Let me clean the data manually to verify, then build the HTML.

Rows:
1001 Texas Q1 1240.00
1002 CA Q1 980
1003 TX Q1 312.50
1004 California Q1 1764.00
1005 Ohio Q1 455
1006 texas Q1 N/A → excluded (but counted in rows)
1007 OH Q1 1102.25
1008 California Q1 2310.00
1009 TX Q1 -450.00
1010 Texas Q1 870.10
1011 Ohio Q1 1999.99
1012 CA Q1 640.00
1008 duplicate → remove (1 duplicate removed)
1013 tx Q1 1580.00
1014 ohio Q1 225.75
1015 California Q1 3050.00
1016 TX Q2 990.00
1017 Ohio Q2 unknown → excluded
1018 CA Q2 1420.50
1019 Texas Q2 2075.00
1020 OH Q2 760.25
1021 California Q2 1111.11
1022 TX Q2 845.00
1023 Ohio Q2 -220.00
1024 texas Q2 1300.00
1025 CA Q2 2940.40
1026 Ohio Q2 515.00
1027 California Q2 1875.25
1028 TX Q2 N/A → excluded
1029 OH Q2 1660.00
1030 Texas Q2 730.90

Total received rows: 31. Duplicates removed: 1. Excluded: 3 (1006, 1017, 1028). Counted: 27.

Q1 by region:
Texas: 1240 + 312.50 + 2310 - 450 + 870.10 + 1580 = 
1240+312.50=1552.50; +2310=3862.50; -450=3412.50; +870.10=4282.60; +1580=5862.60
California: 1764 + 2310 + 3050 = 7124.00
Ohio: 455 + 1999.99 + 225.75 = 2680.74
CA is California: wait — 1002 CA 980, 1012 CA 640 → California. So California Q1: 1764+2310+3050+980+640 = 8744.00

Let me redo: normalize CA → California.
California Q1: 1002 (980), 1004 (1764), 1008 (2310), 1012 (640), 1015 (3050) = 980+1764=2744; +2310=5054; +640=5694; +3050=8744.00
Texas Q1: 1001 1240, 1003 312.50, 1009 -450, 1010 870.10, 1013 1580 → 5862.60
Ohio Q1: 1005 455, 1011 1999.99, 1014 225.75 = 2680.74
Q1 total: 8744+5862.60+2680.74 = 17287.34

Q2:
California: 1018 1420.50, 1021 1111.11, 1027 1875.25 → 4406.86
Texas: 1016 990, 1019 2075, 1022 845, 1024 1300, 1030 730.90 → 5940.90
Ohio: 1020 760.25, 1023 -220, 1026 515, 1029 1660 → 2715.25
Q2 total: 4406.86+5940.90+2715.25 = 13063.01

Totals: California 8744+4406.86=13150.86; Texas 5862.60+5940.90=11803.50; Ohio 2680.74+2715.25=5395.99
Grand total: 13150.86+11803.50+5395.99 = 30350.35
Q1+Q2: 17287.34+13063.01=30350.35 ✓

Top region: California, $13,150.86.

Now, the task says the dashboard must "clean this data" — the HTML should do the cleaning in JS (parse CSV inline), compute the numbers, and render. Output only the complete HTML file. I'll embed the CSV as a string and parse in JS.

Parsing: need a CSV parser that handles quotes. Date formats: YYYY-MM-DD, M/D/YYYY, "Jan 21, 2025" / "Feb 14, 2025". Amounts: quoted with commas, $, N/A, unknown, negatives.

Region normalization: map by lowercase: tx/texas→Texas; ca/california→California; oh/ohio→Ohio.

JS: parse CSV lines with a small quote-aware parser. Then process.

Chart: SVG bar chart of revenue by region (total). Values: CA 13150.86, TX 11803.50, OH 5395.99.

Write clean code. Bar chart: compute max, scale, draw rects with labels.

Let me write the file.

Date parsing:
- if /^\d{4}-\d{2}-\d{2}$/ → parts
- if /^\d{1,2}\/\d{1,2}\/\d{4}$/ → M/D/YYYY
- else month-name: /^(Jan|Feb|...)\s+\d{1,2},\s+\d{4}$/

Quarter from month: 1-3 is Q1, 4-6 is Q2.

Amount parsing: strip $ and commas and spaces; if N/A/unknown/empty → null; parse with parseFloat; check isFinite.

CSV parser: function parseLine(line) that handles quotes.

HTML structure: headline, data-quality panel, pivot table, chart.

Let me write it compactly but completely.