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Dylan Huang
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tasks 8-12
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‎tests/chinook/dataset/task_11/ground_truth.md‎

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‎tests/chinook/dataset/task_11/task.txt‎

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Analyze seasonal purchasing trends by customer country and genre preference.
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Show country, genre, month, average purchase value, customer count, and seasonal
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trend indicator.

‎tests/chinook/dataset/task_12/ground_truth.md‎

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| customer_id | customer_name | genre_diversity_score | avg_track_price | total_spending | diversity_to_spending_ratio |
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| ----------- | --------------------- | --------------------- | ---------------------- | -------------- | --------------------------- |
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| 57 | Luis Rojas | 12 | 1.2268421052631579 | 46.62 | 0.25740025740025740026 |
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| 45 | Ladislav Kovács | 11 | 1.2005263157894737 | 45.62 | 0.24112231477422183253 |
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| 14 | Mark Philips | 10 | 0.99000000000000000000 | 37.62 | 0.26581605528973950027 |
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| 34 | João Fernandes | 10 | 1.0426315789473684 | 39.62 | 0.25239777889954568400 |
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| 3 | François Tremblay | 10 | 1.0426315789473684 | 39.62 | 0.25239777889954568400 |
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| 17 | Jack Smith | 10 | 1.0426315789473684 | 39.62 | 0.25239777889954568400 |
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| 37 | Fynn Zimmermann | 10 | 1.1478947368421053 | 43.62 | 0.22925263640531866116 |
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| 24 | Frank Ralston | 10 | 1.1478947368421053 | 43.62 | 0.22925263640531866116 |
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| 27 | Patrick Gray | 9 | 0.99000000000000000000 | 37.62 | 0.23923444976076555024 |
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| 23 | John Gordon | 9 | 0.99000000000000000000 | 37.62 | 0.23923444976076555024 |
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| 21 | Kathy Chase | 9 | 0.99000000000000000000 | 37.62 | 0.23923444976076555024 |
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| 41 | Marc Dubois | 9 | 0.99000000000000000000 | 37.62 | 0.23923444976076555024 |
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| 19 | Tim Goyer | 9 | 1.01631578947368421053 | 38.62 | 0.23303987571206628690 |
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| 43 | Isabelle Mercier | 9 | 1.0689473684210526 | 40.62 | 0.22156573116691285081 |
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| 7 | Astrid Gruber | 9 | 1.1215789473684211 | 42.62 | 0.21116846550915063351 |
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| 6 | Helena Holý | 9 | 1.3057894736842105 | 49.62 | 0.18137847642079806530 |
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| 54 | Steve Murray | 8 | 0.99000000000000000000 | 37.62 | 0.21265284423179160021 |
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| 15 | Jennifer Peterson | 8 | 1.01631578947368421053 | 38.62 | 0.20714655618850336613 |
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| 58 | Manoj Pareek | 8 | 1.01631578947368421053 | 38.62 | 0.20714655618850336613 |
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| 1 | Luís Gonçalves | 8 | 1.0426315789473684 | 39.62 | 0.20191822311963654720 |
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| 4 | Bjørn Hansen | 8 | 1.0426315789473684 | 39.62 | 0.20191822311963654720 |
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| 22 | Heather Leacock | 8 | 1.0426315789473684 | 39.62 | 0.20191822311963654720 |
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| 5 | František Wichterlová | 8 | 1.0689473684210526 | 40.62 | 0.19694731659281142294 |
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| 25 | Victor Stevens | 8 | 1.1215789473684211 | 42.62 | 0.18770530267480056312 |
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| 46 | Hugh O'Reilly | 8 | 1.2005263157894737 | 45.62 | 0.17536168347216133275 |
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| 59 | Puja Srivastava | 7 | 1.01777777777777777778 | 36.64 | 0.19104803493449781659 |
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| 56 | Diego Gutiérrez | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 50 | Enrique Muñoz | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 32 | Aaron Mitchell | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 10 | Eduardo Martins | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 13 | Fernanda Ramos | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 38 | Niklas Schröder | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 2 | Leonie Köhler | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 16 | Frank Harris | 7 | 0.99000000000000000000 | 37.62 | 0.18607123870281765019 |
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| 39 | Camille Bernard | 7 | 1.01631578947368421053 | 38.62 | 0.18125323666494044537 |
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| 51 | Joakim Johansson | 7 | 1.01631578947368421053 | 38.62 | 0.18125323666494044537 |
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| 40 | Dominique Lefebvre | 7 | 1.01631578947368421053 | 38.62 | 0.18125323666494044537 |
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| 20 | Dan Miller | 7 | 1.0426315789473684 | 39.62 | 0.17667844522968197880 |
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| 42 | Wyatt Girard | 7 | 1.0426315789473684 | 39.62 | 0.17667844522968197880 |
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| 48 | Johannes Van der Berg | 7 | 1.0689473684210526 | 40.62 | 0.17232890201870999508 |
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| 44 | Terhi Hämäläinen | 7 | 1.0952631578947368 | 41.62 | 0.16818837097549255166 |
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| 26 | Richard Cunningham | 7 | 1.2531578947368421 | 47.62 | 0.14699706005879882402 |
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| 35 | Madalena Sampaio | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 33 | Ellie Sullivan | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 47 | Lucas Mancini | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 36 | Hannah Schneider | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 11 | Alexandre Rocha | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 18 | Michelle Brooks | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 31 | Martha Silk | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |
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| 30 | Edward Francis | 6 | 0.99000000000000000000 | 37.62 | 0.15948963317384370016 |

‎tests/chinook/dataset/task_12/task.txt‎

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Find customers with the most diverse music taste and their spending correlation.
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Calculate genre diversity score, average track price, total spending, and
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diversity-to-spending ratio.

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