut', data:{
labels:['BI & Analytics','Machine Learning','Data Engineering'],
datasets:[{data:[4,4,3],backgroundColor:['#4f9cf9','#7c3aed','#06d6a0'],borderWidth:0,hoverOffset:6}]
}, options:{plugins:{legend:{position:'bottom'}},cutout:'65%'}});
},
spotify: () => {
createChart('spotifyCorr', {type:'bar', data:{
labels:['Instrumentalness','Acousticness','Speechiness','Liveness','Valence','Tempo','Loudness','Energy','Danceability'],
datasets:[{label:'Correlation',
data:[-0.48,-0.31,-0.12,-0.08,0.18,0.09,0.35,0.38,0.42],
backgroundColor: d => d.raw < 0 ? '#ef4444' : '#06d6a0',
borderRadius:4}]
}, options:{indexAxis:'y',plugins:{legend:{display:false}},scales:{x:{min:-0.6,max:0.6,grid:{color:'rgba(255,255,255,0.05)'}},y:{grid:{display:false}}}}});
createChart('genreChart', {type:'bar', data:{
labels:['Pop','Hip-Hop','R&B','Latin','Rock','Electronic','Country','Folk','Jazz','Classical'],
datasets:[{label:'Avg Popularity',data:[72,68,65,63,58,55,52,48,42,38],
backgroundColor:['#4f9cf9','#06d6a0','#7c3aed','#f59e0b','#ef4444','#38bdf8','#a78bfa','#86efac','#fcd34d','#fb7185'],
borderRadius:6}]
}, options:{indexAxis:'y',plugins:{legend:{display:false}},scales:{x:{min:0,max:100},y:{grid:{display:false}}}}});
createChart('yearTrend', {type:'line', data:{
labels:['2018','2019','2020','2021','2022','2023'],
datasets:[
{label:'Avg Popularity',data:[58,60,62,65,68,71],borderColor:'#4f9cf9',tension:0.4,fill:true,backgroundColor:'rgba(79,156,249,0.1)'},
{label:'Danceability',data:[0.62,0.64,0.66,0.68,0.70,0.72],borderColor:'#06d6a0',tension:0.4,yAxisID:'y1'}
]
}, options:{scales:{y:{position:'left'},y1:{position:'right',grid:{drawOnChartArea:false},min:0.5,max:0.85}}}});
createChart('hitDist', {type:'doughnut', data:{
labels:['Hit (80+)','High (60-80)','Medium (40-60)','Low (<40)'],
datasets:[{data:[18,32,35,15],backgroundColor:['#06d6a0','#4f9cf9','#f59e0b','#ef4444'],borderWidth:0}]
}, options:{plugins:{legend:{position:'right'}},cutout:'60%'}});
},
obesity: () => {
createChart('obesityModels', {type:'bar', data:{
labels:['Random Forest','Gradient Boosting','Decision Tree','KNN'],
datasets:[
{label:'Test Accuracy',data:[0.942,0.928,0.871,0.834],backgroundColor:BLUES,borderRadius:6},
{label:'CV Score',data:[0.938,0.921,0.865,0.828],backgroundColor:GREENS,borderRadius:6}
]
}, options:{scales:{y:{min:0.7,max:1.0}},plugins:{legend:{position:'top'}}}});
createChart('riskTier', {type:'doughnut', data:{
labels:['High Risk','Medium Risk','Low Risk'],
datasets:[{data:[28,35,37],backgroundColor:['#ef4444','#f59e0b','#06d6a0'],borderWidth:0,hoverOffset:8}]
}, options:{plugins:{legend:{position:'bottom'}},cutout:'65%'}});
createChart('obesityFeatures', {type:'bar', data:{
labels:['Family History','Veg. Consumption','Meals/Day','Physical Activity','Transport Mode','Water Intake','Tech Device Use','Age','Gender','Height'],
datasets:[{label:'Importance',data:[0.24,0.18,0.15,0.12,0.09,0.07,0.06,0.05,0.03,0.01],
backgroundColor:'#4f9cf9',borderRadius:4}]
}, options:{indexAxis:'y',plugins:{legend:{display:false}},scales:{y:{grid:{display:false}}}}});
},
diabetes: () => {
const diabModels = ['XGBoost','Bagging','Voting Ensemble','Gradient Boosting','Random Forest','SVM','Logistic Reg.','KNN','Neural Net','Naive Bayes'];
const diabAUC = [0.84,0.82,0.81,0.80,0.79,0.78,0.76,0.74,0.72,0.68];
createChart('diabetesAUC', {type:'bar', data:{
labels:diabModels,
datasets:[{label:'AUC-ROC',data:diabAUC,
backgroundColor: diabAUC.map((v,i) => i===0?'#06d6a0':i<3?'#4f9cf9':'rgba(79,156,249,0.4)'),
borderRadius:6}]
}, options:{indexAxis:'y',scales:{x:{min:0.5,max:0.9},y:{grid:{display:false}}},plugins:{legend:{display:false}}}});
createChart('diabetesFeatures', {type:'bar', data:{
labels:['Glucose','BMI','Age','Insulin','DiabetesPedigree','BloodPressure','SkinThickness','Pregnancies'],
datasets:[{label:'XGBoost Importance',data:[0.32,0.22,0.15,0.12,0.09,0.05,0.03,0.02],
backgroundColor:'#7c3aed',borderRadius:4}]
}, options:{indexAxis:'y',plugins:{legend:{display:false}},scales:{y:{grid:{display:false}}}}});
},
yelp: () => {
createChart('sentimentDist', {type:'bar', data:{
labels:['-1.0','-0.8','-0.6','-0.4','-0.2','0.0','0.2','0.4','0.6','0.8','1.0'],
datasets:[
{label:'Real Reviews',data:[2,8,15,25,40,120,150,180,200,90,10],backgroundColor:'rgba(6,214,160,0.7)',borderRadius:2},
{label:'Fake Reviews',data:[25,5,2,1,2,5,2,1,2,5,55],backgroundColor:'rgba(239,68,68,0.7)',borderRadius:2}
]
}, options:{plugins:{legend:{position:'top'}},scales:{x:{title:{display:true,text:'VADER Compound Score',color:'#7a8499'}}}}});
createChart('pipelinePerf', {type:'bar', data:{
labels:['Isolation Forest\n(Stage 1)','XGBoost\n(Stage 2)','Combined\nPipeline'],
datasets:[
{label:'Precision',data:[0.71,0.98,0.99],backgroundColor:'#4f9cf9',borderRadius:4},
{label:'Recall',data:[0.82,0.97,0.98],backgroundColor:'#06d6a0',borderRadius:4},
{label:'F1',data:[0.76,0.975,0.985],backgroundColor:'#7c3aed',borderRadius:4}
]
}, options:{scales:{y:{min:0.6,max:1.05}},plugins:{legend:{position:'top'}}}});
},
cars: () => {
createChart('carsModels', {type:'bar', data:{
labels:['Polynomial\nRegression','Gradient\nBoosting','Ridge\nRegression','Linear\nRegression'],
datasets:[
{label:'R²',data:[0.93,0.96,0.84,0.82],backgroundColor:'#4f9cf9',borderRadius:4},
{label:'1/RMSE (normalized)',data:[0.83,0.92,0.65,0.62],backgroundColor:'#06d6a0',borderRadius:4}
]
}, options:{scales:{y:{min:0.5,max:1.05}},plugins:{legend:{position:'top'}}}});
createChart('pricingStatus', {type:'doughnut', data:{
labels:['FAIR (±10%)','UNDERPRICED','OVERPRICED'],
datasets:[{data:[62,22,16],backgroundColor:['#f59e0b','#06d6a0','#ef4444'],borderWidth:0,hoverOffset:8}]
}, options:{plugins:{legend:{position:'bottom'}},cutout:'60%'}});
},
monte: () => {
const caps = ['20K units','25K units','30K units','35K units','40K units','45K units'];
createChart('npvDist', {type:'bar', data:{
labels:caps,
datasets:[
{label:'Expected NPV ($M)',data:[612,748,883,841,752,680],backgroundColor:['#4f9cf9','#4f9cf9','#06d6a0','#f59e0b','#ef4444','#ef4444'],borderRadius:6},
]
}, options:{plugins:{legend:{display:false}},scales:{y:{title:{display:true,text:'Expected 5Y NPV ($M)',color:'#7a8499'}}}}});
createChart('riskReward', {type:'line', data:{
labels:caps,
datasets:[
{label:'P(Loss) %',data:[3,5,8,19,31,45],borderColor:'#ef4444',tension:0.3,yAxisID:'y',fill:false},
{label:'Expected NPV ($M)',data:[612,748,883,841,752,680],borderColor:'#4f9cf9',tension:0.3,yAxisID:'y1',fill:false}
]
}, options:{scales:{y:{position:'left',title:{display:true,text:'P(Loss) %',color:'#7a8499'}},y1:{position:'right',title:{display:true,text:'NPV $M',color:'#7a8499'},grid:{drawOnChartArea:false}}}}});
},
lss: () => {
createChart('lssImprove', {type:'bar', data:{
labels:['Avg Cycle Time (days)','Sigma Level','DPMO (thousands)','Rework Rate %'],
datasets:[
{label:'Before',data:[31.6,2.08,281,12],backgroundColor:'rgba(239,68,68,0.7)',borderRadius:4},
{label:'After',data:[26.9,2.78,158,2],backgroundColor:'rgba(6,214,160,0.7)',borderRadius:4}
]
}, options:{plugins:{legend:{position:'top'}},scales:{y:{beginAtZero:true}}}});
createChart('paretoChart', {type:'bar', data:{
labels:['Manual CRM Entry','Sequential Reviews','Approval Loops','Training Gaps','Tool Issues'],
datasets:[
{label:'Defect Count',data:[142,98,67,45,28],backgroundColor:'#4f9cf9',borderRadius:4,yAxisID:'y'},
{label:'Cumulative %',data:[37,63,80,92,100],type:'line',borderColor:'#f59e0b',yAxisID:'y1',tension:0.2}
]
}, options:{scales:{y:{position:'left',title:{display:true,text:'Defect Count',color:'#7a8499'}},y1:{position:'right',min:0,max:110,title:{display:true,text:'Cumulative %',color:'#7a8499'},grid:{drawOnChartArea:false}}}}});
},
solar: () => {
createChart('solarF1', {type:'bar', data:{
labels:['Clean','Electrical\nDamage','Physical\nDamage','Dusty','Bird Drop'],
datasets:[
{label:'Precision',data:[0.97,0.95,0.96,0.94,0.91],backgroundColor:'#4f9cf9',borderRadius:4},
{label:'Recall',data:[0.98,0.94,0.95,0.92,0.89],backgroundColor:'#06d6a0',borderRadius:4},
{label:'F1',data:[0.975,0.945,0.955,0.930,0.900],backgroundColor:'#7c3aed',borderRadius:4}
]
}, options:{scales:{y:{min:0.8,max:1.0}},plugins:{legend:{position:'top'}}}});
createChart('faultDist', {type:'doughnut', data:{
labels:['Clean','Dusty','Bird Drop','Electrical Damage','Physical Damage'],
datasets:[{data:[42,28,15,10,5],backgroundColor:['#06d6a0','#f59e0b','#7c3aed','#ef4444','#ec4899'],borderWidth:0,hoverOffset:8}]
}, options:{plugins:{legend:{position:'right'}},cutout:'55%'}});
},
escoot: () => {
createChart('stationCost', {type:'bar', data:{
labels:['Sun Devil\nFitness','Hassayampa\nDorms','Tooker\nHouse','Manzanita\nHall','Light Rail\nStop','Greek Row','Research\nPark','Perimeter Rd'],
datasets:[
{label:'Fixed Cost ($K)',data:[8.5,7.2,9.1,6.8,10.2,7.5,8.8,6.2],backgroundColor:d=>[0,1,2,3].includes(d.dataIndex)?'#06d6a0':'rgba(79,156,249,0.3)',borderRadius:4},
{label:'Coverage Score',data:[92,88,85,80,72,68,65,45],type:'line',borderColor:'#f59e0b',yAxisID:'y1'}
]
}, options:{scales:{y:{title:{display:true,text:'Fixed Cost ($K)',color:'#7a8499'}},y1:{position:'right',min:0,max:110,grid:{drawOnChartArea:false}}}}});
createChart('budgetBreak', {type:'doughnut', data:{
labels:['Fixed Construction','Charger Units','Installation','Contingency'],
datasets:[{data:[14200,8500,4200,1450],backgroundColor:['#4f9cf9','#06d6a0','#7c3aed','#f59e0b'],borderWidth:0}]
}, options:{plugins:{legend:{position:'right'}},cutout:'60%'}});
},
grip: () => {
const days = Array.from({length:14},(_,i)=>`Day ${i+1}`);
const hrv = [82,78,80,75,71,73,76,72,68,65,70,74,71,69];
const corr = [79,74,77,68,59,65,71,64,57,52,63,70,66,61];
createChart('gripScore', {type:'line', data:{
labels:days,
datasets:[
{label:'HRV-Only Score',data:hrv,borderColor:'#7a8499',tension:0.3,borderDash:[5,5]},
{label:'Grip-Corrected Score',data:corr,borderColor:'#4f9cf9',tension:0.3,fill:true,backgroundColor:'rgba(79,156,249,0.1)'}
]
}, options:{scales:{y:{min:40,max:100,title:{display:true,text:'Readiness Score',color:'#7a8499'}}},plugins:{legend:{position:'top'}}}});
createChart('gripFeatures', {type:'bar', data:{
labels:['Grip Force Decay','Neural Asymmetry','30d Rolling Avg','HRV','Left Grip Abs.','Right Grip Abs.','Sleep Score','Resting HR'],
datasets:[{label:'XGBoost Importance',data:[0.28,0.24,0.18,0.12,0.07,0.05,0.04,0.02],
backgroundColor:'#a78bfa',borderRadius:4}]
}, options:{indexAxis:'y',plugins:{legend:{display:false}},scales:{y:{grid:{display:false}}}}});
},
doe: () => {
createChart('mainEffects', {type:'bar', data:{
labels:['Width (Large vs Small)','Length (Big vs Small)','Load (Back vs Front)','Tyre Size'],
datasets:[
{label:'Effect Size (cm)',data:[23,19,8,1.2],backgroundColor:['#06d6a0','#4f9cf9','#f59e0b','rgba(120,120,120,0.5)'],borderRadius:6},
{label:'F-value',data:[42.3,38.7,8.2,2.1],backgroundColor:'transparent',borderColor:'#ef4444',type:'line',yAxisID:'y1'}
]
}, options:{scales:{y:{title:{display:true,text:'Effect (cm)',color:'#7a8499'}},y1:{position:'right',title:{display:true,text:'F-value',color:'#7a8499'},grid:{drawOnChartArea:false}}}}});
createChart('configPerf', {type:'bar', data:{
labels:['Big+Front+Small+Big','Big+Back+Small+Big','Big+Front+Large+Big','Big+Back+Large+Small','Big+Back+Large+Big','OPTIMAL\n(Big+Back+Lrg+Sm)'],
datasets:[{label:'Avg Distance (cm)',
data:[98,106,118,138,147,151],
backgroundColor:d=>d.dataIndex===5?'#06d6a0':'#4f9cf9',
borderRadius:6}]
}, options:{plugins:{legend:{display:false}},scales:{y:{min:80,max:165,title:{display:true,text:'Distance (cm)',color:'#7a8499'}},x:{ticks:{font:{size:9}}}}}});
}
};
function initChartsForPage(id) {
if (pageCharts[id]) {
setTimeout(() => pageCharts[id](), 50);
}
}
// Init overview on load
window.addEventListener('load', () => {
setTimeout(() => pageCharts.overview(), 100);
});
</script>
</body>
</html>