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Personl age 1 2 3 4 5 sex 19 female 18 male 28 male 33 male 32 male bmi 27.9 33.77 33 22.705 28.88 childrer smoker O yes 1 no 3 no 0 no O no region charges southwest 16884.9 southeast 1725.55 southeast 4449.46 northwest 21984.4 northwest 3866.85

Personl age 1 2 3 4 5 sex 19 female 18 male 28 male 33 male 32 male bmi 27.9 33.77 33 22.705 28.88 childrer smoker O yes 1 no 3 no 0 no O no region charges southwest 16884.9 southeast 1725.55 southeast 4449.46 northwest 21984.4 northwest 3866.85

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Use decision tree to further explore the dataset, where the dependent variable is ‘smoker’. Please explain the approach taken. [No more than 300 words]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 A Personl 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 B age с sex 19 female 18 male 28 male 33 male 32 male 31 F 46 F 37 F 37 M 60 F 25 M 62 female 23 male 56 female 27 male 19 male 52 female 23 male 56 male 30 male bmi D 27.9 33.77 33 22.705 28.88 25.74 33.44 27.74 29.83 25.84 26.22 26.29 34.4 39.82 42.13 24.6 30.78 23.845 40.3 35.3 E F childrer smoker 0 yes 1 no 3 no 0 no 0 no O no 1 no 3 no 2 no 0 no 0 no 0 yes O no O no O yes 1 no 1 no O no O no O yes G H charges southwest 16884.92 southeast 1725.552 southeast 4449.462 northwest 21984.47 northwest 3866.855 southeast 3756.622 southeast 8240.59 northwest 7281.506 northeast 6406.411 northwest 28923.14 northeast 2721.321 southeast 27808.73 southwest 1826.843 southeast 11090.72 southeast 39611.76 southwest 1837.237 northeast 10797.34 northeast 2395.172 southwest 10602.39 southwest 36837.47 ▾ region
Transcribed Image Text:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 A Personl 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 B age с sex 19 female 18 male 28 male 33 male 32 male 31 F 46 F 37 F 37 M 60 F 25 M 62 female 23 male 56 female 27 male 19 male 52 female 23 male 56 male 30 male bmi D 27.9 33.77 33 22.705 28.88 25.74 33.44 27.74 29.83 25.84 26.22 26.29 34.4 39.82 42.13 24.6 30.78 23.845 40.3 35.3 E F childrer smoker 0 yes 1 no 3 no 0 no 0 no O no 1 no 3 no 2 no 0 no 0 no 0 yes O no O no O yes 1 no 1 no O no O no O yes G H charges southwest 16884.92 southeast 1725.552 southeast 4449.462 northwest 21984.47 northwest 3866.855 southeast 3756.622 southeast 8240.59 northwest 7281.506 northeast 6406.411 northwest 28923.14 northeast 2721.321 southeast 27808.73 southwest 1826.843 southeast 11090.72 southeast 39611.76 southwest 1837.237 northeast 10797.34 northeast 2395.172 southwest 10602.39 southwest 36837.47 ▾ region
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Can u generate the python code for me as that i can generate the decision tree in Jupyter notebook? An example of a decision tree has been attached as image. Explain using the variables inside the decision tree.

- ples vidas1003. class No Smo - harges-133 454) -0.003 ampl 205 waha1705, 11 un-D T Was-10:41 Une Sich - Smoker on - samples 1 11 US EDE g-U.S sampla-2 value=11.11 en Smer Charges <14453 74 -0.014 sam-717 age - 210 0:00 sample-21 [22.11 class Nat-In -1712,51 DING -0.0 CHYTILA vula 1L Nandit DO UN 172, 1 Lina in Ren-Bre LODE LLED FO The UN 181 at Sinir εκτείνει τη 13 P rive Seeker 50 Sen aplica - 11 17,4] clat Non Smer ra 420. thurg4L (2) 0211 campi 15 14.11 2002-> - 463 sples AN 15511 -18 ST 2521, 6161 JAS Uut 2 a 12.07 02-1 S Senk 4 4374 M ார். . 2.11 ri L chagua 3--23.10 1671-30 Seker charg singles 9-1322 -993 sak-(777 296 das-master - sala LADa-Sing SO WEAST -3057 - --- MIM 18-202 cines-Tera NETRES HOLL Ident PAVE. DE wa Serek PETREDEN 2.61 PR aplicad e effi 15,11 SUTADES -2 Sales-Pil in 11- REDAT ---23.2 Svar ges 30-8339 -250 165 TE -Ste wght 2132015 Band HEND Tym 213.79 S -1291 choa-Nasan BELL-> 32329 531 -PATER ENG SEDA STUNGE cstar 15,11 -Ma CHECER ww - N 2. 52=TAB coca-fr 31 1111 Sab berk -20 cupuces coating SIN cal 11.4 T-sanus -12.01 21 L. J ST 368 SMF, ->346.mp tal-ar gm-0.250 WI-12.3 and nate: -11.61 HALS-D 245 340 aus -15 14,91 gin BEST-SE gm-d - 30 11.91 gn-D2 Samples 2 12,01 [-secues www.de.mens 0055 sample-111 ala-2.10 clas salar-11,01 -Sko 11.01 Namun MIONS L CASIE ge-001 DE 10, ST WELK SAD latt al 44-205 Smoker Come 106 13,105 119 -0.0 aca-1 WELL To 11-1 411100 N 6334 applica - 13 11,141 ran fris Smi V age 43.0 sample-2 ON vermit -0.0 saka-121 ca-Man-Gim STARLIG - Spgs -0.5 14 Tot nl-mon ampus ww ON-DO De comple UN [1.01 la Not-beseur
Transcribed Image Text:- ples vidas1003. class No Smo - harges-133 454) -0.003 ampl 205 waha1705, 11 un-D T Was-10:41 Une Sich - Smoker on - samples 1 11 US EDE g-U.S sampla-2 value=11.11 en Smer Charges <14453 74 -0.014 sam-717 age - 210 0:00 sample-21 [22.11 class Nat-In -1712,51 DING -0.0 CHYTILA vula 1L Nandit DO UN 172, 1 Lina in Ren-Bre LODE LLED FO The UN 181 at Sinir εκτείνει τη 13 P rive Seeker 50 Sen aplica - 11 17,4] clat Non Smer ra 420. thurg4L (2) 0211 campi 15 14.11 2002-> - 463 sples AN 15511 -18 ST 2521, 6161 JAS Uut 2 a 12.07 02-1 S Senk 4 4374 M ார். . 2.11 ri L chagua 3--23.10 1671-30 Seker charg singles 9-1322 -993 sak-(777 296 das-master - sala LADa-Sing SO WEAST -3057 - --- MIM 18-202 cines-Tera NETRES HOLL Ident PAVE. DE wa Serek PETREDEN 2.61 PR aplicad e effi 15,11 SUTADES -2 Sales-Pil in 11- REDAT ---23.2 Svar ges 30-8339 -250 165 TE -Ste wght 2132015 Band HEND Tym 213.79 S -1291 choa-Nasan BELL-> 32329 531 -PATER ENG SEDA STUNGE cstar 15,11 -Ma CHECER ww - N 2. 52=TAB coca-fr 31 1111 Sab berk -20 cupuces coating SIN cal 11.4 T-sanus -12.01 21 L. J ST 368 SMF, ->346.mp tal-ar gm-0.250 WI-12.3 and nate: -11.61 HALS-D 245 340 aus -15 14,91 gin BEST-SE gm-d - 30 11.91 gn-D2 Samples 2 12,01 [-secues www.de.mens 0055 sample-111 ala-2.10 clas salar-11,01 -Sko 11.01 Namun MIONS L CASIE ge-001 DE 10, ST WELK SAD latt al 44-205 Smoker Come 106 13,105 119 -0.0 aca-1 WELL To 11-1 411100 N 6334 applica - 13 11,141 ran fris Smi V age 43.0 sample-2 ON vermit -0.0 saka-121 ca-Man-Gim STARLIG - Spgs -0.5 14 Tot nl-mon ampus ww ON-DO De comple UN [1.01 la Not-beseur
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