Prediction with categorical and continuous Variables

I want to predict the result of a match in a video game (win or loose).

It's 5 players against 5 players game, who each plays a specific character.

• I have : the ID of each character (there are 150 different characters, so it's a number between 1 and 150)
• I have different statistiques about each character (attaque, defense : number between 1 and 10)
• I have the different ID of the two abilities chosen by each players (player 1 chose abilities number 1 and 3, player 2 chose abilities number 2 and 3, etc ...)
• I have the number of game played by each player, and the number of game won by each player.

At the end, I have categorical variables (character and ability IDs), but also continuous variables (number of game played by players, etc ..)

I have two questions :

1 - How should I do to handle these two types of variable ? I tried KNN and Decision Tree. But KNN doesn't handle categorical variable (so I tried with a vector of dummy variables, but how should I scale my data ?) And decision tree doesn't handle continuous variables.

2 - How can I integrate the fact that in my input vector, a specific variable (like one ability) is correlate with a specific character variable. Don't know if is clear, but imagine I have 30 columns as an input (10 = 5+5 characters, and 20 = 5*2+5*2 abilities). How can I integrate the fact that the abilities in columns 11 and 12 is linked with the character in column 1 ?

If my question is not adapted for with website, I will be very grateful if you could tell me where I could get an answer !

Thank you
{"ft_lang_label":"__label__en","ft_lang_prob":0.9646261,"math_prob":0.9053719,"size":1554,"snap":"2019-26-2019-30","text_gpt3_token_len":369,"char_repetition_ratio":0.15225807,"word_repetition_ratio":0.020477816,"special_character_ratio":0.25804377,"punctuation_ratio":0.10410095,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95372444,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-06-20T17:19:45Z\",\"WARC-Record-ID\":\"<urn:uuid:ceb23c0b-e1ee-43af-b3db-8eab0da838b7>\",\"Content-Length\":\"133759\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:fdd8f7cb-6cd5-4a21-8f87-7bb15adab693>\",\"WARC-Concurrent-To\":\"<urn:uuid:7a98c0d6-a34d-4a34-93f6-bc9c8d3cee6c>\",\"WARC-IP-Address\":\"151.101.193.69\",\"WARC-Target-URI\":\"https://stats.stackexchange.com/questions/363617/prediction-with-categorical-and-continuous-variables\",\"WARC-Payload-Digest\":\"sha1:QETY7FUWJ2QZA2SVIHK33SMKS6772663\",\"WARC-Block-Digest\":\"sha1:EWCQVJDYMPLSQLKQ6VG2OGXV5CTHF642\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-26/CC-MAIN-2019-26_segments_1560627999263.6_warc_CC-MAIN-20190620165805-20190620191805-00406.warc.gz\"}"}