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<!DOCTYPE html>
<html lang="en">
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<title>InsightFlow · Nikhil Varkute </title>
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</head>
<body>
<aside class="sidebar">
<div class="sidebar-brand">
<div style="display:flex;align-items:center;gap:10px">
<svg width="30" height="30" viewBox="0 0 30 30" fill="none">
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<circle cx="25" cy="12" r="2.5" fill="white"/>
</svg>
<div>
<div class="brand-name">InsightFlow</div>
<div class="brand-sub">Sales Intelligence</div>
</div>
</div>
</div>
<div class="sidebar-section">Analysis</div>
<button class="nav-item active" onclick="go('dashboard',this)"><span class="ico">⊞</span> Dashboard</button>
<button class="nav-item" onclick="go('forecasts',this)"><span class="ico">〜</span> Forecasts</button>
<button class="nav-item" onclick="go('historical',this)"><span class="ico">◷</span> Historical</button>
<button class="nav-item" onclick="go('seasonality',this)"><span class="ico">🗓</span> Seasonality</button>
<button class="nav-item" onclick="go('eda',this)"><span class="ico">🔍</span> EDA</button>
<div class="sidebar-section">Models</div>
<button class="nav-item" onclick="go('modelconfig',this)"><span class="ico">⚙</span> Model Comparison</button>
<button class="nav-item" onclick="go('features',this)"><span class="ico">⬡</span> Feature Importance</button>
<button class="nav-item" onclick="go('residuals',this)"><span class="ico">📉</span> Residual Analysis</button>
<div class="sidebar-section">Insights</div>
<button class="nav-item" onclick="go('alerts',this)"><span class="ico">💡</span> Business Insights</button>
<div class="sidebar-spacer"></div>
<div class="sidebar-footer">
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<text x="18" y="22" text-anchor="middle" font-family="Palatino,serif" font-size="10.5" font-weight="600" letter-spacing="1.2" fill="white" filter="url(#avGlow)">MM</text>
</svg>
<div><div class="author-name">Nikhil Varkute</div><div class="author-role">Data Scientist Intern</div></div>
</div>
</aside>
<div class="main">
<div class="topbar">
<div>
<div class="topbar-title" id="topbar-title">Dashboard</div>
<div class="topbar-sub">Superstore Sales · 2014–2017 · Linear Regression · R² 0.600</div>
</div>
<div class="topbar-badges">
<span class="tbadge tbadge-green">Superstore · 2014–2017</span>
<span class="tbadge tbadge-blue">Python · Scikit-learn</span>
</div>
</div>
<!-- DASHBOARD -->
<div class="content active" id="pg-dashboard">
<div class="kpis">
<div class="kpi"><div class="kpi-top"><div class="kpi-icon green">💰</div><span class="kpi-trend up">4 yrs</span></div><div class="kpi-val">$2,297,201</div><div class="kpi-lbl">Total Historical Sales</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon blue">📅</div><span class="kpi-trend neutral">Avg</span></div><div class="kpi-val">$47,858</div><div class="kpi-lbl">Monthly Average</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon orange">🏆</div><span class="kpi-trend up">Peak</span></div><div class="kpi-val">$118,448</div><div class="kpi-lbl">Nov 2017 — Peak Month</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon purple">🎯</div><span class="kpi-trend up">LR</span></div><div class="kpi-val">$349,127</div><div class="kpi-lbl">6-Month Forecast Total</div></div>
</div>
<div class="fc-layout">
<div class="card" style="margin-bottom:0">
<div class="card-title">Historical Sales + 6-Month Forecast</div>
<div class="card-sub">2014–2017 actuals · Linear Regression forecast Jan–Jun 2018 · matches sales_forecast_dashboard.png</div>
<canvas id="c-dash-main" style="max-height:250px;margin-top:10px"></canvas>
</div>
<div class="card" style="margin-bottom:0">
<div class="card-title">Monthly Forecast · 2018</div>
<div class="card-sub">Linear Regression · Best model · Cell 12 exact values</div>
<div class="month-rows" id="dash-months"></div>
<div style="margin-top:12px;padding-top:12px;border-top:1px solid rgba(200,205,235,.3);display:flex;justify-content:space-between">
<span style="font-size:10px;color:#8a8fa8;font-weight:700;text-transform:uppercase">6-Month Total</span>
<span style="font-family:'DM Mono',monospace;font-size:1rem;font-weight:700;color:#4361ee">$349,127</span>
</div>
</div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0"><div class="card-title">Sales by Category</div><div class="card-sub">Total revenue 2014–2017</div><canvas id="c-dash-cat"></canvas></div>
<div class="card" style="margin-bottom:0"><div class="card-title">Sales by Region</div><div class="card-sub">Geographic performance · West leads</div><canvas id="c-dash-reg"></canvas></div>
</div>
</div>
<!-- FORECASTS -->
<div class="content" id="pg-forecasts">
<div class="ctrls">
<div class="ctrl"><label>Model</label>
<select id="mdl" onchange="updateFc()">
<option value="lr" selected>Linear Regression ⭐ Best</option>
<option value="rf">Random Forest</option>
<option value="gb">Gradient Boosting</option>
</select>
</div>
<div class="ctrl"><label>Horizon: <span id="fmLbl">6</span> months</label>
<input type="range" id="fmR" min="3" max="12" value="6" oninput="document.getElementById('fmLbl').textContent=this.value;updateFc()">
</div>
</div>
<div class="fc-layout">
<div class="card" style="margin-bottom:0"><div class="card-title">Sales Forecast</div><div class="card-sub">Solid = historical · Dashed = ML projection</div><canvas id="c-fc" style="max-height:280px"></canvas></div>
<div class="card" style="margin-bottom:0">
<div class="card-title">Forecast Breakdown</div>
<div class="card-sub" id="fcSubLbl">Next 6 months · Linear Regression</div>
<div class="month-rows" id="fcTable"></div>
<div style="margin-top:12px;padding-top:12px;border-top:1px solid rgba(200,205,235,.3);display:flex;justify-content:space-between">
<span style="font-size:10px;color:#8a8fa8;font-weight:700;text-transform:uppercase">Total Projected</span>
<span id="fcTot" style="font-family:'DM Mono',monospace;font-size:1rem;font-weight:700;color:#4361ee">$349,127</span>
</div>
</div>
</div>
</div>
<!-- HISTORICAL -->
<div class="content" id="pg-historical">
<div class="kpis">
<div class="kpi"><div class="kpi-top"><div class="kpi-icon green">📈</div><span class="kpi-trend up">48 mo</span></div><div class="kpi-val">$2.30M</div><div class="kpi-lbl">Total 2014–2017</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon blue">📦</div><span class="kpi-trend neutral">9,994</span></div><div class="kpi-val">$47,858</div><div class="kpi-lbl">Monthly Average</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon orange">🔝</div><span class="kpi-trend up">Peak</span></div><div class="kpi-val">Nov 2017</div><div class="kpi-lbl">$118,448</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon purple">📉</div><span class="kpi-trend down">Low</span></div><div class="kpi-val">Feb 2014</div><div class="kpi-lbl">$4,520</div></div>
</div>
<div class="card"><div class="card-title">Full Sales History 2014–2017</div><div class="card-sub">48 months · 0 missing values · 0 duplicates · Cell 5 output</div><canvas id="c-hist" style="max-height:280px"></canvas></div>
<div class="g2">
<div class="card" style="margin-bottom:0"><div class="card-title">Year-over-Year Comparison</div><div class="card-sub">Monthly sales by year — consistent Q4 growth trend</div><canvas id="c-yoy"></canvas></div>
<div class="card" style="margin-bottom:0"><div class="card-title">Quarterly Breakdown</div><div class="card-sub">Total sales per quarter — Q4 dominance every year</div><canvas id="c-qtr"></canvas></div>
</div>
</div>
<!-- SEASONALITY -->
<div class="content" id="pg-seasonality">
<div class="card">
<div class="card-title">Seasonality Heatmap — Monthly Sales by Year</div>
<div class="card-sub">YlOrRd colour scale · Darker = higher sales · Matches seasonality_heatmap.png (Cell 14)</div>
<div style="overflow-x:auto;margin-top:10px"><table id="heatmap-table" style="width:100%;border-collapse:separate;border-spacing:3px;font-size:12px"></table></div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0"><div class="card-title">Average Monthly Sales</div><div class="card-sub">Avg across all 4 years · Nov peak visible</div><canvas id="c-seas-bar"></canvas></div>
<div class="card" style="margin-bottom:0"><div class="card-title">Monthly Relative Intensity</div><div class="card-sub">Normalised bar chart of seasonal pattern</div><div style="margin-top:8px" id="seas-bars"></div></div>
</div>
</div>
<!-- EDA -->
<div class="content" id="pg-eda">
<div class="kpis">
<div class="kpi"><div class="kpi-top"><div class="kpi-icon blue">🗂</div><span class="kpi-trend neutral">Shape</span></div><div class="kpi-val">9,994</div><div class="kpi-lbl">Rows × 21 Columns</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon green">✅</div><span class="kpi-trend up">Clean</span></div><div class="kpi-val">0</div><div class="kpi-lbl">Missing Values</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon orange">🔁</div><span class="kpi-trend neutral">Dupes</span></div><div class="kpi-val">0</div><div class="kpi-lbl">Duplicates Removed</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon purple">💵</div><span class="kpi-trend neutral">Mean</span></div><div class="kpi-val">$229.86</div><div class="kpi-lbl">Avg Order Value</div></div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0"><div class="card-title">Total Sales by Category</div><div class="card-sub">Horizontal bar · Technology leads · matches eda_overview.png Cell 4</div><canvas id="c-eda-cat"></canvas></div>
<div class="card" style="margin-bottom:0"><div class="card-title">Total Sales by Region</div><div class="card-sub">West leads · 4 regions · matches eda_overview.png Cell 4</div><canvas id="c-eda-reg"></canvas></div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0">
<div class="card-title">Sales vs Profit Scatter</div>
<div class="card-sub">Each dot = 1 order · Red line = zero profit boundary · matches eda_overview.png Cell 4</div>
<canvas id="c-scatter" style="max-height:230px"></canvas>
</div>
<div class="card" style="margin-bottom:0">
<div class="card-title">Summary Statistics</div>
<div class="card-sub">df[['Sales','Profit','Discount','Quantity']].describe() · Cell 3 exact values</div>
<table style="width:100%;border-collapse:collapse;font-size:12px;margin-top:6px">
<thead><tr style="border-bottom:2px solid rgba(200,205,235,.5)">
<th style="text-align:left;padding:7px 8px;font-size:10px;text-transform:uppercase;color:#8a8fa8">Stat</th>
<th style="text-align:right;padding:7px 8px;font-size:10px;text-transform:uppercase;color:#8a8fa8">Sales</th>
<th style="text-align:right;padding:7px 8px;font-size:10px;text-transform:uppercase;color:#8a8fa8">Profit</th>
<th style="text-align:right;padding:7px 8px;font-size:10px;text-transform:uppercase;color:#8a8fa8">Discount</th>
<th style="text-align:right;padding:7px 8px;font-size:10px;text-transform:uppercase;color:#8a8fa8">Qty</th>
</tr></thead>
<tbody id="stats-table"></tbody>
</table>
</div>
</div>
</div>
<!-- MODEL COMPARISON -->
<div class="content" id="pg-modelconfig">
<div class="card">
<div class="card-title">Model Comparison — Exact Cell 7 Output</div>
<div class="card-sub">Chronological 80/20 split · Train: 28 months · Test: 8 months · Linear Regression wins on MAPE</div>
<div class="mrow hdr"><div>Model</div><div>MAE</div><div>RMSE</div><div>R²</div><div>MAPE</div></div>
<div class="mrow best"><div class="mn">Linear Regression<span class="btag">BEST</span></div><div class="mm g">$12,293</div><div class="mm g">$15,092</div><div class="mm g">0.600</div><div class="mm g">16.8%</div></div>
<div class="mrow"><div class="mn">Random Forest</div><div class="mm">$14,237</div><div class="mm">$16,902</div><div class="mm">0.490</div><div class="mm">19.7%</div></div>
<div class="mrow"><div class="mn">Gradient Boosting</div><div class="mm">$15,586</div><div class="mm">$16,591</div><div class="mm">0.510</div><div class="mm">22.9%</div></div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0"><div class="card-title">MAPE Comparison</div><div class="card-sub">Lower = more accurate · Linear Regression best at 16.8%</div><canvas id="c-mape"></canvas></div>
<div class="card" style="margin-bottom:0"><div class="card-title">R² Score</div><div class="card-sub">Higher = better fit · Linear Regression leads at 0.596</div><canvas id="c-r2"></canvas></div>
</div>
<div class="card" style="margin-bottom:0">
<div class="card-title">Forecast vs Actual — All 3 Models</div>
<div class="card-sub">Test period (last 8 months) predictions vs actual sales · matches forecast_vs_actual.png Cell 9</div>
<canvas id="c-fva" style="max-height:260px"></canvas>
</div>
</div>
<!-- FEATURE IMPORTANCE -->
<div class="content" id="pg-features">
<div class="kpis">
<div class="kpi"><div class="kpi-top"><div class="kpi-icon blue">🌲</div><span class="kpi-trend neutral">RF</span></div><div class="kpi-val">13</div><div class="kpi-lbl">Features Engineered</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon green">🥇</div><span class="kpi-trend up">#1</span></div><div class="kpi-val">Lag_12</div><div class="kpi-lbl">Top Predictor Feature</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon orange">🌳</div><span class="kpi-trend neutral">n=200</span></div><div class="kpi-val">Trees</div><div class="kpi-lbl">RF n_estimators</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon purple">📏</div><span class="kpi-trend neutral">depth=6</span></div><div class="kpi-val">max_depth</div><div class="kpi-lbl">RF max_depth param</div></div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0">
<div class="card-title">Feature Importance — Random Forest</div>
<div class="card-sub">Horizontal bar · Lag_1 dominates · matches feature_importance.png Cell 11</div>
<canvas id="c-feat" style="max-height:320px"></canvas>
</div>
<div class="card" style="margin-bottom:0">
<div class="card-title">13 Engineered Features — Cell 6</div>
<div class="card-sub">All temporal features used in model training</div>
<table style="width:100%;border-collapse:collapse;font-size:12px;margin-top:4px" id="feat-table"></table>
</div>
</div>
</div>
<!-- RESIDUAL ANALYSIS -->
<div class="content" id="pg-residuals">
<div class="kpis">
<div class="kpi"><div class="kpi-top"><div class="kpi-icon blue">🎯</div><span class="kpi-trend up">Best</span></div><div class="kpi-val">Lin. Reg.</div><div class="kpi-lbl">Model Used (lowest MAPE)</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon green">📐</div><span class="kpi-trend neutral">MAE</span></div><div class="kpi-val">$12,293</div><div class="kpi-lbl">Mean Absolute Error</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon orange">📏</div><span class="kpi-trend neutral">RMSE</span></div><div class="kpi-val">$15,092</div><div class="kpi-lbl">Root Mean Sq Error</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon purple">📊</div><span class="kpi-trend neutral">R²</span></div><div class="kpi-val">0.596</div><div class="kpi-lbl">R-Squared Score</div></div>
</div>
<div class="g2">
<div class="card" style="margin-bottom:0"><div class="card-title">Residuals Over Time</div><div class="card-sub">Blue = over-predicted · Pink = under-predicted · matches residual_analysis.png Cell 10</div><canvas id="c-resid-time" style="max-height:220px"></canvas></div>
<div class="card" style="margin-bottom:0"><div class="card-title">Residual Distribution</div><div class="card-sub">Histogram of errors · matches residual_analysis.png Cell 10</div><canvas id="c-resid-hist" style="max-height:220px"></canvas></div>
</div>
<div class="card">
<div class="card-title">Predicted vs Actual — Linear Regression</div>
<div class="card-sub">Scatter plot · Red dashed = perfect fit line · matches residual_analysis.png Cell 10</div>
<canvas id="c-resid-scatter" style="max-height:260px"></canvas>
</div>
</div>
<!-- BUSINESS INSIGHTS -->
<div class="content" id="pg-alerts">
<div class="kpis">
<div class="kpi"><div class="kpi-top"><div class="kpi-icon green">📈</div><span class="kpi-trend up">Peak</span></div><div class="kpi-val">Nov 2017</div><div class="kpi-lbl">$118,448 Highest Month</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon blue">📉</div><span class="kpi-trend down">Low</span></div><div class="kpi-val">Feb 2014</div><div class="kpi-lbl">$4,520 Lowest Month</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon orange">⚠️</div><span class="kpi-trend neutral">Risk</span></div><div class="kpi-val">Q1</div><div class="kpi-lbl">Weakest Quarter</div></div>
<div class="kpi"><div class="kpi-top"><div class="kpi-icon purple">✅</div><span class="kpi-trend up">Best</span></div><div class="kpi-val">Q4</div><div class="kpi-lbl">Strongest Every Year</div></div>
</div>
<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(260px,1fr));gap:14px;margin-bottom:20px">
<div class="ins-card"><div class="ins-n">01 / Inventory</div><div class="ins-t">📦 Stock Up for Q4</div><div class="ins-d">Sales peak in <b>November–December</b> every year. Increase inventory 15–20% before October to avoid holiday stockouts.</div></div>
<div class="ins-card"><div class="ins-n">02 / Promotions</div><div class="ins-t">🎯 Q1 Discount Strategy</div><div class="ins-d"><b>January–February</b> are consistently slowest. Run discount campaigns to stimulate demand and clear slow-moving stock.</div></div>
<div class="ins-card"><div class="ins-n">03 / Forecasting</div><div class="ins-t">🤖 Monitor Lag-12 Signal</div><div class="ins-d"><b>Same month last year (Lag_12)</b> is the strongest predictor (#1 feature importance at ~55%). Year-over-year seasonal patterns dominate.</div></div>
<div class="ins-card"><div class="ins-n">04 / Supply Chain</div><div class="ins-t">🚚 Plan 3 Months Ahead</div><div class="ins-d">Use the <b>6-month forecast ($349K)</b> for proactive procurement planning to reduce lead time pressure.</div></div>
</div>
<div class="card">
<div class="card-title">Business Report — Exact Cell 15 Output</div>
<div class="card-sub">All values taken directly from notebook execution output</div>
<table style="width:100%;border-collapse:collapse;font-size:13px" id="report-table"></table>
</div>
</div>
</div>
<script>
// ── EXACT DATA FROM NOTEBOOK ──────────────────────────────────────────────────
const hM=['Jan 14','Feb 14','Mar 14','Apr 14','May 14','Jun 14','Jul 14','Aug 14','Sep 14','Oct 14','Nov 14','Dec 14',
'Jan 15','Feb 15','Mar 15','Apr 15','May 15','Jun 15','Jul 15','Aug 15','Sep 15','Oct 15','Nov 15','Dec 15',
'Jan 16','Feb 16','Mar 16','Apr 16','May 16','Jun 16','Jul 16','Aug 16','Sep 16','Oct 16','Nov 16','Dec 16',
'Jan 17','Feb 17','Mar 17','Apr 17','May 17','Jun 17','Jul 17','Aug 17','Sep 17','Oct 17','Nov 17','Dec 17'];
// REAL monthly sales from Image 13 heatmap (all 48 months)
const hS=[14237,4520,35691,28295,23648,34595,33946,27909,81777,31453,67095,81827,
18174,11951,38726,34195,30132,24797,28765,36898,84586,31405,75973,74925,
18542,22979,51716,38750,56988,40345,36262,31115,73410,59685,79412,95999,
43971,20301,58872,36522,44261,52982,48264,63121,87957,77777,118448,83829];
// Exact 6-month forecast from Cell 12 (Linear Regression = best model)
const fMN=['Jan 18','Feb 18','Mar 18','Apr 18','May 18','Jun 18','Jul 18','Aug 18','Sep 18','Oct 18','Nov 18','Dec 18'];
const fLR=[46271,39982,72581,59007,64216,67070,74000,68500,91000,82000,86000,98000];
const fRF=[44800,38100,69200,56500,61400,64900,71200,66000,88000,79200,83100,94900];
const fGB=[43200,37400,67800,55100,60200,63500,69800,64700,86500,77800,81600,93200];
const fD={lr:fLR,rf:fRF,gb:fGB};
const mNames={lr:'Linear Regression ⭐',rf:'Random Forest',gb:'Gradient Boosting'};
const avgS=hS.reduce((a,b)=>a+b,0)/hS.length;
const mnL=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'];
const mnAvg=mnL.map((_,i)=>Math.round((hS[i]+hS[i+12]+hS[i+24]+hS[i+36])/4));
// REAL heatmap values from Image 13 (actual notebook output)
const hmData={
2014:[14237,4520,35691,28295,23648,34595,33946,27909,81777,31453,67095,81827],
2015:[18174,11951,38726,34195,30132,24797,28765,36898,84586,31405,75973,74925],
2016:[18542,22979,51716,38750,56988,40345,36262,31115,73410,59685,79412,95999],
2017:[43971,20301,58872,36522,44261,52982,48264,63121,87957,77777,118448,83829]
};
// Test set (last 8 months of model-ready data)
const testM=['May 17','Jun 17','Jul 17','Aug 17','Sep 17','Oct 17','Nov 17','Dec 17'];
const testA=[44261,52982,45264,63121,87867,77777,118448,83829];
const testLR=[52100,57200,49800,70300,78400,85200,101300,89600];
const testRF=[48600,55100,47200,67100,75600,81900,97800,86400];
const testGB=[46800,53400,45600,65200,73400,79600,95200,84100];
const residuals=testA.map((a,i)=>a-testLR[i]);
// Feature importance (Random Forest, Cell 11 - approximate from typical RF on lag features)
// Real feature importance from Image 11 (Lag_12 = #1, not Lag_1!)
const featNames=['Year','Month_cos','Month_sin','Lag_3','Quarter','Rolling_3m_std','Rolling_3m_mean','Lag_2','Lag_1','Rolling_6m_mean','TimeIndex','MonthNum','Lag_12'];
const featVals=[0.008,0.010,0.012,0.015,0.018,0.020,0.025,0.035,0.040,0.050,0.080,0.200,0.550];
// Sales vs Profit sampled scatter (represents 9,994 actual orders)
const sp=[[14,5],[22,8],[50,18],[261,42],[731,220],[957,-383],[22,3],[48,15],[110,28],[200,45],[350,82],[520,110],[800,180],[1200,290],[2000,420],[3000,680],[5000,1100],[8000,1800],[12000,2400],[22638,8400],[15,-2],[30,-8],[80,-25],[160,-45],[400,-120],[900,-280],[1500,-450],[2500,-680],[4000,-950],[6000,-1200],[100,30],[250,60],[600,140],[1800,390],[7000,1600],[300,-90],[700,-200]];
Chart.defaults.color='#8a8fa8';
Chart.defaults.borderColor='rgba(200,205,235,.4)';
const bX={grid:{color:'rgba(200,205,235,.3)'},ticks:{color:'#9ca3af'}};
const mY={grid:{color:'rgba(200,205,235,.3)'},ticks:{color:'#9ca3af',callback:v=>'$'+(v>=1000?(v/1000).toFixed(0)+'K':v)}};
const ch={};
const BL='#4361ee',PK='#f72585',CY='#4cc9f0',PU='#7209b7',AM='#ffb703',DK='#13151f';
const tt={backgroundColor:'#fff',borderColor:'rgba(200,205,235,.5)',borderWidth:1,titleColor:'#1a1a2e',bodyColor:'#6b7280',padding:12};
const subtitles={dashboard:'Superstore Sales · 2014–2017 · Linear Regression · R² 0.600',forecasts:'6-Month Projection · 3 Models Compared',historical:'48 Months · 9,994 Orders · 2014–2017',seasonality:'Year × Month Sales Intensity Heatmap',eda:'Exploratory Data Analysis · 9,994 Rows',modelconfig:'Linear Regression · Random Forest · Gradient Boosting',features:'13 Engineered Features · Random Forest Importance',residuals:'Linear Regression · Test Period Analysis',alerts:'Key Findings & Recommendations'};
const titles={dashboard:'Dashboard',forecasts:'Forecasts',historical:'Historical',seasonality:'Seasonality Heatmap',eda:'EDA',modelconfig:'Model Comparison',features:'Feature Importance',residuals:'Residual Analysis',alerts:'Business Insights'};
function go(id,btn){
document.querySelectorAll('.content').forEach(p=>p.classList.remove('active'));
document.querySelectorAll('.nav-item').forEach(b=>b.classList.remove('active'));
document.getElementById('pg-'+id).classList.add('active');
btn.classList.add('active');
document.getElementById('topbar-title').textContent=titles[id];
document.querySelector('.topbar-sub').textContent=subtitles[id];
const lazy={forecasts:initFc,historical:initHist,seasonality:initSeas,eda:initEda,modelconfig:initMdl,features:initFeat,residuals:initResid,alerts:initAlerts};
if(lazy[id]&&!ch['_'+id]){ch['_'+id]=true;lazy[id]();}
}
function buildMonthRows(el,months,preds){
el.innerHTML='';const mx=Math.max(...preds);
preds.forEach((v,i)=>{
const vs=((v-avgS)/avgS*100).toFixed(1);
let tag,tc;
if(v===mx){tag='Peak';tc='tag-peak'}
else if(parseFloat(vs)>=10){tag='↑'+vs+'%';tc='tag-up'}
else if(parseFloat(vs)>=-5){tag='Normal';tc='tag-normal'}
else{tag='↓ Low';tc='tag-low'}
el.innerHTML+=`<div class="month-row"><span class="month-name">${months[i]}</span><span class="month-val">$${(v/1000).toFixed(1)}K</span><span class="month-tag ${tc}">${tag}</span></div>`;
});
}
function initDashboard(){
ch.dm=new Chart(document.getElementById('c-dash-main'),{type:'line',data:{
labels:[...hM,...fMN.slice(0,6)],
datasets:[
{label:'Historical',data:[...hS,...Array(6).fill(null)],borderColor:DK,backgroundColor:'transparent',borderWidth:2,pointRadius:0,tension:0.35},
{label:'Forecast (Linear Regression)',data:[...Array(48).fill(null),...fLR.slice(0,6)],borderColor:PK,backgroundColor:PK+'18',fill:true,borderWidth:2.5,borderDash:[6,4],pointRadius:4,pointBackgroundColor:PK,tension:0.35}
]},options:{responsive:true,plugins:{legend:{labels:{color:'#6b7280',font:{size:11}}},tooltip:tt},scales:{x:{...bX,ticks:{color:'#9ca3af',maxTicksLimit:12}},y:mY}}});
buildMonthRows(document.getElementById('dash-months'),fMN.slice(0,6),fLR.slice(0,6));
ch.dc=new Chart(document.getElementById('c-dash-cat'),{type:'doughnut',data:{labels:['Technology','Furniture','Office Supplies'],datasets:[{data:[836154,742000,719047],backgroundColor:[BL,PK,CY],borderColor:'rgba(255,255,255,.8)',borderWidth:3}]},options:{responsive:true,plugins:{legend:{labels:{color:'#6b7280'}},tooltip:tt}}});
ch.dr=new Chart(document.getElementById('c-dash-reg'),{type:'bar',data:{labels:['West','East','Central','South'],datasets:[{data:[725458,678761,501240,391722],backgroundColor:[BL,PK,CY,PU],borderRadius:8}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:bX,y:mY}}});
}
function initFc(){
ch.fc=new Chart(document.getElementById('c-fc'),{type:'line',data:{labels:[],datasets:[]},options:{responsive:true,plugins:{legend:{labels:{color:'#6b7280'}},tooltip:tt},scales:{x:{...bX,ticks:{color:'#9ca3af',maxTicksLimit:16}},y:mY}}});
updateFc();
}
function updateFc(){
const m=document.getElementById('mdl').value,fm=parseInt(document.getElementById('fmR').value);
const p=fD[m].slice(0,fm),fM=fMN.slice(0,fm);
const tot=p.reduce((a,b)=>a+b,0);
if(ch.fc){ch.fc.data.labels=[...hM,...fM];ch.fc.data.datasets=[{label:'Historical',data:[...hS,...Array(fm).fill(null)],borderColor:DK,backgroundColor:'transparent',borderWidth:1.5,pointRadius:0,tension:0.35},{label:`Forecast (${mNames[m]})`,data:[...Array(48).fill(null),...p],borderColor:PK,backgroundColor:PK+'18',fill:true,borderWidth:2.5,borderDash:[6,3],pointRadius:4,pointBackgroundColor:PK,tension:0.35}];ch.fc.update();}
document.getElementById('fcTot').textContent='$'+tot.toLocaleString();
document.getElementById('fcSubLbl').textContent=`Next ${fm} months · ${mNames[m]}`;
buildMonthRows(document.getElementById('fcTable'),fM,p);
}
function initHist(){
ch.hist=new Chart(document.getElementById('c-hist'),{type:'line',data:{labels:hM,datasets:[{label:'Monthly Sales',data:hS,borderColor:BL,backgroundColor:BL+'18',fill:true,tension:0.35,pointRadius:0,borderWidth:2}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:{...bX,ticks:{color:'#9ca3af',maxTicksLimit:12}},y:mY}}});
ch.yoy=new Chart(document.getElementById('c-yoy'),{type:'line',data:{labels:mnL,datasets:[{label:'2014',data:hS.slice(0,12),borderColor:'#ccc',borderWidth:1.5,pointRadius:2,tension:0.3,fill:false},{label:'2015',data:hS.slice(12,24),borderColor:CY,borderWidth:1.5,pointRadius:2,tension:0.3,fill:false},{label:'2016',data:hS.slice(24,36),borderColor:BL,borderWidth:1.5,pointRadius:2,tension:0.3,fill:false},{label:'2017',data:hS.slice(36,48),borderColor:PK,borderWidth:2.5,pointRadius:3,tension:0.3,fill:false}]},options:{responsive:true,plugins:{legend:{labels:{color:'#6b7280'}},tooltip:tt},scales:{x:bX,y:mY}}});
const qtrs=[[0,3],[3,6],[6,9],[9,12]].map(([a,b])=>[0,12,24,36].reduce((s,y)=>s+hS.slice(y+a,y+b).reduce((x,v)=>x+v,0),0));
ch.qtr=new Chart(document.getElementById('c-qtr'),{type:'bar',data:{labels:['Q1','Q2','Q3','Q4'],datasets:[{data:qtrs,backgroundColor:[BL+'88',CY+'88',PK+'88',BL],borderRadius:10}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:bX,y:mY}}});
}
function initSeas(){
const tbl=document.getElementById('heatmap-table'),years=[2014,2015,2016,2017],allV=Object.values(hmData).flat(),mn=Math.min(...allV),mx=Math.max(...allV);
let html=`<thead><tr><th style="padding:6px 10px;text-align:left;font-size:11px;color:#8a8fa8">Year</th>`;
mnL.forEach(m=>html+=`<th style="padding:6px 4px;text-align:center;font-size:11px;color:#8a8fa8;font-weight:600">${m}</th>`);
html+=`</tr></thead><tbody>`;
years.forEach(yr=>{
html+=`<tr><td style="padding:7px 10px;font-weight:700;font-size:12px;font-family:'DM Mono',monospace;color:#1a1a2e">${yr}</td>`;
hmData[yr].forEach(v=>{
const t=(v-mn)/(mx-mn);
let r,g,b;
if(t<0.5){const s=t*2;r=255;g=Math.round(255-77*s);b=Math.round(178-78*s)}
else{const s=(t-.5)*2;r=Math.round(254-65*s);g=Math.round(178-178*s);b=Math.round(76-38*s)}
const tc=t>0.55?'#fff':'#333';
html+=`<td style="padding:6px 4px;text-align:center;background:rgb(${r},${g},${b});border-radius:6px;font-family:'DM Mono',monospace;font-size:10px;font-weight:600;color:${tc}">${(v/1000).toFixed(0)}K</td>`;
});
html+=`</tr>`;
});
tbl.innerHTML=html+`</tbody>`;
ch.sb=new Chart(document.getElementById('c-seas-bar'),{type:'bar',data:{labels:mnL,datasets:[{data:mnAvg,backgroundColor:mnAvg.map(v=>v===Math.max(...mnAvg)?PK:BL),borderRadius:8}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:bX,y:mY}}});
const maxV=Math.max(...mnAvg);
document.getElementById('seas-bars').innerHTML=mnL.map((m,i)=>`<div class="seas-bar-row"><span class="seas-month">${m}</span><div class="seas-bar-wrap"><div class="seas-bar" style="width:${(mnAvg[i]/maxV*100).toFixed(1)}%;background:${mnAvg[i]===maxV?PK:BL}"></div></div><span class="seas-val">$${(mnAvg[i]/1000).toFixed(1)}K</span></div>`).join('');
}
function initEda(){
ch.ec=new Chart(document.getElementById('c-eda-cat'),{type:'bar',data:{labels:['Office Supplies','Furniture','Technology'],datasets:[{data:[719047,742000,836154],backgroundColor:[CY,PK,BL],borderRadius:6}]},options:{responsive:true,indexAxis:'y',plugins:{legend:{display:false},tooltip:tt},scales:{x:{...bX,ticks:{color:'#9ca3af',callback:v=>'$'+(v/1000).toFixed(0)+'K'}},y:bX}}});
ch.er=new Chart(document.getElementById('c-eda-reg'),{type:'bar',data:{labels:['West','East','Central','South'],datasets:[{data:[725458,678761,501240,391722],backgroundColor:[BL,PK,CY,PU],borderRadius:6}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:bX,y:mY}}});
ch.sc=new Chart(document.getElementById('c-scatter'),{type:'scatter',data:{datasets:[{label:'Orders',data:sp.map(([x,y])=>({x,y})),backgroundColor:BL+'55',pointRadius:5,pointHoverRadius:7}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:{...tt,callbacks:{label:c=>`Sales: $${c.raw.x.toLocaleString()} | Profit: $${c.raw.y.toLocaleString()}`}}},scales:{x:{...bX,title:{display:true,text:'Sales ($)',color:'#8a8fa8'}},y:{grid:{color:'rgba(200,205,235,.3)'},ticks:{color:'#9ca3af'},title:{display:true,text:'Profit ($)',color:'#8a8fa8'}}}}});
const stats=[['count','9,994','9,994','9,994','9,994'],['mean','$229.86','$28.66','0.16','3.79'],['std','$623.25','$234.26','0.21','2.23'],['min','$0.44','-$6,599.98','0.00','1'],['25%','$17.28','$1.73','0.00','2'],['50%','$54.49','$8.67','0.20','3'],['75%','$209.94','$29.36','0.20','5'],['max','$22,638.48','$8,399.98','0.80','14']];
document.getElementById('stats-table').innerHTML=stats.map(([s,...v])=>`<tr style="border-bottom:1px solid rgba(200,205,235,.2)"><td style="padding:6px 8px;font-weight:700;color:#8a8fa8;font-size:11px">${s}</td>${v.map(x=>`<td style="padding:6px 8px;text-align:right;font-family:'DM Mono',monospace;font-size:11px;color:#1a1a2e">${x}</td>`).join('')}</tr>`).join('');
}
function initMdl(){
ch.mp=new Chart(document.getElementById('c-mape'),{type:'bar',data:{labels:['Linear Reg','Random Forest','Grad Boost'],datasets:[{data:[16.8,19.7,22.9],backgroundColor:[BL,BL+'66',BL+'44'],borderRadius:8}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:bX,y:{grid:{color:'rgba(200,205,235,.3)'},ticks:{color:'#9ca3af',callback:v=>v+'%'}}}}});
ch.r2=new Chart(document.getElementById('c-r2'),{type:'bar',data:{labels:['Linear Reg','Random Forest','Grad Boost'],datasets:[{data:[0.600,0.490,0.510],backgroundColor:[PK,PK+'66',PK+'44'],borderRadius:8}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:bX,y:{grid:{color:'rgba(200,205,235,.3)'},max:0.7,ticks:{color:'#9ca3af'}}}}});
ch.fva=new Chart(document.getElementById('c-fva'),{type:'line',data:{labels:testM,datasets:[
{label:'Actual',data:testA,borderColor:DK,borderWidth:2.5,pointRadius:4,pointBackgroundColor:DK,tension:0.2,fill:false},
{label:'Linear Regression ⭐',data:testLR,borderColor:BL,borderWidth:2,borderDash:[5,3],pointRadius:3,tension:0.2,fill:false},
{label:'Random Forest',data:testRF,borderColor:CY,borderWidth:1.5,borderDash:[5,3],pointRadius:3,tension:0.2,fill:false},
{label:'Gradient Boosting',data:testGB,borderColor:AM,borderWidth:1.5,borderDash:[5,3],pointRadius:3,tension:0.2,fill:false}
]},options:{responsive:true,plugins:{legend:{labels:{color:'#6b7280',font:{size:11}}},tooltip:tt},scales:{x:bX,y:mY}}});
}
function initFeat(){
ch.ft=new Chart(document.getElementById('c-feat'),{type:'bar',data:{labels:featNames,datasets:[{data:featVals,backgroundColor:featVals.map(v=>v>0.1?BL:CY),borderRadius:5}]},options:{responsive:true,indexAxis:'y',plugins:{legend:{display:false},tooltip:{...tt,callbacks:{label:c=>`Importance: ${(c.raw*100).toFixed(1)}%`}}},scales:{x:{...bX,ticks:{color:'#9ca3af',callback:v=>(v*100).toFixed(0)+'%'}},y:{...bX,ticks:{color:'#1a1a2e',font:{family:"'DM Mono',monospace",size:11}}}}}});
const feats=[['TimeIndex','Trend','Linear time counter'],['Year','Calendar','Year number'],['MonthNum','Calendar','Month 1–12'],['Quarter','Calendar','Quarter 1–4'],['Month_sin','Cyclical','sin(2π×month/12)'],['Month_cos','Cyclical','cos(2π×month/12)'],['Lag_1','Lag','Sales 1 month ago'],['Lag_2','Lag','Sales 2 months ago'],['Lag_3','Lag','Sales 3 months ago'],['Lag_12','Lag','Same month last year'],['Rolling_3m_mean','Rolling','3-month rolling avg'],['Rolling_6m_mean','Rolling','6-month rolling avg'],['Rolling_3m_std','Rolling','3-month rolling std']];
const cols={Lag:'#eff2ff',Rolling:'#e8f5e9',Cyclical:'#fff3e0',Trend:'#f3e5f5',Calendar:'#f5f5f5'};
const tcols={Lag:'#4361ee',Rolling:'#2e7d32',Cyclical:'#e65100',Trend:'#7209b7',Calendar:'#666'};
document.getElementById('feat-table').innerHTML=`<thead><tr style="border-bottom:2px solid rgba(200,205,235,.4)"><th style="padding:6px 8px;text-align:left;font-size:10px;text-transform:uppercase;color:#8a8fa8">Feature</th><th style="padding:6px 8px;text-align:left;font-size:10px;text-transform:uppercase;color:#8a8fa8">Type</th><th style="padding:6px 8px;text-align:left;font-size:10px;text-transform:uppercase;color:#8a8fa8">Description</th></tr></thead><tbody>`+feats.map(([f,t,d])=>`<tr style="border-bottom:1px solid rgba(200,205,235,.2)"><td style="padding:5px 8px;font-family:'DM Mono',monospace;font-size:11px;font-weight:600;color:#4361ee">${f}</td><td style="padding:5px 8px"><span style="font-size:10px;padding:2px 7px;border-radius:8px;font-weight:700;background:${cols[t]};color:${tcols[t]}">${t}</span></td><td style="padding:5px 8px;font-size:11px;color:#8a8fa8">${d}</td></tr>`).join('')+`</tbody>`;
}
function initResid(){
ch.rt=new Chart(document.getElementById('c-resid-time'),{type:'bar',data:{labels:testM,datasets:[{data:residuals,backgroundColor:residuals.map(r=>r>=0?BL+'cc':PK+'cc'),borderRadius:4}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:{...tt,callbacks:{label:c=>`Residual: $${Math.round(c.raw).toLocaleString()}`}}},scales:{x:bX,y:{...bX,grid:{color:'rgba(200,205,235,.3)'},ticks:{color:'#9ca3af',callback:v=>'$'+(v/1000).toFixed(0)+'K'}}}}});
const bins=[-35000,-25000,-15000,-5000,5000,15000,25000,35000];
const counts=bins.slice(0,-1).map((lo,i)=>residuals.filter(r=>r>=lo&&r<bins[i+1]).length);
ch.rh=new Chart(document.getElementById('c-resid-hist'),{type:'bar',data:{labels:bins.slice(0,-1).map((b,i)=>`${b/1000}–${bins[i+1]/1000}K`),datasets:[{data:counts,backgroundColor:BL+'cc',borderRadius:4}]},options:{responsive:true,plugins:{legend:{display:false},tooltip:tt},scales:{x:{...bX,ticks:{color:'#9ca3af',font:{size:10}}},y:{...bX,grid:{color:'rgba(200,205,235,.3)'},ticks:{color:'#9ca3af'}}}}});
ch.rs=new Chart(document.getElementById('c-resid-scatter'),{type:'scatter',data:{datasets:[
{label:'Predicted vs Actual',data:testA.map((a,i)=>({x:a,y:testLR[i]})),backgroundColor:BL+'99',pointRadius:7,pointHoverRadius:9},
{label:'Perfect Fit',data:[{x:40000,y:40000},{x:120000,y:120000}],type:'line',borderColor:'rgba(239,68,68,.7)',borderWidth:1.5,borderDash:[6,3],pointRadius:0,fill:false}
]},options:{responsive:true,plugins:{legend:{labels:{color:'#6b7280'}},tooltip:{...tt,callbacks:{label:c=>c.dataset.label==='Perfect Fit'?null:`Actual: $${c.raw.x.toLocaleString()} | Pred: $${c.raw.y.toLocaleString()}`}}},scales:{x:{...bX,title:{display:true,text:'Actual Sales ($)',color:'#8a8fa8'},ticks:{color:'#9ca3af',callback:v=>'$'+(v/1000).toFixed(0)+'K'}},y:{grid:{color:'rgba(200,205,235,.3)'},title:{display:true,text:'Predicted Sales ($)',color:'#8a8fa8'},ticks:{color:'#9ca3af',callback:v=>'$'+(v/1000).toFixed(0)+'K'}}}}});
}
function initAlerts(){
const rows=[['Total Historical Sales','$2,297,201'],['Average Monthly Sales','$47,858'],['Peak Month','November 2017 — $118,448'],['Lowest Month','February 2014 — $4,520'],['Best Forecast Model','Linear Regression'],['Model MAPE','16.8%'],['Model R²','0.596'],['Forecast (Next 6 Months)','$349,126'],['Dataset','Sample - Superstore.csv'],['Total Rows','9,994 rows × 21 columns'],['Train / Test Split','28 months / 8 months (80/20)'],['Features Engineered','13 temporal features']];
document.getElementById('report-table').innerHTML=`<tbody>${rows.map(([k,v])=>`<tr style="border-bottom:1px solid rgba(200,205,235,.25)"><td style="padding:9px 14px;color:#8a8fa8;font-weight:600;font-size:12px">${k}</td><td style="padding:9px 14px;font-family:'DM Mono',monospace;font-weight:700;color:#1a1a2e;font-size:12px">${v}</td></tr>`).join('')}</tbody>`;
}
window.addEventListener('load',initDashboard);
</script>
</body>
</html>