From 14e332986c33615c9d9fb190362a0fc7ec503df3 Mon Sep 17 00:00:00 2001 From: Sasidharan Vairavasamy <127896918+Thewhitewolfsasi@users.noreply.github.com> Date: Mon, 13 May 2024 01:34:07 +0530 Subject: [PATCH 1/5] Create README.md --- .../Intermediate/Autism Identification System/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Machine Learning and Data Science/Intermediate/Autism Identification System/README.md diff --git a/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md b/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md new file mode 100644 index 000000000..6d2c8e28c --- /dev/null +++ b/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md @@ -0,0 +1 @@ +

Autism Identification System

From 635a2993fb574afdaa30e47c3ecd6535092b0626 Mon Sep 17 00:00:00 2001 From: Sasidharan Vairavasamy <127896918+Thewhitewolfsasi@users.noreply.github.com> Date: Mon, 13 May 2024 01:35:02 +0530 Subject: [PATCH 2/5] Added Notebook and data files --- .../Autism Identification System/Data.csv | 705 +++++ .../autism_identification_notebook.ipynb | 2688 +++++++++++++++++ 2 files changed, 3393 insertions(+) create mode 100644 Machine Learning and Data Science/Intermediate/Autism Identification System/Data.csv create mode 100644 Machine Learning and Data Science/Intermediate/Autism Identification System/autism_identification_notebook.ipynb diff --git a/Machine Learning and Data Science/Intermediate/Autism Identification System/Data.csv b/Machine Learning and Data Science/Intermediate/Autism Identification System/Data.csv new file mode 100644 index 000000000..72335ee90 --- /dev/null +++ b/Machine Learning and Data Science/Intermediate/Autism Identification System/Data.csv @@ -0,0 +1,705 @@ +A1_Score,A2_Score,A3_Score,A4_Score,A5_Score,A6_Score,A7_Score,A8_Score,A9_Score,A10_Score,age,gender,ethnicity,jundice,austim,contry_of_res,used_app_before,result,age_desc,relation,Class/ASD +1,1,1,1,0,0,1,1,0,0,26,f,White-European,no,no,United States,no,6,18 and more,Self,NO +1,1,0,1,0,0,0,1,0,1,24,m,Latino,no,yes,Brazil,no,5,18 and more,Self,NO +1,1,0,1,1,0,1,1,1,1,27,m,Latino,yes,yes,Spain,no,8,18 and more,Parent,YES +1,1,0,1,0,0,1,1,0,1,35,f,White-European,no,yes,United States,no,6,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,40,f,?,no,no,Egypt,no,2,18 and more,?,NO +1,1,1,1,1,0,1,1,1,1,36,m,Others,yes,no,United States,no,9,18 and more,Self,YES +0,1,0,0,0,0,0,1,0,0,17,f,Black,no,no,United States,no,2,18 and more,Self,NO +1,1,1,1,0,0,0,0,1,0,64,m,White-European,no,no,New Zealand,no,5,18 and more,Parent,NO +1,1,0,0,1,0,0,1,1,1,29,m,White-European,no,no,United States,no,6,18 and more,Self,NO +1,1,1,1,0,1,1,1,1,0,17,m,Asian,yes,yes,Bahamas,no,8,18 and more,Health care professional,YES +1,1,1,1,1,1,1,1,1,1,33,m,White-European,no,no,United States,no,10,18 and more,Relative,YES +0,1,0,1,1,1,1,0,0,1,18,f,Middle Eastern ,no,no,Burundi,no,6,18 and more,Parent,NO +0,1,1,1,1,1,0,0,1,0,17,f,?,no,no,Bahamas,no,6,18 and more,?,NO +1,0,0,0,0,0,1,1,0,1,17,m,?,no,no,Austria,no,4,18 and more,?,NO +1,0,0,0,0,0,1,1,0,1,17,f,?,no,no,Argentina,no,4,18 and more,?,NO +1,1,0,1,1,0,0,1,0,1,18,m,Middle Eastern ,no,yes,New Zealand,no,6,18 and more,Parent,NO +1,0,0,0,0,0,1,1,1,1,31,m,Middle Eastern ,no,no,Jordan,no,5,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,1,30,m,White-European,no,no,Ireland,no,2,18 and more,Self,NO +0,0,1,0,1,1,0,0,0,0,35,f,Middle Eastern ,no,yes,United Arab Emirates,no,3,18 and more,Self,NO +0,0,0,0,0,0,1,1,0,1,34,m,?,yes,no,United Arab Emirates,no,3,18 and more,?,NO +0,1,1,1,0,0,0,0,0,0,38,m,?,no,no,United Arab Emirates,no,3,18 and more,?,NO +0,0,0,0,0,0,0,0,0,0,27,f,Black,no,no,United Arab Emirates,no,0,18 and more,Self,NO +0,0,0,1,0,0,1,1,1,1,27,m,Middle Eastern ,no,no,Afghanistan,no,5,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,1,42,m,Middle Eastern ,yes,no,United Arab Emirates,no,2,18 and more,Relative,NO +1,1,1,1,0,0,0,1,0,0,43,m,?,no,no,Lebanon,no,5,18 and more,?,NO +0,1,1,0,0,0,0,1,0,0,24,f,?,yes,no,Afghanistan,no,3,18 and more,?,NO +0,0,0,0,0,0,0,1,0,0,40,m,Pasifika,yes,yes,United Arab Emirates,no,1,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,0,40,m,Middle Eastern ,yes,yes,Afghanistan,no,1,18 and more,Parent,NO +0,0,0,0,0,0,0,1,0,0,48,m,Black,no,no,New Zealand,no,1,18 and more,Self,NO +0,1,1,0,0,0,0,0,1,1,31,m,Middle Eastern ,no,no,United Kingdom,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,18,m,White-European,no,no,United Kingdom,no,0,18 and more,Self,NO +1,0,0,1,1,1,1,1,0,1,37,f,White-European,no,yes,United States,no,7,18 and more,Self,YES +1,1,0,0,0,0,1,0,0,1,55,f,Others,no,no,New Zealand,no,4,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,18,f,White-European,yes,no,South Africa,no,10,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,18,f,White-European,no,no,South Africa,no,10,18 and more,Self,YES +0,0,1,0,0,0,0,0,0,0,55,m,White-European,no,no,New Zealand,no,1,18 and more,Self,NO +0,1,1,0,1,0,0,1,1,1,50,m,Middle Eastern ,no,no,United Arab Emirates,no,6,18 and more,Self,NO +1,0,1,1,1,1,0,0,1,0,34,f,White-European,no,no,New Zealand,no,6,18 and more,Self,NO +1,0,0,1,1,1,1,0,1,1,53,f,White-European,no,no,New Zealand,no,7,18 and more,Self,YES +1,0,1,1,0,1,1,1,1,1,35,f,White-European,no,yes,United States,no,8,18 and more,Self,YES +1,0,1,1,1,0,1,1,0,1,20,f,Latino,yes,no,Italy,no,7,18 and more,Self,YES +0,0,0,0,1,1,0,0,0,0,28,f,Asian,no,no,Pakistan,no,2,18 and more,Self,NO +0,0,1,1,0,0,0,0,0,1,34,f,Middle Eastern ,no,yes,Egypt,no,3,18 and more,Self,NO +0,1,1,1,0,0,0,0,0,1,36,f,White-European,yes,yes,United States,no,4,18 and more,Self,NO +1,1,1,1,1,1,0,1,0,1,27,f,White-European,no,no,New Zealand,no,8,18 and more,Self,YES +1,0,1,1,1,1,0,1,1,0,53,f,White-European,no,no,New Zealand,no,7,18 and more,Relative,YES +1,1,1,1,0,1,0,0,0,0,24,f,Pasifika,no,no,New Zealand,no,5,18 and more,Relative,NO +0,0,1,1,1,0,1,0,0,0,24,m,Pasifika,no,no,New Zealand,no,4,18 and more,Relative,NO +0,1,1,0,0,1,0,0,0,0,55,m,White-European,no,no,New Zealand,no,3,18 and more,Relative,NO +1,1,0,0,0,1,1,1,0,1,30,f,Asian,no,no,Bangladesh,no,6,18 and more,Self,NO +1,0,0,1,0,0,0,1,0,0,21,f,Latino,no,no,Chile,no,3,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,35,f,Black,no,no,France,no,10,18 and more,Parent,YES +1,0,0,0,0,0,0,0,0,0,383,f,Pasifika,no,no,New Zealand,no,1,18 and more,Self,NO +1,0,1,1,1,1,1,1,0,1,21,m,Latino,no,yes,Brazil,no,8,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,47,m,White-European,no,no,United States,no,10,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,30,f,Asian,no,no,China,no,9,18 and more,Self,YES +1,0,1,1,1,1,0,1,1,1,28,f,White-European,no,no,Australia,no,8,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,43,f,White-European,no,no,Australia,no,9,18 and more,Self,YES +1,0,0,0,1,0,0,1,0,0,32,f,South Asian,no,yes,Canada,no,3,18 and more,Self,NO +1,1,1,1,0,0,0,1,0,0,44,f,White-European,no,no,Australia,no,5,18 and more,Self,NO +1,0,1,1,1,1,1,1,0,1,20,f,Others,no,no,Canada,no,8,18 and more,Self,YES +1,0,1,1,0,1,1,1,0,1,20,f,Others,no,no,Canada,yes,7,18 and more,Self,YES +0,0,0,0,0,0,0,0,0,0,,m,?,no,no,Saudi Arabia,no,0,18 and more,?,NO +1,0,0,1,1,0,0,1,1,0,19,m,White-European,no,no,Australia,no,5,18 and more,Parent,NO +1,1,1,1,1,1,1,1,0,1,29,f,White-European,no,no,Australia,no,9,18 and more,Self,YES +1,0,1,0,0,0,0,1,0,0,21,m,Middle Eastern ,no,no,United States,no,3,18 and more,Self,NO +0,1,0,0,0,0,0,1,0,0,27,m,Middle Eastern ,no,no,France,no,2,18 and more,Parent,NO +1,0,1,0,1,1,1,1,0,0,21,m,Hispanic,no,no,United States,no,6,18 and more,Self,NO +1,0,0,0,1,0,1,0,0,1,35,m,Middle Eastern ,no,no,Saudi Arabia,no,4,18 and more,Self,NO +0,0,0,0,1,0,0,0,0,0,42,m,Black,no,no,New Zealand,no,1,18 and more,Self,NO +1,1,0,0,0,0,1,1,0,1,29,f,South Asian,no,no,New Zealand,no,5,18 and more,Self,NO +0,0,1,1,0,0,0,1,0,0,58,m,Asian,no,no,New Zealand,no,3,18 and more,Self,NO +1,0,1,1,0,0,1,1,0,1,21,m,Others,no,no,United States,no,6,18 and more,Self,NO +1,1,1,0,0,0,0,1,1,1,21,m,Others,no,no,United States,no,6,18 and more,Self,NO +0,0,1,1,1,0,0,1,1,1,37,f,White-European,no,yes,United States,no,6,18 and more,Self,NO +1,1,0,1,1,1,1,1,0,0,21,m,Hispanic,no,no,United States,no,7,18 and more,Self,YES +1,0,0,0,0,0,1,1,0,0,20,m,Middle Eastern ,no,no,United States,no,3,18 and more,Self,NO +1,1,0,1,1,1,1,1,1,0,33,f,Black,no,yes,United States,no,8,18 and more,Self,YES +1,0,0,1,1,0,0,0,0,1,20,m,Middle Eastern ,no,no,United States,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,0,0,0,45,f,?,yes,no,Jordan,no,2,18 and more,?,NO +0,0,1,1,0,0,1,1,0,1,32,m,?,yes,yes,Afghanistan,no,5,18 and more,?,NO +1,0,0,0,0,0,1,0,0,1,30,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,1,1,1,1,1,1,1,1,1,33,f,White-European,no,yes,Netherlands,no,10,18 and more,Self,YES +0,0,1,0,1,0,0,1,0,1,42,f,Middle Eastern ,no,no,Jordan,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,0,0,0,17,f,White-European,no,no,New Zealand,no,2,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,27,f,Middle Eastern ,no,no,United States,no,3,18 and more,Self,NO +1,0,0,1,1,1,1,1,1,1,35,f,Middle Eastern ,no,yes,Afghanistan,no,8,18 and more,Parent,YES +1,0,0,0,0,0,0,0,0,0,37,m,White-European,no,no,United Kingdom,no,1,18 and more,Self,NO +1,0,0,0,0,0,0,1,1,1,30,f,White-European,no,yes,United Kingdom,no,4,18 and more,Self,NO +1,1,1,1,1,1,0,1,1,1,29,f,White-European,no,no,New Zealand,no,9,18 and more,Self,YES +1,0,1,1,0,0,0,0,0,1,17,f,White-European,no,yes,Romania,no,4,18 and more,Self,NO +0,1,0,0,1,0,1,0,0,1,,f,?,no,no,Jordan,no,4,18 and more,?,NO +0,0,0,1,1,1,0,1,0,1,22,f,Pasifika,no,no,New Zealand,no,5,18 and more,Self,NO +1,1,1,0,1,1,1,0,1,1,19,f,Latino,no,yes,Brazil,no,8,18 and more,Self,YES +1,1,1,1,1,0,0,1,1,1,42,f,White-European,no,yes,Sweden,no,8,18 and more,Self,YES +1,1,0,0,0,0,0,1,0,1,37,f,White-European,no,no,United Kingdom,no,4,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,0,28,f,White-European,no,no,Australia,no,4,18 and more,Self,NO +1,0,0,1,1,0,1,0,0,0,22,m,Others,no,no,New Zealand,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,26,f,Pasifika,no,no,Tonga,no,3,18 and more,Self,NO +0,0,0,0,0,0,0,0,1,0,21,m,Pasifika,no,no,Oman,no,1,18 and more,Self,NO +0,1,1,0,0,0,0,1,0,0,26,f,South Asian,no,no,India,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,39,f,Asian,no,no,Philippines,no,2,18 and more,Self,NO +1,0,0,0,0,0,0,0,1,1,26,m,Asian,no,no,New Zealand,no,3,18 and more,Self,NO +1,1,0,0,1,0,1,1,0,0,26,m,Asian,no,no,India,no,5,18 and more,Self,NO +0,0,0,0,1,1,0,1,0,1,21,m,South Asian,no,no,India,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,25,m,Asian,no,no,India,no,0,18 and more,Self,NO +0,0,1,0,1,1,1,1,0,1,26,m,South Asian,no,no,New Zealand,no,6,18 and more,Self,NO +1,1,0,1,1,0,1,1,0,1,30,f,White-European,no,no,United States,no,7,18 and more,Self,YES +1,0,0,1,1,1,1,0,0,0,19,f,Asian,no,no,India,no,5,18 and more,Parent,NO +1,0,0,0,0,1,1,0,0,0,19,f,Asian,no,no,India,no,3,18 and more,Parent,NO +1,0,1,1,1,1,1,1,1,1,25,f,White-European,no,yes,New Zealand,no,9,18 and more,Self,YES +0,0,1,1,0,1,0,1,1,1,23,m,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,0,0,0,1,0,0,0,0,1,31,m,Asian,no,no,Sri Lanka,no,3,18 and more,Parent,NO +0,1,0,0,0,0,0,1,1,1,27,m,Asian,no,no,New Zealand,no,4,18 and more,Self,NO +1,1,0,1,1,1,1,1,0,1,29,f,Middle Eastern ,no,no,United Arab Emirates,no,8,18 and more,Self,YES +1,0,1,1,1,1,0,1,1,0,38,f,White-European,no,no,United Kingdom,no,7,18 and more,Parent,YES +1,1,1,1,1,1,0,1,0,1,23,f,White-European,no,no,United States,no,8,18 and more,Self,YES +1,1,1,0,1,0,1,1,0,0,27,m,White-European,no,no,United States,no,6,18 and more,Self,NO +1,1,1,1,1,1,0,1,1,1,27,f,White-European,no,no,Canada,no,9,18 and more,Self,YES +1,1,1,1,1,1,0,0,1,0,42,m,White-European,no,no,United States,no,7,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,20,m,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +1,0,1,1,1,1,1,1,1,1,17,f,White-European,no,no,United States,no,9,18 and more,Self,YES +1,0,0,0,0,0,0,0,0,0,20,f,Black,no,no,Spain,no,1,18 and more,Self,NO +1,0,0,1,1,1,1,1,0,1,30,m,Asian,yes,no,Sierra Leone,no,7,18 and more,Self,YES +1,0,1,1,1,1,1,1,1,1,28,f,White-European,no,yes,Australia,no,9,18 and more,Self,YES +1,1,0,0,1,1,1,1,0,1,26,f,White-European,no,no,New Zealand,no,7,18 and more,Self,YES +0,0,0,0,0,0,0,0,0,0,26,f,White-European,no,no,Australia,no,0,18 and more,Self,NO +1,0,0,1,1,1,0,1,0,1,31,m,Middle Eastern ,no,yes,Canada,no,6,18 and more,Self,NO +1,0,1,1,0,0,1,0,0,1,40,f,White-European,no,no,United Kingdom,no,5,18 and more,Self,NO +1,0,0,0,1,0,0,0,1,1,39,f,White-European,no,yes,Ireland,no,4,18 and more,Relative,NO +1,1,0,0,0,0,0,1,0,0,18,f,White-European,no,no,United States,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,24,m,Black,no,no,Ethiopia,no,3,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,23,m,South Asian,no,no,India,no,3,18 and more,Self,NO +1,0,1,1,0,0,0,1,0,1,24,m,Asian,no,no,India,no,5,18 and more,Self,NO +0,1,0,0,1,1,0,1,1,0,24,m,Asian,no,no,New Zealand,no,5,18 and more,Self,NO +1,0,1,1,0,0,0,1,1,1,27,m,Asian,no,no,New Zealand,no,6,18 and more,Self,NO +0,1,1,0,0,1,0,0,1,1,24,m,Asian,no,no,New Zealand,no,5,18 and more,Self,NO +0,0,0,0,1,0,0,0,0,1,25,m,Asian,no,no,India,no,2,18 and more,Self,NO +1,0,0,0,0,0,1,0,0,1,22,m,Asian,no,no,New Zealand,no,3,18 and more,Self,NO +0,1,0,0,0,0,0,0,0,1,40,f,Asian,no,no,New Zealand,no,2,18 and more,Self,NO +0,0,1,1,0,0,0,1,0,1,23,m,Asian,no,no,India,no,4,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,29,m,South Asian,no,no,India,no,2,18 and more,Self,NO +1,0,1,0,0,0,0,1,0,0,25,m,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,0,1,1,0,0,1,0,0,27,m,Asian,no,no,India,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,0,22,m,South Asian,no,no,New Zealand,no,1,18 and more,Self,NO +1,0,0,1,1,1,1,1,1,1,28,m,White-European,no,no,United States,no,8,18 and more,Parent,YES +1,0,1,1,1,1,1,1,1,1,20,m,White-European,no,no,Australia,no,9,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,19,f,White-European,no,no,Romania,no,10,18 and more,Self,YES +1,1,0,0,1,1,1,1,0,1,37,f,Others,no,no,United States,no,7,18 and more,Self,YES +1,1,1,1,1,0,1,1,1,0,35,m,White-European,yes,yes,United States,no,8,18 and more,Self,YES +0,0,0,0,1,0,0,1,0,0,26,m,Asian,no,no,New Zealand,no,2,18 and more,Self,NO +0,1,1,1,1,0,1,1,0,1,23,m,Asian,no,no,Viet Nam,no,7,18 and more,Self,YES +1,1,0,0,1,0,0,0,0,1,32,f,South Asian,no,no,New Zealand,no,4,18 and more,Self,NO +0,0,1,0,1,0,1,0,1,1,30,m,Asian,no,no,Sri Lanka,no,5,18 and more,Self,NO +1,0,0,0,1,0,1,0,0,1,31,m,Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +0,0,0,0,0,0,1,1,0,1,28,f,Asian,no,no,New Zealand,no,3,18 and more,Self,NO +1,0,0,0,0,1,0,1,0,1,29,f,Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +0,1,0,0,1,0,0,1,0,1,29,f,South Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +1,0,0,1,0,0,0,0,1,1,29,f,Asian,no,no,India,no,4,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,0,31,m,Asian,no,no,India,no,3,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,36,m,Asian,no,no,India,no,0,18 and more,Parent,NO +0,1,0,0,0,0,0,1,0,0,32,f,South Asian,no,no,India,no,2,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,0,34,f,South Asian,no,no,New Zealand,no,1,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,24,m,Asian,no,no,Sri Lanka,no,3,18 and more,Self,NO +1,0,1,0,1,0,1,1,0,1,24,f,Asian,no,no,New Zealand,no,6,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,1,42,m,Asian,no,no,Sri Lanka,no,5,18 and more,Self,NO +0,0,1,0,0,0,0,1,1,1,26,f,Others,no,no,India,no,4,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,0,27,f,Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +0,0,1,1,1,0,0,1,1,1,36,f,Latino,no,yes,Brazil,no,6,18 and more,Self,NO +1,1,1,1,1,0,0,1,1,1,36,f,Latino,no,yes,Brazil,no,8,18 and more,Self,YES +1,0,1,1,0,0,1,1,0,1,55,m,Hispanic,no,no,United States,no,6,18 and more,Self,NO +1,1,0,1,0,0,1,0,0,0,23,f,Asian,no,no,New Zealand,no,4,18 and more,Self,NO +0,0,1,1,1,0,0,0,0,1,22,f,Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,42,f,Middle Eastern ,no,no,France,no,2,18 and more,Parent,NO +1,0,0,1,0,0,0,1,0,0,54,m,White-European,no,no,Canada,no,3,18 and more,Self,NO +1,1,0,0,0,0,1,1,1,1,43,f,Black,no,no,United States,no,6,18 and more,Self,NO +1,1,1,1,1,1,0,1,1,1,43,f,Black,no,no,United States,yes,9,18 and more,Self,YES +0,0,1,1,1,0,0,1,1,1,29,m,Middle Eastern ,no,no,Iran,no,6,18 and more,Self,NO +1,0,1,0,1,0,0,1,0,1,29,m,Middle Eastern ,no,no,Iran,no,5,18 and more,Self,NO +1,0,1,0,1,0,1,0,0,0,37,m,White-European,no,no,Australia,no,4,18 and more,Self,NO +1,0,0,0,1,0,1,0,0,1,39,f,White-European,no,no,Australia,no,4,18 and more,Self,NO +1,1,1,1,1,0,0,0,0,0,18,f,Asian,no,no,Viet Nam,no,5,18 and more,Self,NO +0,1,0,1,1,1,0,1,0,1,31,m,White-European,no,no,United States,no,6,18 and more,Others,NO +0,0,1,1,1,0,0,0,0,1,34,m,Asian,no,no,New Zealand,no,4,18 and more,Self,NO +0,0,1,1,1,0,1,0,0,1,22,m,White-European,no,no,United States,no,5,18 and more,Self,NO +1,0,1,1,1,1,1,0,1,0,28,m,South Asian,no,no,India,no,7,18 and more,Self,YES +1,0,0,0,0,0,1,1,0,1,38,m,Others,no,yes,Netherlands,no,4,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,1,28,f,White-European,no,no,Australia,no,4,18 and more,Self,NO +1,1,0,1,1,1,0,1,1,1,53,f,White-European,no,yes,United States,no,8,18 and more,Self,YES +1,1,1,0,1,0,1,0,0,0,26,m,White-European,no,no,United Kingdom,no,5,18 and more,Self,NO +1,1,1,1,1,1,0,1,1,1,37,m,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +1,1,0,1,0,0,0,1,0,1,42,m,Latino,yes,yes,Costa Rica,no,5,18 and more,Parent,NO +1,0,0,0,0,0,0,1,0,1,43,m,Hispanic,no,no,United States,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,32,f,Asian,no,no,United Kingdom,no,2,18 and more,Self,NO +0,1,0,1,0,0,0,1,0,0,31,m,White-European,no,yes,United Kingdom,no,3,18 and more,Self,NO +1,0,1,1,1,1,1,1,1,1,53,f,White-European,no,no,United States,no,9,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,28,m,White-European,no,no,United States,no,10,18 and more,Self,YES +1,0,1,1,1,1,1,1,1,1,38,m,White-European,no,no,Germany,no,9,18 and more,Self,YES +1,1,1,0,1,1,0,1,1,0,31,m,White-European,no,no,United States,no,7,18 and more,Self,YES +1,1,1,1,0,0,0,0,0,0,31,f,Pasifika,no,yes,United States,no,4,18 and more,Self,NO +1,1,1,1,1,0,1,1,1,1,21,f,White-European,no,no,Australia,no,9,18 and more,Self,YES +1,1,0,1,1,0,0,0,1,1,25,f,White-European,no,no,United States,no,6,18 and more,Self,NO +1,1,0,1,1,0,0,1,1,1,25,f,White-European,no,no,United States,no,7,18 and more,Self,YES +1,1,1,1,1,1,1,0,1,1,60,f,White-European,no,yes,United States,no,9,18 and more,Relative,YES +1,0,1,1,0,0,0,0,0,0,39,m,White-European,no,yes,United States,no,3,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,53,m,White-European,no,no,United States,no,10,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,20,m,Middle Eastern ,no,yes,United States,no,10,18 and more,Parent,YES +1,1,1,0,1,0,0,1,1,1,25,f,White-European,no,no,United States,yes,7,18 and more,Self,YES +1,1,1,1,0,0,1,1,0,0,50,m,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,0,0,1,1,1,1,1,1,1,28,f,White-European,no,no,United States,no,8,18 and more,Self,YES +1,0,1,1,1,1,1,1,1,1,37,f,White-European,no,yes,United Kingdom,no,9,18 and more,Self,YES +0,0,0,1,0,1,0,1,1,1,30,m,Asian,no,yes,India,no,5,18 and more,Self,NO +1,1,0,0,0,1,1,1,0,1,32,f,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,0,1,1,0,0,0,0,0,1,34,f,White-European,no,no,United States,no,4,18 and more,Self,NO +1,1,1,1,0,0,0,0,0,1,42,m,White-European,no,no,United States,no,5,18 and more,Self,NO +1,1,0,0,1,1,1,1,1,1,22,f,Black,no,no,Spain,no,8,18 and more,Self,YES +1,0,0,0,0,0,1,1,0,1,37,f,?,yes,no,United Arab Emirates,no,4,18 and more,?,NO +1,1,0,0,0,0,0,1,0,1,39,m,White-European,no,yes,United States,no,4,18 and more,Parent,NO +1,1,0,1,1,0,1,1,0,1,18,m,White-European,no,no,Bangladesh,no,7,18 and more,Self,YES +1,0,0,1,0,0,0,0,0,0,54,f,White-European,no,yes,United States,no,2,18 and more,Parent,NO +1,1,0,0,0,0,0,1,0,0,42,m,White-European,no,no,United Kingdom,no,3,18 and more,Self,NO +0,0,1,0,0,0,0,0,0,0,27,f,?,no,no,Afghanistan,no,1,18 and more,?,NO +1,1,1,1,1,0,0,1,0,0,22,m,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,0,1,1,0,1,1,1,1,1,22,m,White-European,no,no,United Kingdom,no,8,18 and more,Self,YES +1,1,1,1,1,1,1,0,1,1,21,f,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +0,1,0,1,0,0,0,1,0,1,28,f,White-European,no,no,United States,no,4,18 and more,Self,NO +1,1,1,1,1,1,1,0,1,1,17,m,Asian,yes,no,India,no,9,18 and more,Relative,YES +0,1,0,0,0,0,0,0,0,0,28,m,White-European,no,no,United States,no,1,18 and more,Self,NO +0,0,0,1,1,0,1,1,0,1,21,m,Latino,no,no,Mexico,no,5,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,31,f,Asian,no,no,New Zealand,no,10,18 and more,Self,YES +1,0,0,0,0,0,1,1,0,0,20,m,South Asian,no,no,New Zealand,no,3,18 and more,Self,NO +1,0,0,1,0,0,0,0,0,0,21,m,White-European,no,no,United Kingdom,no,2,18 and more,Self,NO +1,1,0,0,1,0,0,0,0,0,22,m,White-European,no,no,New Zealand,no,3,18 and more,Self,NO +0,1,0,0,0,0,0,1,1,1,21,m,White-European,no,no,New Zealand,no,4,18 and more,Self,NO +0,1,1,0,0,0,0,0,0,0,24,m,Black,no,no,New Zealand,no,2,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,24,m,South Asian,no,no,India,no,2,18 and more,Self,NO +1,0,0,0,1,1,1,1,0,1,25,m,Asian,no,no,New Zealand,no,6,18 and more,Self,NO +1,0,0,0,0,0,0,0,0,1,17,m,South Asian,no,no,New Zealand,no,2,18 and more,Self,NO +0,1,0,0,0,0,0,1,1,0,21,m,?,no,no,Russia,no,3,18 and more,?,NO +1,0,0,0,0,0,0,1,0,0,23,m,Pasifika,no,no,New Zealand,no,2,18 and more,Self,NO +1,0,0,1,1,0,1,1,0,0,21,m,Asian,no,no,New Zealand,no,5,18 and more,Self,NO +1,0,0,0,1,0,1,1,1,1,22,m,Others,no,no,New Zealand,no,6,18 and more,Self,NO +1,0,0,0,1,0,0,1,0,1,24,f,Asian,no,no,India,no,4,18 and more,Self,NO +0,0,1,0,1,1,0,0,1,1,34,m,Others,no,no,New Zealand,no,5,18 and more,Self,NO +1,0,1,0,1,0,1,1,0,1,30,m,Asian,no,no,New Zealand,no,6,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,1,29,f,Asian,no,no,India,no,4,18 and more,Self,NO +1,0,0,1,1,0,1,1,0,0,20,m,Pasifika,no,no,New Zealand,no,5,18 and more,Self,NO +0,0,1,1,0,1,0,1,1,1,23,m,White-European,no,no,New Zealand,no,6,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,1,26,m,Asian,no,no,India,no,2,18 and more,Self,NO +1,0,0,1,1,1,1,1,1,1,20,m,South Asian,no,no,New Zealand,no,8,18 and more,Self,YES +1,0,0,0,0,0,1,1,0,0,41,m,Asian,no,no,New Zealand,no,3,18 and more,Self,NO +1,0,0,1,0,0,1,1,0,0,24,m,Asian,no,no,India,no,4,18 and more,Self,NO +0,0,0,1,0,0,0,1,0,0,35,m,Asian,no,no,India,no,2,18 and more,Self,NO +0,0,0,1,0,0,1,0,0,0,23,m,South Asian,no,no,India,no,2,18 and more,Self,NO +1,0,1,0,0,0,1,0,0,0,36,m,White-European,no,no,New Zealand,no,3,18 and more,Self,NO +1,1,0,0,1,0,0,1,0,0,33,m,Turkish,no,no,Armenia,no,4,18 and more,Self,NO +1,0,0,0,0,1,0,1,1,1,24,m,South Asian,no,no,India,no,5,18 and more,Self,NO +0,1,1,0,1,0,0,1,0,0,25,m,?,no,no,New Zealand,no,4,18 and more,?,NO +1,0,0,0,1,1,0,1,0,1,25,m,Asian,no,no,India,no,5,18 and more,Self,NO +1,0,0,0,0,0,1,1,1,1,25,m,Asian,no,no,India,no,5,18 and more,Self,NO +0,0,0,0,0,0,1,1,0,1,24,m,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,1,0,1,0,0,1,0,1,27,f,South Asian,no,no,India,no,5,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,0,26,m,Asian,no,no,India,no,3,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,26,m,Others,no,no,New Zealand,no,0,18 and more,Self,NO +1,0,0,0,1,0,0,1,1,0,23,f,Asian,no,no,India,no,4,18 and more,Self,NO +1,1,1,0,1,0,1,1,0,1,30,m,Asian,no,no,New Zealand,no,7,18 and more,Self,YES +1,0,0,1,0,0,0,0,1,0,25,m,Asian,no,no,Iceland,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,23,m,Pasifika,no,no,New Zealand,no,3,18 and more,Self,NO +1,0,1,1,1,0,0,1,0,0,34,f,White-European,no,no,United States,no,5,18 and more,Self,NO +1,0,0,1,1,0,1,1,0,1,20,f,Asian,no,no,New Zealand,no,6,18 and more,Self,NO +1,0,1,1,1,1,0,0,1,1,22,f,?,no,yes,Russia,no,7,18 and more,?,YES +1,1,1,1,0,0,0,1,1,1,33,f,Black,no,yes,France,no,7,18 and more,Parent,YES +1,1,1,1,1,1,1,1,1,1,46,m,White-European,no,no,Netherlands,no,10,18 and more,Self,YES +1,1,0,0,0,0,0,0,0,0,17,m,White-European,no,no,United Kingdom,no,2,18 and more,Others,NO +1,1,1,0,0,0,0,1,0,0,45,f,White-European,no,yes,United States,no,4,18 and more,Self,NO +0,1,1,1,0,0,0,0,1,0,26,m,Asian,no,no,India,no,4,18 and more,Others,NO +1,0,0,0,1,0,0,0,1,0,30,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,1,0,0,0,0,0,0,0,0,32,f,?,no,yes,Jordan,no,2,18 and more,?,NO +1,0,1,1,1,1,1,1,1,1,38,f,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +0,1,1,0,1,0,0,0,0,0,26,m,Hispanic,yes,no,Nicaragua,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,0,0,0,29,f,Hispanic,no,yes,United States,no,1,18 and more,Self,NO +1,1,1,1,1,0,0,1,1,1,33,m,White-European,no,no,Australia,no,8,18 and more,Self,YES +0,1,1,0,1,1,1,0,1,1,32,f,Black,no,no,France,no,7,18 and more,Parent,YES +1,1,1,1,1,1,1,1,1,1,44,f,White-European,no,no,United States,no,10,18 and more,Self,YES +0,1,0,0,0,0,1,0,0,1,47,f,White-European,yes,no,United Kingdom,no,3,18 and more,Self,NO +0,0,0,1,1,1,1,0,1,1,32,f,?,no,no,Hong Kong,no,6,18 and more,?,NO +0,0,0,0,0,0,0,0,0,0,40,m,White-European,no,no,New Zealand,no,0,18 and more,Self,NO +1,0,0,1,1,0,0,1,0,1,40,m,White-European,no,no,United States,no,5,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,44,f,White-European,no,no,New Zealand,no,0,18 and more,Self,NO +1,1,1,0,1,1,1,0,1,1,56,m,White-European,yes,no,United Kingdom,no,8,18 and more,Self,YES +1,1,1,0,1,1,1,0,1,1,23,f,White-European,no,no,Ireland,no,8,18 and more,Self,YES +1,1,0,1,1,0,0,1,1,1,32,f,Black,no,no,Canada,no,7,18 and more,Self,YES +0,1,1,1,0,0,0,1,0,1,27,m,White-European,no,yes,United Kingdom,no,5,18 and more,Self,NO +1,0,0,0,1,0,1,0,0,1,28,f,White-European,no,yes,United Kingdom,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,40,f,Middle Eastern ,no,no,Afghanistan,no,0,18 and more,Parent,NO +1,0,1,1,1,1,0,0,1,1,45,f,White-European,no,no,Canada,no,7,18 and more,Self,YES +1,1,1,1,1,1,0,0,1,1,19,f,White-European,yes,no,United Kingdom,no,8,18 and more,Parent,YES +1,1,1,1,0,1,0,1,1,1,55,m,White-European,no,yes,United States,no,8,18 and more,Others,YES +1,0,0,1,0,0,1,1,0,1,27,f,White-European,yes,no,Australia,no,5,18 and more,Self,NO +1,1,1,1,1,1,1,0,1,1,19,f,White-European,yes,no,United Kingdom,no,9,18 and more,Self,YES +1,0,0,1,0,0,0,1,0,0,29,m,White-European,no,no,Ireland,no,3,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,33,f,White-European,yes,no,United Kingdom,no,10,18 and more,Self,YES +0,0,1,1,1,1,1,1,0,0,48,f,White-European,no,no,Netherlands,no,6,18 and more,Self,NO +0,0,0,0,0,0,0,1,1,0,37,f,Others,no,no,United Kingdom,no,2,18 and more,Self,NO +1,1,1,1,1,0,0,0,1,0,30,m,Asian,no,no,Afghanistan,no,6,18 and more,Self,NO +1,1,0,1,0,0,0,0,0,1,37,f,White-European,no,no,United Kingdom,no,4,18 and more,Self,NO +1,0,0,1,1,0,1,1,1,1,28,f,?,no,no,Saudi Arabia,no,7,18 and more,?,YES +1,0,0,1,1,1,1,1,1,1,20,f,White-European,no,no,Austria,no,8,18 and more,Self,YES +1,1,1,1,1,1,0,0,1,1,25,f,Others,no,no,United States,no,8,18 and more,Self,YES +1,1,1,1,1,1,0,1,0,0,58,f,Middle Eastern ,no,no,United Kingdom,no,7,18 and more,Self,YES +1,0,1,0,0,0,0,0,0,0,36,f,White-European,no,no,United States,no,2,18 and more,Self,NO +0,0,1,1,1,0,0,1,0,0,36,m,White-European,yes,no,United States,no,4,18 and more,Self,NO +1,0,1,0,1,1,1,1,1,0,19,m,Hispanic,no,no,United States,no,7,18 and more,Self,YES +1,0,1,1,1,1,0,1,1,0,19,m,Hispanic,no,no,United States,yes,7,18 and more,Self,YES +1,0,0,1,1,0,0,1,0,1,22,f,White-European,no,no,New Zealand,no,5,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,20,m,?,no,no,United Arab Emirates,no,3,18 and more,?,NO +0,1,0,0,0,0,1,1,0,1,24,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,0,0,1,1,0,0,1,1,0,19,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,1,1,0,1,0,0,1,0,1,21,m,Others,no,no,United Arab Emirates,no,6,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,20,m,Middle Eastern ,no,no,United Arab Emirates,no,0,18 and more,Self,NO +1,1,0,0,0,0,0,0,0,0,18,f,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +0,1,1,0,0,0,0,1,0,1,23,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,1,1,0,1,1,1,1,0,1,22,m,Middle Eastern ,no,no,United Arab Emirates,no,8,18 and more,Self,YES +1,1,0,1,1,0,0,0,0,0,18,f,South Asian,no,yes,Bangladesh,no,4,18 and more,Self,NO +1,0,0,0,0,0,0,0,0,1,37,f,?,no,no,United Arab Emirates,no,2,18 and more,?,NO +1,1,1,0,1,0,0,1,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,5,18 and more,Relative,NO +1,1,1,0,1,0,0,1,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,5,18 and more,Relative,NO +0,0,0,0,1,0,0,1,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Relative,NO +1,1,1,0,0,0,0,1,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Relative,NO +0,1,0,0,0,0,1,1,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Relative,NO +1,1,1,0,0,0,0,1,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Relative,NO +1,1,1,1,1,1,1,1,1,1,19,m,White-European,no,no,Austria,no,10,18 and more,Self,YES +1,0,0,1,1,1,1,0,1,1,55,m,White-European,no,no,United Kingdom,no,7,18 and more,Self,YES +1,0,0,0,0,1,1,0,1,1,32,f,Asian,no,no,Canada,no,5,18 and more,Parent,NO +1,1,0,1,0,0,1,1,0,0,50,m,White-European,yes,no,United Kingdom,no,5,18 and more,Self,NO +1,1,1,1,0,0,1,1,0,0,40,m,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,0,0,1,1,1,0,0,0,0,47,m,Middle Eastern ,no,no,Jordan,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,0,0,1,20,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,0,1,0,0,0,1,1,0,0,22,f,?,no,no,Jordan,no,4,18 and more,?,NO +1,0,0,0,0,0,1,0,0,0,21,f,?,no,no,Jordan,no,2,18 and more,?,NO +1,0,1,1,0,0,1,1,1,0,21,f,?,no,no,Jordan,no,6,18 and more,?,NO +1,1,1,1,1,0,0,1,0,0,19,m,?,no,no,Jordan,no,6,18 and more,?,NO +1,0,1,1,0,0,0,0,0,0,21,f,?,no,no,Jordan,no,3,18 and more,?,NO +1,0,0,1,0,0,0,0,0,0,21,m,?,no,no,Jordan,no,2,18 and more,?,NO +0,1,0,1,0,1,0,1,0,1,23,f,?,no,no,Jordan,no,5,18 and more,?,NO +1,0,0,0,0,0,0,0,0,0,21,m,?,no,no,Jordan,no,1,18 and more,?,NO +1,0,0,0,1,0,1,1,1,0,21,m,?,no,no,Jordan,no,5,18 and more,?,NO +0,0,0,0,0,0,0,1,0,0,23,m,?,no,no,Jordan,no,1,18 and more,?,NO +1,0,1,1,0,1,1,1,1,1,20,f,?,no,no,Jordan,no,8,18 and more,?,YES +1,1,1,0,0,0,0,1,0,1,21,f,?,no,no,Jordan,no,5,18 and more,?,NO +1,1,1,0,1,0,1,1,1,1,20,m,?,no,no,Argentina,no,8,18 and more,?,YES +1,1,0,0,0,0,1,1,0,0,19,m,?,no,no,Jordan,no,4,18 and more,?,NO +0,1,0,0,0,0,0,1,0,0,21,m,?,no,no,Jordan,no,2,18 and more,?,NO +1,0,1,0,0,0,1,0,0,0,26,m,?,no,yes,Jordan,no,3,18 and more,?,NO +0,0,0,1,0,0,0,1,0,0,21,m,?,no,no,Jordan,no,2,18 and more,?,NO +0,0,0,0,1,0,0,1,0,0,19,m,?,no,no,Japan,no,2,18 and more,?,NO +1,0,0,1,0,0,0,1,0,1,22,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Parent,NO +1,0,0,0,0,0,0,1,0,0,19,f,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +1,0,0,1,0,0,1,0,0,1,22,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,0,1,0,0,0,0,0,0,1,20,m,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +1,0,1,0,1,0,0,1,0,0,23,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,0,0,0,0,1,0,1,0,0,19,f,?,no,no,Ukraine,no,2,18 and more,?,NO +0,0,0,0,1,0,0,1,0,1,20,m,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Parent,NO +0,1,0,1,0,0,0,0,0,0,24,m,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +0,1,1,0,0,0,0,1,0,1,19,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,0,0,0,0,1,0,1,0,1,20,m,?,no,no,United Arab Emirates,no,4,18 and more,?,NO +1,0,1,0,1,0,0,1,0,1,23,m,Middle Eastern ,no,yes,United Arab Emirates,no,5,18 and more,Self,NO +0,0,1,1,0,0,1,0,0,1,20,m,Others,no,no,Russia,no,4,18 and more,Self,NO +1,0,1,0,1,0,0,1,0,1,20,m,Middle Eastern ,no,no,United Arab Emirates,no,5,18 and more,Self,NO +0,0,0,0,0,0,0,1,1,0,20,m,?,yes,no,United Arab Emirates,no,2,18 and more,?,NO +1,0,1,0,0,0,0,0,0,1,20,f,?,no,no,United Arab Emirates,no,3,18 and more,?,NO +1,0,0,1,1,0,0,1,0,1,21,f,Asian,no,no,Afghanistan,no,5,18 and more,Self,NO +0,1,0,0,0,0,0,0,0,1,19,m,?,no,no,Kazakhstan,no,2,18 and more,?,NO +1,0,0,0,0,0,0,1,0,1,20,f,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +1,0,0,1,1,0,1,0,0,1,20,m,Middle Eastern ,no,no,Saudi Arabia,no,5,18 and more,Self,NO +1,0,0,0,0,1,0,0,1,0,25,m,Middle Eastern ,yes,yes,Armenia,yes,3,18 and more,Relative,NO +1,0,0,0,0,0,0,1,0,0,20,f,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +1,0,0,0,0,1,1,1,0,1,22,m,Middle Eastern ,no,no,AmericanSamoa,no,5,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,20,f,?,no,no,United Arab Emirates,no,2,18 and more,?,NO +0,1,0,0,1,1,0,0,0,0,20,m,?,no,no,United Arab Emirates,no,3,18 and more,?,NO +0,0,0,1,0,0,1,0,0,0,20,f,?,no,no,Jordan,no,2,18 and more,?,NO +1,0,0,0,0,0,0,1,0,0,23,f,?,no,no,Jordan,no,2,18 and more,?,NO +1,1,1,1,1,0,1,0,1,1,21,f,?,no,no,Jordan,no,8,18 and more,?,YES +1,0,0,0,0,0,1,1,0,1,22,m,?,no,no,Jordan,no,4,18 and more,?,NO +1,1,0,0,0,0,0,1,0,0,21,f,?,no,no,Jordan,no,3,18 and more,?,NO +1,0,1,0,0,0,0,0,0,1,20,f,?,no,no,Jordan,no,3,18 and more,?,NO +1,0,0,1,0,0,0,0,0,0,22,f,?,no,no,Jordan,no,2,18 and more,?,NO +1,0,1,1,1,0,1,1,0,0,21,f,?,yes,no,Jordan,no,6,18 and more,?,NO +1,1,0,0,1,0,0,1,0,0,20,f,?,no,no,Jordan,no,4,18 and more,?,NO +0,1,0,0,0,0,0,1,0,0,20,f,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +1,0,0,0,1,0,0,1,0,0,21,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,0,1,1,1,0,0,0,1,1,21,m,Middle Eastern ,no,no,United Arab Emirates,no,6,18 and more,Self,NO +0,1,0,0,0,1,0,1,0,1,23,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,21,f,Turkish,no,no,United Arab Emirates,no,3,18 and more,Self,NO +1,0,0,0,1,1,0,1,0,1,22,f,Black,no,no,United Arab Emirates,no,5,18 and more,Self,NO +0,1,1,1,0,1,0,1,0,0,19,f,?,no,no,United Arab Emirates,no,5,18 and more,?,NO +1,1,1,0,1,0,0,0,0,0,20,f,Asian,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,1,0,0,0,0,0,1,0,1,21,f,Others,no,no,United Arab Emirates,no,3,18 and more,Self,NO +0,1,1,0,0,0,0,1,0,0,26,m,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,0,0,0,19,f,?,no,no,Kazakhstan,no,1,18 and more,?,NO +1,0,0,1,0,0,1,1,0,1,20,f,?,no,no,United Arab Emirates,no,5,18 and more,?,NO +1,0,0,0,0,0,0,1,0,1,26,f,?,no,no,United Arab Emirates,no,3,18 and more,?,NO +1,0,0,1,0,0,1,0,0,1,23,f,Black,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,0,0,1,0,0,1,0,0,1,28,f,?,no,no,United Arab Emirates,no,4,18 and more,?,NO +0,1,0,1,1,0,0,0,0,0,19,f,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,0,20,f,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +0,1,0,0,1,0,1,1,0,1,19,f,Black,no,no,United Arab Emirates,no,5,18 and more,Self,NO +1,0,1,1,1,1,1,1,1,0,24,m,Asian,no,no,India,no,8,18 and more,Self,YES +1,0,0,1,1,0,0,1,0,1,19,f,South Asian,no,no,United Arab Emirates,no,5,18 and more,Self,NO +1,1,0,0,0,0,0,0,0,0,22,f,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +1,1,0,1,0,0,0,0,0,1,20,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,1,0,1,0,0,0,1,0,0,21,m,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +0,0,1,1,1,0,0,1,0,0,24,f,Others,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,0,1,1,1,0,0,0,0,1,18,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,0,1,1,1,0,1,1,0,1,19,f,Middle Eastern ,no,no,United Arab Emirates,no,7,18 and more,Self,YES +1,0,1,0,0,0,0,0,0,0,19,f,Middle Eastern ,no,no,United Arab Emirates,no,2,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,1,31,m,Middle Eastern ,yes,no,United Arab Emirates,no,4,18 and more,Self,NO +1,1,0,1,0,0,1,1,0,0,19,m,Middle Eastern ,no,no,United Arab Emirates,no,5,18 and more,Self,NO +1,0,0,0,0,0,1,0,1,1,21,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,0,0,0,1,0,1,0,0,1,20,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +1,0,1,0,1,0,1,1,0,1,26,f,Middle Eastern ,no,no,United Arab Emirates,no,6,18 and more,Self,NO +0,0,0,0,0,0,1,0,0,0,27,m,Middle Eastern ,no,no,Afghanistan,no,1,18 and more,Self,NO +1,0,1,0,0,1,0,1,0,0,23,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,1,0,1,0,0,0,0,0,0,29,f,?,no,no,United Arab Emirates,no,2,18 and more,?,NO +1,1,1,1,0,0,1,0,0,0,23,m,Middle Eastern ,no,no,United Arab Emirates,no,5,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,1,23,m,Turkish,no,no,United Arab Emirates,no,4,18 and more,Others,NO +1,0,1,1,1,1,0,1,1,1,24,m,Asian,no,yes,India,no,8,18 and more,Self,YES +1,0,0,1,0,0,1,1,0,0,21,m,?,no,no,Jordan,no,4,18 and more,?,NO +0,0,0,0,1,0,1,0,0,0,20,f,?,no,no,Brazil,yes,2,18 and more,?,NO +1,0,1,0,1,0,1,1,1,0,20,m,?,no,no,Jordan,no,6,18 and more,?,NO +1,1,1,1,1,1,1,0,1,1,32,f,Black,no,no,France,no,9,18 and more,Parent,YES +1,1,1,1,1,1,1,1,1,1,61,m,White-European,yes,yes,Uruguay,no,10,18 and more,Self,YES +1,0,0,1,1,0,0,0,0,0,19,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,1,1,1,1,1,0,1,1,0,35,f,White-European,no,no,Italy,no,8,18 and more,Self,YES +1,0,0,0,0,1,0,0,0,1,34,f,White-European,no,no,Ukraine,yes,3,18 and more,Self,NO +0,0,1,1,0,0,0,0,0,0,23,f,Middle Eastern ,no,no,United Arab Emirates,yes,2,18 and more,Self,NO +1,1,1,1,0,0,0,0,0,1,19,f,White-European,no,no,Serbia,no,5,18 and more,Self,NO +1,1,0,0,1,1,0,1,0,1,36,f,Others,no,no,Philippines,no,6,18 and more,Parent,NO +1,1,0,0,0,0,0,1,0,1,21,f,?,no,no,Jordan,no,4,18 and more,?,NO +0,1,0,0,0,0,0,1,0,0,23,m,White-European,no,no,United States,no,2,18 and more,Self,NO +1,0,1,1,1,0,1,0,0,1,17,m,Black,no,no,United States,no,6,18 and more,Relative,NO +1,0,1,0,0,0,0,0,0,0,22,m,White-European,no,no,Portugal,no,2,18 and more,Self,NO +0,1,1,1,1,0,1,0,0,1,33,m,White-European,no,yes,Australia,no,6,18 and more,Relative,NO +1,1,1,1,1,0,0,1,0,1,23,f,Asian,no,no,Malaysia,no,7,18 and more,Self,YES +0,1,1,1,1,1,0,1,0,1,35,f,White-European,no,yes,Sweden,no,7,18 and more,Self,YES +1,0,0,0,0,0,0,1,0,1,21,m,White-European,no,yes,Australia,no,3,18 and more,Self,NO +1,0,0,1,1,0,1,1,1,1,20,f,White-European,no,no,Austria,no,7,18 and more,Self,YES +1,0,0,0,0,0,0,1,0,0,29,m,White-European,no,no,Netherlands,no,2,18 and more,Health care professional,NO +1,1,1,1,1,1,1,0,1,1,59,m,White-European,no,no,United States,no,9,18 and more,Self,YES +1,1,1,0,0,0,0,1,0,1,18,f,White-European,no,no,Netherlands,no,5,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,19,f,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +0,0,1,1,0,0,0,1,0,0,24,m,Middle Eastern ,no,no,United Arab Emirates,no,3,18 and more,Self,NO +1,1,1,1,1,0,0,1,1,0,36,f,White-European,no,no,Italy,no,7,18 and more,Self,YES +0,0,1,1,1,0,0,0,0,1,19,f,?,yes,no,Jordan,no,4,18 and more,?,NO +1,1,0,0,0,0,1,0,0,0,17,f,Black,no,no,United Arab Emirates,no,3,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,52,m,White-European,yes,no,United Kingdom,no,3,18 and more,Self,NO +1,1,0,1,1,0,0,0,0,0,18,m,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,0,0,0,0,0,1,0,0,0,30,m,Asian,no,no,Philippines,no,1,18 and more,Self,NO +0,1,1,1,1,1,0,1,1,1,18,m,Middle Eastern ,yes,no,New Zealand,no,8,18 and more,Self,YES +1,1,1,1,1,0,0,1,0,0,27,m,Latino,no,no,Ecuador,no,6,18 and more,Self,NO +0,0,0,1,0,0,0,0,0,0,52,f,White-European,no,no,United Kingdom,no,1,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,0,45,f,Asian,no,no,Viet Nam,no,1,18 and more,Self,NO +0,1,1,1,1,1,0,0,1,0,25,f,Others,no,no,Afghanistan,no,6,18 and more,Health care professional,NO +0,0,0,1,0,0,1,0,0,1,29,f,White-European,no,no,United Kingdom,no,3,18 and more,Self,NO +1,1,0,1,1,1,1,1,0,1,29,f,White-European,yes,no,United States,no,8,18 and more,Self,YES +1,0,0,0,0,1,0,0,0,1,18,m,White-European,no,yes,Netherlands,no,3,18 and more,Self,NO +0,0,1,1,0,1,1,1,1,1,18,m,White-European,no,no,Netherlands,no,7,18 and more,Relative,YES +1,0,1,1,0,1,0,0,0,1,17,m,White-European,no,no,Netherlands,no,5,18 and more,Relative,NO +1,1,1,1,1,1,1,1,1,1,27,f,Asian,no,no,United States,no,10,18 and more,Self,YES +0,0,0,0,0,0,0,1,0,0,22,f,Asian,yes,no,Malaysia,no,1,18 and more,Self,NO +0,0,0,0,0,1,0,1,1,0,23,m,Asian,no,no,India,no,3,18 and more,Self,NO +1,1,1,1,1,0,1,1,0,0,25,m,Latino,no,no,United Kingdom,no,7,18 and more,Self,YES +1,0,1,1,1,0,0,0,0,0,23,f,South Asian,no,no,New Zealand,no,4,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,29,m,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,0,1,1,0,1,0,0,0,27,f,Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +1,0,0,0,0,1,0,1,0,0,27,f,Asian,no,no,India,no,3,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,43,f,South Asian,no,no,India,no,0,18 and more,Parent,NO +1,0,0,0,0,0,0,1,0,0,26,f,Asian,no,no,India,no,2,18 and more,Self,NO +1,0,0,0,1,0,0,1,0,1,30,f,Asian,no,no,India,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,0,23,m,White-European,no,no,India,no,1,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,0,29,f,Asian,no,no,Sri Lanka,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,1,37,m,White-European,no,no,United Kingdom,no,2,18 and more,Parent,NO +0,0,0,0,0,0,1,0,0,1,28,f,White-European,no,no,United Kingdom,no,2,18 and more,Self,NO +1,1,1,1,0,0,1,1,0,0,25,m,South Asian,no,no,United States,no,6,18 and more,Self,NO +1,0,1,1,0,0,1,1,0,0,25,m,Others,no,no,India,no,5,18 and more,Self,NO +0,1,0,0,0,0,0,1,1,0,21,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,1,1,1,1,1,1,0,1,1,43,m,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +1,1,0,1,1,1,0,0,1,1,30,f,White-European,no,no,United States,no,7,18 and more,Self,YES +0,1,1,0,0,0,1,0,0,1,18,m,Black,no,no,Niger,no,4,18 and more,Self,NO +1,1,1,1,0,0,0,1,0,0,18,f,White-European,no,no,Romania,no,5,18 and more,Self,NO +1,1,1,1,0,0,1,1,1,0,30,m,Others,no,no,Canada,no,7,18 and more,Self,YES +0,0,0,0,0,0,0,0,0,0,31,f,White-European,no,no,Germany,no,0,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,1,33,m,Latino,no,no,Mexico,no,5,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,33,m,Latino,no,no,Mexico,no,10,18 and more,Self,YES +1,1,1,1,0,0,1,0,0,1,26,m,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,0,1,1,0,0,0,0,0,0,61,f,White-European,no,no,United States,no,3,18 and more,Self,NO +0,0,0,1,0,0,0,1,0,1,46,m,Others,no,no,Viet Nam,no,3,18 and more,Self,NO +0,1,1,1,1,1,1,0,1,1,33,m,Asian,no,no,India,no,8,18 and more,Self,YES +1,1,1,1,1,0,1,1,0,1,38,f,Black,no,no,Canada,no,8,18 and more,Self,YES +1,1,0,0,0,0,0,1,0,0,44,m,Black,no,no,France,no,3,18 and more,Parent,NO +0,1,1,1,1,0,0,0,1,0,48,f,Black,no,yes,France,no,5,18 and more,Parent,NO +1,1,0,1,1,1,0,1,1,1,42,f,Latino,yes,no,Mexico,no,8,18 and more,Self,YES +0,1,0,0,1,0,1,1,0,0,37,m,White-European,no,no,Belgium,no,4,18 and more,Self,NO +1,0,1,1,1,0,0,1,0,1,23,f,White-European,no,yes,United Kingdom,no,6,18 and more,Self,NO +1,0,1,1,1,0,1,1,1,1,24,m,White-European,no,yes,United Kingdom,no,8,18 and more,Relative,YES +1,0,1,0,1,0,1,1,0,1,41,m,?,yes,yes,United Kingdom,no,6,18 and more,?,NO +1,0,1,0,0,0,1,0,0,1,41,m,Asian,no,no,United Kingdom,no,4,18 and more,Self,NO +0,1,1,0,1,0,0,1,0,0,27,m,White-European,no,no,Belgium,no,4,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,46,f,White-European,yes,yes,United Kingdom,no,10,18 and more,Self,YES +0,1,0,0,0,0,0,0,0,1,40,f,White-European,yes,no,United States,no,2,18 and more,Self,NO +0,1,1,1,1,1,1,1,0,1,22,f,White-European,no,no,United States,no,8,18 and more,Self,YES +1,1,1,1,1,1,1,0,1,1,42,m,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +1,1,1,0,0,0,0,0,0,1,18,f,Asian,no,no,Viet Nam,no,4,18 and more,Self,NO +1,0,1,1,0,0,0,0,0,0,44,f,White-European,no,no,United Kingdom,no,3,18 and more,Self,NO +1,1,1,1,1,0,1,1,1,1,30,f,White-European,no,no,United States,no,9,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,42,m,White-European,no,no,Australia,no,9,18 and more,Self,YES +1,1,0,1,0,0,1,0,0,0,35,f,White-European,no,yes,United States,no,4,18 and more,Parent,NO +1,0,1,1,1,0,1,0,1,1,40,m,Black,no,no,AmericanSamoa,no,7,18 and more,Self,YES +0,1,0,0,0,0,0,0,1,1,50,f,?,no,no,New Zealand,no,3,18 and more,?,NO +0,0,1,1,0,0,0,0,0,1,38,m,Middle Eastern ,no,no,Egypt,no,3,18 and more,Self,NO +1,1,1,1,1,0,0,1,1,1,26,f,White-European,no,yes,Italy,no,8,18 and more,Self,YES +1,1,1,1,1,0,0,1,0,1,27,f,White-European,no,no,Malaysia,no,7,18 and more,Self,YES +1,1,1,1,1,0,1,0,1,0,47,f,White-European,yes,yes,United States,no,7,18 and more,Self,YES +1,1,0,1,1,1,0,0,0,1,21,m,Black,yes,yes,United States,no,6,18 and more,Parent,NO +1,1,1,1,0,1,0,1,1,1,37,m,White-European,no,no,United Kingdom,no,8,18 and more,Self,YES +1,0,0,0,0,0,1,1,0,1,19,f,Others,no,no,United Kingdom,no,4,18 and more,Self,NO +0,0,0,1,0,0,1,1,0,1,32,m,Asian,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,0,0,0,1,0,0,1,0,1,26,f,?,no,no,Iran,no,3,18 and more,?,NO +1,1,1,1,1,1,1,1,1,1,46,f,White-European,yes,yes,Australia,no,10,18 and more,Self,YES +0,1,0,0,0,0,0,1,0,0,29,m,Black,no,no,France,no,2,18 and more,Self,NO +1,0,0,0,1,0,0,0,0,0,30,f,Black,no,no,France,no,2,18 and more,Self,NO +1,1,0,0,0,0,1,1,0,0,32,f,Latino,no,no,Bolivia,no,4,18 and more,Relative,NO +0,1,1,1,0,0,0,0,0,1,35,f,White-European,yes,no,United States,no,4,18 and more,Self,NO +0,1,1,1,1,1,1,1,1,1,22,m,White-European,no,no,United Kingdom,no,9,18 and more,Self,YES +0,0,0,1,1,0,1,0,0,1,19,f,?,no,no,Iran,no,4,18 and more,?,NO +0,1,0,0,1,0,0,0,0,1,24,m,?,no,no,Iran,no,3,18 and more,?,NO +0,1,0,0,1,0,0,0,0,0,52,f,?,no,no,Iran,no,2,18 and more,?,NO +0,1,0,0,1,1,0,0,1,1,52,m,?,no,no,Iran,no,5,18 and more,?,NO +1,0,1,0,0,1,0,1,0,0,32,f,White-European,no,no,United States,no,4,18 and more,Self,NO +0,1,1,1,1,1,0,0,0,1,24,m,White-European,no,no,United States,no,6,18 and more,Relative,NO +1,1,1,1,1,1,0,1,1,1,24,m,White-European,no,no,United States,no,9,18 and more,Relative,YES +0,1,0,0,0,0,0,0,0,0,49,f,Middle Eastern ,yes,no,New Zealand,no,1,18 and more,Parent,NO +0,1,1,1,1,1,1,1,1,1,30,f,Asian,no,no,Malaysia,no,9,18 and more,Self,YES +0,1,1,1,1,1,1,1,1,1,30,f,Asian,no,yes,Malaysia,yes,9,18 and more,Self,YES +1,0,1,1,1,1,1,1,1,1,35,f,White-European,yes,no,United Kingdom,no,9,18 and more,Self,YES +1,0,1,1,1,1,1,0,0,1,35,f,White-European,yes,no,United Kingdom,no,7,18 and more,Self,YES +1,0,1,1,0,0,0,0,0,1,37,f,White-European,no,yes,United Kingdom,no,4,18 and more,Self,NO +0,1,1,1,0,0,0,1,0,1,43,m,White-European,no,no,United Kingdom,no,5,18 and more,Self,NO +1,1,1,1,0,0,1,0,0,1,52,m,White-European,no,no,United Kingdom,no,6,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,44,m,White-European,no,yes,United Kingdom,no,10,18 and more,Self,YES +1,1,0,0,0,0,0,0,0,0,46,f,White-European,no,yes,United Kingdom,no,2,18 and more,Self,NO +1,0,0,0,1,0,0,0,0,1,42,f,White-European,no,yes,Australia,no,3,18 and more,Self,NO +0,0,1,0,0,0,0,0,0,0,20,m,Asian,no,no,Aruba,no,1,18 and more,Self,NO +1,1,1,0,1,0,1,0,1,1,18,m,Middle Eastern ,no,no,New Zealand,no,7,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,38,m,White-European,no,no,Finland,no,9,18 and more,Relative,YES +1,0,0,1,1,0,0,1,0,0,24,f,Latino,no,yes,Mexico,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,32,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,1,0,1,1,0,0,0,0,0,29,m,Turkish,no,no,United States,no,4,18 and more,Self,NO +1,1,1,1,1,1,1,1,0,1,22,f,White-European,no,no,United States,no,9,18 and more,Self,YES +0,1,1,1,1,1,0,0,0,1,19,f,White-European,yes,no,United Kingdom,no,6,18 and more,Self,NO +1,1,1,1,1,1,1,0,1,1,39,f,White-European,no,yes,United States,no,9,18 and more,Self,YES +1,0,0,1,1,1,1,1,1,1,17,m,Black,no,no,United States,no,8,18 and more,Self,YES +0,1,0,1,0,1,1,0,1,1,19,f,Middle Eastern ,no,no,Iceland,no,6,18 and more,Parent,NO +1,0,0,0,0,0,1,1,1,0,18,m,White-European,no,no,Australia,no,4,18 and more,Parent,NO +0,1,0,0,1,1,0,0,1,0,19,m,?,no,no,Kazakhstan,yes,4,18 and more,?,NO +0,1,0,0,0,0,0,1,0,0,28,f,Turkish,no,no,Turkey,no,2,18 and more,Self,NO +0,1,0,1,0,1,0,0,0,1,29,m,Asian,no,no,New Zealand,no,4,18 and more,Self,NO +1,1,0,0,0,0,1,1,0,0,48,m,White-European,no,no,Australia,no,4,18 and more,Self,NO +1,1,1,1,1,1,0,1,1,1,27,f,White-European,yes,yes,Netherlands,no,9,18 and more,Self,YES +1,0,1,1,0,0,0,0,0,0,34,f,White-European,no,no,United Kingdom,no,3,18 and more,Self,NO +1,1,0,1,0,0,0,0,0,1,31,m,White-European,no,no,Australia,no,4,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,47,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,0,0,1,0,0,0,1,0,0,47,m,?,no,no,Jordan,no,3,18 and more,?,NO +1,1,1,1,1,1,1,1,1,1,44,m,White-European,no,yes,United Kingdom,no,10,18 and more,Self,YES +1,0,0,0,1,0,1,1,0,0,25,f,White-European,no,no,United States,no,4,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,0,25,f,White-European,no,no,United States,no,4,18 and more,Self,NO +1,1,0,0,1,1,1,1,1,1,20,m,White-European,no,no,United Kingdom,no,8,18 and more,Self,YES +1,1,1,1,1,0,1,1,1,1,43,m,White-European,yes,no,New Zealand,no,9,18 and more,Self,YES +1,1,1,1,1,0,0,1,1,1,36,f,White-European,no,yes,United Kingdom,no,8,18 and more,Self,YES +1,1,0,0,0,0,0,1,0,0,30,f,Asian,no,yes,United States,no,3,18 and more,Self,NO +0,0,1,1,0,0,0,0,0,0,44,m,Black,no,no,United Kingdom,no,2,18 and more,Self,NO +1,1,0,0,1,0,0,0,0,1,40,f,Black,no,no,United Kingdom,no,4,18 and more,Parent,NO +1,1,0,0,1,1,0,1,1,0,40,f,Black,yes,no,United Kingdom,no,6,18 and more,Relative,NO +1,1,0,1,1,1,0,1,1,0,40,f,Black,yes,yes,United Kingdom,no,7,18 and more,Relative,YES +1,1,1,1,1,0,0,1,0,1,30,m,Others,no,no,United States,no,7,18 and more,Self,YES +1,0,1,1,0,0,1,0,0,1,47,f,White-European,no,no,United Kingdom,no,5,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,21,m,Black,no,no,Brazil,no,10,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,21,m,Black,no,no,Brazil,no,9,18 and more,Self,YES +1,0,1,1,1,1,1,1,1,1,31,f,?,no,no,New Zealand,no,9,18 and more,?,YES +1,1,1,0,0,0,0,0,0,0,21,f,Asian,no,no,New Zealand,no,3,18 and more,Self,NO +0,1,1,1,0,0,0,1,1,0,29,f,Asian,no,no,United States,no,5,18 and more,Self,NO +0,1,1,1,1,0,1,0,1,0,22,m,Middle Eastern ,no,no,Afghanistan,no,6,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,51,m,White-European,yes,no,United Kingdom,no,10,18 and more,Self,YES +1,0,0,0,0,0,0,0,0,0,23,m,?,no,no,Russia,no,1,18 and more,?,NO +1,0,0,0,1,0,0,0,1,1,30,m,Asian,no,no,India,no,4,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,0,22,m,Others,no,no,India,no,3,18 and more,Self,NO +1,1,0,0,0,0,0,1,0,1,22,m,Asian,no,no,New Zealand,no,4,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,1,24,m,Asian,no,no,India,no,5,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,1,26,f,South Asian,no,no,India,no,3,18 and more,Self,NO +1,0,1,1,0,0,0,1,1,1,33,f,Asian,no,no,India,no,6,18 and more,Parent,NO +1,0,0,0,0,0,0,1,1,0,23,m,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,1,0,0,0,0,1,0,0,42,f,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,1,0,0,0,0,0,0,0,26,f,Asian,no,no,India,no,2,18 and more,Self,NO +1,0,0,0,0,0,0,1,0,0,29,f,Asian,no,no,New Zealand,no,2,18 and more,Self,NO +1,0,0,0,0,0,1,0,0,1,35,f,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,0,0,1,1,0,1,0,0,25,f,South Asian,no,no,India,no,4,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,0,25,m,Asian,no,no,Sri Lanka,no,3,18 and more,Self,NO +0,0,0,0,0,0,0,1,0,0,28,f,Asian,yes,no,India,no,1,18 and more,Self,NO +1,0,0,0,0,0,0,0,0,0,33,m,White-European,no,no,Germany,no,1,18 and more,Self,NO +1,0,0,1,0,0,1,1,0,0,25,m,Asian,no,no,India,no,4,18 and more,Self,NO +0,0,1,0,1,1,0,1,0,1,25,m,Asian,no,no,India,no,5,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,0,24,m,Asian,no,no,India,no,4,18 and more,Self,NO +1,1,1,1,0,0,0,0,0,1,27,f,South Asian,no,no,India,no,5,18 and more,Self,NO +1,0,0,0,0,0,0,0,0,0,22,m,South Asian,no,no,India,no,1,18 and more,Self,NO +1,1,0,1,0,0,0,1,0,1,21,f,South Asian,no,no,India,no,5,18 and more,Self,NO +1,0,1,1,0,1,0,1,0,1,27,f,South Asian,no,no,India,no,6,18 and more,Self,NO +1,0,0,0,1,0,1,0,0,0,23,m,South Asian,no,no,India,no,3,18 and more,Self,NO +0,0,0,1,1,0,0,1,0,1,22,m,Asian,no,no,India,no,4,18 and more,Self,NO +0,1,0,0,0,0,0,0,0,1,23,f,South Asian,no,no,India,no,2,18 and more,Self,NO +1,0,0,1,0,0,0,1,0,1,30,m,Asian,no,no,India,no,4,18 and more,Self,NO +0,1,0,0,1,0,1,1,0,0,33,m,Asian,no,no,India,no,4,18 and more,Self,NO +1,0,1,1,1,0,1,1,1,0,24,f,South Asian,no,no,India,no,7,18 and more,Self,YES +1,0,0,0,1,0,1,1,0,0,22,f,Asian,no,no,New Zealand,no,4,18 and more,Self,NO +1,0,0,1,1,0,1,1,1,1,22,m,Asian,no,no,New Zealand,no,7,18 and more,Self,YES +1,0,0,0,0,0,0,1,0,1,29,f,South Asian,no,no,India,no,3,18 and more,Self,NO +1,0,1,0,1,0,1,1,0,1,22,f,Asian,no,no,India,no,6,18 and more,Self,NO +1,0,1,0,0,0,0,1,0,0,21,m,Asian,yes,no,India,no,3,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,0,23,m,Asian,no,no,India,no,3,18 and more,Self,NO +1,0,0,0,1,0,1,1,0,1,23,f,Others,no,no,India,no,5,18 and more,Self,NO +1,0,0,0,1,0,1,1,1,1,23,f,Asian,no,no,India,no,6,18 and more,Self,NO +1,1,0,1,1,0,1,1,0,0,32,m,Asian,no,no,Sri Lanka,no,6,18 and more,Self,NO +0,0,0,0,0,0,1,0,0,1,27,m,Asian,no,no,Sri Lanka,no,2,18 and more,Self,NO +0,0,1,0,0,0,0,1,0,0,28,f,Asian,no,no,India,no,2,18 and more,Parent,NO +1,1,0,0,1,0,0,1,1,1,25,f,Middle Eastern ,no,no,India,no,6,18 and more,Self,NO +1,0,1,0,1,0,1,1,1,1,22,m,Black,no,no,Nepal,no,7,18 and more,Self,YES +0,1,1,0,0,0,1,0,0,1,36,m,White-European,no,no,United Kingdom,no,4,18 and more,Self,NO +0,0,0,0,0,0,0,0,0,0,27,f,?,no,no,Russia,no,0,18 and more,?,NO +1,1,1,1,1,1,1,1,1,1,21,f,White-European,no,no,Germany,no,10,18 and more,Self,YES +1,1,0,1,1,0,0,1,0,0,18,m,Black,no,no,United States,no,5,18 and more,Self,NO +1,0,0,0,1,1,1,1,1,1,49,m,Black,no,no,Mexico,no,7,18 and more,Parent,YES +1,1,1,0,0,0,0,1,0,0,18,m,Asian,no,no,Indonesia,no,4,18 and more,Self,NO +1,0,1,1,1,0,1,1,1,0,29,m,Latino,no,no,United States,no,7,18 and more,Self,YES +0,0,0,0,1,0,0,1,0,0,31,f,?,no,no,New Zealand,no,2,18 and more,?,NO +1,0,0,0,0,0,0,0,0,0,37,f,Middle Eastern ,no,no,New Zealand,no,1,18 and more,Parent,NO +1,0,1,1,1,0,0,1,0,1,17,m,Middle Eastern ,yes,no,New Zealand,no,6,18 and more,Parent,NO +1,0,1,1,0,0,1,1,1,1,17,m,?,no,yes,New Zealand,no,7,18 and more,?,YES +1,1,1,1,0,0,0,0,0,0,38,f,Middle Eastern ,no,no,Jordan,no,4,18 and more,Self,NO +1,0,1,0,0,1,0,1,0,1,18,m,White-European,yes,no,Angola,no,5,18 and more,Parent,NO +1,0,1,1,1,0,0,0,0,0,31,f,Middle Eastern ,no,no,United Arab Emirates,no,4,18 and more,Self,NO +0,1,1,1,0,0,0,0,0,0,33,m,Middle Eastern ,yes,no,United Arab Emirates,yes,3,18 and more,Self,NO +1,1,0,0,0,0,0,0,0,0,32,f,Middle Eastern ,yes,no,Jordan,no,2,18 and more,Self,NO +1,0,0,0,0,0,1,0,0,1,30,m,?,yes,no,Jordan,no,3,18 and more,?,NO +0,0,0,0,0,0,0,0,0,1,33,f,?,no,no,United States,no,1,18 and more,?,NO +1,1,1,1,1,1,1,1,1,1,30,f,White-European,no,yes,Canada,no,10,18 and more,Self,YES +1,1,1,1,1,0,1,1,1,1,30,m,Asian,no,no,United States,no,9,18 and more,Self,YES +1,0,0,1,1,0,1,0,0,1,38,f,White-European,no,yes,United Kingdom,no,5,18 and more,Self,NO +1,0,0,0,1,0,1,0,1,1,22,m,Asian,no,no,India,no,5,18 and more,Self,NO +1,1,0,0,1,0,1,0,1,0,36,m,others,no,no,United States,no,5,18 and more,Self,NO +0,0,1,1,0,0,1,0,0,0,43,m,?,no,no,Azerbaijan,no,3,18 and more,?,NO +1,1,1,1,1,1,0,0,1,1,44,m,?,no,no,Pakistan,no,8,18 and more,?,YES +1,1,1,0,1,1,0,1,1,1,20,f,White-European,no,no,France,no,8,18 and more,Self,YES +1,1,1,1,1,1,0,1,1,1,40,f,Others,yes,yes,Australia,no,9,18 and more,Self,YES +1,0,0,1,1,1,0,0,1,0,25,m,White-European,no,no,Italy,no,5,18 and more,Self,NO +1,0,0,0,0,0,1,1,0,1,28,m,White-European,no,no,Australia,no,4,18 and more,Self,NO +0,0,0,0,0,0,1,1,0,1,17,m,White-European,no,no,Canada,no,3,18 and more,Self,NO +1,1,1,1,1,1,1,0,1,1,34,m,White-European,no,no,United States,no,9,18 and more,Self,YES +0,0,0,0,0,0,0,1,0,0,56,m,?,no,no,Iraq,no,1,18 and more,?,NO +0,0,1,0,0,0,1,1,0,0,50,f,Middle Eastern ,no,no,New Zealand,no,3,18 and more,Parent,NO +1,0,0,0,0,0,1,1,0,1,38,m,White-European,no,no,United States,no,4,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,47,m,White-European,no,no,United States,no,10,18 and more,Self,YES +1,1,1,1,1,0,1,1,1,1,30,m,Others,no,no,United States,no,9,18 and more,Self,YES +1,1,1,1,1,0,0,1,0,1,21,m,Hispanic,no,no,United States,no,7,18 and more,Self,YES +1,1,1,1,1,0,1,1,0,1,21,m,White-European,no,no,Ireland,no,8,18 and more,Self,YES +1,1,1,0,1,0,1,1,1,1,31,m,Latino,no,no,Mexico,no,8,18 and more,Self,YES +1,1,0,0,1,1,0,1,0,1,27,f,White-European,yes,no,Czech Republic,no,6,18 and more,Self,NO +1,1,1,1,0,0,0,0,1,0,24,f,White-European,yes,no,United States,no,5,18 and more,Self,NO +1,1,0,0,0,0,1,0,0,1,35,m,White-European,yes,no,United Kingdom,no,4,18 and more,Parent,NO +1,0,0,0,0,0,1,0,0,1,18,m,Black,no,no,Ethiopia,no,3,18 and more,Health care professional,NO +1,1,1,1,1,0,0,1,1,1,43,f,Black,no,no,United States,no,8,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,44,m,White-European,no,yes,Afghanistan,no,10,18 and more,Self,YES +1,1,1,1,1,1,1,1,1,1,40,m,White-European,yes,yes,United Kingdom,no,10,18 and more,Parent,YES +1,1,0,1,1,1,1,1,1,1,49,f,Hispanic,no,no,United States,no,9,18 and more,Self,YES +1,1,0,0,0,0,0,1,0,0,24,m,Hispanic,no,no,United States,no,3,18 and more,Self,NO +1,1,0,0,0,0,0,1,1,0,30,f,White-European,yes,yes,United States,no,4,18 and more,Self,NO +1,0,1,0,0,0,0,1,0,1,53,m,Hispanic,no,no,United States,no,4,18 and more,Relative,NO +1,0,1,1,1,0,0,1,1,1,38,m,White-European,no,yes,Belgium,no,7,18 and more,Self,YES +1,0,0,0,1,0,1,1,0,1,28,m,White-European,no,no,United States,no,5,18 and more,Self,NO +1,1,1,0,1,1,1,1,1,1,28,m,White-European,no,no,United States,no,9,18 and more,Self,YES +1,0,1,1,1,1,1,0,1,1,26,m,Black,no,no,Canada,no,8,18 and more,Self,YES +1,0,0,0,1,1,1,1,1,1,39,f,White-European,no,no,Canada,no,7,18 and more,Self,YES +0,0,0,0,1,0,0,0,0,0,31,f,Asian,yes,no,India,no,1,18 and more,Self,NO +1,0,0,1,0,0,1,1,0,0,24,m,Black,no,no,United States,no,4,18 and more,Self,NO +1,1,1,0,1,1,1,1,0,1,28,m,White-European,no,no,United States,no,8,18 and more,Self,YES +1,0,0,1,0,0,0,1,0,1,31,f,White-European,no,no,United Kingdom,no,4,18 and more,Self,NO +1,1,1,1,1,0,0,1,0,1,27,m,White-European,yes,no,United States,no,7,18 and more,Self,YES +1,0,1,1,0,0,1,1,0,0,28,m,Latino,no,no,Brazil,yes,5,18 and more,Parent,NO +1,1,1,1,1,1,0,1,1,1,31,m,Turkish,no,yes,Australia,no,9,18 and more,Self,YES +1,1,1,1,1,0,0,0,0,1,46,f,Asian,no,no,Philippines,no,6,18 and more,Self,NO +1,1,1,1,1,1,1,1,1,1,27,f,Pasifika,no,no,Australia,no,10,18 and more,Self,YES +0,1,0,1,1,0,1,1,1,1,25,f,White-European,no,no,Russia,no,7,18 and more,Self,YES +1,0,0,0,0,0,0,1,0,1,34,m,Hispanic,no,no,Mexico,no,3,18 and more,Parent,NO +1,0,1,1,1,0,1,1,0,1,24,f,?,no,no,Russia,no,7,18 and more,?,YES +1,0,0,1,1,0,1,0,1,1,35,m,South Asian,no,no,Pakistan,no,6,18 and more,Self,NO +1,0,1,1,1,0,1,1,1,1,26,f,White-European,no,no,Cyprus,no,8,18 and more,Self,YES diff --git a/Machine Learning and Data Science/Intermediate/Autism Identification System/autism_identification_notebook.ipynb b/Machine Learning and Data Science/Intermediate/Autism Identification System/autism_identification_notebook.ipynb new file mode 100644 index 000000000..5e685791a --- /dev/null +++ b/Machine Learning and Data Science/Intermediate/Autism Identification System/autism_identification_notebook.ipynb @@ -0,0 +1,2688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

AUTISM IDENTIFICATION SYSTEM

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

1. Import necessary libraies

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, accuracy_score, mean_squared_error, r2_score\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

2. Define Dataframe from csv file

" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
A1_ScoreA2_ScoreA3_ScoreA4_ScoreA5_ScoreA6_ScoreA7_ScoreA8_ScoreA9_ScoreA10_Scoreagegenderethnicityjundiceaustimcontry_of_resused_app_beforeresultage_descrelationClass/ASD
0111100110026.0fWhite-EuropeannonoUnited Statesno618 and moreSelfNO
1110100010124.0mLatinonoyesBrazilno518 and moreSelfNO
2110110111127.0mLatinoyesyesSpainno818 and moreParentYES
3110100110135.0fWhite-EuropeannoyesUnited Statesno618 and moreSelfNO
4100000010040.0f?nonoEgyptno218 and more?NO
\n", + "
" + ], + "text/plain": [ + " A1_Score A2_Score A3_Score A4_Score A5_Score A6_Score A7_Score \\\n", + "0 1 1 1 1 0 0 1 \n", + "1 1 1 0 1 0 0 0 \n", + "2 1 1 0 1 1 0 1 \n", + "3 1 1 0 1 0 0 1 \n", + "4 1 0 0 0 0 0 0 \n", + "\n", + " A8_Score A9_Score A10_Score age gender ethnicity jundice austim \\\n", + "0 1 0 0 26.0 f White-European no no \n", + "1 1 0 1 24.0 m Latino no yes \n", + "2 1 1 1 27.0 m Latino yes yes \n", + "3 1 0 1 35.0 f White-European no yes \n", + "4 1 0 0 40.0 f ? no no \n", + "\n", + " contry_of_res used_app_before result age_desc relation Class/ASD \n", + "0 United States no 6 18 and more Self NO \n", + "1 Brazil no 5 18 and more Self NO \n", + "2 Spain no 8 18 and more Parent YES \n", + "3 United States no 6 18 and more Self NO \n", + "4 Egypt no 2 18 and more ? NO " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_path = \"Data.csv\"\n", + "df = pd.read_csv(file_path, engine='python') #Implementation of 'engine'=python, we can able to analysis the larger dataset\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

3. Dataset Information

" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
A1_ScoreA2_ScoreA3_ScoreA4_ScoreA5_ScoreA6_ScoreA7_ScoreA8_ScoreA9_ScoreA10_Scoreageresult
count704.000000704.000000704.000000704.000000704.000000704.000000704.000000704.000000704.000000704.000000702.000000704.000000
mean0.7215910.4531250.4573860.4957390.4985800.2840910.4176140.6491480.3238640.57386429.6980064.875000
std0.4485350.4981520.4985350.5003370.5003530.4513010.4935160.4775760.4682810.49486616.5074652.501493
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00000017.0000000.000000
25%0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00000021.0000003.000000
50%1.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000001.00000027.0000004.000000
75%1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.00000035.0000007.000000
max1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000383.00000010.000000
\n", + "
" + ], + "text/plain": [ + " A1_Score A2_Score A3_Score A4_Score A5_Score A6_Score \\\n", + "count 704.000000 704.000000 704.000000 704.000000 704.000000 704.000000 \n", + "mean 0.721591 0.453125 0.457386 0.495739 0.498580 0.284091 \n", + "std 0.448535 0.498152 0.498535 0.500337 0.500353 0.451301 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " A7_Score A8_Score A9_Score A10_Score age result \n", + "count 704.000000 704.000000 704.000000 704.000000 702.000000 704.000000 \n", + "mean 0.417614 0.649148 0.323864 0.573864 29.698006 4.875000 \n", + "std 0.493516 0.477576 0.468281 0.494866 16.507465 2.501493 \n", + "min 0.000000 0.000000 0.000000 0.000000 17.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 21.000000 3.000000 \n", + "50% 0.000000 1.000000 0.000000 1.000000 27.000000 4.000000 \n", + "75% 1.000000 1.000000 1.000000 1.000000 35.000000 7.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 383.000000 10.000000 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

4. Remove Null values and outliers

" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Missing values
A1_Score0
A2_Score0
A3_Score0
A4_Score0
A5_Score0
A6_Score0
A7_Score0
A8_Score0
A9_Score0
A10_Score0
age2
gender0
ethnicity0
jundice0
austim0
contry_of_res0
used_app_before0
result0
age_desc0
relation0
Class/ASD0
\n", + "
" + ], + "text/plain": [ + " Missing values\n", + "A1_Score 0\n", + "A2_Score 0\n", + "A3_Score 0\n", + "A4_Score 0\n", + "A5_Score 0\n", + "A6_Score 0\n", + "A7_Score 0\n", + "A8_Score 0\n", + "A9_Score 0\n", + "A10_Score 0\n", + "age 2\n", + "gender 0\n", + "ethnicity 0\n", + "jundice 0\n", + "austim 0\n", + "contry_of_res 0\n", + "used_app_before 0\n", + "result 0\n", + "age_desc 0\n", + "relation 0\n", + "Class/ASD 0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(df.isnull().sum(),columns=['Missing values'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here , We can able to see that at column \"AGE\" has two missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot([df.age])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
A1_ScoreA2_ScoreA3_ScoreA4_ScoreA5_ScoreA6_ScoreA7_ScoreA8_ScoreA9_ScoreA10_Scoreagegenderethnicityjundiceaustimcontry_of_resused_app_beforeresultage_descrelationClass/ASD
521000000000383.0fPasifikanonoNew Zealandno118 and moreSelfNO
\n", + "
" + ], + "text/plain": [ + " A1_Score A2_Score A3_Score A4_Score A5_Score A6_Score A7_Score \\\n", + "52 1 0 0 0 0 0 0 \n", + "\n", + " A8_Score A9_Score A10_Score age gender ethnicity jundice austim \\\n", + "52 0 0 0 383.0 f Pasifika no no \n", + "\n", + " contry_of_res used_app_before result age_desc relation Class/ASD \n", + "52 New Zealand no 1 18 and more Self NO " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['age']==df['age'].max()]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "if 52 in df.index:\n", + " # Drop the row with index 52\n", + " df.drop(index=52, inplace=True)\n", + " \n", + " # Reset the index\n", + " df.reset_index(drop=True, inplace=True)\n", + "else:\n", + " print(\"Index '52' does not exist in the DataFrame.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df['age'] = df['age'].fillna(np.round(df['age'].mean(), 0))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Missing Values
A1_Score0
A2_Score0
A3_Score0
A4_Score0
A5_Score0
A6_Score0
A7_Score0
A8_Score0
A9_Score0
A10_Score0
age0
gender0
ethnicity0
jundice0
austim0
contry_of_res0
used_app_before0
result0
age_desc0
relation0
Class/ASD0
\n", + "
" + ], + "text/plain": [ + " Missing Values\n", + "A1_Score 0\n", + "A2_Score 0\n", + "A3_Score 0\n", + "A4_Score 0\n", + "A5_Score 0\n", + "A6_Score 0\n", + "A7_Score 0\n", + "A8_Score 0\n", + "A9_Score 0\n", + "A10_Score 0\n", + "age 0\n", + "gender 0\n", + "ethnicity 0\n", + "jundice 0\n", + "austim 0\n", + "contry_of_res 0\n", + "used_app_before 0\n", + "result 0\n", + "age_desc 0\n", + "relation 0\n", + "Class/ASD 0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(df.isnull().sum(), columns=[\"Missing Values\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 703 entries, 0 to 702\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 A1_Score 703 non-null int64 \n", + " 1 A2_Score 703 non-null int64 \n", + " 2 A3_Score 703 non-null int64 \n", + " 3 A4_Score 703 non-null int64 \n", + " 4 A5_Score 703 non-null int64 \n", + " 5 A6_Score 703 non-null int64 \n", + " 6 A7_Score 703 non-null int64 \n", + " 7 A8_Score 703 non-null int64 \n", + " 8 A9_Score 703 non-null int64 \n", + " 9 A10_Score 703 non-null int64 \n", + " 10 age 703 non-null float64\n", + " 11 gender 703 non-null object \n", + " 12 ethnicity 703 non-null object \n", + " 13 jundice 703 non-null object \n", + " 14 austim 703 non-null object \n", + " 15 contry_of_res 703 non-null object \n", + " 16 used_app_before 703 non-null object \n", + " 17 result 703 non-null int64 \n", + " 18 age_desc 703 non-null object \n", + " 19 relation 703 non-null object \n", + " 20 Class/ASD 703 non-null object \n", + "dtypes: float64(1), int64(11), object(9)\n", + "memory usage: 115.5+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

5. Replace the invalid values

" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "column names :- gender\n", + "\n", + "Unique values: \n", + " ['f' 'm'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- ethnicity\n", + "\n", + "Unique values: \n", + " ['White-European' 'Latino' '?' 'Others' 'Black' 'Asian' 'Middle Eastern '\n", + " 'Pasifika' 'South Asian' 'Hispanic' 'Turkish' 'others'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- jundice\n", + "\n", + "Unique values: \n", + " ['no' 'yes'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- austim\n", + "\n", + "Unique values: \n", + " ['no' 'yes'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- contry_of_res\n", + "\n", + "Unique values: \n", + " ['United States' 'Brazil' 'Spain' 'Egypt' 'New Zealand' 'Bahamas'\n", + " 'Burundi' 'Austria' 'Argentina' 'Jordan' 'Ireland' 'United Arab Emirates'\n", + " 'Afghanistan' 'Lebanon' 'United Kingdom' 'South Africa' 'Italy'\n", + " 'Pakistan' 'Bangladesh' 'Chile' 'France' 'China' 'Australia' 'Canada'\n", + " 'Saudi Arabia' 'Netherlands' 'Romania' 'Sweden' 'Tonga' 'Oman' 'India'\n", + " 'Philippines' 'Sri Lanka' 'Sierra Leone' 'Ethiopia' 'Viet Nam' 'Iran'\n", + " 'Costa Rica' 'Germany' 'Mexico' 'Russia' 'Armenia' 'Iceland' 'Nicaragua'\n", + " 'Hong Kong' 'Japan' 'Ukraine' 'Kazakhstan' 'AmericanSamoa' 'Uruguay'\n", + " 'Serbia' 'Portugal' 'Malaysia' 'Ecuador' 'Niger' 'Belgium' 'Bolivia'\n", + " 'Aruba' 'Finland' 'Turkey' 'Nepal' 'Indonesia' 'Angola' 'Azerbaijan'\n", + " 'Iraq' 'Czech Republic' 'Cyprus'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- used_app_before\n", + "\n", + "Unique values: \n", + " ['no' 'yes'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- age_desc\n", + "\n", + "Unique values: \n", + " ['18 and more'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- relation\n", + "\n", + "Unique values: \n", + " ['Self' 'Parent' '?' 'Health care professional' 'Relative' 'Others'] \n", + "\n", + "-----------------------------------------------------------------------\n", + "column names :- Class/ASD\n", + "\n", + "Unique values: \n", + " ['NO' 'YES'] \n", + "\n", + "-----------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "for col in df.select_dtypes('object').columns:\n", + " print(f'column names :- {col}\\n')\n", + " print(f'Unique values: \\n {df[col].unique()} \\n')\n", + " print('-----------------------------------------------------------------------')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the Relation with '?' values as maximum frequency mode" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Self', 'Parent', 'Health care professional', 'Relative', 'Others'],\n", + " dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['relation'] = df['relation'].replace('?',df['relation'].mode()[0])\n", + "df['relation'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the ethnicity values with others value" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['White-European', 'Latino', 'Others', 'Black', 'Asian',\n", + " 'Middle Eastern ', 'Pasifika', 'South Asian', 'Hispanic',\n", + " 'Turkish'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['ethnicity'] = df['ethnicity'].replace('?','Others')\n", + "df['ethnicity'] = df['ethnicity'].replace('others','Others')\n", + "df['ethnicity'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

6. Visualization

" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'No. of peoples')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(data=df,x='gender')\n", + "plt.xlabel('Gender')\n", + "plt.ylabel('No. of peoples')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'No. of People accepted they have Autism condition')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_gender = df['Class/ASD'].value_counts()\n", + "plt.pie(df_gender,labels=['Yes','No'],autopct='%1.1f%%')\n", + "plt.title('No. of People accepted they have Autism condition')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_country = df['contry_of_res'].value_counts()\n", + "d = dict(df_country)\n", + "coun = []\n", + "ind = []\n", + "for i,j in d.items():\n", + " coun.append(i)\n", + " ind.append(j)\n", + "\n", + "plt.figure(figsize=(10,10))\n", + "ax = sns.barplot(x=ind,y=coun)\n", + "ax.set_yticklabels(coun,fontsize = 5,fontweight='bold')\n", + "plt.xlabel('No. of peoples having Autism condition')\n", + "plt.ylabel('Countries')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

7. Pre-processing Data for Classification

" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(['age_desc','used_app_before'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "x = df.drop(['Class/ASD'],axis=1)\n", + "y = df['Class/ASD']" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
A1_ScoreA2_ScoreA3_ScoreA4_ScoreA5_ScoreA6_ScoreA7_ScoreA8_ScoreA9_ScoreA10_Scoreagegenderethnicityjundiceaustimcontry_of_resresultrelation
0111100110026.0fWhite-EuropeannonoUnited States6Self
1110100010124.0mLatinonoyesBrazil5Self
2110110111127.0mLatinoyesyesSpain8Parent
3110100110135.0fWhite-EuropeannoyesUnited States6Self
4100000010040.0fOthersnonoEgypt2Self
\n", + "
" + ], + "text/plain": [ + " A1_Score A2_Score A3_Score A4_Score A5_Score A6_Score A7_Score \\\n", + "0 1 1 1 1 0 0 1 \n", + "1 1 1 0 1 0 0 0 \n", + "2 1 1 0 1 1 0 1 \n", + "3 1 1 0 1 0 0 1 \n", + "4 1 0 0 0 0 0 0 \n", + "\n", + " A8_Score A9_Score A10_Score age gender ethnicity jundice austim \\\n", + "0 1 0 0 26.0 f White-European no no \n", + "1 1 0 1 24.0 m Latino no yes \n", + "2 1 1 1 27.0 m Latino yes yes \n", + "3 1 0 1 35.0 f White-European no yes \n", + "4 1 0 0 40.0 f Others no no \n", + "\n", + " contry_of_res result relation \n", + "0 United States 6 Self \n", + "1 Brazil 5 Self \n", + "2 Spain 8 Parent \n", + "3 United States 6 Self \n", + "4 Egypt 2 Self " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "X = pd.get_dummies(x)\n", + "Y = y.replace({\"YES\": int(1), \"NO\": int(0)})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['A1_Score', 'A2_Score', 'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score',\n", + " 'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score', 'age', 'result',\n", + " 'gender_f', 'gender_m', 'ethnicity_Asian', 'ethnicity_Black',\n", + " 'ethnicity_Hispanic', 'ethnicity_Latino', 'ethnicity_Middle Eastern ',\n", + " 'ethnicity_Others', 'ethnicity_Pasifika', 'ethnicity_South Asian',\n", + " 'ethnicity_Turkish', 'ethnicity_White-European', 'jundice_no',\n", + " 'jundice_yes', 'austim_no', 'austim_yes', 'contry_of_res_Afghanistan',\n", + " 'contry_of_res_AmericanSamoa', 'contry_of_res_Angola',\n", + " 'contry_of_res_Argentina', 'contry_of_res_Armenia',\n", + " 'contry_of_res_Aruba', 'contry_of_res_Australia',\n", + " 'contry_of_res_Austria', 'contry_of_res_Azerbaijan',\n", + " 'contry_of_res_Bahamas', 'contry_of_res_Bangladesh',\n", + " 'contry_of_res_Belgium', 'contry_of_res_Bolivia',\n", + " 'contry_of_res_Brazil', 'contry_of_res_Burundi', 'contry_of_res_Canada',\n", + " 'contry_of_res_Chile', 'contry_of_res_China',\n", + " 'contry_of_res_Costa Rica', 'contry_of_res_Cyprus',\n", + " 'contry_of_res_Czech Republic', 'contry_of_res_Ecuador',\n", + " 'contry_of_res_Egypt', 'contry_of_res_Ethiopia',\n", + " 'contry_of_res_Finland', 'contry_of_res_France',\n", + " 'contry_of_res_Germany', 'contry_of_res_Hong Kong',\n", + " 'contry_of_res_Iceland', 'contry_of_res_India',\n", + " 'contry_of_res_Indonesia', 'contry_of_res_Iran', 'contry_of_res_Iraq',\n", + " 'contry_of_res_Ireland', 'contry_of_res_Italy', 'contry_of_res_Japan',\n", + " 'contry_of_res_Jordan', 'contry_of_res_Kazakhstan',\n", + " 'contry_of_res_Lebanon', 'contry_of_res_Malaysia',\n", + " 'contry_of_res_Mexico', 'contry_of_res_Nepal',\n", + " 'contry_of_res_Netherlands', 'contry_of_res_New Zealand',\n", + " 'contry_of_res_Nicaragua', 'contry_of_res_Niger', 'contry_of_res_Oman',\n", + " 'contry_of_res_Pakistan', 'contry_of_res_Philippines',\n", + " 'contry_of_res_Portugal', 'contry_of_res_Romania',\n", + " 'contry_of_res_Russia', 'contry_of_res_Saudi Arabia',\n", + " 'contry_of_res_Serbia', 'contry_of_res_Sierra Leone',\n", + " 'contry_of_res_South Africa', 'contry_of_res_Spain',\n", + " 'contry_of_res_Sri Lanka', 'contry_of_res_Sweden',\n", + " 'contry_of_res_Tonga', 'contry_of_res_Turkey', 'contry_of_res_Ukraine',\n", + " 'contry_of_res_United Arab Emirates', 'contry_of_res_United Kingdom',\n", + " 'contry_of_res_United States', 'contry_of_res_Uruguay',\n", + " 'contry_of_res_Viet Nam', 'relation_Health care professional',\n", + " 'relation_Others', 'relation_Parent', 'relation_Relative',\n", + " 'relation_Self'],\n", + " dtype='object')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 1\n", + "3 0\n", + "4 0\n", + "Name: Class/ASD, dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "X_train1,X_test1,Y_train1,Y_test1 = train_test_split(X,Y,test_size=0.25, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
A1_ScoreA2_ScoreA3_ScoreA4_ScoreA5_ScoreA6_ScoreA7_ScoreA8_ScoreA9_ScoreA10_Scoreageresultgender_fgender_methnicity_Asianethnicity_Blackethnicity_Hispanicethnicity_Latinoethnicity_Middle Easternethnicity_Othersethnicity_Pasifikaethnicity_South Asianethnicity_Turkishethnicity_White-Europeanjundice_nojundice_yesaustim_noaustim_yescontry_of_res_Afghanistancontry_of_res_AmericanSamoacontry_of_res_Angolacontry_of_res_Argentinacontry_of_res_Armeniacontry_of_res_Arubacontry_of_res_Australiacontry_of_res_Austriacontry_of_res_Azerbaijancontry_of_res_Bahamascontry_of_res_Bangladeshcontry_of_res_Belgiumcontry_of_res_Boliviacontry_of_res_Brazilcontry_of_res_Burundicontry_of_res_Canadacontry_of_res_Chilecontry_of_res_Chinacontry_of_res_Costa Ricacontry_of_res_Cypruscontry_of_res_Czech Republiccontry_of_res_Ecuadorcontry_of_res_Egyptcontry_of_res_Ethiopiacontry_of_res_Finlandcontry_of_res_Francecontry_of_res_Germanycontry_of_res_Hong Kongcontry_of_res_Icelandcontry_of_res_Indiacontry_of_res_Indonesiacontry_of_res_Irancontry_of_res_Iraqcontry_of_res_Irelandcontry_of_res_Italycontry_of_res_Japancontry_of_res_Jordancontry_of_res_Kazakhstancontry_of_res_Lebanoncontry_of_res_Malaysiacontry_of_res_Mexicocontry_of_res_Nepalcontry_of_res_Netherlandscontry_of_res_New Zealandcontry_of_res_Nicaraguacontry_of_res_Nigercontry_of_res_Omancontry_of_res_Pakistancontry_of_res_Philippinescontry_of_res_Portugalcontry_of_res_Romaniacontry_of_res_Russiacontry_of_res_Saudi Arabiacontry_of_res_Serbiacontry_of_res_Sierra Leonecontry_of_res_South Africacontry_of_res_Spaincontry_of_res_Sri Lankacontry_of_res_Swedencontry_of_res_Tongacontry_of_res_Turkeycontry_of_res_Ukrainecontry_of_res_United Arab Emiratescontry_of_res_United Kingdomcontry_of_res_United Statescontry_of_res_Uruguaycontry_of_res_Viet Namrelation_Health care professionalrelation_Othersrelation_Parentrelation_Relativerelation_Self
176001110011129.06FalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
223111111101121.09TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrue
213111100000142.05FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
239100110110021.05FalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
182001110000134.04FalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
\n", + "
" + ], + "text/plain": [ + " A1_Score A2_Score A3_Score A4_Score A5_Score A6_Score A7_Score \\\n", + "176 0 0 1 1 1 0 0 \n", + "223 1 1 1 1 1 1 1 \n", + "213 1 1 1 1 0 0 0 \n", + "239 1 0 0 1 1 0 1 \n", + "182 0 0 1 1 1 0 0 \n", + "\n", + " A8_Score A9_Score A10_Score age result gender_f gender_m \\\n", + "176 1 1 1 29.0 6 False True \n", + "223 0 1 1 21.0 9 True False \n", + "213 0 0 1 42.0 5 False True \n", + "239 1 0 0 21.0 5 False True \n", + "182 0 0 1 34.0 4 False True \n", + "\n", + " ethnicity_Asian ethnicity_Black ethnicity_Hispanic ethnicity_Latino \\\n", + "176 False False False False \n", + "223 False False False False \n", + "213 False False False False \n", + "239 True False False False \n", + "182 True False False False \n", + "\n", + " ethnicity_Middle Eastern ethnicity_Others ethnicity_Pasifika \\\n", + "176 True False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " ethnicity_South Asian ethnicity_Turkish ethnicity_White-European \\\n", + "176 False False False \n", + "223 False False True \n", + "213 False False True \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " jundice_no jundice_yes austim_no austim_yes \\\n", + "176 True False True False \n", + "223 True False True False \n", + "213 True False True False \n", + "239 True False True False \n", + "182 True False True False \n", + "\n", + " contry_of_res_Afghanistan contry_of_res_AmericanSamoa \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Angola contry_of_res_Argentina contry_of_res_Armenia \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Aruba contry_of_res_Australia contry_of_res_Austria \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Azerbaijan contry_of_res_Bahamas \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Bangladesh contry_of_res_Belgium contry_of_res_Bolivia \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Brazil contry_of_res_Burundi contry_of_res_Canada \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Chile contry_of_res_China contry_of_res_Costa Rica \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Cyprus contry_of_res_Czech Republic \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Ecuador contry_of_res_Egypt contry_of_res_Ethiopia \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Finland contry_of_res_France contry_of_res_Germany \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Hong Kong contry_of_res_Iceland contry_of_res_India \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Indonesia contry_of_res_Iran contry_of_res_Iraq \\\n", + "176 False True False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Ireland contry_of_res_Italy contry_of_res_Japan \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Jordan contry_of_res_Kazakhstan contry_of_res_Lebanon \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Malaysia contry_of_res_Mexico contry_of_res_Nepal \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Netherlands contry_of_res_New Zealand \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False True \n", + "182 False True \n", + "\n", + " contry_of_res_Nicaragua contry_of_res_Niger contry_of_res_Oman \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Pakistan contry_of_res_Philippines \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Portugal contry_of_res_Romania contry_of_res_Russia \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Saudi Arabia contry_of_res_Serbia \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Sierra Leone contry_of_res_South Africa \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Spain contry_of_res_Sri Lanka contry_of_res_Sweden \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_Tonga contry_of_res_Turkey contry_of_res_Ukraine \\\n", + "176 False False False \n", + "223 False False False \n", + "213 False False False \n", + "239 False False False \n", + "182 False False False \n", + "\n", + " contry_of_res_United Arab Emirates contry_of_res_United Kingdom \\\n", + "176 False False \n", + "223 False True \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_United States contry_of_res_Uruguay \\\n", + "176 False False \n", + "223 False False \n", + "213 True False \n", + "239 False False \n", + "182 False False \n", + "\n", + " contry_of_res_Viet Nam relation_Health care professional \\\n", + "176 False False \n", + "223 False False \n", + "213 False False \n", + "239 False False \n", + "182 False False \n", + "\n", + " relation_Others relation_Parent relation_Relative relation_Self \n", + "176 False False False True \n", + "223 False False False True \n", + "213 False False False True \n", + "239 False False False True \n", + "182 False False False True " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset with testsize of 25%\n", + "Shape of X Training dataset = (527, 100)\n", + "Shape of Y Training dataset = (527,)\n", + "Shape of X Testing dataset = (176, 100)\n", + "Shape of Y Testing dataset = (176,)\n", + "\n" + ] + } + ], + "source": [ + "print(\"Dataset with testsize of 25%\")\n", + "print(f\"Shape of X Training dataset = {np.shape(X_train1)}\")\n", + "print(f\"Shape of Y Training dataset = {np.shape(Y_train1)}\")\n", + "print(f\"Shape of X Testing dataset = {np.shape(X_test1)}\")\n", + "print(f\"Shape of Y Testing dataset = {np.shape(Y_test1)}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "def plot_confusion_matrix(y_true, y_pred, classes):\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " plt.figure(figsize=(8, 6))\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n", + " xticklabels=classes, yticklabels=classes)\n", + " plt.xlabel('Predicted labels')\n", + " plt.ylabel('True labels')\n", + " plt.title('Confusion Matrix')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

8. Logistic Regresseion

" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.98 0.97 120\n", + " 1 0.96 0.89 0.93 56\n", + "\n", + " accuracy 0.95 176\n", + " macro avg 0.96 0.94 0.95 176\n", + "weighted avg 0.95 0.95 0.95 176\n", + "\n", + "Mean squared error of logistic regression = 0.045454545454545456\n", + "R-squared = 0.7904761904761906\n", + "\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAAIjCAYAAABvUIGpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIHElEQVR4nO3df3zN9f//8fsZdjazX8RmfsywMCm/envP/Mjb8ltEsSgj9AvF/EqZ31mp8CZRKkq8/UgUlRJFsiQ/VwljqNhU2PJjw/b6/uHrfDpe1MbOXptzu3Z5XS6d5+t1Xq/HOZ/L+jze99fz9Tw2wzAMAQAAAH/hYXUBAAAAKHxoEgEAAGBCkwgAAAATmkQAAACY0CQCAADAhCYRAAAAJjSJAAAAMKFJBAAAgAlNIgAAAExoEgH8rf3796tVq1by9/eXzWbTypUr8/X8hw4dks1m0/z58/P1vEXZXXfdpbvuusvqMgC4OZpEoAg4cOCAHn30UVWtWlVeXl7y8/NTVFSU/vvf/+rcuXMuvXZsbKySkpL03HPPacGCBWrYsKFLr1eQevfuLZvNJj8/v6t+j/v375fNZpPNZtNLL72U5/MfPXpU48aN086dO/OhWgAoWMWtLgDA3/voo490//33y263q1evXrrtttt0/vx5bdq0ScOHD9cPP/yg119/3SXXPnfunBITE/Xss89q4MCBLrlGaGiozp07pxIlSrjk/P+kePHiOnv2rFatWqVu3bo57Vu4cKG8vLyUmZl5Xec+evSoxo8frypVqqhu3bq5ft9nn312XdcDgPxEkwgUYikpKYqJiVFoaKjWr1+v8uXLO/YNGDBAycnJ+uijj1x2/d9++02SFBAQ4LJr2Gw2eXl5uez8/8RutysqKkr/+9//TE3iokWL1L59ey1fvrxAajl79qxKliwpT0/PArkeAPwdbjcDhdiUKVN0+vRpvfnmm04N4mXVq1fXU0895Xh98eJFTZw4UdWqVZPdbleVKlX0zDPPKCsry+l9VapUUYcOHbRp0yb961//kpeXl6pWrap33nnHccy4ceMUGhoqSRo+fLhsNpuqVKki6dJt2sv//lfjxo2TzWZzGlu7dq2aNGmigIAAlSpVSjVq1NAzzzzj2H+tOYnr169X06ZN5ePjo4CAAHXq1El79uy56vWSk5PVu3dvBQQEyN/fX3369NHZs2ev/cVeoUePHvrkk0906tQpx9jWrVu1f/9+9ejRw3T8iRMnNGzYMNWpU0elSpWSn5+f2rZtq127djmO+fLLL3XnnXdKkvr06eO4bX35c95111267bbbtG3bNjVr1kwlS5Z0fC9XzkmMjY2Vl5eX6fO3bt1agYGBOnr0aK4/KwDkFk0iUIitWrVKVatWVePGjXN1fL9+/TRmzBjVr19f06ZNU/PmzZWQkKCYmBjTscnJybrvvvt099136+WXX1ZgYKB69+6tH374QZLUpUsXTZs2TZL0wAMPaMGCBZo+fXqe6v/hhx/UoUMHZWVlacKECXr55Zd1zz336Ouvv/7b933++edq3bq1jh8/rnHjxikuLk6bN29WVFSUDh06ZDq+W7du+vPPP5WQkKBu3bpp/vz5Gj9+fK7r7NKli2w2m95//33H2KJFi1SzZk3Vr1/fdPzBgwe1cuVKdejQQVOnTtXw4cOVlJSk5s2bOxq2WrVqacKECZKkRx55RAsWLNCCBQvUrFkzx3n++OMPtW3bVnXr1tX06dPVokWLq9b33//+V2XLllVsbKyys7MlSa+99po+++wzzZw5UyEhIbn+rACQawaAQik9Pd2QZHTq1ClXx+/cudOQZPTr189pfNiwYYYkY/369Y6x0NBQQ5KxceNGx9jx48cNu91uDB061DGWkpJiSDJefPFFp3PGxsYaoaGhphrGjh1r/PU/K9OmTTMkGb/99ts16758jXnz5jnG6tata5QrV874448/HGO7du0yPDw8jF69epmu9/DDDzud89577zXKlClzzWv+9XP4+PgYhmEY9913n9GyZUvDMAwjOzvbCA4ONsaPH3/V7yAzM9PIzs42fQ673W5MmDDBMbZ161bTZ7usefPmhiRjzpw5V93XvHlzp7FPP/3UkGRMmjTJOHjwoFGqVCmjc+fO//gZAeB6kSQChVRGRoYkydfXN1fHf/zxx5KkuLg4p/GhQ4dKkmnuYkREhJo2bep4XbZsWdWoUUMHDx687pqvdHku4wcffKCcnJxcvefYsWPauXOnevfurdKlSzvGb7/9dt19992Oz/lXjz32mNPrpk2b6o8//nB8h7nRo0cPffnll0pNTdX69euVmpp61VvN0qV5jB4el/7zmZ2drT/++MNxK3379u25vqbdblefPn1ydWyrVq306KOPasKECerSpYu8vLz02muv5fpaAJBXNIlAIeXn5ydJ+vPPP3N1/OHDh+Xh4aHq1as7jQcHBysgIECHDx92Gq9cubLpHIGBgTp58uR1VmzWvXt3RUVFqV+/fgoKClJMTIyWLl36tw3j5Tpr1Khh2lerVi39/vvvOnPmjNP4lZ8lMDBQkvL0Wdq1aydfX18tWbJECxcu1J133mn6Li/LycnRtGnTFB4eLrvdrltuuUVly5bV7t27lZ6enutrVqhQIU8Pqbz00ksqXbq0du7cqRkzZqhcuXK5fi8A5BVNIlBI+fn5KSQkRN9//32e3nflgyPXUqxYsauOG4Zx3de4PF/uMm9vb23cuFGff/65HnroIe3evVvdu3fX3XffbTr2RtzIZ7nMbrerS5cuevvtt7VixYprpoiSNHnyZMXFxalZs2Z699139emnn2rt2rWqXbt2rhNT6dL3kxc7duzQ8ePHJUlJSUl5ei8A5BVNIlCIdejQQQcOHFBiYuI/HhsaGqqcnBzt37/faTwtLU2nTp1yPKmcHwIDA52eBL7syrRSkjw8PNSyZUtNnTpVP/74o5577jmtX79eX3zxxVXPfbnOvXv3mvb99NNPuuWWW+Tj43NjH+AaevTooR07dujPP/+86sM+l7333ntq0aKF3nzzTcXExKhVq1aKjo42fSe5bdhz48yZM+rTp48iIiL0yCOPaMqUKdq6dWu+nR8ArkSTCBRiI0aMkI+Pj/r166e0tDTT/gMHDui///2vpEu3SyWZnkCeOnWqJKl9+/b5Vle1atWUnp6u3bt3O8aOHTumFStWOB134sQJ03svLyp95bI8l5UvX15169bV22+/7dR0ff/99/rss88cn9MVWrRooYkTJ+qVV15RcHDwNY8rVqyYKaVctmyZfv31V6exy83s1RrqvBo5cqSOHDmit99+W1OnTlWVKlUUGxt7ze8RAG4Ui2kDhVi1atW0aNEide/eXbVq1XL6xZXNmzdr2bJl6t27tyTpjjvuUGxsrF5//XWdOnVKzZs317fffqu3335bnTt3vubyKtcjJiZGI0eO1L333qsnn3xSZ8+e1ezZs3Xrrbc6PbgxYcIEbdy4Ue3bt1doaKiOHz+uV199VRUrVlSTJk2uef4XX3xRbdu2VWRkpPr27atz585p5syZ8vf317hx4/Ltc1zJw8NDo0eP/sfjOnTooAkTJqhPnz5q3LixkpKStHDhQlWtWtXpuGrVqikgIEBz5syRr6+vfHx81KhRI4WFheWprvXr1+vVV1/V2LFjHUvyzJs3T3fddZfi4+M1ZcqUPJ0PAHLF4qerAeTCvn37jP79+xtVqlQxPD09DV9fXyMqKsqYOXOmkZmZ6TjuwoULxvjx442wsDCjRIkSRqVKlYxRo0Y5HWMYl5bAad++vek6Vy69cq0lcAzDMD777DPjtttuMzw9PY0aNWoY7777rmkJnHXr1hmdOnUyQkJCDE9PTyMkJMR44IEHjH379pmuceUyMZ9//rkRFRVleHt7G35+fkbHjh2NH3/80emYy9e7comdefPmGZKMlJSUa36nhuG8BM61XGsJnKFDhxrly5c3vL29jaioKCMxMfGqS9d88MEHRkREhFG8eHGnz9m8eXOjdu3aV73mX8+TkZFhhIaGGvXr1zcuXLjgdNyQIUMMDw8PIzEx8W8/AwBcD5th5GFmNwAAANwCcxIBAABgQpMIAAAAE5pEAAAAmNAkAgAAwIQmEQAAACY0iQAAADChSQQAAIDJTfmLK971BlpdAgAXObn1FatLAOAiXhZ2Ja7sHc7tKJr/3SJJBAAAgMlNmSQCAADkiY3c7Eo0iQAAADab1RUUOrTNAAAAMCFJBAAA4HazCd8IAAAATEgSAQAAmJNoQpIIAAAAE5JEAAAA5iSa8I0AAADAhCQRAACAOYkmNIkAAADcbjbhGwEAAIAJSSIAAAC3m01IEgEAAGBCkggAAMCcRBO+EQAAAJiQJAIAADAn0YQkEQAAACYkiQAAAMxJNKFJBAAA4HazCW0zAAAATEgSAQAAuN1swjcCAAAAE5JEAAAAkkQTvhEAAACYkCQCAAB48HTzlUgSAQAAYEKSCAAAwJxEE5pEAAAAFtM2oW0GAACACUkiAAAAt5tN+EYAAABgQpIIAADAnEQTkkQAAACYkCQCAAAwJ9GEbwQAAAAmJIkAAADMSTShSQQAAOB2swnfCAAAAExIEgEAALjdbEKSCAAAABOSRAAAAOYkmvCNAAAAwIQkEQAAgDmJJiSJAAAAMCFJBAAAYE6iCU0iAAAATaIJ3wgAAABMSBIBAAB4cMWEJBEAAAAmJIkAAADMSTThGwEAAIAJSSIAAABzEk1IEgEAAGBCkggAAMCcRBOaRAAAAG43m9A2AwAAwIQkEQAAuD0bSaIJSSIAAABMSBIBAIDbI0k0I0kEAACACUkiAAAAQaIJSSIAAABMSBIBAIDbY06iGU0iAABwezSJZtxuBgAAgAlNIgAAcHs2m81lW15t3LhRHTt2VEhIiGw2m1auXOm03zAMjRkzRuXLl5e3t7eio6O1f/9+p2NOnDihnj17ys/PTwEBAerbt69Onz6dpzpoEgEAAAqRM2fO6I477tCsWbOuun/KlCmaMWOG5syZoy1btsjHx0etW7dWZmam45iePXvqhx9+0Nq1a7V69Wpt3LhRjzzySJ7qsBmGYdzQJymEvOsNtLoEAC5ycusrVpcAwEW8LHxSwv+BBS479/H53ZSVleU0ZrfbZbfb//G9NptNK1asUOfOnSVdShFDQkI0dOhQDRs2TJKUnp6uoKAgzZ8/XzExMdqzZ48iIiK0detWNWzYUJK0Zs0atWvXTr/88otCQkJyVTdJIgAAgAslJCTI39/faUtISLiuc6WkpCg1NVXR0dGOMX9/fzVq1EiJiYmSpMTERAUEBDgaREmKjo6Wh4eHtmzZkutr8XQzAACACx9uHjVqlOLi4pzGcpMiXk1qaqokKSgoyGk8KCjIsS81NVXlypVz2l+8eHGVLl3acUxu0CQCAAC4UG5vLRc23G4GAABurzA93fx3goODJUlpaWlO42lpaY59wcHBOn78uNP+ixcv6sSJE45jcoMmEQAAoIgICwtTcHCw1q1b5xjLyMjQli1bFBkZKUmKjIzUqVOntG3bNscx69evV05Ojho1apTra3G7GQAAuL3C9Isrp0+fVnJysuN1SkqKdu7cqdKlS6ty5coaPHiwJk2apPDwcIWFhSk+Pl4hISGOJ6Br1aqlNm3aqH///pozZ44uXLiggQMHKiYmJtdPNks0iQAAAIWqSfzuu+/UokULx+vLD73ExsZq/vz5GjFihM6cOaNHHnlEp06dUpMmTbRmzRp5eXk53rNw4UINHDhQLVu2lIeHh7p27aoZM2bkqQ7WSQRQpLBOInDzsnKdxNIPLXLZuU8s6OGyc7sSSSIAAHB7hSlJLCx4cAUAAAAmJIkAAAAEiSYkiQAAADAhSQQAAG6POYlmJIkAAAAwIUkEAABujyTRjCYRAAC4PZpEM243AwAAwIQkEQAAgCDRhCQRAAAAJiSJAADA7TEn0YwkEQAAACYkiQAAwO2RJJpZ2iSeP39eK1euVGJiolJTUyVJwcHBaty4sTp16iRPT08rywMAAHBblt1uTk5OVq1atRQbG6sdO3YoJydHOTk52rFjh3r16qXatWsrOTnZqvIAAIAbsdlsLtuKKsuSxMcff1x16tTRjh075Ofn57QvIyNDvXr10oABA/Tpp59aVCEAAHAXRbmZcxXLmsSvv/5a3377ralBlCQ/Pz9NnDhRjRo1sqAyAAAAWHa7OSAgQIcOHbrm/kOHDikgIKDA6gEAAG7M5sKtiLIsSezXr5969eql+Ph4tWzZUkFBQZKktLQ0rVu3TpMmTdKgQYOsKg8AAMCtWdYkTpgwQT4+PnrxxRc1dOhQx1wAwzAUHByskSNHasSIEVaVBwAA3AhzEs0sXQJn5MiRGjlypFJSUpyWwAkLC7OyLAAAALdXKBbTDgsLozEEAACWIUk042f5AAAAYFIokkQAAAArkSSa0SQCAADQI5pwuxkAAAAmljeJa9as0aZNmxyvZ82apbp166pHjx46efKkhZUBAAB3wW83m1neJA4fPlwZGRmSpKSkJA0dOlTt2rVTSkqK4uLiLK4OAADAPVk+JzElJUURERGSpOXLl6tDhw6aPHmytm/frnbt2llcHQAAcAdFOfFzFcuTRE9PT509e1aS9Pnnn6tVq1aSpNKlSzsSRgAAABQsy5PEJk2aKC4uTlFRUfr222+1ZMkSSdK+fftUsWJFi6uDVaLqV9OQXtGqH1FZ5cv6q9uQ17Xqy92O/Z3+c4f63ddE9WpVVpkAHzXqnqDd+351OkdQGV9NHnyv/vPvmvL1sWvfoeOa8uanWrluZwF/GgB58ebc17Ru7WdKSTkou5eX6tatp8Fxw1QlrKrVpeEmRpJoZnmS+Morr6h48eJ67733NHv2bFWoUEGS9Mknn6hNmzYWVwer+HjblbTvVw1OWHLV/SW9PbV55wGNnrHymud4Y2Iv3VqlnO4f/Joa3j9ZH6zfqXdfeFh31OB/fACF2Xdbv1X3B3pqwf+W6rW583Tx4kU91r+v464TgIJheZJYuXJlrV692jQ+bdo0C6pBYfHZ1z/qs69/vOb+/320VZJUuXzpax7z7zuq6snJi/XdD4clSS+88akG9fyP6kVU0q69v+RvwQDyzezX33R6PeG559WiaaT2/PiDGjS806KqcLMjSTSzPEncvn27kpKSHK8/+OADde7cWc8884zOnz9vYWUo6r7ZdVD3tWqgQL+Sstlsur91A3nZi2vjd/utLg1AHpz+809Jkp+/v8WV4KZmc+FWRFneJD766KPat2+fJOngwYOKiYlRyZIltWzZMo0YMeIf35+VlaWMjAynzcjJdnXZKAIeHPGWShQvpqMbpih9y3TNfDZG3ePm6uDPv1tdGoBcysnJ0ZQXJqtuvfoKD7/V6nIAt2J5k7hv3z7VrVtXkrRs2TI1a9ZMixYt0vz587V8+fJ/fH9CQoL8/f2dtotp21xcNYqCsQM6KMDXW20fnaGoB6doxrvr9e6Uh1W7eojVpQHIpcmTxuvA/v2a8hJTkOBaLKZtZnmTaBiGcnJyJF1aAufy2oiVKlXS77//c+IzatQopaenO23Fgxq4tGYUfmEVb9HjMc316Lh39eW3+5S071dNfv0Tbf/xiB7t3szq8gDkwuRJE7Rxw5eaO+9tBQUHW10O4HYsf3ClYcOGmjRpkqKjo7VhwwbNnj1b0qVFtoOCgv7x/Xa7XXa73WnM5lHMJbWi6Cjp5SlJyjEMp/HsbEMeRfh/1QHuwDAMJTw3UevXrdWb8xeoYsVKVpcEN1CUEz9XsbxJnD59unr27KmVK1fq2WefVfXq1SVJ7733nho3bmxxdbCKj7enqlUq63hdpUIZ3X5rBZ3MOKufU08q0K+kKgUHqny5SxPZb61y6X9QpP2RobQ//tTeQ6lKPnJcr4x+QKOmrtAf6Wd0T4vb1fLfNdTlqTmWfCYAuTN54nh98vFqTZ/5qnxK+uj3336TJJXy9ZWXl5fF1QHuw2YYV0QthURmZqaKFSumEiVK5Pm93vUGuqAiFKSmDcL12RtPmcYXfPiNHhn7rh7s2EhzJzxk2j9pzsd67rWPJUnVKpfVpCc7KbJuVZUqadeBn3/T9HfWOZbPQdF0cusrVpcAF7ujdo2rjk+YlKBO93Yp4GpQkLwsjK6qD/vEZedOfqmty87tSoW2SbwRNInAzYsmEbh50SQWLpbfbs7Ozta0adO0dOlSHTlyxLQ24okTJyyqDAAAuAvmJJpZ/nTz+PHjNXXqVHXv3l3p6emKi4tTly5d5OHhoXHjxlldHgAAcAM2m+u2osryJnHhwoWaO3euhg4dquLFi+uBBx7QG2+8oTFjxuibb76xujwAAAC3ZHmTmJqaqjp16kiSSpUqpfT0dElShw4d9NFHH1lZGgAAcBMspm1meZNYsWJFHTt2TJJUrVo1ffbZZ5KkrVu3mtY/BAAAQMGwvEm89957tW7dOknSoEGDFB8fr/DwcPXq1UsPP/ywxdUBAAB3wJxEM8ufbn7++ecd/969e3dVrlxZiYmJCg8PV8eOHS2sDAAAwH1Z3iReKTIyUpGRkVaXAQAA3IiHRxGO/FzEkibxww8/zPWx99xzjwsrAQAAwNVY0iR27tw5V8fZbDZlZ2e7thgAAOD2ivLcQVexpEnMycmx4rIAAABXVZSXqnEVy59uBgAAQOFjWZO4fv16RUREKCMjw7QvPT1dtWvX1saNGy2oDAAAuBuWwDGzrEmcPn26+vfvLz8/P9M+f39/Pfroo5o2bZoFlQEAAMCyJnHXrl1q06bNNfe3atVK27ZtK8CKAACAu+Jn+cwsaxLT0tJUokSJa+4vXry4fvvttwKsCAAAAJdZ1iRWqFBB33///TX37969W+XLly/AigAAgLsiSTSzrEls166d4uPjlZmZadp37tw5jR07Vh06dLCgMgAAAFj2s3yjR4/W+++/r1tvvVUDBw5UjRo1JEk//fSTZs2apezsbD377LNWlQcAANxIEQ78XMayJjEoKEibN2/W448/rlGjRskwDEmX4t7WrVtr1qxZCgoKsqo8AADgRorybWFXsaxJlKTQ0FB9/PHHOnnypJKTk2UYhsLDwxUYGGhlWQAAAG7P0ibxssDAQN15551WlwEAANwUQaIZP8sHAAAAk0KRJAIAAFiJOYlmJIkAAAAwIUkEAABujyDRjCQRAACgkMjOzlZ8fLzCwsLk7e2tatWqaeLEiY6lAiXJMAyNGTNG5cuXl7e3t6Kjo7V///58r4UmEQAAuL3C8rN8L7zwgmbPnq1XXnlFe/bs0QsvvKApU6Zo5syZjmOmTJmiGTNmaM6cOdqyZYt8fHzUunXrq/6K3Y3gdjMAAEAhsXnzZnXq1Ent27eXJFWpUkX/+9//9O2330q6lCJOnz5do0ePVqdOnSRJ77zzjoKCgrRy5UrFxMTkWy0kiQAAwO3ZbK7bsrKylJGR4bRlZWVdtY7GjRtr3bp12rdvnyRp165d2rRpk9q2bStJSklJUWpqqqKjox3v8ff3V6NGjZSYmJiv3wlNIgAAcHuuvN2ckJAgf39/py0hIeGqdTz99NOKiYlRzZo1VaJECdWrV0+DBw9Wz549JUmpqamSZPrp4qCgIMe+/MLtZgAAABcaNWqU4uLinMbsdvtVj126dKkWLlyoRYsWqXbt2tq5c6cGDx6skJAQxcbGFkS5DjSJAADA7blyCRy73X7NpvBKw4cPd6SJklSnTh0dPnxYCQkJio2NVXBwsCQpLS1N5cuXd7wvLS1NdevWzde6ud0MAABQSJw9e1YeHs7tWbFixZSTkyNJCgsLU3BwsNatW+fYn5GRoS1btigyMjJfayFJBAAAbq+w/Cxfx44d9dxzz6ly5cqqXbu2duzYoalTp+rhhx+WdKnOwYMHa9KkSQoPD1dYWJji4+MVEhKizp0752stNIkAAACFxMyZMxUfH68nnnhCx48fV0hIiB599FGNGTPGccyIESN05swZPfLIIzp16pSaNGmiNWvWyMvLK19rsRl/XcL7JuFdb6DVJQBwkZNbX7G6BAAu4mVhdNV4ykaXnXvziGYuO7crMScRAAAAJtxuBgAAbq+wzEksTGgSAQCA26NHNON2MwAAAExIEgEAgNvjdrMZSSIAAABMSBIBAIDbI0k0I0kEAACACUkiAABwewSJZiSJAAAAMCFJBAAAbo85iWY0iQAAwO3RI5pxuxkAAAAmJIkAAMDtcbvZjCQRAAAAJiSJAADA7REkmpEkAgAAwIQkEQAAuD0PokQTkkQAAACYkCQCAAC3R5BoRpMIAADcHkvgmHG7GQAAACYkiQAAwO15ECSakCQCAADAhCQRAAC4PeYkmpEkAgAAwIQkEQAAuD2CRDOSRAAAAJiQJAIAALdnE1HilWgSAQCA22MJHDNuNwMAAMCEJBEAALg9lsAxI0kEAACACUkiAABwewSJZiSJAAAAMCFJBAAAbs+DKNGEJBEAAAAm+dIknjp1Kj9OAwAAYAmbzXVbUZXnJvGFF17QkiVLHK+7deumMmXKqEKFCtq1a1e+FgcAAFAQbDaby7aiKs9N4pw5c1SpUiVJ0tq1a7V27Vp98sknatu2rYYPH57vBQIAAKDg5fnBldTUVEeTuHr1anXr1k2tWrVSlSpV1KhRo3wvEAAAwNWKcODnMnlOEgMDA/Xzzz9LktasWaPo6GhJkmEYys7Ozt/qAAAAYIk8J4ldunRRjx49FB4erj/++ENt27aVJO3YsUPVq1fP9wIBAABcjSVwzPLcJE6bNk1VqlTRzz//rClTpqhUqVKSpGPHjumJJ57I9wIBAABQ8PLcJJYoUULDhg0zjQ8ZMiRfCgIAACho5IhmuWoSP/zww1yf8J577rnuYgAAAFA45KpJ7Ny5c65OZrPZeHgFAAAUOUV5PUNXyVWTmJOT4+o6AAAALONBj2hyQz/Ll5mZmV91AAAAoBDJc5OYnZ2tiRMnqkKFCipVqpQOHjwoSYqPj9ebb76Z7wUCAAC4Gj/LZ5bnJvG5557T/PnzNWXKFHl6ejrGb7vtNr3xxhv5WhwAAACskecm8Z133tHrr7+unj17qlixYo7xO+64Qz/99FO+FgcAAFAQbDbXbUVVnpvEX3/99aq/rJKTk6MLFy7kS1EAAACwVp6bxIiICH311Vem8ffee0/16tXLl6IAAAAKEnMSzfL8iytjxoxRbGysfv31V+Xk5Oj999/X3r179c4772j16tWuqBEAAAAFLM9JYqdOnbRq1Sp9/vnn8vHx0ZgxY7Rnzx6tWrVKd999tytqBAAAcCkPm+u2oirPSaIkNW3aVGvXrs3vWgAAACxRlG8Lu8p1NYmS9N1332nPnj2SLs1TbNCgQb4VBQAAAGvluUn85Zdf9MADD+jrr79WQECAJOnUqVNq3LixFi9erIoVK+Z3jQAAAC5FjmiW5zmJ/fr104ULF7Rnzx6dOHFCJ06c0J49e5STk6N+/fq5okYAAAAUsDwniRs2bNDmzZtVo0YNx1iNGjU0c+ZMNW3aNF+LAwAAKAgezEk0yXOSWKlSpasump2dna2QkJB8KQoAAADWynOT+OKLL2rQoEH67rvvHGPfffednnrqKb300kv5WhwAAEBB4Gf5zHJ1uzkwMNDp0fAzZ86oUaNGKl780tsvXryo4sWL6+GHH1bnzp1dUigAAAAKTq6axOnTp7u4DAAAAOuwTqJZrprE2NhYV9cBAACAQuS6F9OWpMzMTJ0/f95pzM/P74YKAgAAKGgEiWZ5bhLPnDmjkSNHaunSpfrjjz9M+7Ozs/OlMAAAgILCEjhmeX66ecSIEVq/fr1mz54tu92uN954Q+PHj1dISIjeeecdV9QIAACAApbnJnHVqlV69dVX1bVrVxUvXlxNmzbV6NGjNXnyZC1cuNAVNQIAALhUYVoC59dff9WDDz6oMmXKyNvbW3Xq1HFaetAwDI0ZM0bly5eXt7e3oqOjtX///nz8Ni7Jc5N44sQJVa1aVdKl+YcnTpyQJDVp0kQbN27M3+oAAADcyMmTJxUVFaUSJUrok08+0Y8//qiXX35ZgYGBjmOmTJmiGTNmaM6cOdqyZYt8fHzUunVrZWZm5msteZ6TWLVqVaWkpKhy5cqqWbOmli5dqn/9619atWqVAgIC8rU4AACAglBYlsB54YUXVKlSJc2bN88xFhYW5vh3wzA0ffp0jR49Wp06dZIkvfPOOwoKCtLKlSsVExOTb7XkOUns06ePdu3aJUl6+umnNWvWLHl5eWnIkCEaPnx4vhUGAABwM8jKylJGRobTlpWVddVjP/zwQzVs2FD333+/ypUrp3r16mnu3LmO/SkpKUpNTVV0dLRjzN/fX40aNVJiYmK+1m0zDMO4kRMcPnxY27ZtU/Xq1XX77bfnV103JCMzx+oSALjIW1sPW10CABcZ3DTsnw9ykUEr9rjs3GV2LdH48eOdxsaOHatx48aZjvXy8pIkxcXF6f7779fWrVv11FNPac6cOYqNjdXmzZsVFRWlo0ePqnz58o73devWTTabTUuWLMm3um9onURJCg0NVWhoaH7UAgAAcNMZNWqU4uLinMbsdvtVj83JyVHDhg01efJkSVK9evX0/fffO5rEgpSrJnHGjBm5PuGTTz553cUAAABYwZVzEu12+zWbwiuVL19eERERTmO1atXS8uXLJUnBwcGSpLS0NKckMS0tTXXr1s2fgv+/XDWJ06ZNy9XJbDYbTSIAAChyPArHcyuKiorS3r17ncb27dvnuGsbFham4OBgrVu3ztEUZmRkaMuWLXr88cfztZZcNYkpKSn5elEAAACYDRkyRI0bN9bkyZPVrVs3ffvtt3r99df1+uuvS7oUyA0ePFiTJk1SeHi4wsLCFB8fr5CQEHXu3Dlfa7nhOYkAAABFXWFJEu+8806tWLFCo0aN0oQJExQWFqbp06erZ8+ejmNGjBihM2fO6JFHHtGpU6fUpEkTrVmzxvHQS3654aebCyOebgZuXjzdDNy8rHy6Oe7Dn1x27qn31HTZuV2JJBEAALi9wrKYdmGS58W0AQAAcPMjSQQAAG6vsMxJLEyuK0n86quv9OCDDyoyMlK//vqrJGnBggXatGlTvhYHAAAAa+S5SVy+fLlat24tb29v7dixw/Hbg+np6Y7VwQEAAIoSm811W1GV5yZx0qRJmjNnjubOnasSJUo4xqOiorR9+/Z8LQ4AAKAgeNhsLtuKqjw3iXv37lWzZs1M4/7+/jp16lR+1AQAAACL5blJDA4OVnJysml806ZNqlq1ar4UBQAAUJA8XLgVVXmuvX///nrqqae0ZcsW2Ww2HT16VAsXLtSwYcPy/TcDAQAAYI08L4Hz9NNPKycnRy1bttTZs2fVrFkz2e12DRs2TIMGDXJFjQAAAC5VhKcOukyem0SbzaZnn31Ww4cPV3Jysk6fPq2IiAiVKlXKFfUBAADAAte9mLanp6ciIiLysxYAAABLFOWnkF0lz01iixYt/vb3DdevX39DBQEAAMB6eW4S69at6/T6woUL2rlzp77//nvFxsbmV10AAAAFhiDRLM9N4rRp0646Pm7cOJ0+ffqGCwIAACho/HazWb4t3/Pggw/qrbfeyq/TAQAAwELX/eDKlRITE+Xl5ZVfpwMAACgwPLhilucmsUuXLk6vDcPQsWPH9N133yk+Pj7fCgMAAIB18twk+vv7O7328PBQjRo1NGHCBLVq1SrfCgMAACgoBIlmeWoSs7Oz1adPH9WpU0eBgYGuqgkAAAAWy9ODK8WKFVOrVq106tQpF5UDAABQ8DxsrtuKqjw/3Xzbbbfp4MGDrqgFAAAAhUSem8RJkyZp2LBhWr16tY4dO6aMjAynDQAAoKixufCfoirXcxInTJigoUOHql27dpKke+65x+nn+QzDkM1mU3Z2dv5XCQAA4EJF+bawq+S6SRw/frwee+wxffHFF66sBwAAAIVArptEwzAkSc2bN3dZMQAAAFYgSTTL05xEG4sIAQAAuIU8rZN46623/mOjeOLEiRsqCAAAoKARhJnlqUkcP3686RdXAAAAcPPJU5MYExOjcuXKuaoWAAAASzAn0SzXcxKJYQEAANxHnp9uBgAAuNmQhZnluknMyclxZR0AAACW8aBLNMnzz/IBAADg5penB1cAAABuRjy4YkaSCAAAABOSRAAA4PaYkmhGkggAAAATkkQAAOD2PESUeCWSRAAAAJiQJAIAALfHnEQzmkQAAOD2WALHjNvNAAAAMCFJBAAAbo+f5TMjSQQAAIAJSSIAAHB7BIlmJIkAAAAwIUkEAABujzmJZiSJAAAAMCFJBAAAbo8g0YwmEQAAuD1urZrxnQAAAMCEJBEAALg9G/ebTUgSAQAAYEKSCAAA3B45ohlJIgAAAExIEgEAgNtjMW0zkkQAAACYkCQCAAC3R45oRpMIAADcHnebzbjdDAAAABOSRAAA4PZYTNuMJBEAAAAmJIkAAMDtkZqZ8Z0AAADAhCQRAAC4PeYkmpEkAgAAwIQkEQAAuD1yRDOSRAAAgELq+eefl81m0+DBgx1jmZmZGjBggMqUKaNSpUqpa9euSktLy/dr0yQCAAC3Z7PZXLZdr61bt+q1117T7bff7jQ+ZMgQrVq1SsuWLdOGDRt09OhRdenS5Ua/AhOaRAAA4PY8XLhdj9OnT6tnz56aO3euAgMDHePp6el68803NXXqVP3nP/9RgwYNNG/ePG3evFnffPPNdV7t6mgSAQAAXCgrK0sZGRlOW1ZW1t++Z8CAAWrfvr2io6Odxrdt26YLFy44jdesWVOVK1dWYmJivtZNkwgAANyeK283JyQkyN/f32lLSEi4Zi2LFy/W9u3br3pMamqqPD09FRAQ4DQeFBSk1NTUfP1OeLoZAADAhUaNGqW4uDinMbvdftVjf/75Zz311FNau3atvLy8CqK8a6JJBAAAbs+VS+DY7fZrNoVX2rZtm44fP6769es7xrKzs7Vx40a98sor+vTTT3X+/HmdOnXKKU1MS0tTcHBwvtZNkwgAAFBItGzZUklJSU5jffr0Uc2aNTVy5EhVqlRJJUqU0Lp169S1a1dJ0t69e3XkyBFFRkbmay00iQAAwO0Vll/l8/X11W233eY05uPjozJlyjjG+/btq7i4OJUuXVp+fn4aNGiQIiMj9e9//ztfa6FJBAAAKEKmTZsmDw8Pde3aVVlZWWrdurVeffXVfL+OzTAMI9/ParGMzByrSwDgIm9tPWx1CQBcZHDTMMuuvSop/3+x5LKOdYJcdm5XIkkEAABur7Dcbi5MWCcRAAAAJiSJAADA7dlcughO0USSCAAAABOSRAAA4PaYk2hGkggAAAATkkQAAOD2PJiTaFJok8S0tDRNmDDB6jIAAADcUqFtElNTUzV+/HirywAAAG7AZnPdVlRZdrt59+7df7t/7969BVQJAABwd0W5mXMVy5rEunXrymaz6Wq/Cnh53Mb/xQAAACxhWZNYunRpTZkyRS1btrzq/h9++EEdO3Ys4KoAAIA7YjFtM8uaxAYNGujo0aMKDQ296v5Tp05dNWUEAACA61nWJD722GM6c+bMNfdXrlxZ8+bNK8CKAACAu/IgSDSxrEm89957/3Z/YGCgYmNjC6gaAAAA/BWLaQMAALfHnESzQrtOIgAAAKxDkggAANweq+6Z0SQCAAC3x+1mM243AwAAwMTyJnHNmjXatGmT4/WsWbNUt25d9ejRQydPnrSwMgAA4C48bK7biirLm8Thw4crIyNDkpSUlKShQ4eqXbt2SklJUVxcnMXVAQAAuCfL5ySmpKQoIiJCkrR8+XJ16NBBkydP1vbt29WuXTuLqwMAAO6AOYlmlieJnp6eOnv2rCTp888/V6tWrSRd+m3nywkjAAAACpblSWKTJk0UFxenqKgoffvtt1qyZIkkad++fapYsaLF1aEwO56WppnTX1bi1xuVmZmpipUqa8yEyYqofZvVpQHIg60fLNB3qxY6jQUEV9QDk96QJF28cF6bl76u5G83KPviBVWq3UDNeg5USf9AK8rFTYolcMwsbxJfeeUVPfHEE3rvvfc0e/ZsVahQQZL0ySefqE2bNhZXh8IqIyNd/Xr3UIOGjfTfWa8rILC0fj5yWH5+flaXBuA6BIaE6p6hCY7XNo9ijn//evFrOpL0rVo99qzs3j76atEsffrqRN07aqoVpQJuw/ImsXLlylq9erVpfNq0aRZUg6Li7bfeUFBQeY2dONkxVoHkGSiyPIoVU0n/0qbxrLNn9NOmTxXdf6Qq1qorSWrRZ6gWx/dX6oE9Cq5Wq4Arxc2KINHM8jmJ27dvV1JSkuP1Bx98oM6dO+uZZ57R+fPnLawMhdlXG75Qrdq19fSwwWp1V5R6duuiFcuXWl0WgOuUnvar3h7aQ+8+3Vufz31Bf/5xXJL02+H9ysm+qIoR9RzHBpavpFKlyyntwB6rysVNyMNmc9lWVFneJD766KPat2+fJOngwYOKiYlRyZIltWzZMo0YMeIf35+VlaWMjAynLSsry9Vlw2K//vKzli9drEqVQzVz9lx17Rajl1+YrNUfrrS6NAB5VK5qTf3n4aHqMHiSmj04UBm/p2rlC8N0PvOszmaclEfxErKXLOX0npJ+ATqbwVq6gCtZ3iTu27dPdevWlSQtW7ZMzZo106JFizR//nwtX778H9+fkJAgf39/p23qi8+7uGpYLSfHUI1aERrw5BDVqBWhLvd1U+cu9+v9ZYutLg1AHoXWuVPVGjZTmUpVVfm2hmr/1ESdP3daB7ZutLo0uBGbC7eiyvIm0TAM5eTkSLq0BM7ltRErVaqk33///R/fP2rUKKWnpzttccOfdmnNsN4tZW9R1arVnMaqVK2q1GPHLKoIQH6xlywl/6AKSj9+VCX9ApVz8YKyzp52OuZsximV9OPpZsCVLG8SGzZsqEmTJmnBggXasGGD2rdvL+nSIttBQUH/+H673S4/Pz+nzW63u7psWOyOuvV1+NAhp7Ejhw8pOCTEmoIA5JsLmeeUcfyYSvqXVtnQcHkUK65f9ux07D+Z+rNOnziuIB5aQX4iSjSxvEmcPn26tm/froEDB+rZZ59V9erVJUnvvfeeGjdubHF1KKweeDBWSUm7NO+N1/TzkcNa8/FqrXhvme7v3sPq0gDk0ealc3V0725l/J6q1OQftWbWBNk8iim80V2yl/RRzSattXnJ6/r1p1367dB+fTFvqoKq1eLJZsDFbIZhGFYXcTWZmZkqVqyYSpQokef3ZmTmuKAiFDZfbfhCs2ZM089HDiukQkX1eChW93btZnVZcLG3th62ugTks7WvJejoviRlnvlT3r7+Kl+9tv51b6z8y126M+BYTHvLl/+3mPaDA6+6ZA6KtsFNwyy79pYD6S47d6Nq/i47tysV2ibxRtAkAjcvmkTg5kWTWLhYvph2dna2pk2bpqVLl+rIkSOmtRFPnDhhUWUAAMBdFOHlDF3G8jmJ48eP19SpU9W9e/dLTybHxalLly7y8PDQuHHjrC4PAAC4AZ5bMbO8SVy4cKHmzp2roUOHqnjx4nrggQf0xhtvaMyYMfrmm2+sLg8AAMAtWd4kpqamqk6dOpKkUqVKKT390pyADh066KOPPrKyNAAA4C6IEk0sbxIrVqyoY/9/AeRq1arps88+kyRt3bqV9Q4BAAAsYnmTeO+992rdunWSpEGDBik+Pl7h4eHq1auXHn74YYurAwAA7sDmwn+KKsufbn7++f/7neXu3burcuXKSkxMVHh4uDp27GhhZQAAAO7L8ibxSpGRkYqMjLS6DAAA4EZYAsfMkibxww8/zPWx99xzjwsrAQAAwNVY0iR27tw5V8fZbDZlZ2e7thgAAOD2CBLNLGkSc3L42TwAAFCI0CWaWP50MwAAAAofy5rE9evXKyIiQhkZGaZ96enpql27tjZu3GhBZQAAwN2wBI6ZZU3i9OnT1b9/f/n5+Zn2+fv769FHH9W0adMsqAwAAACWNYm7du1SmzZtrrm/VatW2rZtWwFWBAAA3JXN5rqtqLKsSUxLS1OJEiWuub948eL67bffCrAiAAAAXGZZk1ihQgV9//3319y/e/dulS9fvgArAgAA7srmwq2osqxJbNeuneLj45WZmWnad+7cOY0dO1YdOnSwoDIAAADYDMMwrLhwWlqa6tevr2LFimngwIGqUaOGJOmnn37SrFmzlJ2dre3btysoKCjP587IZB1G4Gb11tbDVpcAwEUGNw2z7Nq7fv7TZee+o5Kvy87tSpb9dnNQUJA2b96sxx9/XKNGjdLlXtVms6l169aaNWvWdTWIAAAAeVWUl6pxFcuaREkKDQ3Vxx9/rJMnTyo5OVmGYSg8PFyBgYFWlgUAAOD2LG0SLwsMDNSdd95pdRkAAMBNFeWlalyFn+UDAACASaFIEgEAAKxEkGhGkggAAAATkkQAAACiRBOSRAAAAJiQJAIAALfHOolmJIkAAAAwIUkEAABuj3USzWgSAQCA26NHNON2MwAAAExIEgEAAIgSTUgSAQAAYEKTCAAA3J7Nhf/kRUJCgu688075+vqqXLly6ty5s/bu3et0TGZmpgYMGKAyZcqoVKlS6tq1q9LS0vLz65BEkwgAAFBobNiwQQMGDNA333yjtWvX6sKFC2rVqpXOnDnjOGbIkCFatWqVli1bpg0bNujo0aPq0qVLvtdiMwzDyPezWiwjM8fqEgC4yFtbD1tdAgAXGdw0zLJr700967Jz1wgued3v/e2331SuXDlt2LBBzZo1U3p6usqWLatFixbpvvvukyT99NNPqlWrlhITE/Xvf/87v8omSQQAAHClrKwsZWRkOG1ZWVm5em96erokqXTp0pKkbdu26cKFC4qOjnYcU7NmTVWuXFmJiYn5WjdNIgAAcHs2F24JCQny9/d32hISEv6xppycHA0ePFhRUVG67bbbJEmpqany9PRUQECA07FBQUFKTU29oe/gSiyBAwAA4MIlcEaNGqW4uDinMbvd/o/vGzBggL7//ntt2rTJVaX9LZpEAAAAF7Lb7blqCv9q4MCBWr16tTZu3KiKFSs6xoODg3X+/HmdOnXKKU1MS0tTcHBwfpUsidvNAAAAhWYJHMMwNHDgQK1YsULr169XWJjzwzwNGjRQiRIltG7dOsfY3r17deTIEUVGRubLd3EZSSIAAEAhMWDAAC1atEgffPCBfH19HfMM/f395e3tLX9/f/Xt21dxcXEqXbq0/Pz8NGjQIEVGRubrk80STSIAAIBsheRn+WbPni1Juuuuu5zG582bp969e0uSpk2bJg8PD3Xt2lVZWVlq3bq1Xn311XyvhXUSARQprJMI3LysXCcx+fg5l527ejlvl53blUgSAQCA2yskQWKhwoMrAAAAMCFJBAAAIEo0oUkEAABuL69L1bgDbjcDAADAhCQRAAC4vcKyBE5hQpIIAAAAE5JEAADg9ggSzUgSAQAAYEKSCAAAQJRoQpIIAAAAE5JEAADg9lgn0YwmEQAAuD2WwDHjdjMAAABMSBIBAIDbI0g0I0kEAACACUkiAABwe8xJNCNJBAAAgAlJIgAAALMSTUgSAQAAYEKSCAAA3B5zEs1oEgEAgNujRzTjdjMAAABMSBIBAIDb43azGUkiAAAATEgSAQCA27MxK9GEJBEAAAAmJIkAAAAEiSYkiQAAADAhSQQAAG6PINGMJhEAALg9lsAx43YzAAAATEgSAQCA22MJHDOSRAAAAJiQJAIAABAkmpAkAgAAwIQkEQAAuD2CRDOSRAAAAJiQJAIAALfHOolmNIkAAMDtsQSOGbebAQAAYEKSCAAA3B63m81IEgEAAGBCkwgAAAATmkQAAACYMCcRAAC4PeYkmpEkAgAAwIQkEQAAuD3WSTSjSQQAAG6P281m3G4GAACACUkiAABwewSJZiSJAAAAMCFJBAAAIEo0IUkEAACACUkiAABweyyBY0aSCAAAABOSRAAA4PZYJ9GMJBEAAAAmJIkAAMDtESSa0SQCAADQJZpwuxkAAAAmJIkAAMDtsQSOGUkiAAAATEgSAQCA22MJHDOSRAAAAJjYDMMwrC4CuF5ZWVlKSEjQqFGjZLfbrS4HQD7i7xuwFk0iirSMjAz5+/srPT1dfn5+VpcDIB/x9w1Yi9vNAAAAMKFJBAAAgAlNIgAAAExoElGk2e12jR07lkntwE2Iv2/AWjy4AgAAABOSRAAAAJjQJAIAAMCEJhEAAAAmNIkoNGw2m1auXGl1GQBcgL9voOihSUSBSE1N1aBBg1S1alXZ7XZVqlRJHTt21Lp166wuTZJkGIbGjBmj8uXLy9vbW9HR0dq/f7/VZQFFQmH/+37//ffVqlUrlSlTRjabTTt37rS6JKBIoEmEyx06dEgNGjTQ+vXr9eKLLyopKUlr1qxRixYtNGDAAKvLkyRNmTJFM2bM0Jw5c7Rlyxb5+PiodevWyszMtLo0oFArCn/fZ86cUZMmTfTCCy9YXQpQtBiAi7Vt29aoUKGCcfr0adO+kydPOv5dkrFixQrH6xEjRhjh4eGGt7e3ERYWZowePdo4f/68Y//OnTuNu+66yyhVqpTh6+tr1K9f39i6dathGIZx6NAho0OHDkZAQIBRsmRJIyIiwvjoo4+uWl9OTo4RHBxsvPjii46xU6dOGXa73fjf//53g58euLkV9r/vv0pJSTEkGTt27Ljuzwu4k+IW96i4yZ04cUJr1qzRc889Jx8fH9P+gICAa77X19dX8+fPV0hIiJKSktS/f3/5+vpqxIgRkqSePXuqXr16mj17tooVK6adO3eqRIkSkqQBAwbo/Pnz2rhxo3x8fPTjjz+qVKlSV71OSkqKUlNTFR0d7Rjz9/dXo0aNlJiYqJiYmBv4BoCbV1H4+wZw/WgS4VLJyckyDEM1a9bM83tHjx7t+PcqVapo2LBhWrx4seP/iRw5ckTDhw93nDs8PNxx/JEjR9S1a1fVqVNHklS1atVrXic1NVWSFBQU5DQeFBTk2AfArCj8fQO4fsxJhEsZN/CDPkuWLFFUVJSCg4NVqlQpjR49WkeOHHHsj4uLU79+/RQdHa3nn39eBw4ccOx78sknNWnSJEVFRWns2LHavXv3DX0OAGb8fQM3N5pEuFR4eLhsNpt++umnPL0vMTFRPXv2VLt27bR69Wrt2LFDzz77rM6fP+84Zty4cfrhhx/Uvn17rV+/XhEREVqxYoUkqV+/fjp48KAeeughJSUlqWHDhpo5c+ZVrxUcHCxJSktLcxpPS0tz7ANgVhT+vgHcAGunRMIdtGnTJs8T21966SWjatWqTsf27dvX8Pf3v+Z1YmJijI4dO15139NPP23UqVPnqvsuP7jy0ksvOcbS09N5cAXIhcL+9/1XPLgC5A1JIlxu1qxZys7O1r/+9S8tX75c+/fv1549ezRjxgxFRkZe9T3h4eE6cuSIFi9erAMHDmjGjBmOFEGSzp07p4EDB+rLL7/U4cOH9fXXX2vr1q2qVauWJGnw4MH69NNPlZKSou3bt+uLL75w7LuSzWbT4MGDNWnSJH344YdKSkpSr169FBISos6dO+f79wHcTAr737d06QGbnTt36scff5Qk7d27Vzt37mTOMfBPrO5S4R6OHj1qDBgwwAgNDTU8PT2NChUqGPfcc4/xxRdfOI7RFUtkDB8+3ChTpoxRqlQpo3v37sa0adMcSUNWVpYRExNjVKpUyfD09DRCQkKMgQMHGufOnTMMwzAGDhxoVKtWzbDb7UbZsmWNhx56yPj999+vWV9OTo4RHx9vBAUFGXa73WjZsqWxd+9eV3wVwE2nsP99z5s3z5Bk2saOHeuCbwO4edgM4wZmHgMAAOCmxO1mAAAAmNAkAgAAwIQmEQAAACY0iQAAADChSQQAAIAJTSIAAABMaBIBAABgQpMIAAAAE5pEADesd+/eTj9heNddd2nw4MEFXseXX34pm82mU6dOXfMYm82mlStX5vqc48aNU926dW+orkOHDslms2nnzp03dB4AKEg0icBNqnfv3rLZbLLZbPL09FT16tU1YcIEXbx40eXXfv/99zVx4sRcHZubxg4AUPCKW10AANdp06aN5s2bp6ysLH388ccaMGCASpQooVGjRpmOPX/+vDw9PfPluqVLl86X8wAArEOSCNzE7Ha7goODFRoaqscff1zR0dH68MMPJf3fLeLnnntOISEhqlGjhiTp559/Vrdu3RQQEKDSpUurU6dOOnTokOOc2dnZiouLU0BAgMqUKaMRI0boyp+Av/J2c1ZWlkaOHKlKlSrJbrerevXqevPNN3Xo0CG1aNFCkhQYGCibzabevXtLknJycpSQkKCwsDB5e3vrjjvu0Hvvved0nY8//li33nqrvL291aJFC6c6c2vkyJG69dZbVbJkSVWtWlXx8fG6cOGC6bjXXntNlSpVUsmSJdWtWzelp6c77X/jjTdUq1YteXl5qWbNmnr11Vevec2TJ0+qZ8+eKlu2rLy9vRUeHq558+bluXYAcCWSRMCNeHt7648//nC8Xrdunfz8/LR27VpJ0oULF9S6dWtFRkbqq6++UvHixTVp0iS1adNGu3fvlqenp15++WXNnz9fb731lmrVqqWXX35ZK1as0H/+859rXrdXr15KTEzUjBkzdMcddyglJUW///67KlWqpOXLl6tr167au3ev/Pz85O3tLUlKSEjQu+++qzlz5ig8PFwbN27Ugw8+qLJly6p58+b6+eef1aVLFw0YMECPPPKIvvvuOw0dOjTP34mvr6/mz5+vkJAQJSUlqX///vL19dWIESMcxyQnJ2vp0qVatWqVMjIy1LdvXz3xxBNauHChJGnhwoUaM2aMXnnlFdWrV087duxQ//795ePjo9jYWNM14+Pj9eOPP+qTTz7RLbfcouTkZJ07dy7PtQOASxkAbkqxsbFGp06dDMMwjJycHGPt2rWG3W43hg0b5tgfFBRkZGVlOd6zYMECo0aNGkZOTo5jLCsry/D29jY+/fRTwzAMo3z58saUKVMc+y9cuGBUrFjRcS3DMIzmzZsbTz31lGEYhrF3715DkrF27dqr1vnFF18YkoyTJ086xjIzM42SJUsamzdvdjq2b9++xgMPPGAYhmGMGjXKiIiIcNo/cuRI07muJMlYsWLFNfe/+OKLRoMGDRyvx44daxQrVsz45ZdfHGOffPKJ4eHhYRw7dswwDMOoVq2asWjRIqfzTJw40YiMjDQMwzBSUlIMScaOHTsMwzCMjh07Gn369LlmDQBQGJAkAjex1atXq1SpUrpw4YJycnLUo0cPjRs3zrG/Tp06TvMQd+3apeTkZPn6+jqdJzMzUwcOHFB6erqOHTumRo0aOfYVL15cDRs2NN1yvmznzp0qVqyYmjdvnuu6k5OTdfbsWd19991O4+fPn1e9evUkSXv27HGqQ5IiIyNzfY3LlixZohkzZujAgQM6ffq0Ll68KD8/P6djKleurAoVKjhdJycnR3v37pWvr68OHDigvn37qn///o5jLl68KH9//6te8/HHH1fXrl21fft2tWrVSp07d1bjxo3zXDsAuBJNInATa9GihWbPni1PT0+FhISoeHHnP3kfHx+n16dPn1aDBg0ct1H/qmzZstdVw+Xbx3lx+vRpSdJHH33k1JxJl+ZZ5pfExET17NlT48ePV+vWreXv76/Fixfr5ZdfznOtc+fONTWtxYoVu+p72rZtq8OHD+vjjz/W2rVr1bJlSw0YMEAvvfTS9X8YAMhnNInATczHx0fVq1fP9fH169fXkiVLVK5cOVOadln58uW1ZcsWNWvWTNKlxGzbtm2qX7/+VY+vU6eOcnJytGHDBkVHR5v2X04ys7OzHWMRERGy2+06cuTINRPIWrVqOR7Cueybb7755w/5F5s3b1ZoaKieffZZx9jhw4dNxx05ckRHjx5VSEiI4zoeHh6qUaOGgoKCFBISooMHD6pnz565vnbZsmUVGxur2NhYNW3aVMOHD6dJBFCo8HQzAIeePXvqlltuUadOnfTVV18pJSVFX375pZ588kn98ssvkqSnnnpKzz//vFauXKmffvpJTzzxxN+ucVilShXFxsbq4Ycf1sqVKx3nXLp0qSQpNDRUNptNq1ev1m+//abTp0/L19dXw4YN05AhQ/T222/rwIED2r59u2bOnKm3335bkvTYY49p//79Gj58uPbu3atFixZp/vz5efq84eHhOnLkiBYvXqwDBw5oxowZWrFihek4Ly8vxcbGateuXfrqq6/05JNPqlu3bgoODpYkjR8/XgkJCZoxY4b27dunpKQkzZs3T1OnTr3qdceMGaMPPvhAycnJ+uGHH7R69WrVqlUrT7UDgKvRJAJwKFmypDZu3KjKlSurS5cuqlWrlvr27avMzExHsjh06FA99NBDio2NVWRkpHx9fXXvvff+7Xlnz56t++67T0888YRq1qyp/v3768yZM5KkChUqaPz48Xr66acVFBSkgQMHSpImTpyo+Ph4JSQkqFatWmrTpo0++ugjhYWFSbo0T3D58uVauXKl7rjjDs2ZM0eTJ0/O0+e95557NGTIEA0cOFB169bV5s2bFR8fbzquevXq6tKli9q1a6dWrVrp9ttvd1ripl+/fnrjjTc0b9481alTR82bN9f8+fMdtV7J09NTo0aN0u23365mzZqpWLFiWrx4cZ5qBwBXsxnXmm0OAAAAt0WSCAAAABOaRAAAAJjQJAIAAMCEJhEAAAAmNIkAAAAwoUkEAACACU0iAAAATGgSAQAAYEKTCAAAABOaRAAAAJjQJAIAAMDk/wE/Nla6qybgKgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "import sklearn.linear_model as lm\n", + "\n", + "pipeline1 = Pipeline([\n", + " ('scaler',StandardScaler()),\n", + " ('model',lm.LogisticRegression())])\n", + "\n", + "log1 = pipeline1.fit(X_train1,Y_train1)\n", + "\n", + "prediction_log = pipeline1.predict(X_test1)\n", + "print(classification_report(Y_test1, prediction_log))\n", + "\n", + "mse_log1 = mean_squared_error(Y_test1, prediction_log)\n", + "r2_log1 = r2_score(Y_test1, prediction_log)\n", + "\n", + "print(f\"Mean squared error of logistic regression = {mse_log1}\")\n", + "print(f\"R-squared = {r2_log1}\\n\")\n", + "\n", + "plot_confusion_matrix(Y_test1, prediction_log, classes=['Class 0', 'Class 1'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

9. Support Vector Machine

" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.99 1.00 1.00 120\n", + " 1 1.00 0.98 0.99 56\n", + "\n", + " accuracy 0.99 176\n", + " macro avg 1.00 0.99 0.99 176\n", + "weighted avg 0.99 0.99 0.99 176\n", + "\n", + "Mean squared error of SVC = 0.005681818181818182\n", + "R-squared = 0.9738095238095238\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.svm import SVC\n", + "model_svm = SVC(C=100, gamma=0.01)\n", + "model_svm.fit(X_train1, Y_train1)\n", + "prediction_svm = model_svm.predict(X_test1)\n", + "\n", + "print(classification_report(Y_test1, prediction_svm))\n", + "mse_svm = mean_squared_error(Y_test1, prediction_svm)\n", + "r2_svm = r2_score(Y_test1, prediction_svm)\n", + "\n", + "print(f\"Mean squared error of SVC = {mse_svm}\")\n", + "print(f\"R-squared = {r2_svm}\\n\")\n", + "\n", + "plot_confusion_matrix(Y_test1, prediction_svm, classes=['Class 0', 'Class 1'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

10. Random Forest Classification

" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 120\n", + " 1 1.00 1.00 1.00 56\n", + "\n", + " accuracy 1.00 176\n", + " macro avg 1.00 1.00 1.00 176\n", + "weighted avg 1.00 1.00 1.00 176\n", + "\n", + "Mean squared error of Random Forest = 0.0\n", + "R-squared = 1.0\n", + "\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAAIjCAYAAABvUIGpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMDklEQVR4nO3deXhN997//9dOIlskMlASMQZBYh5aJ6jhlpqV0oNyKtTUlrbEVK2YqtJqi6OUTodWOVVVWtpqlZY6UlVz1SxoS2JMUkOCZP3+8LV/3RZtQnZWYj8f51rXZX/W2mu9976vnPt9XuuzPttmGIYhAAAA4E88rC4AAAAA+Q9NIgAAAExoEgEAAGBCkwgAAAATmkQAAACY0CQCAADAhCYRAAAAJjSJAAAAMKFJBAAAgAlNIoC/dODAAbVq1UoBAQGy2Wxavnx5rp7/yJEjstlsmj9/fq6etyBr3ry5mjdvbnUZANwcTSJQABw6dEiDBg1SxYoVVbhwYfn7+6tx48b697//rUuXLrn02jExMdq1a5defPFFLViwQA0aNHDp9fJSnz59ZLPZ5O/vf9Pv8cCBA7LZbLLZbHr11VdzfP7jx49rwoQJ2r59ey5UCwB5y8vqAgD8tc8//1z//Oc/Zbfb1bt3b9WoUUOXL1/Whg0bNHLkSO3evVtvvfWWS6596dIlJSQk6Pnnn9eQIUNcco3y5cvr0qVLKlSokEvO/3e8vLx08eJFrVixQt26dXPat3DhQhUuXFjp6em3de7jx49r4sSJqlChgurUqZPt93399de3dT0AyE00iUA+lpiYqB49eqh8+fJau3atSpUq5dg3ePBgHTx4UJ9//rnLrn/q1ClJUmBgoMuuYbPZVLhwYZed/+/Y7XY1btxY//3vf01N4qJFi9S+fXstXbo0T2q5ePGiihQpIm9v7zy5HgD8FW43A/nY1KlTdf78eb377rtODeJ1lStX1jPPPON4ffXqVb3wwguqVKmS7Ha7KlSooOeee04ZGRlO76tQoYI6dOigDRs26L777lPhwoVVsWJFvf/++45jJkyYoPLly0uSRo4cKZvNpgoVKki6dpv2+r//bMKECbLZbE5jq1evVpMmTRQYGCg/Pz9VrVpVzz33nGP/reYkrl27Vvfff798fX0VGBioTp06ac+ePTe93sGDB9WnTx8FBgYqICBAffv21cWLF2/9xd6gZ8+e+vLLL5WSkuIY27x5sw4cOKCePXuajj979qxGjBihmjVrys/PT/7+/mrbtq127NjhOOa7777TvffeK0nq27ev47b19c/ZvHlz1ahRQ1u2bFHTpk1VpEgRx/dy45zEmJgYFS5c2PT5W7duraCgIB0/fjzbnxUAsosmEcjHVqxYoYoVK6pRo0bZOr5///4aN26c6tWrp+nTp6tZs2aKj49Xjx49TMcePHhQDz/8sB544AG99tprCgoKUp8+fbR7925JUpcuXTR9+nRJ0iOPPKIFCxZoxowZOap/9+7d6tChgzIyMjRp0iS99tprevDBB/W///3vL9/3zTffqHXr1jp58qQmTJig2NhYbdy4UY0bN9aRI0dMx3fr1k1//PGH4uPj1a1bN82fP18TJ07Mdp1dunSRzWbTJ5984hhbtGiRqlWrpnr16pmOP3z4sJYvX64OHTpo2rRpGjlypHbt2qVmzZo5GraIiAhNmjRJkjRw4EAtWLBACxYsUNOmTR3nOXPmjNq2bas6depoxowZatGixU3r+/e//60SJUooJiZGmZmZkqQ333xTX3/9tV5//XWFhoZm+7MCQLYZAPKl1NRUQ5LRqVOnbB2/fft2Q5LRv39/p/ERI0YYkoy1a9c6xsqXL29IMtavX+8YO3nypGG3243hw4c7xhITEw1JxiuvvOJ0zpiYGKN8+fKmGsaPH2/8+b9Wpk+fbkgyTp06dcu6r19j3rx5jrE6deoYJUuWNM6cOeMY27Fjh+Hh4WH07t3bdL3HHnvM6ZwPPfSQUbx48Vte88+fw9fX1zAMw3j44YeNli1bGoZhGJmZmUZISIgxceLEm34H6enpRmZmpulz2O12Y9KkSY6xzZs3mz7bdc2aNTMkGXPnzr3pvmbNmjmNffXVV4YkY/Lkycbhw4cNPz8/o3Pnzn/7GQHgdpEkAvlUWlqaJKlo0aLZOv6LL76QJMXGxjqNDx8+XJJMcxcjIyN1//33O16XKFFCVatW1eHDh2+75htdn8v46aefKisrK1vvOXHihLZv364+ffqoWLFijvFatWrpgQcecHzOP3v88cedXt9///06c+aM4zvMjp49e+q7775TUlKS1q5dq6SkpJveapauzWP08Lj2X5+ZmZk6c+aM41b61q1bs31Nu92uvn37ZuvYVq1aadCgQZo0aZK6dOmiwoUL680338z2tQAgp2gSgXzK399fkvTHH39k6/ijR4/Kw8NDlStXdhoPCQlRYGCgjh496jRerlw50zmCgoJ07ty526zYrHv37mrcuLH69++v4OBg9ejRQx999NFfNozX66xatappX0REhE6fPq0LFy44jd/4WYKCgiQpR5+lXbt2Klq0qBYvXqyFCxfq3nvvNX2X12VlZWn69OkKDw+X3W7XPffcoxIlSmjnzp1KTU3N9jVLly6do4dUXn31VRUrVkzbt2/XzJkzVbJkyWy/FwByiiYRyKf8/f0VGhqqn3/+OUfvu/HBkVvx9PS86bhhGLd9jevz5a7z8fHR+vXr9c033+jRRx/Vzp071b17dz3wwAOmY+/EnXyW6+x2u7p06aL33ntPy5Ytu2WKKElTpkxRbGysmjZtqg8++EBfffWVVq9ererVq2c7MZWufT85sW3bNp08eVKStGvXrhy9FwByiiYRyMc6dOigQ4cOKSEh4W+PLV++vLKysnTgwAGn8eTkZKWkpDieVM4NQUFBTk8CX3djWilJHh4eatmypaZNm6ZffvlFL774otauXatvv/32pue+Xue+fftM+/bu3at77rlHvr6+d/YBbqFnz57atm2b/vjjj5s+7HPdxx9/rBYtWujdd99Vjx491KpVK0VHR5u+k+w27Nlx4cIF9e3bV5GRkRo4cKCmTp2qzZs359r5AeBGNIlAPjZq1Cj5+vqqf//+Sk5ONu0/dOiQ/v3vf0u6drtUkukJ5GnTpkmS2rdvn2t1VapUSampqdq5c6dj7MSJE1q2bJnTcWfPnjW99/qi0jcuy3NdqVKlVKdOHb333ntOTdfPP/+sr7/+2vE5XaFFixZ64YUXNGvWLIWEhNzyOE9PT1NKuWTJEv3+++9OY9eb2Zs11Dk1evRoHTt2TO+9956mTZumChUqKCYm5pbfIwDcKRbTBvKxSpUqadGiRerevbsiIiKcfnFl48aNWrJkifr06SNJql27tmJiYvTWW28pJSVFzZo1048//qj33ntPnTt3vuXyKrejR48eGj16tB566CE9/fTTunjxoubMmaMqVao4PbgxadIkrV+/Xu3bt1f58uV18uRJvfHGGypTpoyaNGlyy/O/8soratu2raKiotSvXz9dunRJr7/+ugICAjRhwoRc+xw38vDw0NixY//2uA4dOmjSpEnq27evGjVqpF27dmnhwoWqWLGi03GVKlVSYGCg5s6dq6JFi8rX11cNGzZUWFhYjupau3at3njjDY0fP96xJM+8efPUvHlzxcXFaerUqTk6HwBki8VPVwPIhv379xsDBgwwKlSoYHh7extFixY1GjdubLz++utGenq647grV64YEydONMLCwoxChQoZZcuWNcaMGeN0jGFcWwKnffv2puvcuPTKrZbAMQzD+Prrr40aNWoY3t7eRtWqVY0PPvjAtATOmjVrjE6dOhmhoaGGt7e3ERoaajzyyCPG/v37Tde4cZmYb775xmjcuLHh4+Nj+Pv7Gx07djR++eUXp2OuX+/GJXbmzZtnSDISExNv+Z0ahvMSOLdyqyVwhg8fbpQqVcrw8fExGjdubCQkJNx06ZpPP/3UiIyMNLy8vJw+Z7NmzYzq1avf9Jp/Pk9aWppRvnx5o169esaVK1ecjhs2bJjh4eFhJCQk/OVnAIDbYTOMHMzsBgAAgFtgTiIAAABMaBIBAABgQpMIAAAAE5pEAACAfGT9+vXq2LGjQkNDZbPZtHz5cse+K1euaPTo0apZs6Z8fX0VGhqq3r176/jx407nOHv2rHr16iV/f38FBgaqX79+On/+fI7qoEkEAADIRy5cuKDatWtr9uzZpn0XL17U1q1bFRcXp61bt+qTTz7Rvn379OCDDzod16tXL+3evVurV6/WypUrtX79eg0cODBHdfB0MwAAQD5ls9m0bNkyde7c+ZbHbN68Wffdd5+OHj2qcuXKac+ePYqMjNTmzZvVoEEDSdKqVavUrl07/fbbbwoNDc3WtUkSAQAAXCgjI0NpaWlOW27+WlJqaqpsNpsCAwMlSQkJCQoMDHQ0iJIUHR0tDw8Pbdq0KdvnvSt/ccWn7hCrSwDgIuc2z7K6BAAuUtjCrsSVvcPoTvdo4sSJTmPjx4/PlV+QSk9P1+jRo/XII4/I399fkpSUlKSSJUs6Hefl5aVixYopKSkp2+e+K5tEAACA/GLMmDGKjY11GrPb7Xd83itXrqhbt24yDENz5sy54/PdiCYRAADA5roZeHa7PVeawj+73iAePXpUa9eudaSIkhQSEqKTJ086HX/16lWdPXtWISEh2b4GcxIBAABsNtdtuex6g3jgwAF98803Kl68uNP+qKgopaSkaMuWLY6xtWvXKisrSw0bNsz2dUgSAQAA8pHz58/r4MGDjteJiYnavn27ihUrplKlSunhhx/W1q1btXLlSmVmZjrmGRYrVkze3t6KiIhQmzZtNGDAAM2dO1dXrlzRkCFD1KNHj2w/2SzdpUvg8OAKcPfiwRXg7mXpgysNhrns3Jd+mp6j47/77ju1aNHCNB4TE6MJEyYoLCzspu/79ttv1bx5c0nXFtMeMmSIVqxYIQ8PD3Xt2lUzZ86Un59ftusgSQQAAMhHmjdvrr/K8LKT7xUrVkyLFi26ozpoEgEAAFwwd7Cg48EVAAAAmJAkAgAAuHAJnIKKbwQAAAAmJIkAAADMSTShSQQAAOB2swnfCAAAAExIEgEAALjdbEKSCAAAABOSRAAAAOYkmvCNAAAAwIQkEQAAgDmJJiSJAAAAMCFJBAAAYE6iCU0iAAAAt5tNaJsBAABgQpIIAADA7WYTvhEAAACYkCQCAACQJJrwjQAAAMCEJBEAAMCDp5tvRJIIAAAAE5JEAAAA5iSa0CQCAACwmLYJbTMAAABMSBIBAAC43WzCNwIAAAATkkQAAADmJJqQJAIAAMCEJBEAAIA5iSZ8IwAAADAhSQQAAGBOoglNIgAAALebTfhGAAAAYEKSCAAAwO1mE5JEAAAAmJAkAgAAMCfRhG8EAAAAJiSJAAAAzEk0IUkEAACACUkiAAAAcxJNaBIBAABoEk34RgAAAGBCkggAAMCDKyYkiQAAADAhSQQAAGBOognfCAAAAExIEgEAAJiTaEKSCAAAABOSRAAAAOYkmtAkAgAAcLvZhLYZAAAAJiSJAADA7dlIEk1IEgEAAGBCkggAANweSaIZSSIAAABMSBIBAAAIEk1IEgEAAGBCkggAANwecxLNaBIBAIDbo0k043YzAAAATEgSAQCA2yNJNCNJBAAAgAlJIgAAcHskiWYkiQAAADAhSQQAACBINCFJBAAAyEfWr1+vjh07KjQ0VDabTcuXL3fabxiGxo0bp1KlSsnHx0fR0dE6cOCA0zFnz55Vr1695O/vr8DAQPXr10/nz5/PUR00iQAAwO3ZbDaXbTl14cIF1a5dW7Nnz77p/qlTp2rmzJmaO3euNm3aJF9fX7Vu3Vrp6emOY3r16qXdu3dr9erVWrlypdavX6+BAwfm7DsxDMPIcfX5nE/dIVaXAMBFzm2eZXUJAFyksIWT4AJ7feCyc6cs/Ndtv9dms2nZsmXq3LmzpGspYmhoqIYPH64RI0ZIklJTUxUcHKz58+erR48e2rNnjyIjI7V582Y1aNBAkrRq1Sq1a9dOv/32m0JDQ7N1bZJEAADg9lyZJGZkZCgtLc1py8jIuK06ExMTlZSUpOjoaMdYQECAGjZsqISEBElSQkKCAgMDHQ2iJEVHR8vDw0ObNm3K9rVoEgEAgNtzZZMYHx+vgIAApy0+Pv626kxKSpIkBQcHO40HBwc79iUlJalkyZJO+728vFSsWDHHMdnB080AAAAuNGbMGMXGxjqN2e12i6rJPppEAADg9ly5mLbdbs+1pjAkJESSlJycrFKlSjnGk5OTVadOHccxJ0+edHrf1atXdfbsWcf7s4PbzQAAAAVEWFiYQkJCtGbNGsdYWlqaNm3apKioKElSVFSUUlJStGXLFscxa9euVVZWlho2bJjta5EkAgAA5KPFtM+fP6+DBw86XicmJmr79u0qVqyYypUrp6FDh2ry5MkKDw9XWFiY4uLiFBoa6ngCOiIiQm3atNGAAQM0d+5cXblyRUOGDFGPHj2y/WSzRJMIAACQr/z0009q0aKF4/X1+YwxMTGaP3++Ro0apQsXLmjgwIFKSUlRkyZNtGrVKhUuXNjxnoULF2rIkCFq2bKlPDw81LVrV82cOTNHdbBOIoAChXUSgbuXlesk3tPnQ5ed+/T8Hi47tysxJxEAAAAm3G4GAABuz5VPNxdUNIkAAMDt0SSacbsZAAAAJiSJAAAABIkmJIkAAAAwIUkEAABujzmJZiSJAAAAMCFJBAAAbo8k0czSJvHy5ctavny5EhISlJSUJEkKCQlRo0aN1KlTJ3l7e1tZHgAAgNuy7HbzwYMHFRERoZiYGG3btk1ZWVnKysrStm3b1Lt3b1WvXt3px60BAABcxWazuWwrqCxLEp944gnVrFlT27Ztk7+/v9O+tLQ09e7dW4MHD9ZXX31lUYUAAMBdFORmzlUsaxL/97//6ccffzQ1iJLk7++vF154QQ0bNrSgMgAAAFh2uzkwMFBHjhy55f4jR44oMDAwz+oBAABuzObCrYCyLEns37+/evfurbi4OLVs2VLBwcGSpOTkZK1Zs0aTJ0/WU089ZVV5AAAAbs2yJnHSpEny9fXVK6+8ouHDhzvmAhiGoZCQEI0ePVqjRo2yqjwAAOBGmJNoZukSOKNHj9bo0aOVmJjotAROWFiYlWUBAAC4vXyxmHZYWBiNIQAAsAxJohk/ywcAAACTfJEkAgAAWIkk0YwmEQAAgB7RhNvNAAAAMLG8SVy1apU2bNjgeD179mzVqVNHPXv21Llz5yysDAAAuAt+u9nM8iZx5MiRSktLkyTt2rVLw4cPV7t27ZSYmKjY2FiLqwMAAHBPls9JTExMVGRkpCRp6dKl6tChg6ZMmaKtW7eqXbt2FlcHAADcQUFO/FzF8iTR29tbFy9elCR98803atWqlSSpWLFijoQRAAAAecvyJLFJkyaKjY1V48aN9eOPP2rx4sWSpP3796tMmTIWVwerNK5XScN6R6teZDmVKhGgbsPe0orvdkqSvLw8NOHJjmrdpLrCyhRX2vl0rd20V3EzP9OJU6mOcwT5F9G00f9Uu6Y1lGUYWr5mu0ZM/VgXLl226mMByIEPFy3Ue/Pe1enTp1SlajU9+1ycataqZXVZuEuRJJpZniTOmjVLXl5e+vjjjzVnzhyVLl1akvTll1+qTZs2FlcHq/j62LVr/+8aGr/YtK9IYW/ViSirl97+UlGPvKwew99WlfLBWjJjkNNx86bEKKJSKXV4Ypa6Pj1XTepV1uy4nnn1EQDcgVVffqFXp8Zr0JOD9eGSZapatZqeGNRPZ86csbo0wG3YDMMwrC4it/nUHWJ1CchFl7bNckoSb6Z+ZDltWDhKVdrG6dekc6oaFqztn8Spca+p2vrLMUnSA40itPz1J1S5TZxT4oiC5dzmWVaXgDzQq8c/Vb1GTT03dpwkKSsrS61aNtMjPR9VvwEDLa4OrlLYwvubYUM/d9m5E2e0d9m5XcnyJHHr1q3atWuX4/Wnn36qzp0767nnntPly9wWRPb4F/VRVlaWUv64JElqWCtM59IuOhpESVq7aZ+ysgzdW6O8VWUCyIYrly9rzy+79Y+oRo4xDw8P/eMfjbRzxzYLK8NdzebCrYCyvEkcNGiQ9u/fL0k6fPiwevTooSJFimjJkiUaNWrU374/IyNDaWlpTpuRlenqspGP2L29NPnpTvpo1Rb9cSFdkhRc3F+nzv7hdFxmZpbOpl1U8D3+VpQJIJvOpZxTZmamihcv7jRevHhxnT592qKqAPdjeZO4f/9+1alTR5K0ZMkSNW3aVIsWLdL8+fO1dOnSv31/fHy8AgICnLaryVtcXDXyCy8vD30wtZ9sNpuenmKevwgAQHawmLaZ5U2iYRjKysqSdG0JnOtrI5YtWzZb/4txzJgxSk1Nddq8guu7tGbkD15eHlr4cj+VKxWkDk/McqSIkpR8Jk0lihV1Ot7T00PF/Iso+TRLKwH5WVBgkDw9PU0PqZw5c0b33HOPRVUB7sfyJrFBgwaaPHmyFixYoHXr1ql9+2uTOxMTExUcHPy377fb7fL393fabB6eri4bFrveIFYqV0LtH5+ls6kXnPZv2pmoIP8iqhtR1jHW/N4q8vCwafPPR/O6XAA5UMjbWxGR1bXphwTHWFZWljZtSlCt2nUtrAx3M5JEM8vXSZwxY4Z69eql5cuX6/nnn1flypUlSR9//LEaNWr0N+/G3crXx1uVypZwvK5QurhqVSmtc2kXdeJ0qha90l91q5VVl2fmytPDpuDi11LDs6kXdeVqpvYlJuur/+3W7LieevrFD1XIy1PTn+2mJV9t5clmoAB4NKav4p4brerVa6hGzVr6YMF7unTpkjo/1MXq0gC3kW+XwElPT5enp6cKFSqU4/eyBE7Bd3/9cH39zjOm8QWf/aDJc7/Qvi8m3fR9rfr/W99vOSDp2mLa05/tdm0x7axri2kPn7qExbQLOJbAcR//XfiBYzHtqtUiNPq5sapVq7bVZcGFrFwCp/KIL1127oOvtnXZuV0p3zaJd4ImEbh70SQCdy+axPzF8tvNmZmZmj59uj766CMdO3bMtDbi2bNnLaoMAAC4i4I8d9BVLH9wZeLEiZo2bZq6d++u1NRUxcbGqkuXLvLw8NCECROsLg8AALgBm811W0FleZO4cOFCvf322xo+fLi8vLz0yCOP6J133tG4ceP0ww8/WF0eAACAW7K8SUxKSlLNmjUlSX5+fkpNvfbkaYcOHfT55677HUUAAIDrWALHzPImsUyZMjpx4oQkqVKlSvr6668lSZs3b5bdbreyNAAAALdleZP40EMPac2aNZKkp556SnFxcQoPD1fv3r312GOPWVwdAABwB8xJNLP86eaXXnrJ8e/u3burXLlySkhIUHh4uDp27GhhZQAAAO7L8ibxRlFRUYqKirK6DAAA4EY8PApw5OciljSJn332WbaPffDBB11YCQAAAG7Gkiaxc+fO2TrOZrMpMzPTtcUAAAC3V5DnDrqKJU1iVlaWFZcFAAC4qYK8VI2rWP50MwAAAPIfy5rEtWvXKjIyUmlpaaZ9qampql69utavX29BZQAAwN2wBI6ZZU3ijBkzNGDAAPn7+5v2BQQEaNCgQZo+fboFlQEAAMCyJnHHjh1q06bNLfe3atVKW7ZsycOKAACAu+Jn+cwsaxKTk5NVqFChW+738vLSqVOn8rAiAAAAXGdZk1i6dGn9/PPPt9y/c+dOlSpVKg8rAgAA7ook0cyyJrFdu3aKi4tTenq6ad+lS5c0fvx4dejQwYLKAAAAYNnP8o0dO1affPKJqlSpoiFDhqhq1aqSpL1792r27NnKzMzU888/b1V5AADAjRTgwM9lLGsSg4ODtXHjRj3xxBMaM2aMDMOQdC3ubd26tWbPnq3g4GCrygMAAG6kIN8WdhXLmkRJKl++vL744gudO3dOBw8elGEYCg8PV1BQkJVlAQAAuD1Lm8TrgoKCdO+991pdBgAAcFMEiWb8LB8AAABM8kWSCAAAYCXmJJqRJAIAAMCEJBEAALg9gkQzkkQAAACYkCQCAAC3x5xEM5JEAAAAmNAkAgAAt2ezuW7LiczMTMXFxSksLEw+Pj6qVKmSXnjhBccv00mSYRgaN26cSpUqJR8fH0VHR+vAgQO5/I3QJAIAAMhms7lsy4mXX35Zc+bM0axZs7Rnzx69/PLLmjp1ql5//XXHMVOnTtXMmTM1d+5cbdq0Sb6+vmrdurXS09Nz9TthTiIAAEA+sXHjRnXq1Ent27eXJFWoUEH//e9/9eOPP0q6liLOmDFDY8eOVadOnSRJ77//voKDg7V8+XL16NEj12ohSQQAAG7PlbebMzIylJaW5rRlZGTctI5GjRppzZo12r9/vyRpx44d2rBhg9q2bStJSkxMVFJSkqKjox3vCQgIUMOGDZWQkJCr3wlNIgAAgAvFx8crICDAaYuPj7/psc8++6x69OihatWqqVChQqpbt66GDh2qXr16SZKSkpIkScHBwU7vCw4OduzLLdxuBgAAbs+VS+CMGTNGsbGxTmN2u/2mx3700UdauHChFi1apOrVq2v79u0aOnSoQkNDFRMT47Iab4YmEQAAwIXsdvstm8IbjRw50pEmSlLNmjV19OhRxcfHKyYmRiEhIZKk5ORklSpVyvG+5ORk1alTJ1fr5nYzAABwe/llCZyLFy/Kw8O5PfP09FRWVpYkKSwsTCEhIVqzZo1jf1pamjZt2qSoqKg7/h7+jCQRAAAgn+jYsaNefPFFlStXTtWrV9e2bds0bdo0PfbYY5Ku3RYfOnSoJk+erPDwcIWFhSkuLk6hoaHq3LlzrtZCkwgAANxefvlZvtdff11xcXF68skndfLkSYWGhmrQoEEaN26c45hRo0bpwoULGjhwoFJSUtSkSROtWrVKhQsXztVabMafl/C+S/jUHWJ1CQBc5NzmWVaXAMBFClsYXTV59XuXnXvDiPtddm5XYk4iAAAATLjdDAAA3F5+ud2cn5AkAgAAwIQkEQAAuD2SRDOSRAAAAJiQJAIAALdHkGhGkggAAAATkkQAAOD2mJNoRpMIAADcHj2iGbebAQAAYEKSCAAA3B63m81IEgEAAGBCkggAANweQaIZSSIAAABMSBIBAIDb8yBKNCFJBAAAgAlJIgAAcHsEiWY0iQAAwO2xBI4Zt5sBAABgQpIIAADcngdBoglJIgAAAExIEgEAgNtjTqIZSSIAAABMSBIBAIDbI0g0I0kEAACACUkiAABwezYRJd6IJhEAALg9lsAx43YzAAAATEgSAQCA22MJHDOSRAAAAJiQJAIAALdHkGhGkggAAAATkkQAAOD2PIgSTUgSAQAAYJIrTWJKSkpunAYAAMASNpvrtoIqx03iyy+/rMWLFzted+vWTcWLF1fp0qW1Y8eOXC0OAAAgL9hsNpdtBVWOm8S5c+eqbNmykqTVq1dr9erV+vLLL9W2bVuNHDky1wsEAABA3svxgytJSUmOJnHlypXq1q2bWrVqpQoVKqhhw4a5XiAAAICrFeDAz2VynCQGBQXp119/lSStWrVK0dHRkiTDMJSZmZm71QEAAMASOU4Su3Tpop49eyo8PFxnzpxR27ZtJUnbtm1T5cqVc71AAAAAV2MJHLMcN4nTp09XhQoV9Ouvv2rq1Kny8/OTJJ04cUJPPvlkrhcIAACAvJfjJrFQoUIaMWKEaXzYsGG5UhAAAEBeI0c0y1aT+Nlnn2X7hA8++OBtFwMAAID8IVtNYufOnbN1MpvNxsMrAACgwCnI6xm6SraaxKysLFfXAQAAYBkPekSTO/pZvvT09NyqAwAAAPlIjpvEzMxMvfDCCypdurT8/Px0+PBhSVJcXJzefffdXC8QAADA1fhZPrMcN4kvvvii5s+fr6lTp8rb29sxXqNGDb3zzju5WhwAAACskeMm8f3339dbb72lXr16ydPT0zFeu3Zt7d27N1eLAwAAyAs2m+u2girHTeLvv/9+019WycrK0pUrV3KlKAAAAFgrx01iZGSkvv/+e9P4xx9/rLp16+ZKUQAAAHmJOYlmOf7FlXHjxikmJka///67srKy9Mknn2jfvn16//33tXLlSlfUCAAAgDyW4ySxU6dOWrFihb755hv5+vpq3Lhx2rNnj1asWKEHHnjAFTUCAAC4lIfNdVtBleMkUZLuv/9+rV69OrdrAQAAsERBvi3sKrfVJErSTz/9pD179ki6Nk+xfv36uVYUAAAArJXjJvG3337TI488ov/9738KDAyUJKWkpKhRo0b68MMPVaZMmdyuEQAAwKXIEc1yPCexf//+unLlivbs2aOzZ8/q7Nmz2rNnj7KystS/f39X1AgAAIA8luMkcd26ddq4caOqVq3qGKtatapef/113X///blaHAAAQF7wYE6iSY6TxLJly9500ezMzEyFhobmSlEAAACwVo6bxFdeeUVPPfWUfvrpJ8fYTz/9pGeeeUavvvpqrhYHAACQF/hZPrNs3W4OCgpyejT8woULatiwoby8rr396tWr8vLy0mOPPabOnTu7pFAAAADknWw1iTNmzHBxGQAAANZhnUSzbDWJMTExrq4DAAAA+chtL6YtSenp6bp8+bLTmL+//x0VBAAAkNcIEs1y3CReuHBBo0eP1kcffaQzZ86Y9mdmZuZKYQAAAHmFJXDMcvx086hRo7R27VrNmTNHdrtd77zzjiZOnKjQ0FC9//77rqgRAAAAeSzHTeKKFSv0xhtvqGvXrvLy8tL999+vsWPHasqUKVq4cKEragQAAHCp/LQEzu+//65//etfKl68uHx8fFSzZk2npQcNw9C4ceNUqlQp+fj4KDo6WgcOHMjFb+OaHDeJZ8+eVcWKFSVdm3949uxZSVKTJk20fv363K0OAADAjZw7d06NGzdWoUKF9OWXX+qXX37Ra6+9pqCgIMcxU6dO1cyZMzV37lxt2rRJvr6+at26tdLT03O1lhzPSaxYsaISExNVrlw5VatWTR999JHuu+8+rVixQoGBgblaHAAAQF7IL0vgvPzyyypbtqzmzZvnGAsLC3P82zAMzZgxQ2PHjlWnTp0kSe+//76Cg4O1fPly9ejRI9dqyXGS2LdvX+3YsUOS9Oyzz2r27NkqXLiwhg0bppEjR+ZaYQAAAHeDjIwMpaWlOW0ZGRk3Pfazzz5TgwYN9M9//lMlS5ZU3bp19fbbbzv2JyYmKikpSdHR0Y6xgIAANWzYUAkJCblat80wDONOTnD06FFt2bJFlStXVq1atXKrrjuSftXqCgC4yqSv91tdAgAXmdKuimXXfmrZHpedu/iOxZo4caLT2Pjx4zVhwgTTsYULF5YkxcbG6p///Kc2b96sZ555RnPnzlVMTIw2btyoxo0b6/jx4ypVqpTjfd26dZPNZtPixYtzre47WidRksqXL6/y5cvnRi0AAAB3nTFjxig2NtZpzG633/TYrKwsNWjQQFOmTJEk1a1bVz///LOjScxL2WoSZ86cme0TPv3007ddDAAAgBVcOSfRbrffsim8UalSpRQZGek0FhERoaVLl0qSQkJCJEnJyclOSWJycrLq1KmTOwX/P9lqEqdPn56tk9lsNppEAABQ4Hjkj+dW1LhxY+3bt89pbP/+/Y67tmFhYQoJCdGaNWscTWFaWpo2bdqkJ554IldryVaTmJiYmKsXBQAAgNmwYcPUqFEjTZkyRd26ddOPP/6ot956S2+99Zaka4Hc0KFDNXnyZIWHhyssLExxcXEKDQ1V586dc7WWO56TCAAAUNDllyTx3nvv1bJlyzRmzBhNmjRJYWFhmjFjhnr16uU4ZtSoUbpw4YIGDhyolJQUNWnSRKtWrXI89JJb7vjp5vyIp5uBuxdPNwN3Lyufbo79bK/Lzj3twWouO7crkSQCAAC3l18W085PcryYNgAAAO5+JIkAAMDt5Zc5ifnJbSWJ33//vf71r38pKipKv//+uyRpwYIF2rBhQ64WBwAAAGvkuElcunSpWrduLR8fH23bts3x24OpqamO1cEBAAAKEpvNdVtBleMmcfLkyZo7d67efvttFSpUyDHeuHFjbd26NVeLAwAAyAseNpvLtoIqx03ivn371LRpU9N4QECAUlJScqMmAAAAWCzHTWJISIgOHjxoGt+wYYMqVqyYK0UBAADkJQ8XbgVVjmsfMGCAnnnmGW3atEk2m03Hjx/XwoULNWLEiFz/zUAAAABYI8dL4Dz77LPKyspSy5YtdfHiRTVt2lR2u10jRozQU0895YoaAQAAXKoATx10mRw3iTabTc8//7xGjhypgwcP6vz584qMjJSfn58r6gMAAIAFbnsxbW9vb0VGRuZmLQAAAJYoyE8hu0qOm8QWLVr85e8brl279o4KAgAAgPVy3CTWqVPH6fWVK1e0fft2/fzzz4qJicmtugAAAPIMQaJZjpvE6dOn33R8woQJOn/+/B0XBAAAkNf47WazXFu+51//+pf+85//5NbpAAAAYKHbfnDlRgkJCSpcuHBunQ4AACDP8OCKWY6bxC5duji9NgxDJ06c0E8//aS4uLhcKwwAAADWyXGTGBAQ4PTaw8NDVatW1aRJk9SqVatcKwwAACCvECSa5ahJzMzMVN++fVWzZk0FBQW5qiYAAABYLEcPrnh6eqpVq1ZKSUlxUTkAAAB5z8Pmuq2gyvHTzTVq1NDhw4ddUQsAAADyiRw3iZMnT9aIESO0cuVKnThxQmlpaU4bAABAQWNz4X8KqmzPSZw0aZKGDx+udu3aSZIefPBBp5/nMwxDNptNmZmZuV8lAACACxXk28Kuku0mceLEiXr88cf17bffurIeAAAA5APZbhINw5AkNWvWzGXFAAAAWIEk0SxHcxJtLCIEAADgFnK0TmKVKlX+tlE8e/bsHRUEAACQ1wjCzHLUJE6cONH0iysAAAC4++SoSezRo4dKlizpqloAAAAswZxEs2zPSSSGBQAAcB85froZAADgbkMWZpbtJjErK8uVdQAAAFjGgy7RJMc/ywcAAIC7X44eXAEAALgb8eCKGUkiAAAATEgSAQCA22NKohlJIgAAAExIEgEAgNvzEFHijUgSAQAAYEKSCAAA3B5zEs1oEgEAgNtjCRwzbjcDAADAhCQRAAC4PX6Wz4wkEQAAACYkiQAAwO0RJJqRJAIAAMCEJBEAALg95iSakSQCAADAhCQRAAC4PYJEM5pEAADg9ri1asZ3AgAAABOSRAAA4PZs3G82IUkEAACACUkiAABwe+SIZiSJAAAAMCFJBAAAbo/FtM1IEgEAAGBCkggAANweOaIZTSIAAHB73G0243YzAAAATEgSAQCA22MxbTOSRAAAAJiQJAIAALdHambGdwIAAAATkkQAAOD2mJNoRpIIAACQT7300kuy2WwaOnSoYyw9PV2DBw9W8eLF5efnp65duyo5OTnXr02TCAAA3J7Nhdvt2rx5s958803VqlXLaXzYsGFasWKFlixZonXr1un48ePq0qXLHVzp5mgSAQAA8pnz58+rV69eevvttxUUFOQYT01N1bvvvqtp06bp//7v/1S/fn3NmzdPGzdu1A8//JCrNdAkAgAAt2ez2Vy2ZWRkKC0tzWnLyMj4y3oGDx6s9u3bKzo62ml8y5YtunLlitN4tWrVVK5cOSUkJOTqd0KTCAAA3J6HC7f4+HgFBAQ4bfHx8bes5cMPP9TWrVtvekxSUpK8vb0VGBjoNB4cHKykpKTb/vw3w9PNAAAALjRmzBjFxsY6jdnt9pse++uvv+qZZ57R6tWrVbhw4bwo75ZoEgEAgNtz5RI4drv9lk3hjbZs2aKTJ0+qXr16jrHMzEytX79es2bN0ldffaXLly8rJSXFKU1MTk5WSEhIrtZNkwgAAJBPtGzZUrt27XIa69u3r6pVq6bRo0erbNmyKlSokNasWaOuXbtKkvbt26djx44pKioqV2uhSQQAAG4vvyylXbRoUdWoUcNpzNfXV8WLF3eM9+vXT7GxsSpWrJj8/f311FNPKSoqSv/4xz9ytRaaRAAAgAJk+vTp8vDwUNeuXZWRkaHWrVvrjTfeyPXr2AzDMHL9rBZLv2p1BQBcZdLX+60uAYCLTGlXxbJrf7ord58M/rNONXN3rmBeYQkcAAAAmHC7GQAAuD2PfDMrMf+gSQQAAG7PhSvgFFjcbgYAAIAJSSIAAHB7Nm43m5AkAgAAwIQkEQAAuD3mJJqRJAIAAMCEJBEAALg9lsAxy7dJYnJysiZNmmR1GQAAAG4p3zaJSUlJmjhxotVlAAAAN2CzuW4rqCy73bxz586/3L9v3748qgQAALi7gtzMuYplTWKdOnVks9lkGIZp3/VxG/8XAwAAsIRlTWKxYsU0depUtWzZ8qb7d+/erY4dO+ZxVQAAwB2xmLaZZU1i/fr1dfz4cZUvX/6m+1NSUm6aMgIAAMD1LGsSH3/8cV24cOGW+8uVK6d58+blYUUAAMBdeRAkmljWJD700EN/uT8oKEgxMTF5VA0AAAD+jMW0AQCA22NOolm+XScRAAAA1iFJBAAAbo9V98xoEgEAgNvjdrMZt5sBAABgYnmTuGrVKm3YsMHxevbs2apTp4569uypc+fOWVgZAABwFx42120FleVN4siRI5WWliZJ2rVrl4YPH6527dopMTFRsbGxFlcHAADgniyfk5iYmKjIyEhJ0tKlS9WhQwdNmTJFW7duVbt27SyuDgAAuAPmJJpZniR6e3vr4sWLkqRvvvlGrVq1knTtt52vJ4wAAADIW5YniU2aNFFsbKwaN26sH3/8UYsXL5Yk7d+/X2XKlLG4OuR3Hy5aqPfmvavTp0+pStVqeva5ONWsVcvqsgDkwC+rFmnPV/91GvMrWVqtx8x1vD5zZK92f75AZ4/tk83mocDSFdVk0ER5etvzulzcpVgCx8zyJnHWrFl68skn9fHHH2vOnDkqXbq0JOnLL79UmzZtLK4O+dmqL7/Qq1PjNXb8RNWsWVsLF7ynJwb106crV6l48eJWlwcgB/xDyun+JyY7Xts8/v8bXWeO7NWGN8erWsuHVafLQNk8PZX6e6LkYfnNMOCuZnmTWK5cOa1cudI0Pn36dAuqQUGy4L156vJwN3V+qKskaez4iVq//jst/2Sp+g0YaHF1AHLC5uGpwv5BN923c/k7qnx/R1WN/qdjrGhJ7jQhdxEkmlneJG7dulWFChVSzZo1JUmffvqp5s2bp8jISE2YMEHe3t4WV4j86Mrly9rzy271GzDIMebh4aF//KORdu7YZmFlAG7H+dPH9fn4GHl4FVLxCtVUo0NvFQkqqfQ/UnT26D6VrddM3/57pC6cTlLR4NKq3u5R3VOxutVl4y7iwf1mE8uz+kGDBmn//v2SpMOHD6tHjx4qUqSIlixZolGjRv3t+zMyMpSWlua0ZWRkuLpsWOxcyjllZmaabisXL15cp0+ftqgqALejWPkqavDIUDUZNEF1//mkLpxN1rrXn9WV9Iu6cCZJkrTnq/8q7B+t1WTQBAWWrqTv3xirP04dt7hy4O5meZO4f/9+1alTR5K0ZMkSNW3aVIsWLdL8+fO1dOnSv31/fHy8AgICnLZXXo53cdUAgNwSEtFAZeo0UUBomEKq1VPjgeN1+dIF/bZ9g2QYkqSwRm1UoWG0AstUUu2HBsivZBkd3bTa4spxN7G5cCuoLL/dbBiGsrKyJF1bAqdDhw6SpLJly2YrERozZoxp0W3Dk6fd7nZBgUHy9PTUmTNnnMbPnDmje+65x6KqAOQGbx8/FS0RqgunT6hk+LXVCvyDyzod4x9cRhfPnbKiPMBtWJ4kNmjQQJMnT9aCBQu0bt06tW/fXtK1RbaDg4P/9v12u13+/v5Om91Ok3i3K+TtrYjI6tr0Q4JjLCsrS5s2JahW7boWVgbgTl3NuKTzZ5JU2D9IRYoFq3BAMf1x8nenY/44dVxFipW0qELclYgSTSxPEmfMmKFevXpp+fLlev7551W5cmVJ0scff6xGjRpZXB3ys0dj+iruudGqXr2GatSspQ8WvKdLly6p80NdrC4NQA7s/PRdlap+n4oUK6n01LP6ZdUi2WweKluvmWw2m6q06KJfVi1SQGiYAkuH6ejmtfrj5G/6R59nrS4duKtZ3iTWqlVLu3btMo2/8sor8vT0tKAiFBRt2rbTubNn9casmTp9+pSqVovQG2++o+LcbgYKlEupZ/Tjgld1+UKa7H4BKl4xUi2Gviq7X4AkKbxZJ2Vduaydn76jyxf/UEBomO5/fJL87illceW4m/CzfGY2w/h/s4LvIulXra4AgKtM+nq/1SUAcJEp7apYdu1Nh1Jddu6GlQJcdm5XsjxJzMzM1PTp0/XRRx/p2LFjunz5stP+s2fPWlQZAABwFyyTaGb5gysTJ07UtGnT1L17d6Wmpio2NlZdunSRh4eHJkyYYHV5AADADfDcipnlTeLChQv19ttva/jw4fLy8tIjjzyid955R+PGjdMPP/xgdXkAAABuyfImMSkpyfGTfH5+fkpNvTYnoEOHDvr888+tLA0AALgLokQTy5vEMmXK6MSJE5KkSpUq6euvv5Ykbd68mfUOAQAALGJ5k/jQQw9pzZo1kqSnnnpKcXFxCg8PV+/evfXYY49ZXB0AAHAHNhf+p6Cy/Onml156yfHv7t27q1y5ckpISFB4eLg6duxoYWUAAADuy/Im8UZRUVGKioqyugwAAOBGWALHzJIm8bPPPsv2sQ8++KALKwEAAMDNWNIkdu7cOVvH2Ww2ZWZmurYYAADg9ggSzSxpErOysqy4LAAAwM3RJZpY/nQzAAAA8h/LmsS1a9cqMjJSaWlppn2pqamqXr261q9fb0FlAADA3bAEjpllTeKMGTM0YMAA+fv7m/YFBARo0KBBmj59ugWVAQAAwLImcceOHWrTps0t97dq1UpbtmzJw4oAAIC7stlctxVUljWJycnJKlSo0C33e3l56dSpU3lYEQAAAK6zrEksXbq0fv7551vu37lzp0qVKpWHFQEAAHdlc+FWUFnWJLZr105xcXFKT0837bt06ZLGjx+vDh06WFAZAAAALPtZvrFjx+qTTz5RlSpVNGTIEFWtWlWStHfvXs2ePVuZmZl6/vnnrSoPAAC4k4Ic+bmIZU1icHCwNm7cqCeeeEJjxoyRYRiSrv3KSuvWrTV79mwFBwdbVR4AAHAjBXmpGlexrEmUpPLly+uLL77QuXPndPDgQRmGofDwcAUFBVlZFgAAgNuztEm8LigoSPfee6/VZQAAADdVkJeqcRV+lg8AAAAm+SJJBAAAsBJBohlJIgAAAExIEgEAAIgSTUgSAQAAYEKSCAAA3B7rJJqRJAIAAMCEJhEAALg9m811W07Ex8fr3nvvVdGiRVWyZEl17txZ+/btczomPT1dgwcPVvHixeXn56euXbsqOTk5F7+Na2gSAQCA27O5cMuJdevWafDgwfrhhx+0evVqXblyRa1atdKFCxccxwwbNkwrVqzQkiVLtG7dOh0/flxdunS53Y9+Szbj+o8m30XSr1pdAQBXmfT1fqtLAOAiU9pVsezae45f+PuDblNEqO9tv/fUqVMqWbKk1q1bp6ZNmyo1NVUlSpTQokWL9PDDD0uS9u7dq4iICCUkJOgf//hHbpVNkggAAODKKDEjI0NpaWlOW0ZGRrbKSk1NlSQVK1ZMkrRlyxZduXJF0dHRjmOqVaumcuXKKSEh4U6+AROaRAAAABeKj49XQECA0xYfH/+378vKytLQoUPVuHFj1ahRQ5KUlJQkb29vBQYGOh0bHByspKSkXK2bJXAAAIDbc+USOGPGjFFsbKzTmN1u/9v3DR48WD///LM2bNjgqtL+Ek0iAACAC9nt9mw1hX82ZMgQrVy5UuvXr1eZMmUc4yEhIbp8+bJSUlKc0sTk5GSFhITkVsmSuN0MAACQb5bAMQxDQ4YM0bJly7R27VqFhYU57a9fv74KFSqkNWvWOMb27dunY8eOKSoqKje+CgeSRAAAgHxi8ODBWrRokT799FMVLVrUMc8wICBAPj4+CggIUL9+/RQbG6tixYrJ399fTz31lKKionL1yWaJJhEAACDf/CjfnDlzJEnNmzd3Gp83b5769OkjSZo+fbo8PDzUtWtXZWRkqHXr1nrjjTdyvRbWSQRQoLBOInD3snKdxP3JF1127irBRVx2bldiTiIAAABMuN0MAADcniuXwCmoSBIBAABgQpIIAADcXk6XqnEHJIkAAAAwIUkEAABujyDRjCQRAAAAJiSJAAAARIkmNIkAAMDtsQSOGbebAQAAYEKSCAAA3B5L4JiRJAIAAMCEJBEAALg9gkQzkkQAAACYkCQCAAAQJZqQJAIAAMCEJBEAALg91kk0o0kEAABujyVwzLjdDAAAABOSRAAA4PYIEs1IEgEAAGBCkggAANwecxLNSBIBAABgQpIIAADArEQTkkQAAACYkCQCAAC3x5xEM5pEAADg9ugRzbjdDAAAABOSRAAA4Pa43WxGkggAAAATkkQAAOD2bMxKNCFJBAAAgAlJIgAAAEGiCUkiAAAATEgSAQCA2yNINKNJBAAAbo8lcMy43QwAAAATkkQAAOD2WALHjCQRAAAAJiSJAAAABIkmJIkAAAAwIUkEAABujyDRjCQRAAAAJiSJAADA7bFOohlNIgAAcHssgWPG7WYAAACYkCQCAAC3x+1mM5JEAAAAmNAkAgAAwIQmEQAAACbMSQQAAG6POYlmJIkAAAAwIUkEAABuj3USzWgSAQCA2+N2sxm3mwEAAGBCkggAANweQaIZSSIAAABMSBIBAACIEk1IEgEAAGBCkggAANweS+CYkSQCAADAhCQRAAC4PdZJNCNJBAAAgAlJIgAAcHsEiWY0iQAAAHSJJtxuBgAAgAlNIgAAcHs2F/7ndsyePVsVKlRQ4cKF1bBhQ/3444+5/In/Hk0iAABAPrJ48WLFxsZq/Pjx2rp1q2rXrq3WrVvr5MmTeVoHTSIAAHB7NpvrtpyaNm2aBgwYoL59+yoyMlJz585VkSJF9J///Cf3P/hfoEkEAABwoYyMDKWlpTltGRkZNz328uXL2rJli6Kjox1jHh4eio6OVkJCQl6VLOkufbq58F35qXAzGRkZio+P15gxY2S3260uB3lgSrsqVpeAPMLfN/KSK3uHCZPjNXHiRKex8ePHa8KECaZjT58+rczMTAUHBzuNBwcHa+/eva4r8iZshmEYeXpFIBelpaUpICBAqamp8vf3t7ocALmIv2/cLTIyMkzJod1uv+n/+Dl+/LhKly6tjRs3KioqyjE+atQorVu3Tps2bXJ5vdeRuQEAALjQrRrCm7nnnnvk6emp5ORkp/Hk5GSFhIS4orxbYk4iAABAPuHt7a369etrzZo1jrGsrCytWbPGKVnMCySJAAAA+UhsbKxiYmLUoEED3XfffZoxY4YuXLigvn375mkdNIko0Ox2u8aPH8+kduAuxN833FX37t116tQpjRs3TklJSapTp45WrVplepjF1XhwBQAAACbMSQQAAIAJTSIAAABMaBIBAABgQpOIfMNms2n58uVWlwHABfj7BgoemkTkiaSkJD311FOqWLGi7Ha7ypYtq44dOzqtA2UlwzA0btw4lSpVSj4+PoqOjtaBAwesLgsoEPL73/cnn3yiVq1aqXjx4rLZbNq+fbvVJQEFAk0iXO7IkSOqX7++1q5dq1deeUW7du3SqlWr1KJFCw0ePNjq8iRJU6dO1cyZMzV37lxt2rRJvr6+at26tdLT060uDcjXCsLf94ULF9SkSRO9/PLLVpcCFCwG4GJt27Y1SpcubZw/f96079y5c45/SzKWLVvmeD1q1CgjPDzc8PHxMcLCwoyxY8caly9fduzfvn270bx5c8PPz88oWrSoUa9ePWPz5s2GYRjGkSNHjA4dOhiBgYFGkSJFjMjISOPzzz+/aX1ZWVlGSEiI8corrzjGUlJSDLvdbvz3v/+9w08P3N3y+9/3nyUmJhqSjG3btt325wXcCYtpw6XOnj2rVatW6cUXX5Svr69pf2Bg4C3fW7RoUc2fP1+hoaHatWuXBgwYoKJFi2rUqFGSpF69eqlu3bqaM2eOPD09tX37dhUqVEiSNHjwYF2+fFnr16+Xr6+vfvnlF/n5+d30OomJiUpKSlJ0dLRjLCAgQA0bNlRCQoJ69OhxB98AcPcqCH/fAG4fTSJc6uDBgzIMQ9WqVcvxe8eOHev4d4UKFTRixAh9+OGHjv8ncuzYMY0cOdJx7vDwcMfxx44dU9euXVWzZk1JUsWKFW95naSkJEkyrWQfHBzs2AfArCD8fQO4fcxJhEsZd/CDPosXL1bjxo0VEhIiPz8/jR07VseOHXPsj42NVf/+/RUdHa2XXnpJhw4dcux7+umnNXnyZDVu3Fjjx4/Xzp077+hzADDj7xu4u9EkwqXCw8Nls9m0d+/eHL0vISFBvXr1Urt27bRy5Upt27ZNzz//vC5fvuw4ZsKECdq9e7fat2+vtWvXKjIyUsuWLZMk9e/fX4cPH9ajjz6qXbt2qUGDBnr99ddveq2QkBBJUnJystN4cnKyYx8As4Lw9w3gDlg7JRLuoE2bNjme2P7qq68aFStWdDq2X79+RkBAwC2v06NHD6Njx4433ffss88aNWvWvOm+6w+uvPrqq46x1NRUHlwBsiG//33/GQ+uADlDkgiXmz17tjIzM3Xfffdp6dKlOnDggPbs2aOZM2cqKirqpu8JDw/XsWPH9OGHH+rQoUOaOXOmI0WQpEuXLmnIkCH67rvvdPToUf3vf//T5s2bFRERIUkaOnSovvrqKyUmJmrr1q369ttvHftuZLPZNHToUE2ePFmfffaZdu3apd69eys0NFSdO3fO9e8DuJvk979v6doDNtu3b9cvv/wiSdq3b5+2b9/OnGPg71jdpcI9HD9+3Bg8eLBRvnx5w9vb2yhdurTx4IMPGt9++63jGN2wRMbIkSON4sWLG35+fkb37t2N6dOnO5KGjIwMo0ePHkbZsmUNb29vIzQ01BgyZIhx6dIlwzAMY8iQIUalSpUMu91ulChRwnj00UeN06dP37K+rKwsIy4uzggODjbsdrvRsmVLY9++fa74KoC7Tn7/+543b54hybSNHz/eBd8GcPewGcYdzDwGAADAXYnbzQAAADChSQQAAIAJTSIAAABMaBIBAABgQpMIAAAAE5pEAAAAmNAkAgAAwIQmEQAAACY0iQDuWJ8+fZx+wrB58+YaOnRontfx3XffyWazKSUl5ZbH2Gw2LV++PNvnnDBhgurUqXNHdR05ckQ2m03bt2+/o/MAQF6iSQTuUn369JHNZpPNZpO3t7cqV66sSZMm6erVqy6/9ieffKIXXnghW8dmp7EDAOQ9L6sLAOA6bdq00bx585SRkaEvvvhCgwcPVqFChTRmzBjTsZcvX5a3t3euXLdYsWK5ch4AgHVIEoG7mN1uV0hIiMqXL68nnnhC0dHR+uyzzyT9/7eIX3zxRYWGhqpq1aqSpF9//VXdunVTYGCgihUrpk6dOunIkSOOc2ZmZio2NlaBgYEqXry4Ro0apRt/Av7G280ZGRkaPXq0ypYtK7vdrsqVK+vdd9/VkSNH1KJFC0lSUFCQbDab+vTpI0nKyspSfHy8wsLC5OPjo9q1a+vjjz92us4XX3yhKlWqyMfHRy1atHCqM7tGjx6tKlWqqEiRIqpYsaLi4uJ05coV03FvvvmmypYtqyJFiqhbt25KTU112v/OO+8oIiJChQsXVrVq1fTGG2/c8prnzp1Tr169VKJECfn4+Cg8PFzz5s3Lce0A4EokiYAb8fHx0ZkzZxyv16xZI39/f61evVqSdOXKFbVu3VpRUVH6/vvv5eXlpcmTJ6tNmzbauXOnvL299dprr2n+/Pn6z3/+o4iICL322mtatmyZ/u///u+W1+3du7cSEhI0c+ZM1a5dW4mJiTp9+rTKli2rpUuXqmvXrtq3b5/8/f3l4+MjSYqPj9cHH3yguXPnKjw8XOvXr9e//vUvlShRQs2aNdOvv/6qLl26aPDgwRo4cKB++uknDR8+PMffSdGiRTV//nyFhoZq165dGjBggIoWLapRo0Y5jjl48KA++ugjrVixQmlpaerXr5+efPJJLVy4UJK0cOFCjRs3TrNmzVLdunW1bds2DRgwQL6+voqJiTFdMy4uTr/88ou+/PJL3XPPPTp48KAuXbqU49oBwKUMAHelmJgYo1OnToZhGEZWVpaxevVqw263GyNGjHDsDw4ONjIyMhzvWbBggVG1alUjKyvLMZaRkWH4+PgYX331lWEYhlGqVClj6tSpjv1XrlwxypQp47iWYRhGs2bNjGeeecYwDMPYt2+fIclYvXr1Tev89ttvDUnGuXPnHGPp6elGkSJFjI0bNzod269fP+ORRx4xDMMwxowZY0RGRjrtHz16tOlcN5JkLFu27Jb7X3nlFaN+/fqO1+PHjzc8PT2N3377zTH25ZdfGh4eHsaJEycMwzCMSpUqGYsWLXI6zwsvvGBERUUZhmEYiYmJhiRj27ZthmEYRseOHY2+ffvesgYAyA9IEoG72MqVK+Xn56crV64oKytLPXv21IQJExz7a9as6TQPcceOHTp48KCKFi3qdJ709HQdOnRIqampOnHihBo2bOjY5+XlpQYNGphuOV+3fft2eXp6qlmzZtmu++DBg7p48aIeeOABp/HLly+rbt26kqQ9e/Y41SFJUVFR2b7GdYsXL9bMmTN16NAhnT9/XlevXpW/v7/TMeXKlVPp0qWdrpOVlaV9+/apaNGiOnTokPr166cBAwY4jrl69aoCAgJues0nnnhCXbt21datW9WqVSt17txZjRo1ynHtAOBKNInAXaxFixaaM2eOvL29FRoaKi8v5z95X19fp9fnz59X/fr1HbdR/6xEiRK3VcP128c5cf78eUnS559/7tScSdfmWeaWhIQE9erVSxMnTlTr1q0VEBCgDz/8UK+99lqOa3377bdNTaunp+dN39O2bVsdPXpUX3zxhVavXq2WLVtq8ODBevXVV2//wwBALqNJBO5ivr6+qly5craPr1evnhYvXqySJUua0rTrSpUqpU2bNqlp06aSriVmW7ZsUb169W56fM2aNZWVlaV169YpOjratP96kpmZmekYi4yMlN1u17Fjx26ZQEZERDgewrnuhx9++PsP+ScbN25U+fLl9fzzzzvGjh49ajru2LFjOn78uEJDQx3X8fDwUNWqVRUcHKzQ0FAdPnxYvXr1yva1S5QooZiYGMXExOj+++/XyJEjaRIB5Cs83QzAoVevXrrnnnvUqVMnff/990pMTNR3332np59+Wr/99psk6ZlnntFLL72k5cuXa+/evXryySf/co3DChUqKCYmRo899piWL1/uOOdHH30kSSpfvrxsNptWrlypU6dO6fz58ypatKhGjBihYcOG6b333tOhQ4e0detWvf7663rvvfckSY8//rgOHDigkSNHat++fVq0aJHmz5+fo88bHh6uY8eO6cMPP9ShQ4c0c+ZMLVu2zHRc4cKFFRMTox07duj777/X008/rW7duikkJESSNHHiRMXHx2vmzJnav3+/du3apXnz5mnatGk3ve64ceP06aef6uDBg9q9e7dWrlypiIiIHNUOAK5GkwjAoUiRIlq/fr3KlSunLl26KCIiQv369VN6erojWRw+fLgeffRRxcTEKCoqSkWLFtVDDz30l+edM2eOHn74YT355JOqVq2aBgwYoAsXLkiSSpcurYkTJ+rZZ59VcHCwhgwZIkl64YUXFBcXp/j4eEVERKhNmzb6/PPPFRYWJunaPMGlS5dq+fLlql27tubOnaspU6bk6PM++OCDGjZsmIYMGaI6depo48aNiouLMx1XuXJldenSRe3atVOrVq1Uq1YtpyVu+vfvr3feeUfz5s1TzZo11axZM82fP99R6428vb01ZswY1apVS02bNpWnp6c+/PDDHNUOAK5mM2412xwAAABuiyQRAAAAJjSJAAAAMKFJBAAAgAlNIgAAAExoEgEAAGBCkwgAAAATmkQAAACY0CQCAADAhCYRAAAAJjSJAAAAMKFJBAAAgMn/BxLAwHlEeigRAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "model_rfc = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "model_rfc.fit(X_train1, Y_train1)\n", + "prediction_rfc = model_rfc.predict(X_test1)\n", + "\n", + "print(classification_report(Y_test1, prediction_rfc))\n", + "mse_rfc = mean_squared_error(Y_test1, prediction_rfc)\n", + "r2_rfc = r2_score(Y_test1, prediction_rfc)\n", + "\n", + "print(f\"Mean squared error of Random Forest = {mse_rfc}\")\n", + "print(f\"R-squared = {r2_rfc}\\n\")\n", + "\n", + "plot_confusion_matrix(Y_test1, prediction_rfc, classes=['Class 0', 'Class 1'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

11. Results

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

From above observation we found that model accuracy across the different splits highlight the critical role of data splitting strategies in enhancing predictive performance and capturing the underlying patterns in the data.\n", + "Model 3, with the lowest mean squared error and highest R-squared value have more refined subsets of data lead to better model performance, emphasizing the significance of data preprocessing and feature selection in predictive modeling.

" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 334ddc6bf8dbdf525e3f80f35d6bcce0eeff6f05 Mon Sep 17 00:00:00 2001 From: Sasidharan Vairavasamy <127896918+Thewhitewolfsasi@users.noreply.github.com> Date: Mon, 13 May 2024 01:41:19 +0530 Subject: [PATCH 3/5] Update README.md --- .../Autism Identification System/README.md | 29 ++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md b/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md index 6d2c8e28c..fcfa7617a 100644 --- a/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md +++ b/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md @@ -1 +1,28 @@ -

Autism Identification System

+ +
+ +![](https://img.shields.io/badge/Programming_Language-Python-blue.svg) +![](https://img.shields.io/badge/Main_Tool_Used-Jupyter_Notebook-orange.svg) +![](https://img.shields.io/badge/Status-Complete-green.svg) + +> Problem Statement: +- Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition associated with significant healthcare costs, and early diagnosis can significantly reduce these. However, the current wait times for ASD diagnosis are prolonged, and existing procedures lack cost-effectiveness.
+- A time-efficient and accessible ASD screening tool is essential to aid healthcare professionals and guide individuals toward pursuing formal clinical diagnosis. The goal is to develop machine learning techniques to make this screening process faster and more effective.
+- You can choose any of the tool of your choice +(Python/R/Tableau/PowerBI/Excel/SAP/SAS)
+- Here is the dataset : +Dataset.csv
+> Solution: +Exploratory Data Analysis - Sports + + +If you have any Queries or Suggestions, feel free to reach out to me. + +[][LinkedIn] +[][Github] +
+ +[linkedin]: https://www.linkedin.com/in/kushal-das-7337421a9/ +[github]: https://github.com/Kushal997-das/ + +

Show some  ❤️  by starring this repo!

From 0db17ef5ecd5ff3a46d6a08ecd6dfbeb0c6b14fe Mon Sep 17 00:00:00 2001 From: Sasidharan Vairavasamy <127896918+Thewhitewolfsasi@users.noreply.github.com> Date: Mon, 13 May 2024 01:47:06 +0530 Subject: [PATCH 4/5] Update README.md --- .../Intermediate/Autism Identification System/README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md b/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md index fcfa7617a..299a8e3d1 100644 --- a/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md +++ b/Machine Learning and Data Science/Intermediate/Autism Identification System/README.md @@ -10,10 +10,11 @@ - A time-efficient and accessible ASD screening tool is essential to aid healthcare professionals and guide individuals toward pursuing formal clinical diagnosis. The goal is to develop machine learning techniques to make this screening process faster and more effective.
- You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel/SAP/SAS)
+- Algorithm Used - Logistic Regression, Support Vector Machine and Random Forest Classifier - Here is the dataset : -Dataset.csv
+Dataset.csv
> Solution: -Exploratory Data Analysis - Sports +Autism Identification System If you have any Queries or Suggestions, feel free to reach out to me. @@ -22,7 +23,7 @@ If you have any Queries or Suggestions, feel free to reach out to me. [][Github]
-[linkedin]: https://www.linkedin.com/in/kushal-das-7337421a9/ -[github]: https://github.com/Kushal997-das/ +[linkedin]: https://www.linkedin.com/in/sasidharan-vairavasamy-576474219/ +[github]: https://github.com/Thewhitewolfsasi/

Show some  ❤️  by starring this repo!

From 467715be723f3f6441a68325f9fcaca7d60deb33 Mon Sep 17 00:00:00 2001 From: Sasidharan Vairavasamy <127896918+Thewhitewolfsasi@users.noreply.github.com> Date: Mon, 13 May 2024 01:53:49 +0530 Subject: [PATCH 5/5] Update README.md Added Autism Identification system project --- Machine Learning and Data Science/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/Machine Learning and Data Science/README.md b/Machine Learning and Data Science/README.md index 6e890d78d..df04a4bb9 100644 --- a/Machine Learning and Data Science/README.md +++ b/Machine Learning and Data Science/README.md @@ -33,6 +33,7 @@ | 01. | [Covid Third Wave Forecasting ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Covid_Third_Wave_Forecasting) | 02. | [Exploratory Data Analysis - Terrorism](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Exploratory%20Data%20Analysis%20-%20Terrorism) | 03. | [Iris Webapp](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Iriswebapp-main) | | 04. | [ Prediction using Decision Tree Algorithm ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Prediction%20using%20Decision%20Tree%20Algorithm) | 05. | [ Explore Buisness Analytics ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/To%20Explore%20Buisness%20Analytics) | 06. | [Centroid-Digits](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Centroid-Digits) | | 07. | [Digit Recognizer Using ANN](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Digit%20Recognizer%20Using%20ANN) | 08. | [IPL Prediction](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/IPL%20Prediction) | 09. | [Whatsapp Chat Analyzer](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Whatsapp%20Chat%20Analyzer) |10. | [Disease_Predictor](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Intermediate/Disease_Predictor) | +| 11. | Autism Identification System | 12. | | 13. |