I am unable to fit the table in overleaf, it doesnt show all columns is there a trick to solve this>?

This is my code below:

newpage

setlength{LTpost}{0mm}
begin{longtable}{lcccccccccc}
caption*{
{large textbf{Descriptive Statistics For The Polish Car Data}}
} \ 
toprule
 & multicolumn{4}{c}{textbf{Variables}} & multicolumn{6}{c}{textbf{Fuel Type}} \ 
cmidrule(lr){2-5} cmidrule(lr){6-11}
textbf{Car Manufacturer} & textbf{Price (Mean, SD)} & textbf{Year (Mean, SD)} & textbf{Mileage (Mean, SD)} & textbf{Volume Engine (Mean, SD)} & textbf{CNG} & textbf{Diesel} & textbf{Electric} & textbf{Gasoline} & textbf{Hybrid} & textbf{LPG} \ 
midruleaddlinespace[2.5pt]
textbf{Overall}, N = 117029 & 14518 (15434) & 2013 (5.6) & 141927 (91986) & 1801 (624) & 47 (<0.1%) & 48237 (41%) & 860 (0.7%) & 61020 (52%) & 2565 (2.2%) & 4300 (3.7%) \ 
textbf{Alfa-Romeo}, N = 704 (0.6%) & 15217 (17457) & 2013 (5.3) & 136647 (92991) & 1786 (343) & 0 (0%) & 256 (36%) & 0 (0%) & 428 (61%) & 0 (0%) & 20 (2.8%) \ 
textbf{Audi}, N = 11880 (10.1%) & 21627 (21592) & 2012 (6.3) & 161820 (103181) & 2149 (660) & 2 (<0.1%) & 6614 (56%) & 103 (0.9%) & 4720 (40%) & 130 (1.1%) & 311 (2.6%) \ 
textbf{BMW}, N = 10700 (9.1%) & 23235 (22102) & 2013 (6.3) & 150607 (103916) & 2275 (720) & 0 (0%) & 5874 (55%) & 150 (1.4%) & 4163 (39%) & 213 (2.0%) & 300 (2.8%) \ 
textbf{Chevrolet}, N = 603 (0.5%) & 8626 (12305) & 2012 (2.8) & 141425 (61700) & 2067 (1296) & 0 (0%) & 123 (20%) & 0 (0%) & 415 (69%) & 0 (0%) & 65 (11%) \ 
textbf{Citroen}, N = 2720 (2.3%) & 7450 (5969) & 2012 (4.2) & 167729 (76524) & 1666 (289) & 0 (0%) & 1544 (57%) & 0 (0%) & 1065 (39%) & 16 (0.6%) & 95 (3.5%) \ 
textbf{Fiat}, N = 2866 (2.4%) & 8601 (6690) & 2014 (5.2) & 106286 (80130) & 1302 (364) & 9 (0.3%) & 534 (19%) & 97 (3.4%) & 1962 (68%) & 51 (1.8%) & 213 (7.4%) \ 
textbf{Ford}, N = 9637 (8.2%) & 11228 (11429) & 2013 (4.9) & 150373 (90753) & 1777 (637) & 1 (<0.1%) & 4526 (47%) & 69 (0.7%) & 4661 (48%) & 95 (1.0%) & 285 (3.0%) \ 
textbf{Honda}, N = 2176 (1.9%) & 11219 (9701) & 2011 (5.5) & 143694 (85395) & 1756 (339) & 0 (0%) & 264 (12%) & 0 (0%) & 1529 (70%) & 216 (9.9%) & 167 (7.7%) \ 
textbf{Hyundai}, N = 4032 (3.4%) & 11980 (6959) & 2015 (4.1) & 107287 (75143) & 1545 (295) & 0 (0%) & 1237 (31%) & 24 (0.6%) & 2617 (65%) & 33 (0.8%) & 121 (3.0%) \ 
textbf{Kia}, N = 3744 (3.2%) & 13048 (8299) & 2015 (4.8) & 105278 (80165) & 1572 (383) & 0 (0%) & 1129 (30%) & 44 (1.2%) & 2333 (62%) & 81 (2.2%) & 157 (4.2%) \ 
textbf{Mazda}, N = 2848 (2.4%) & 12528 (8979) & 2013 (5.1) & 125832 (82978) & 1978 (326) & 0 (0%) & 550 (19%) & 2 (<0.1%) & 2198 (77%) & 3 (0.1%) & 95 (3.3%) \ 
textbf{Mercedes-Benz}, N = 6849 (5.9%) & 25988 (23406) & 2012 (6.6) & 144266 (107084) & 2445 (1089) & 6 (<0.1%) & 3192 (47%) & 15 (0.2%) & 3342 (49%) & 82 (1.2%) & 212 (3.1%) \ 
textbf{Mini}, N = 1087 (0.9%) & 15796 (11984) & 2014 (6.3) & 95557 (80185) & 1616 (315) & 0 (0%) & 161 (15%) & 13 (1.2%) & 882 (81%) & 19 (1.7%) & 12 (1.1%) \ 
textbf{Mitsubishi}, N = 1120 (1.0%) & 11162 (9484) & 2013 (5.0) & 118572 (80382) & 1680 (411) & 0 (0%) & 207 (18%) & 0 (0%) & 760 (68%) & 86 (7.7%) & 67 (6.0%) \ 
textbf{Nissan}, N = 3072 (2.6%) & 10845 (7016) & 2013 (4.7) & 125186 (72351) & 1518 (449) & 0 (0%) & 809 (26%) & 64 (2.1%) & 2079 (68%) & 9 (0.3%) & 111 (3.6%) \ 
textbf{Opel}, N = 11912 (10.2%) & 7653 (5905) & 2012 (5.2) & 154554 (78709) & 1599 (327) & 14 (0.1%) & 4275 (36%) & 45 (0.4%) & 6860 (58%) & 38 (0.3%) & 680 (5.7%) \ 
textbf{Peugeot}, N = 5056 (4.3%) & 10301 (9694) & 2013 (5.0) & 141758 (82624) & 1564 (322) & 0 (0%) & 2525 (50%) & 47 (0.9%) & 2224 (44%) & 147 (2.9%) & 113 (2.2%) \ 
textbf{Renault}, N = 6976 (6.0%) & 9840 (7932) & 2013 (5.3) & 127454 (83623) & 1485 (378) & 0 (0%) & 2682 (38%) & 65 (0.9%) & 3808 (55%) & 67 (1.0%) & 354 (5.1%) \ 
textbf{Seat}, N = 2848 (2.4%) & 8839 (7430) & 2012 (5.4) & 146448 (80713) & 1526 (324) & 2 (<0.1%) & 822 (29%) & 0 (0%) & 1935 (68%) & 6 (0.2%) & 83 (2.9%) \ 
textbf{Skoda}, N = 5887 (5.0%) & 13443 (10519) & 2015 (4.9) & 129038 (87036) & 1511 (402) & 1 (<0.1%) & 2274 (39%) & 64 (1.1%) & 3207 (54%) & 21 (0.4%) & 320 (5.4%) \ 
textbf{Toyota}, N = 5115 (4.4%) & 11862 (8670) & 2013 (5.4) & 126554 (86983) & 1692 (462) & 0 (0%) & 1107 (22%) & 0 (0%) & 2865 (56%) & 931 (18%) & 212 (4.1%) \ 
textbf{Volkswagen}, N = 10835 (9.3%) & 13440 (13600) & 2012 (6.3) & 153210 (98000) & 1687 (442) & 12 (0.1%) & 4965 (46%) & 48 (0.4%) & 5561 (51%) & 40 (0.4%) & 209 (1.9%) \ 
textbf{Volvo}, N = 4362 (3.7%) & 23777 (19296) & 2014 (5.0) & 148111 (94371) & 2028 (355) & 0 (0%) & 2567 (59%) & 10 (0.2%) & 1406 (32%) & 281 (6.4%) & 98 (2.2%) \ 
bottomrule
end{longtable}
begin{minipage}{linewidth}
textsuperscript{textbf{*}}Notes: Mean and The Standard Deviation is presented in parentheses. The variable `Fuel' is separated into different fuel types, and the proportion per fuel type is shown for each mark.
end{minipage}

I have tried using chatgpt which gave some solutions but not what I looked for.