From 2d0baafb55daec9d31257a1fa00df570a232dd0f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B0=D0=BD=D0=B4=D1=80=20?= =?UTF-8?q?=D0=9A=D0=BE=D0=B1=D1=8B=D0=BB=D1=8C=D0=BD=D0=B8=D0=BA=D0=BE?= =?UTF-8?q?=D0=B2?= Date: Thu, 15 May 2025 22:35:39 +0000 Subject: [PATCH] =?UTF-8?q?=D0=97=D0=B0=D0=B3=D1=80=D1=83=D0=B7=D0=B8?= =?UTF-8?q?=D1=82=D1=8C=20=D1=84=D0=B0=D0=B9=D0=BB=D1=8B=20=D0=B2=20=C2=AB?= =?UTF-8?q?/=C2=BB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 + Students Social Media Addiction.csv | 706 ++++++++++++++++++++++++++++ customdatasetweek4.ipynb | 508 ++++++++++++++++++++ requirements.txt | 114 +++++ week4_scikit_learn.ipynb.ipynb | 699 +++++++++++++++++++++++++++ 5 files changed, 2028 insertions(+) create mode 100644 README.md create mode 100644 Students Social Media Addiction.csv create mode 100644 customdatasetweek4.ipynb create mode 100644 requirements.txt create mode 100644 week4_scikit_learn.ipynb.ipynb diff --git a/README.md b/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ + diff --git a/Students Social Media Addiction.csv b/Students Social Media Addiction.csv new file mode 100644 index 0000000..5141935 --- /dev/null +++ b/Students Social Media Addiction.csv @@ -0,0 +1,706 @@ +Student_ID,Age,Gender,Academic_Level,Country,Avg_Daily_Usage_Hours,Most_Used_Platform,Affects_Academic_Performance,Sleep_Hours_Per_Night,Mental_Health_Score,Relationship_Status,Conflicts_Over_Social_Media,Addicted_Score +1,19,Female,Undergraduate,Bangladesh,5.2,Instagram,Yes,6.5,6,In Relationship,3,8 +2,22,Male,Graduate,India,2.1,Twitter,No,7.5,8,Single,0,3 +3,20,Female,Undergraduate,USA,6.0,TikTok,Yes,5.0,5,Complicated,4,9 +4,18,Male,High School,UK,3.0,YouTube,No,7.0,7,Single,1,4 +5,21,Male,Graduate,Canada,4.5,Facebook,Yes,6.0,6,In Relationship,2,7 +6,19,Female,Undergraduate,Australia,7.2,Instagram,Yes,4.5,4,Complicated,5,9 +7,23,Male,Graduate,Germany,1.5,LinkedIn,No,8.0,9,Single,0,2 +8,20,Female,Undergraduate,Brazil,5.8,Snapchat,Yes,6.0,6,In Relationship,2,8 +9,18,Male,High School,Japan,4.0,TikTok,No,6.5,7,Single,1,5 +10,21,Female,Graduate,South Korea,3.3,Instagram,No,7.0,7,In Relationship,1,4 +11,19,Male,Undergraduate,France,4.8,Snapchat,Yes,6.2,5,Complicated,3,7 +12,20,Female,Undergraduate,Spain,5.5,TikTok,Yes,5.8,6,In Relationship,2,8 +13,22,Male,Graduate,Italy,2.8,LinkedIn,No,7.2,8,Single,1,4 +14,18,Female,High School,Mexico,6.5,Instagram,Yes,5.5,5,Single,4,9 +15,21,Male,Undergraduate,Russia,3.7,YouTube,No,6.8,7,In Relationship,2,5 +16,20,Female,Undergraduate,China,4.2,TikTok,Yes,6.0,6,Complicated,3,7 +17,24,Male,Graduate,Sweden,2.0,LinkedIn,No,7.8,8,Single,0,3 +18,19,Female,High School,Norway,5.0,Instagram,Yes,5.7,5,In Relationship,3,8 +19,21,Male,Undergraduate,Denmark,3.5,Facebook,No,6.7,7,Single,1,5 +20,20,Female,Undergraduate,Netherlands,4.7,Snapchat,Yes,5.9,6,Complicated,3,7 +21,18,Male,High School,Belgium,5.3,TikTok,Yes,5.5,5,Single,4,8 +22,23,Female,Graduate,Switzerland,2.5,LinkedIn,No,7.3,8,In Relationship,1,4 +23,19,Male,Undergraduate,Austria,4.9,Instagram,Yes,5.8,6,Complicated,3,7 +24,20,Female,Undergraduate,Portugal,5.7,TikTok,Yes,5.4,5,Single,4,8 +25,22,Male,Graduate,Greece,3.2,Facebook,No,6.9,7,In Relationship,2,5 +26,19,Female,High School,Ireland,6.1,Instagram,Yes,5.2,5,Complicated,4,9 +27,21,Male,Undergraduate,New Zealand,3.8,YouTube,No,6.6,7,Single,1,5 +28,20,Female,Undergraduate,Singapore,4.4,TikTok,Yes,5.9,6,In Relationship,3,7 +29,24,Male,Graduate,Malaysia,2.2,LinkedIn,No,7.4,8,Single,0,3 +30,19,Female,High School,Thailand,5.9,Instagram,Yes,5.3,5,Complicated,4,8 +31,21,Male,Undergraduate,Vietnam,3.6,Facebook,No,6.7,7,Single,1,5 +32,20,Female,Undergraduate,Philippines,4.8,Snapchat,Yes,5.7,6,In Relationship,3,7 +33,18,Male,High School,Indonesia,5.4,TikTok,Yes,5.4,5,Complicated,4,8 +34,23,Female,Graduate,Taiwan,2.6,LinkedIn,No,7.2,8,Single,1,4 +35,19,Male,Undergraduate,Hong Kong,4.7,Instagram,Yes,5.8,6,In Relationship,3,7 +36,20,Female,Undergraduate,Turkey,5.6,TikTok,Yes,5.5,5,Complicated,4,8 +37,22,Male,Graduate,Israel,3.1,Facebook,No,6.8,7,Single,1,5 +38,19,Female,High School,UAE,6.2,Instagram,Yes,5.1,5,In Relationship,4,9 +39,21,Male,Undergraduate,Egypt,3.9,YouTube,No,6.5,7,Complicated,2,6 +40,20,Female,Undergraduate,Morocco,4.5,TikTok,Yes,5.8,6,Single,3,7 +41,24,Male,Graduate,South Africa,2.3,LinkedIn,No,7.3,8,In Relationship,1,4 +42,19,Female,High School,Nigeria,5.8,Instagram,Yes,5.4,5,Complicated,4,8 +43,21,Male,Undergraduate,Kenya,3.7,Facebook,No,6.6,7,Single,2,5 +44,20,Female,Undergraduate,Ghana,4.6,Snapchat,Yes,5.7,6,In Relationship,3,7 +45,18,Male,High School,Argentina,5.5,TikTok,Yes,5.3,5,Single,4,8 +46,23,Female,Graduate,Chile,2.7,LinkedIn,No,7.1,8,Complicated,1,4 +47,19,Male,Undergraduate,Colombia,4.8,Instagram,Yes,5.9,6,In Relationship,3,7 +48,20,Female,Undergraduate,Peru,5.5,TikTok,Yes,5.6,5,Single,4,8 +49,22,Male,Graduate,Venezuela,3.3,Facebook,No,6.7,7,In Relationship,2,5 +50,19,Female,High School,Ecuador,6.3,Instagram,Yes,5.2,5,Complicated,4,9 +51,21,Male,Undergraduate,Uruguay,3.8,YouTube,No,6.4,7,Single,2,6 +52,20,Female,Undergraduate,Paraguay,4.7,TikTok,Yes,5.8,6,In Relationship,3,7 +53,24,Male,Graduate,Bolivia,2.4,LinkedIn,No,7.2,8,Complicated,1,4 +54,19,Female,High School,Costa Rica,5.7,Instagram,Yes,5.5,5,Single,4,8 +55,21,Male,Undergraduate,Panama,3.6,Facebook,No,6.5,7,In Relationship,2,5 +56,20,Female,Undergraduate,Jamaica,4.9,Snapchat,Yes,5.6,6,Complicated,3,7 +57,18,Male,High School,Trinidad,5.6,TikTok,Yes,5.2,5,Single,4,8 +58,23,Female,Graduate,Bahamas,2.8,LinkedIn,No,7.0,8,In Relationship,1,4 +59,19,Male,Undergraduate,Iceland,4.6,Instagram,Yes,5.9,6,Complicated,3,7 +60,20,Female,Undergraduate,Finland,5.4,TikTok,Yes,5.7,5,Single,4,8 +61,22,Male,Graduate,Poland,3.1,Facebook,No,7.1,7,Single,1,5 +62,19,Female,Undergraduate,Romania,5.6,Instagram,Yes,5.6,5,In Relationship,3,8 +63,20,Male,Undergraduate,Hungary,4.2,TikTok,Yes,6.0,6,Complicated,3,7 +64,18,Female,High School,Czech Republic,6.1,Snapchat,Yes,5.2,4,Single,4,9 +65,23,Male,Graduate,Slovakia,2.3,LinkedIn,No,7.4,8,In Relationship,1,3 +66,21,Female,Undergraduate,Croatia,4.8,Instagram,Yes,5.8,6,Single,3,7 +67,20,Male,Undergraduate,Serbia,3.9,YouTube,No,6.5,7,Complicated,2,6 +68,19,Female,High School,Slovenia,5.7,TikTok,Yes,5.4,5,In Relationship,4,8 +69,22,Male,Graduate,Bulgaria,2.8,LinkedIn,No,7.2,8,Single,1,4 +70,20,Female,Undergraduate,Estonia,4.5,Instagram,Yes,5.9,6,Complicated,3,7 +71,18,Male,High School,Latvia,5.4,Snapchat,Yes,5.5,5,Single,4,8 +72,21,Female,Graduate,Lithuania,3.2,Facebook,No,6.8,7,In Relationship,2,5 +73,19,Male,Undergraduate,Ukraine,4.9,TikTok,Yes,5.7,6,Complicated,3,7 +74,20,Female,Undergraduate,Moldova,5.8,Instagram,Yes,5.3,5,Single,4,8 +75,23,Male,Graduate,Belarus,2.5,LinkedIn,No,7.3,8,In Relationship,1,4 +76,21,Female,Undergraduate,Kazakhstan,4.6,Snapchat,Yes,5.8,6,Single,3,7 +77,19,Male,High School,Uzbekistan,5.5,TikTok,Yes,5.4,5,Complicated,4,8 +78,22,Female,Graduate,Kyrgyzstan,2.9,Facebook,No,7.0,7,In Relationship,2,5 +79,20,Male,Undergraduate,Tajikistan,4.7,YouTube,Yes,5.9,6,Single,3,7 +80,18,Female,High School,Armenia,5.9,Instagram,Yes,5.2,5,Complicated,4,9 +81,21,Male,Graduate,Georgia,3.0,LinkedIn,No,7.1,8,In Relationship,1,4 +82,19,Female,Undergraduate,Azerbaijan,4.8,TikTok,Yes,5.7,6,Single,3,7 +83,20,Male,Undergraduate,Cyprus,3.8,Facebook,No,6.6,7,Complicated,2,6 +84,22,Female,Graduate,Malta,2.7,LinkedIn,No,7.2,8,In Relationship,1,4 +85,18,Male,High School,Luxembourg,5.6,Snapchat,Yes,5.3,5,Single,4,8 +86,21,Female,Undergraduate,Monaco,4.5,Instagram,Yes,5.8,6,Complicated,3,7 +87,19,Male,High School,Andorra,5.3,TikTok,Yes,5.5,5,In Relationship,4,8 +88,23,Female,Graduate,San Marino,2.6,LinkedIn,No,7.3,8,Single,1,4 +89,20,Male,Undergraduate,Vatican City,4.4,YouTube,Yes,6.0,6,Complicated,3,7 +90,18,Female,High School,Liechtenstein,5.8,Instagram,Yes,5.2,5,Single,4,9 +91,22,Male,Graduate,Montenegro,2.9,Facebook,No,7.0,7,In Relationship,2,5 +92,19,Female,Undergraduate,Albania,4.7,TikTok,Yes,5.8,6,Complicated,3,7 +93,21,Male,Undergraduate,North Macedonia,3.7,Snapchat,No,6.5,7,Single,2,6 +94,20,Female,High School,Kosovo,5.5,Instagram,Yes,5.4,5,In Relationship,4,8 +95,23,Male,Graduate,Bosnia,2.4,LinkedIn,No,7.4,8,Complicated,1,4 +96,19,Female,Undergraduate,Qatar,4.9,TikTok,Yes,5.7,6,Single,3,7 +97,18,Male,High School,Kuwait,5.7,Snapchat,Yes,5.3,5,In Relationship,4,8 +98,22,Female,Graduate,Bahrain,2.8,LinkedIn,No,7.1,8,Complicated,1,4 +99,20,Male,Undergraduate,Oman,4.6,Instagram,Yes,5.9,6,Single,3,7 +100,21,Female,Undergraduate,Jordan,5.4,TikTok,Yes,5.5,5,In Relationship,4,8 +101,19,Male,High School,Lebanon,5.8,YouTube,Yes,5.2,5,Complicated,4,9 +102,23,Female,Graduate,Iraq,2.5,LinkedIn,No,7.3,8,Single,1,4 +103,20,Male,Undergraduate,Yemen,4.7,Facebook,Yes,5.8,6,In Relationship,3,7 +104,18,Female,High School,Syria,5.6,Instagram,Yes,5.4,5,Single,4,8 +105,22,Male,Graduate,Afghanistan,2.9,LinkedIn,No,7.0,7,Complicated,2,5 +106,19,Female,Undergraduate,Pakistan,4.8,TikTok,Yes,5.7,6,In Relationship,3,7 +107,21,Male,Undergraduate,Nepal,3.8,YouTube,No,6.6,7,Single,2,6 +108,20,Female,High School,Bhutan,5.5,Snapchat,Yes,5.5,5,Complicated,4,8 +109,23,Male,Graduate,Sri Lanka,2.6,LinkedIn,No,7.2,8,In Relationship,1,4 +110,19,Female,Undergraduate,Maldives,4.9,Instagram,Yes,5.8,6,Single,3,7 +111,20,Male,Undergraduate,Bangladesh,6.1,Instagram,Yes,6.2,5,Single,4,8 +112,21,Female,Undergraduate,India,5.8,TikTok,Yes,5.9,6,In Relationship,3,7 +113,19,Male,Undergraduate,Nepal,4.9,Facebook,No,7.1,7,Single,2,5 +114,22,Female,Graduate,Pakistan,5.5,Instagram,Yes,6.0,5,Single,4,8 +115,20,Male,Undergraduate,Sri Lanka,5.2,TikTok,Yes,6.3,6,In Relationship,3,7 +116,19,Female,Undergraduate,Maldives,4.8,Instagram,No,7.2,8,Single,2,5 +117,21,Male,Graduate,Bangladesh,6.0,Facebook,Yes,5.8,5,In Relationship,4,8 +118,20,Female,Undergraduate,India,5.7,Instagram,Yes,6.1,6,Single,3,7 +119,22,Male,Graduate,Nepal,4.7,TikTok,No,7.3,7,Single,2,5 +120,19,Female,Undergraduate,Pakistan,5.4,Instagram,Yes,6.2,5,In Relationship,4,8 +121,20,Male,Undergraduate,Sri Lanka,5.9,Facebook,Yes,5.9,6,Single,3,7 +122,21,Female,Graduate,Maldives,4.6,Instagram,No,7.4,8,In Relationship,2,5 +123,19,Male,Undergraduate,Bangladesh,5.3,TikTok,Yes,6.3,5,Single,4,8 +124,22,Female,Graduate,India,5.8,Instagram,Yes,5.8,6,In Relationship,3,7 +125,20,Male,Undergraduate,Nepal,4.5,Facebook,No,7.5,7,Single,2,5 +126,21,Female,Graduate,Pakistan,5.2,Instagram,Yes,6.4,5,In Relationship,4,8 +127,19,Male,Undergraduate,Sri Lanka,5.7,TikTok,Yes,5.7,6,Single,3,7 +128,20,Female,Undergraduate,Maldives,4.4,Instagram,No,7.6,8,In Relationship,2,5 +129,22,Male,Graduate,Bangladesh,5.1,Facebook,Yes,6.5,5,Single,4,8 +130,21,Female,Graduate,India,5.6,Instagram,Yes,5.6,6,In Relationship,3,7 +131,19,Male,Undergraduate,Nepal,4.3,TikTok,No,7.7,7,Single,2,5 +132,20,Female,Undergraduate,Pakistan,5.0,Instagram,Yes,6.6,5,In Relationship,4,8 +133,22,Male,Graduate,Sri Lanka,5.5,Facebook,Yes,5.5,6,Single,3,7 +134,21,Female,Graduate,Maldives,4.2,Instagram,No,7.8,8,In Relationship,2,5 +135,19,Male,Undergraduate,Bangladesh,4.9,TikTok,Yes,6.7,5,Single,4,8 +136,20,Female,Undergraduate,India,5.4,Instagram,Yes,5.4,6,In Relationship,3,7 +137,22,Male,Graduate,Nepal,4.1,Facebook,No,7.9,7,Single,2,5 +138,21,Female,Graduate,Pakistan,4.8,Instagram,Yes,6.8,5,In Relationship,4,8 +139,19,Male,Undergraduate,Sri Lanka,5.3,TikTok,Yes,5.3,6,Single,3,7 +140,20,Female,Undergraduate,Maldives,4.0,Instagram,No,8.0,8,In Relationship,2,5 +141,22,Male,Graduate,Bangladesh,4.7,Facebook,Yes,6.9,5,Single,4,8 +142,21,Female,Graduate,India,5.2,Instagram,Yes,5.2,6,In Relationship,3,7 +143,19,Male,Undergraduate,Nepal,3.9,TikTok,No,8.1,7,Single,2,5 +144,20,Female,Undergraduate,Pakistan,4.6,Instagram,Yes,7.0,5,In Relationship,4,8 +145,22,Male,Graduate,Sri Lanka,5.1,Facebook,Yes,5.1,6,Single,3,7 +146,21,Female,Graduate,Maldives,3.8,Instagram,No,8.2,8,In Relationship,2,5 +147,19,Male,Undergraduate,Bangladesh,4.5,TikTok,Yes,7.1,5,Single,4,8 +148,20,Female,Undergraduate,India,5.0,Instagram,Yes,5.0,6,In Relationship,3,7 +149,22,Male,Graduate,Nepal,3.7,Facebook,No,8.3,7,Single,2,5 +150,21,Female,Graduate,Pakistan,4.4,Instagram,Yes,7.2,5,In Relationship,4,8 +151,19,Male,Undergraduate,Sri Lanka,4.9,TikTok,Yes,4.9,6,Single,3,7 +152,20,Female,Undergraduate,Maldives,3.6,Instagram,No,8.4,8,In Relationship,2,5 +153,22,Male,Graduate,Bangladesh,4.3,Facebook,Yes,7.3,5,Single,4,8 +154,21,Female,Graduate,India,4.8,Instagram,Yes,4.8,6,In Relationship,3,7 +155,19,Male,Undergraduate,Nepal,3.5,TikTok,No,8.5,7,Single,2,5 +156,20,Female,Undergraduate,Pakistan,4.2,Instagram,Yes,7.4,5,In Relationship,4,8 +157,22,Male,Graduate,Sri Lanka,4.7,Facebook,Yes,4.7,6,Single,3,7 +158,21,Female,Graduate,Maldives,3.4,Instagram,No,8.6,8,In Relationship,2,5 +159,19,Male,Undergraduate,Bangladesh,4.1,TikTok,Yes,7.5,5,Single,4,8 +160,20,Female,Undergraduate,India,4.6,Instagram,Yes,4.6,6,In Relationship,3,7 +161,19,Female,Undergraduate,Bangladesh,5.3,Instagram,Yes,6.1,5,Single,3,7 +162,21,Male,Graduate,India,4.8,Facebook,No,7.2,7,In Relationship,2,6 +163,20,Female,Undergraduate,Nepal,5.5,TikTok,Yes,5.9,6,Single,4,8 +164,22,Male,Graduate,Pakistan,4.7,Instagram,Yes,6.3,5,In Relationship,3,7 +165,19,Female,Undergraduate,Sri Lanka,5.1,Facebook,No,7.0,7,Single,2,5 +166,21,Male,Graduate,Maldives,5.4,TikTok,Yes,6.0,6,In Relationship,4,8 +167,20,Female,Undergraduate,Bangladesh,4.9,Instagram,Yes,6.4,5,Single,3,7 +168,22,Male,Graduate,India,5.2,Facebook,No,7.1,7,In Relationship,2,6 +169,19,Female,Undergraduate,Nepal,5.6,TikTok,Yes,5.8,6,Single,4,8 +170,21,Male,Graduate,Pakistan,4.6,Instagram,Yes,6.5,5,In Relationship,3,7 +171,20,Female,Undergraduate,Sri Lanka,5.0,Facebook,No,7.3,7,Single,2,5 +172,22,Male,Graduate,Maldives,5.3,TikTok,Yes,5.7,6,In Relationship,4,8 +173,19,Female,Undergraduate,Bangladesh,4.8,Instagram,Yes,6.6,5,Single,3,7 +174,21,Male,Graduate,India,5.1,Facebook,No,7.4,7,In Relationship,2,6 +175,20,Female,Undergraduate,Nepal,5.7,TikTok,Yes,5.6,6,Single,4,8 +176,22,Male,Graduate,Pakistan,4.5,Instagram,Yes,6.7,5,In Relationship,3,7 +177,19,Female,Undergraduate,Sri Lanka,4.9,Facebook,No,7.5,7,Single,2,5 +178,21,Male,Graduate,Maldives,5.2,TikTok,Yes,5.5,6,In Relationship,4,8 +179,20,Female,Undergraduate,Bangladesh,4.7,Instagram,Yes,6.8,5,Single,3,7 +180,22,Male,Graduate,India,5.0,Facebook,No,7.6,7,In Relationship,2,6 +181,19,Female,Undergraduate,Nepal,5.8,TikTok,Yes,5.4,6,Single,4,8 +182,21,Male,Graduate,Pakistan,4.4,Instagram,Yes,6.9,5,In Relationship,3,7 +183,20,Female,Undergraduate,Sri Lanka,4.8,Facebook,No,7.7,7,Single,2,5 +184,22,Male,Graduate,Maldives,5.1,TikTok,Yes,5.3,6,In Relationship,4,8 +185,19,Female,Undergraduate,Bangladesh,4.6,Instagram,Yes,7.0,5,Single,3,7 +186,21,Male,Graduate,India,4.9,Facebook,No,7.8,7,In Relationship,2,6 +187,20,Female,Undergraduate,Nepal,5.9,TikTok,Yes,5.2,6,Single,4,8 +188,22,Male,Graduate,Pakistan,4.3,Instagram,Yes,7.1,5,In Relationship,3,7 +189,19,Female,Undergraduate,Sri Lanka,4.7,Facebook,No,7.9,7,Single,2,5 +190,21,Male,Graduate,Maldives,5.0,TikTok,Yes,5.1,6,In Relationship,4,8 +191,20,Female,Undergraduate,Bangladesh,4.5,Instagram,Yes,7.2,5,Single,3,7 +192,22,Male,Graduate,India,4.8,Facebook,No,8.0,7,In Relationship,2,6 +193,19,Female,Undergraduate,Nepal,6.0,TikTok,Yes,5.0,6,Single,4,8 +194,21,Male,Graduate,Pakistan,4.2,Instagram,Yes,7.3,5,In Relationship,3,7 +195,20,Female,Undergraduate,Sri Lanka,4.6,Facebook,No,8.1,7,Single,2,5 +196,22,Male,Graduate,Maldives,4.9,TikTok,Yes,4.9,6,In Relationship,4,8 +197,19,Female,Undergraduate,Bangladesh,4.4,Instagram,Yes,7.4,5,Single,3,7 +198,21,Male,Graduate,India,4.7,Facebook,No,8.2,7,In Relationship,2,6 +199,20,Female,Undergraduate,Nepal,6.1,TikTok,Yes,4.8,6,Single,4,8 +200,22,Male,Graduate,Pakistan,4.1,Instagram,Yes,7.5,5,In Relationship,3,7 +201,19,Female,Undergraduate,Sri Lanka,4.5,Facebook,No,8.3,7,Single,2,5 +202,21,Male,Graduate,Maldives,4.8,TikTok,Yes,4.7,6,In Relationship,4,8 +203,20,Female,Undergraduate,Bangladesh,4.3,Instagram,Yes,7.6,5,Single,3,7 +204,22,Male,Graduate,India,4.6,Facebook,No,8.4,7,In Relationship,2,6 +205,19,Female,Undergraduate,Nepal,6.2,TikTok,Yes,4.6,6,Single,4,8 +206,21,Male,Graduate,Pakistan,4.0,Instagram,Yes,7.7,5,In Relationship,3,7 +207,20,Female,Undergraduate,Sri Lanka,4.4,Facebook,No,8.5,7,Single,2,5 +208,22,Male,Graduate,Maldives,4.7,TikTok,Yes,4.5,6,In Relationship,4,8 +209,19,Female,Undergraduate,Bangladesh,4.2,Instagram,Yes,7.8,5,Single,3,7 +210,21,Male,Graduate,India,4.5,Facebook,No,8.6,7,In Relationship,2,6 +211,20,Female,Undergraduate,Nepal,6.3,TikTok,Yes,4.4,6,Single,4,8 +212,22,Male,Graduate,Pakistan,3.9,Instagram,Yes,7.9,5,In Relationship,3,7 +213,19,Female,Undergraduate,Sri Lanka,4.3,Facebook,No,8.7,7,Single,2,5 +214,21,Male,Graduate,Maldives,4.6,TikTok,Yes,4.3,6,In Relationship,4,8 +215,20,Female,Undergraduate,Bangladesh,4.1,Instagram,Yes,8.0,5,Single,3,7 +216,22,Male,Graduate,India,4.4,Facebook,No,8.8,7,In Relationship,2,6 +217,19,Female,Undergraduate,Nepal,6.4,TikTok,Yes,4.2,6,Single,4,8 +218,21,Male,Graduate,Pakistan,3.8,Instagram,Yes,8.1,5,In Relationship,3,7 +219,20,Female,Undergraduate,Sri Lanka,4.2,Facebook,No,8.9,7,Single,2,5 +220,22,Male,Graduate,Maldives,4.5,TikTok,Yes,4.1,6,In Relationship,4,8 +221,19,Female,Undergraduate,USA,6.5,Instagram,Yes,6.0,5,Single,4,9 +222,21,Male,Graduate,UK,5.8,TikTok,Yes,6.5,6,In Relationship,3,7 +223,20,Female,Undergraduate,Australia,4.5,Instagram,No,7.5,8,Single,2,5 +224,22,Male,Graduate,Germany,4.2,Facebook,No,7.8,7,In Relationship,2,4 +225,19,Female,Undergraduate,Japan,3.8,LINE,No,7.9,8,Single,1,3 +226,21,Male,Graduate,Italy,5.5,Instagram,Yes,6.8,6,Single,3,7 +227,20,Female,Undergraduate,South Korea,5.2,KakaoTalk,Yes,6.5,6,In Relationship,3,6 +228,22,Male,Graduate,Russia,4.8,VKontakte,No,7.2,7,Single,2,5 +229,19,Female,Undergraduate,USA,7.0,TikTok,Yes,5.8,4,In Relationship,4,9 +230,21,Male,Graduate,UK,5.5,Instagram,Yes,6.7,6,Single,3,7 +231,20,Female,Undergraduate,Australia,4.7,Facebook,No,7.4,7,In Relationship,2,5 +232,22,Male,Graduate,Germany,4.0,Instagram,No,7.9,8,Single,1,4 +233,19,Female,Undergraduate,Japan,3.5,LINE,No,8.0,8,Single,1,3 +234,21,Male,Graduate,Italy,5.7,TikTok,Yes,6.5,5,In Relationship,3,8 +235,20,Female,Undergraduate,South Korea,5.0,KakaoTalk,Yes,6.8,6,Single,3,6 +236,22,Male,Graduate,Russia,4.5,VKontakte,No,7.4,7,In Relationship,2,5 +237,19,Female,Undergraduate,USA,6.8,Instagram,Yes,5.9,4,Single,4,9 +238,21,Male,Graduate,UK,5.6,Facebook,Yes,6.6,6,In Relationship,3,7 +239,20,Female,Undergraduate,Australia,4.6,Instagram,No,7.3,7,Single,2,5 +240,22,Male,Graduate,Germany,4.1,Facebook,No,7.7,8,In Relationship,2,4 +241,19,Female,Undergraduate,Japan,3.7,LINE,No,7.8,8,Single,1,3 +242,21,Male,Graduate,Italy,5.4,Instagram,Yes,6.7,5,Single,3,7 +243,20,Female,Undergraduate,South Korea,5.1,KakaoTalk,Yes,6.6,6,In Relationship,3,6 +244,22,Male,Graduate,Russia,4.7,VKontakte,No,7.3,7,Single,2,5 +245,19,Female,Undergraduate,USA,6.9,TikTok,Yes,5.7,4,In Relationship,4,9 +246,21,Male,Graduate,UK,5.7,Instagram,Yes,6.4,6,Single,3,7 +247,20,Female,Undergraduate,Australia,4.8,Facebook,No,7.2,7,In Relationship,2,5 +248,22,Male,Graduate,Germany,3.9,Instagram,No,7.8,8,Single,1,4 +249,19,Female,Undergraduate,Japan,3.6,LINE,No,8.1,8,Single,1,3 +250,21,Male,Graduate,Italy,5.6,TikTok,Yes,6.6,5,In Relationship,3,8 +251,20,Female,Undergraduate,South Korea,4.9,KakaoTalk,Yes,6.9,6,Single,3,6 +252,22,Male,Graduate,Russia,4.6,VKontakte,No,7.5,7,In Relationship,2,5 +253,19,Female,Undergraduate,USA,6.7,Instagram,Yes,5.8,4,Single,4,9 +254,21,Male,Graduate,UK,5.4,Facebook,Yes,6.5,6,In Relationship,3,7 +255,20,Female,Undergraduate,Australia,4.4,Instagram,No,7.4,7,Single,2,5 +256,22,Male,Graduate,Germany,4.0,Facebook,No,7.6,8,In Relationship,2,4 +257,19,Female,Undergraduate,Japan,3.4,LINE,No,8.2,8,Single,1,3 +258,21,Male,Graduate,Italy,5.3,Instagram,Yes,6.8,5,Single,3,7 +259,20,Female,Undergraduate,South Korea,5.0,KakaoTalk,Yes,6.7,6,In Relationship,3,6 +260,22,Male,Graduate,Russia,4.4,VKontakte,No,7.6,7,Single,2,5 +261,19,Female,Undergraduate,USA,6.6,TikTok,Yes,5.6,4,In Relationship,4,9 +262,21,Male,Graduate,UK,5.3,Instagram,Yes,6.3,6,Single,3,7 +263,20,Female,Undergraduate,Australia,4.3,Facebook,No,7.5,7,In Relationship,2,5 +264,22,Male,Graduate,Germany,3.8,Instagram,No,7.7,8,Single,1,4 +265,19,Female,Undergraduate,Japan,3.3,LINE,No,8.3,8,Single,1,3 +266,21,Male,Graduate,Italy,5.2,TikTok,Yes,6.9,5,In Relationship,3,8 +267,20,Female,Undergraduate,South Korea,4.8,KakaoTalk,Yes,7.0,6,Single,3,6 +268,22,Male,Graduate,Russia,4.3,VKontakte,No,7.7,7,In Relationship,2,5 +269,19,Female,Undergraduate,USA,6.4,Instagram,Yes,5.7,4,Single,4,9 +270,21,Male,Graduate,UK,5.2,Facebook,Yes,6.4,6,In Relationship,3,7 +271,20,Female,Undergraduate,Australia,4.5,Instagram,No,7.3,7,Single,2,5 +272,22,Male,Graduate,Germany,3.7,Facebook,No,7.8,8,In Relationship,1,4 +273,19,Female,Undergraduate,Japan,3.2,LINE,No,8.4,8,Single,1,3 +274,21,Male,Graduate,Italy,5.4,Instagram,Yes,6.7,5,Single,3,7 +275,20,Female,Undergraduate,South Korea,4.7,KakaoTalk,Yes,7.1,6,In Relationship,3,6 +276,22,Male,Graduate,Russia,4.2,VKontakte,No,7.8,7,Single,2,5 +277,19,Female,Undergraduate,USA,6.6,TikTok,Yes,5.5,4,In Relationship,4,9 +278,21,Male,Graduate,UK,5.1,Instagram,Yes,6.5,6,Single,3,7 +279,20,Female,Undergraduate,Australia,4.4,Facebook,No,7.4,7,In Relationship,2,5 +280,22,Male,Graduate,Germany,3.6,Instagram,No,7.9,8,Single,1,4 +281,19,Female,Undergraduate,Japan,3.1,LINE,No,8.5,8,Single,1,3 +282,21,Male,Graduate,Italy,5.3,TikTok,Yes,6.8,5,In Relationship,3,8 +283,20,Female,Undergraduate,South Korea,4.6,KakaoTalk,Yes,7.2,6,Single,3,6 +284,22,Male,Graduate,Russia,4.1,VKontakte,No,7.9,7,In Relationship,2,5 +285,19,Female,Undergraduate,USA,6.7,Instagram,Yes,5.4,4,Single,4,9 +286,21,Male,Graduate,UK,5.0,Facebook,Yes,6.6,6,In Relationship,3,7 +287,20,Female,Undergraduate,Australia,4.3,Instagram,No,7.5,7,Single,2,5 +288,22,Male,Graduate,Germany,3.5,Facebook,No,8.0,8,In Relationship,1,4 +289,19,Female,Undergraduate,Japan,3.0,LINE,No,8.6,8,Single,1,3 +290,21,Male,Graduate,Italy,5.2,Instagram,Yes,6.9,5,Single,3,7 +291,20,Female,Undergraduate,South Korea,4.5,KakaoTalk,Yes,7.3,6,In Relationship,3,6 +292,22,Male,Graduate,Russia,4.0,VKontakte,No,8.0,7,Single,2,5 +293,19,Female,Undergraduate,USA,6.8,TikTok,Yes,5.3,4,In Relationship,4,9 +294,21,Male,Graduate,UK,4.9,Instagram,Yes,6.7,6,Single,3,7 +295,20,Female,Undergraduate,Australia,4.2,Facebook,No,7.6,7,In Relationship,2,5 +296,22,Male,Graduate,Germany,3.4,Instagram,No,8.1,8,Single,1,4 +297,19,Female,Undergraduate,Japan,2.9,LINE,No,8.7,8,Single,1,3 +298,21,Male,Graduate,Italy,5.1,TikTok,Yes,7.0,5,In Relationship,3,8 +299,20,Female,Undergraduate,South Korea,4.4,KakaoTalk,Yes,7.4,6,Single,3,6 +300,22,Male,Graduate,Russia,3.9,VKontakte,No,8.1,7,In Relationship,2,5 +301,19,Female,Undergraduate,USA,6.9,Instagram,Yes,5.2,4,Single,4,9 +302,21,Male,Graduate,UK,4.8,Facebook,Yes,6.8,6,In Relationship,3,7 +303,20,Female,Undergraduate,Australia,4.1,Instagram,No,7.7,7,Single,2,5 +304,22,Male,Graduate,Germany,3.3,Facebook,No,8.2,8,In Relationship,1,4 +305,19,Female,Undergraduate,Japan,2.8,LINE,No,8.8,8,Single,1,3 +306,21,Male,Graduate,Italy,5.0,Instagram,Yes,7.1,5,Single,3,7 +307,20,Female,Undergraduate,South Korea,4.3,KakaoTalk,Yes,7.5,6,In Relationship,3,6 +308,22,Male,Graduate,Russia,3.8,VKontakte,No,8.2,7,Single,2,5 +309,19,Female,Undergraduate,USA,7.0,TikTok,Yes,5.1,4,In Relationship,4,9 +310,21,Male,Graduate,UK,4.7,Instagram,Yes,6.9,6,Single,3,7 +311,20,Female,Undergraduate,Australia,4.0,Facebook,No,7.8,7,In Relationship,2,5 +312,22,Male,Graduate,Germany,3.2,Instagram,No,8.3,8,Single,1,4 +313,19,Female,Undergraduate,Japan,2.7,LINE,No,8.9,8,Single,1,3 +314,21,Male,Graduate,Italy,4.9,TikTok,Yes,7.2,5,In Relationship,3,8 +315,20,Female,Undergraduate,South Korea,4.2,KakaoTalk,Yes,7.6,6,Single,3,6 +316,22,Male,Graduate,Russia,3.7,VKontakte,No,8.3,7,In Relationship,2,5 +317,19,Female,Undergraduate,USA,7.1,Instagram,Yes,5.0,4,Single,4,9 +318,21,Male,Graduate,UK,4.6,Facebook,Yes,7.0,6,In Relationship,3,7 +319,20,Female,Undergraduate,Australia,3.9,Instagram,No,7.9,7,Single,2,5 +320,22,Male,Graduate,Germany,3.1,Facebook,No,8.4,8,In Relationship,1,4 +321,19,Female,Undergraduate,Spain,5.2,Instagram,Yes,6.8,6,Single,3,7 +322,21,Male,Graduate,Denmark,4.1,Facebook,No,7.8,8,In Relationship,2,4 +323,20,Female,Undergraduate,Ireland,5.0,Instagram,Yes,7.0,7,Single,3,6 +324,22,Male,Graduate,India,5.8,WhatsApp,Yes,6.5,5,In Relationship,4,8 +325,19,Female,Undergraduate,Switzerland,4.0,Instagram,No,7.9,8,Single,2,4 +326,21,Male,Graduate,Turkey,5.5,Instagram,Yes,6.7,6,Single,3,7 +327,20,Female,Undergraduate,USA,6.8,TikTok,Yes,5.5,5,In Relationship,4,9 +328,22,Male,Graduate,Mexico,5.6,WhatsApp,Yes,6.6,6,Single,3,7 +329,19,Female,Undergraduate,France,4.5,Instagram,No,7.5,7,In Relationship,2,5 +330,21,Male,Graduate,Canada,5.3,Instagram,Yes,6.9,6,Single,3,7 +331,20,Female,Undergraduate,Spain,5.1,TikTok,Yes,6.7,6,Single,3,7 +332,22,Male,Graduate,Denmark,3.9,Facebook,No,7.9,8,In Relationship,2,4 +333,19,Female,Undergraduate,Ireland,4.8,Instagram,Yes,7.1,7,Single,3,6 +334,21,Male,Graduate,India,5.9,WhatsApp,Yes,6.4,5,In Relationship,4,8 +335,20,Female,Undergraduate,Switzerland,3.8,Instagram,No,8.0,8,Single,2,4 +336,22,Male,Graduate,Turkey,5.4,TikTok,Yes,6.8,6,Single,3,7 +337,19,Female,Undergraduate,USA,6.9,Instagram,Yes,5.4,5,In Relationship,4,9 +338,21,Male,Graduate,Mexico,5.7,WhatsApp,Yes,6.5,6,Single,3,7 +339,20,Female,Undergraduate,France,4.4,Instagram,No,7.6,7,In Relationship,2,5 +340,22,Male,Graduate,Canada,5.2,TikTok,Yes,7.0,6,Single,3,7 +341,19,Female,Undergraduate,Spain,5.0,Instagram,Yes,6.9,6,Single,3,7 +342,21,Male,Graduate,Denmark,3.8,Facebook,No,8.0,8,In Relationship,2,4 +343,20,Female,Undergraduate,Ireland,4.7,TikTok,Yes,7.2,7,Single,3,6 +344,22,Male,Graduate,India,6.0,WhatsApp,Yes,6.3,5,In Relationship,4,8 +345,19,Female,Undergraduate,Switzerland,3.7,Instagram,No,8.1,8,Single,2,4 +346,21,Male,Graduate,Turkey,5.3,Instagram,Yes,6.9,6,Single,3,7 +347,20,Female,Undergraduate,USA,7.0,TikTok,Yes,5.3,5,In Relationship,4,9 +348,22,Male,Graduate,Mexico,5.8,WhatsApp,Yes,6.4,6,Single,3,7 +349,19,Female,Undergraduate,France,4.3,Instagram,No,7.7,7,In Relationship,2,5 +350,21,Male,Graduate,Canada,5.1,Instagram,Yes,7.1,6,Single,3,7 +351,20,Female,Undergraduate,Spain,4.9,TikTok,Yes,7.0,6,Single,3,7 +352,22,Male,Graduate,Denmark,3.7,Facebook,No,8.1,8,In Relationship,2,4 +353,19,Female,Undergraduate,Ireland,4.6,Instagram,Yes,7.3,7,Single,3,6 +354,21,Male,Graduate,India,6.1,WhatsApp,Yes,6.2,5,In Relationship,4,8 +355,20,Female,Undergraduate,Switzerland,3.6,Instagram,No,8.2,8,Single,2,4 +356,22,Male,Graduate,Turkey,5.2,TikTok,Yes,7.0,6,Single,3,7 +357,19,Female,Undergraduate,USA,7.1,Instagram,Yes,5.2,5,In Relationship,4,9 +358,21,Male,Graduate,Mexico,5.9,WhatsApp,Yes,6.3,6,Single,3,7 +359,20,Female,Undergraduate,France,4.2,Instagram,No,7.8,7,In Relationship,2,5 +360,22,Male,Graduate,Canada,5.0,TikTok,Yes,7.2,6,Single,3,7 +361,19,Female,Undergraduate,Spain,4.8,Instagram,Yes,7.1,6,Single,3,7 +362,21,Male,Graduate,Denmark,3.6,Facebook,No,8.2,8,In Relationship,2,4 +363,20,Female,Undergraduate,Ireland,4.5,TikTok,Yes,7.4,7,Single,3,6 +364,22,Male,Graduate,India,6.2,WhatsApp,Yes,6.1,5,In Relationship,4,8 +365,19,Female,Undergraduate,Switzerland,3.5,Instagram,No,8.3,8,Single,2,4 +366,21,Male,Graduate,Turkey,5.1,Instagram,Yes,7.1,6,Single,3,7 +367,20,Female,Undergraduate,USA,7.2,TikTok,Yes,5.1,5,In Relationship,4,9 +368,22,Male,Graduate,Mexico,6.0,WhatsApp,Yes,6.2,6,Single,3,7 +369,19,Female,Undergraduate,France,4.1,Instagram,No,7.9,7,In Relationship,2,5 +370,21,Male,Graduate,Canada,4.9,Instagram,Yes,7.3,6,Single,3,7 +371,20,Female,Undergraduate,Spain,4.7,TikTok,Yes,7.2,6,Single,3,7 +372,22,Male,Graduate,Denmark,3.5,Facebook,No,8.3,8,In Relationship,2,4 +373,19,Female,Undergraduate,Ireland,4.4,Instagram,Yes,7.5,7,Single,3,6 +374,21,Male,Graduate,India,6.3,WhatsApp,Yes,6.0,5,In Relationship,4,8 +375,20,Female,Undergraduate,Switzerland,3.4,Instagram,No,8.4,8,Single,2,4 +376,22,Male,Graduate,Turkey,5.0,TikTok,Yes,7.2,6,Single,3,7 +377,19,Female,Undergraduate,USA,7.3,Instagram,Yes,5.0,5,In Relationship,4,9 +378,21,Male,Graduate,Mexico,6.1,WhatsApp,Yes,6.1,6,Single,3,7 +379,20,Female,Undergraduate,France,4.0,Instagram,No,8.0,7,In Relationship,2,5 +380,22,Male,Graduate,Canada,4.8,TikTok,Yes,7.4,6,Single,3,7 +381,19,Female,Undergraduate,Spain,4.6,Instagram,Yes,7.3,6,Single,3,7 +382,21,Male,Graduate,Denmark,3.4,Facebook,No,8.4,8,In Relationship,2,4 +383,20,Female,Undergraduate,Ireland,4.3,TikTok,Yes,7.6,7,Single,3,6 +384,22,Male,Graduate,India,6.4,WhatsApp,Yes,5.9,5,In Relationship,4,8 +385,19,Female,Undergraduate,Switzerland,3.3,Instagram,No,8.5,8,Single,2,4 +386,21,Male,Graduate,Turkey,4.9,Instagram,Yes,7.3,6,Single,3,7 +387,20,Female,Undergraduate,USA,7.4,TikTok,Yes,4.9,5,In Relationship,4,9 +388,22,Male,Graduate,Mexico,6.2,WhatsApp,Yes,6.0,6,Single,3,7 +389,19,Female,Undergraduate,France,3.9,Instagram,No,8.1,7,In Relationship,2,5 +390,21,Male,Graduate,Canada,4.7,Instagram,Yes,7.5,6,Single,3,7 +391,20,Female,Undergraduate,Spain,4.5,TikTok,Yes,7.4,6,Single,3,7 +392,22,Male,Graduate,Denmark,3.3,Facebook,No,8.5,8,In Relationship,2,4 +393,19,Female,Undergraduate,Ireland,4.2,Instagram,Yes,7.7,7,Single,3,6 +394,21,Male,Graduate,India,6.5,WhatsApp,Yes,5.8,5,In Relationship,4,8 +395,20,Female,Undergraduate,Switzerland,3.2,Instagram,No,8.6,8,Single,2,4 +396,22,Male,Graduate,Turkey,4.8,TikTok,Yes,7.4,6,Single,3,7 +397,19,Female,Undergraduate,USA,7.5,Instagram,Yes,4.8,5,In Relationship,4,9 +398,21,Male,Graduate,Mexico,6.3,WhatsApp,Yes,5.9,6,Single,3,7 +399,20,Female,Undergraduate,France,3.8,Instagram,No,8.2,7,In Relationship,2,5 +400,22,Male,Graduate,Canada,4.6,TikTok,Yes,7.6,6,Single,3,7 +401,19,Female,Undergraduate,Spain,4.4,Instagram,Yes,7.5,6,Single,3,7 +402,21,Male,Graduate,Denmark,3.2,Facebook,No,8.6,8,In Relationship,2,4 +403,20,Female,Undergraduate,Ireland,4.1,TikTok,Yes,7.8,7,Single,3,6 +404,22,Male,Graduate,India,6.6,WhatsApp,Yes,5.7,5,In Relationship,4,8 +405,19,Female,Undergraduate,Switzerland,3.1,Instagram,No,8.7,8,Single,2,4 +406,21,Male,Graduate,Turkey,4.7,Instagram,Yes,7.5,6,Single,3,7 +407,20,Female,Undergraduate,USA,7.6,TikTok,Yes,4.7,5,In Relationship,4,9 +408,22,Male,Graduate,Mexico,6.4,WhatsApp,Yes,5.8,6,Single,3,7 +409,19,Female,Undergraduate,France,3.7,Instagram,No,8.3,7,In Relationship,2,5 +410,21,Male,Graduate,Canada,4.5,Instagram,Yes,7.7,6,Single,3,7 +411,20,Female,Undergraduate,Spain,4.3,TikTok,Yes,7.6,6,Single,3,7 +412,22,Male,Graduate,Denmark,3.1,Facebook,No,8.7,8,In Relationship,2,4 +413,19,Female,Undergraduate,Ireland,4.0,Instagram,Yes,7.9,7,Single,3,6 +414,21,Male,Graduate,India,6.7,WhatsApp,Yes,5.6,5,In Relationship,4,8 +415,20,Female,Undergraduate,Switzerland,3.0,Instagram,No,8.8,8,Single,2,4 +416,22,Male,Graduate,Turkey,4.6,TikTok,Yes,7.6,6,Single,3,7 +417,19,Female,Undergraduate,USA,7.7,Instagram,Yes,4.6,5,In Relationship,4,9 +418,21,Male,Graduate,Mexico,6.5,WhatsApp,Yes,5.7,6,Single,3,7 +419,20,Female,Undergraduate,France,3.6,Instagram,No,8.4,7,In Relationship,2,5 +420,22,Male,Graduate,Canada,4.4,TikTok,Yes,7.8,6,Single,3,7 +421,19,Female,Undergraduate,Spain,4.2,Instagram,Yes,7.7,6,Single,3,7 +422,21,Male,Graduate,Denmark,3.0,Facebook,No,8.8,8,In Relationship,2,4 +423,20,Female,Undergraduate,Ireland,3.9,TikTok,Yes,8.0,7,Single,3,6 +424,22,Male,Graduate,India,6.8,WhatsApp,Yes,5.5,5,In Relationship,4,8 +425,19,Female,Undergraduate,Switzerland,2.9,Instagram,No,8.9,8,Single,2,4 +426,21,Male,Graduate,Turkey,4.5,Instagram,Yes,7.7,6,Single,3,7 +427,20,Female,Undergraduate,USA,7.8,TikTok,Yes,4.5,5,In Relationship,4,9 +428,22,Male,Graduate,Mexico,6.6,WhatsApp,Yes,5.6,6,Single,3,7 +429,19,Female,Undergraduate,France,3.5,Instagram,No,8.5,7,In Relationship,2,5 +430,21,Male,Graduate,Canada,4.3,Instagram,Yes,7.9,6,Single,3,7 +431,20,Female,Undergraduate,Spain,4.1,TikTok,Yes,7.8,6,Single,3,7 +432,22,Male,Graduate,Denmark,2.9,Facebook,No,8.9,8,In Relationship,2,4 +433,19,Female,Undergraduate,Ireland,3.8,Instagram,Yes,8.1,7,Single,3,6 +434,21,Male,Graduate,India,6.9,WhatsApp,Yes,5.4,5,In Relationship,4,8 +435,20,Female,Undergraduate,Switzerland,2.8,Instagram,No,9.0,8,Single,2,4 +436,22,Male,Graduate,Turkey,4.4,TikTok,Yes,7.8,6,Single,3,7 +437,19,Female,Undergraduate,USA,7.9,Instagram,Yes,4.4,5,In Relationship,4,9 +438,21,Male,Graduate,Mexico,6.7,WhatsApp,Yes,5.5,6,Single,3,7 +439,20,Female,Undergraduate,France,3.4,Instagram,No,8.6,7,In Relationship,2,5 +440,22,Male,Graduate,Canada,4.2,TikTok,Yes,8.0,6,Single,3,7 +441,19,Female,Undergraduate,Spain,4.0,Instagram,Yes,7.9,6,Single,3,7 +442,21,Male,Graduate,Denmark,2.8,Facebook,No,9.0,8,In Relationship,2,4 +443,20,Female,Undergraduate,Ireland,3.7,TikTok,Yes,8.2,7,Single,3,6 +444,22,Male,Graduate,India,7.0,WhatsApp,Yes,5.3,5,In Relationship,4,8 +445,19,Female,Undergraduate,Switzerland,2.7,Instagram,No,9.1,8,Single,2,4 +446,21,Male,Graduate,Turkey,4.3,Instagram,Yes,7.9,6,Single,3,7 +447,20,Female,Undergraduate,USA,8.0,TikTok,Yes,4.3,5,In Relationship,4,9 +448,22,Male,Graduate,Mexico,6.8,WhatsApp,Yes,5.4,6,Single,3,7 +449,19,Female,Undergraduate,France,3.3,Instagram,No,8.7,7,In Relationship,2,5 +450,21,Male,Graduate,Canada,4.1,Instagram,Yes,8.1,6,Single,3,7 +451,20,Female,Undergraduate,Spain,3.9,TikTok,Yes,8.0,6,Single,3,7 +452,22,Male,Graduate,Denmark,2.7,Facebook,No,9.1,8,In Relationship,2,4 +453,19,Female,Undergraduate,Ireland,3.6,Instagram,Yes,8.3,7,Single,3,6 +454,21,Male,Graduate,India,7.1,WhatsApp,Yes,5.2,5,In Relationship,4,8 +455,20,Female,Undergraduate,Switzerland,2.6,Instagram,No,9.2,8,Single,2,4 +456,22,Male,Graduate,Turkey,4.2,TikTok,Yes,8.0,6,Single,3,7 +457,19,Female,Undergraduate,USA,8.1,Instagram,Yes,4.2,5,In Relationship,4,9 +458,21,Male,Graduate,Mexico,6.9,WhatsApp,Yes,5.3,6,Single,3,7 +459,20,Female,Undergraduate,France,3.2,Instagram,No,8.8,7,In Relationship,2,5 +460,22,Male,Graduate,Canada,4.0,TikTok,Yes,8.2,6,Single,3,7 +461,19,Female,Undergraduate,Spain,3.8,Instagram,Yes,8.1,6,Single,3,7 +462,21,Male,Graduate,Denmark,2.6,Facebook,No,9.2,8,In Relationship,2,4 +463,20,Female,Undergraduate,Ireland,3.5,TikTok,Yes,8.4,7,Single,3,6 +464,22,Male,Graduate,India,7.2,WhatsApp,Yes,5.1,5,In Relationship,4,8 +465,19,Female,Undergraduate,Switzerland,2.5,Instagram,No,9.3,8,Single,2,4 +466,21,Male,Graduate,Turkey,4.1,Instagram,Yes,8.1,6,Single,3,7 +467,20,Female,Undergraduate,USA,8.2,TikTok,Yes,4.1,5,In Relationship,4,9 +468,22,Male,Graduate,Mexico,7.0,WhatsApp,Yes,5.2,6,Single,3,7 +469,19,Female,Undergraduate,France,3.1,Instagram,No,8.9,7,In Relationship,2,5 +470,21,Male,Graduate,Canada,3.9,Instagram,Yes,8.3,6,Single,3,7 +471,20,Female,Undergraduate,Spain,3.7,TikTok,Yes,8.2,6,Single,3,7 +472,22,Male,Graduate,Denmark,2.5,Facebook,No,9.3,8,In Relationship,2,4 +473,19,Female,Undergraduate,Ireland,3.4,Instagram,Yes,8.5,7,Single,3,6 +474,21,Male,Graduate,India,7.3,WhatsApp,Yes,5.0,5,In Relationship,4,8 +475,20,Female,Undergraduate,Switzerland,2.4,Instagram,No,9.4,8,Single,2,4 +476,22,Male,Graduate,Turkey,4.0,TikTok,Yes,8.2,6,Single,3,7 +477,19,Female,Undergraduate,USA,8.3,Instagram,Yes,4.0,5,In Relationship,4,9 +478,21,Male,Graduate,Mexico,7.1,WhatsApp,Yes,5.1,6,Single,3,7 +479,20,Female,Undergraduate,France,3.0,Instagram,No,9.0,7,In Relationship,2,5 +480,22,Male,Graduate,Canada,3.8,TikTok,Yes,8.4,6,Single,3,7 +481,19,Female,Undergraduate,Spain,3.6,Instagram,Yes,8.3,6,Single,3,7 +482,21,Male,Graduate,Denmark,2.4,Facebook,No,9.4,8,In Relationship,2,4 +483,20,Female,Undergraduate,Ireland,3.3,TikTok,Yes,8.6,7,Single,3,6 +484,22,Male,Graduate,India,7.4,WhatsApp,Yes,4.9,5,In Relationship,4,8 +485,19,Female,Undergraduate,Switzerland,2.3,Instagram,No,9.5,8,Single,2,4 +486,21,Male,Graduate,Turkey,3.9,Instagram,Yes,8.3,6,Single,3,7 +487,20,Female,Undergraduate,USA,8.4,TikTok,Yes,3.9,5,In Relationship,4,9 +488,22,Male,Graduate,Mexico,7.2,WhatsApp,Yes,5.0,6,Single,3,7 +489,19,Female,Undergraduate,France,2.9,Instagram,No,9.1,7,In Relationship,2,5 +490,21,Male,Graduate,Canada,3.7,Instagram,Yes,8.5,6,Single,3,7 +491,20,Female,Undergraduate,Spain,3.5,TikTok,Yes,8.4,6,Single,3,7 +492,22,Male,Graduate,Denmark,2.3,Facebook,No,9.5,8,In Relationship,2,4 +493,19,Female,Undergraduate,Ireland,3.2,Instagram,Yes,8.7,7,Single,3,6 +494,21,Male,Graduate,India,7.5,WhatsApp,Yes,4.8,5,In Relationship,4,8 +495,20,Female,Undergraduate,Switzerland,2.2,Instagram,No,9.6,8,Single,2,4 +496,22,Male,Graduate,Turkey,3.8,TikTok,Yes,8.4,6,Single,3,7 +497,19,Female,Undergraduate,USA,8.5,Instagram,Yes,3.8,5,In Relationship,4,9 +498,21,Male,Graduate,Mexico,7.3,WhatsApp,Yes,4.9,6,Single,3,7 +499,20,Female,Undergraduate,France,2.8,Instagram,No,9.2,7,In Relationship,2,5 +500,22,Male,Graduate,Canada,3.6,TikTok,Yes,8.6,6,Single,3,7 +501,19,Female,Undergraduate,Brazil,6.2,WhatsApp,Yes,6.3,6,Single,3,7 +502,21,Male,Graduate,China,4.5,WeChat,No,7.8,7,In Relationship,2,5 +503,20,Female,Undergraduate,Netherlands,3.8,Instagram,No,8.2,8,Single,2,4 +504,22,Male,Graduate,New Zealand,4.7,Instagram,Yes,7.5,7,In Relationship,3,6 +505,19,Female,Undergraduate,Singapore,5.1,TikTok,Yes,7.0,6,Single,3,7 +506,21,Male,Graduate,Malaysia,5.5,WhatsApp,Yes,6.8,6,Single,3,7 +507,20,Female,Undergraduate,UAE,6.5,Instagram,Yes,6.5,5,In Relationship,4,8 +508,22,Male,Graduate,Poland,4.2,Facebook,No,7.9,7,Single,2,5 +509,19,Female,Undergraduate,India,6.8,WhatsApp,Yes,6.0,5,In Relationship,4,8 +510,21,Male,Graduate,Canada,4.8,Instagram,Yes,7.4,7,Single,3,6 +511,20,Female,Undergraduate,Brazil,6.1,Instagram,Yes,6.4,6,Single,3,7 +512,22,Male,Graduate,China,4.4,WeChat,No,7.9,7,In Relationship,2,5 +513,19,Female,Undergraduate,Netherlands,3.7,TikTok,No,8.3,8,Single,2,4 +514,21,Male,Graduate,New Zealand,4.6,Instagram,Yes,7.6,7,In Relationship,3,6 +515,20,Female,Undergraduate,Singapore,5.2,TikTok,Yes,6.9,6,Single,3,7 +516,22,Male,Graduate,Malaysia,5.6,WhatsApp,Yes,6.7,6,Single,3,7 +517,19,Female,Undergraduate,UAE,6.6,Instagram,Yes,6.4,5,In Relationship,4,8 +518,21,Male,Graduate,Poland,4.1,Facebook,No,8.0,7,Single,2,5 +519,20,Female,Undergraduate,India,6.9,WhatsApp,Yes,5.9,5,In Relationship,4,8 +520,22,Male,Graduate,Canada,4.7,TikTok,Yes,7.5,7,Single,3,6 +521,19,Female,Undergraduate,Brazil,6.0,WhatsApp,Yes,6.5,6,Single,3,7 +522,21,Male,Graduate,China,4.3,WeChat,No,8.0,7,In Relationship,2,5 +523,20,Female,Undergraduate,Netherlands,3.6,Instagram,No,8.4,8,Single,2,4 +524,22,Male,Graduate,New Zealand,4.5,Facebook,Yes,7.7,7,In Relationship,3,6 +525,19,Female,Undergraduate,Singapore,5.3,TikTok,Yes,6.8,6,Single,3,7 +526,21,Male,Graduate,Malaysia,5.7,WhatsApp,Yes,6.6,6,Single,3,7 +527,20,Female,Undergraduate,UAE,6.7,Instagram,Yes,6.3,5,In Relationship,4,8 +528,22,Male,Graduate,Poland,4.0,Facebook,No,8.1,7,Single,2,5 +529,19,Female,Undergraduate,India,7.0,WhatsApp,Yes,5.8,5,In Relationship,4,8 +530,21,Male,Graduate,Canada,4.6,Instagram,Yes,7.6,7,Single,3,6 +531,20,Female,Undergraduate,Brazil,5.9,TikTok,Yes,6.6,6,Single,3,7 +532,22,Male,Graduate,China,4.2,WeChat,No,8.1,7,In Relationship,2,5 +533,19,Female,Undergraduate,Netherlands,3.5,Instagram,No,8.5,8,Single,2,4 +534,21,Male,Graduate,New Zealand,4.4,Facebook,Yes,7.8,7,In Relationship,3,6 +535,20,Female,Undergraduate,Singapore,5.4,TikTok,Yes,6.7,6,Single,3,7 +536,22,Male,Graduate,Malaysia,5.8,WhatsApp,Yes,6.5,6,Single,3,7 +537,19,Female,Undergraduate,UAE,6.8,Instagram,Yes,6.2,5,In Relationship,4,8 +538,21,Male,Graduate,Poland,3.9,Facebook,No,8.2,7,Single,2,5 +539,20,Female,Undergraduate,India,7.1,WhatsApp,Yes,5.7,5,In Relationship,4,8 +540,22,Male,Graduate,Canada,4.5,TikTok,Yes,7.7,7,Single,3,6 +541,19,Female,Undergraduate,Brazil,5.8,WhatsApp,Yes,6.7,6,Single,3,7 +542,21,Male,Graduate,China,4.1,WeChat,No,8.2,7,In Relationship,2,5 +543,20,Female,Undergraduate,Netherlands,3.4,Instagram,No,8.6,8,Single,2,4 +544,22,Male,Graduate,New Zealand,4.3,Instagram,Yes,7.9,7,In Relationship,3,6 +545,19,Female,Undergraduate,Singapore,5.5,TikTok,Yes,6.6,6,Single,3,7 +546,21,Male,Graduate,Malaysia,5.9,WhatsApp,Yes,6.4,6,Single,3,7 +547,20,Female,Undergraduate,UAE,6.9,Instagram,Yes,6.1,5,In Relationship,4,8 +548,22,Male,Graduate,Poland,3.8,Facebook,No,8.3,7,Single,2,5 +549,19,Female,Undergraduate,India,7.2,WhatsApp,Yes,5.6,5,In Relationship,4,8 +550,21,Male,Graduate,Canada,4.4,Instagram,Yes,7.8,7,Single,3,6 +551,20,Female,Undergraduate,Brazil,5.7,TikTok,Yes,6.8,6,Single,3,7 +552,22,Male,Graduate,China,4.0,WeChat,No,8.3,7,In Relationship,2,5 +553,19,Female,Undergraduate,Netherlands,3.3,Instagram,No,8.7,8,Single,2,4 +554,21,Male,Graduate,New Zealand,4.2,Facebook,Yes,8.0,7,In Relationship,3,6 +555,20,Female,Undergraduate,Singapore,5.6,TikTok,Yes,6.5,6,Single,3,7 +556,22,Male,Graduate,Malaysia,6.0,WhatsApp,Yes,6.3,6,Single,3,7 +557,19,Female,Undergraduate,UAE,7.0,Instagram,Yes,6.0,5,In Relationship,4,8 +558,21,Male,Graduate,Poland,3.7,Facebook,No,8.4,7,Single,2,5 +559,20,Female,Undergraduate,India,7.3,WhatsApp,Yes,5.5,5,In Relationship,4,8 +560,22,Male,Graduate,Canada,4.3,TikTok,Yes,7.9,7,Single,3,6 +561,19,Female,Undergraduate,Brazil,5.6,WhatsApp,Yes,6.9,6,Single,3,7 +562,21,Male,Graduate,China,3.9,WeChat,No,8.4,7,In Relationship,2,5 +563,20,Female,Undergraduate,Netherlands,3.2,Instagram,No,8.8,8,Single,2,4 +564,22,Male,Graduate,New Zealand,4.1,Instagram,Yes,8.1,7,In Relationship,3,6 +565,19,Female,Undergraduate,Singapore,5.7,TikTok,Yes,6.4,6,Single,3,7 +566,21,Male,Graduate,Malaysia,6.1,WhatsApp,Yes,6.2,6,Single,3,7 +567,20,Female,Undergraduate,UAE,7.1,Instagram,Yes,5.9,5,In Relationship,4,8 +568,22,Male,Graduate,Poland,3.6,Facebook,No,8.5,7,Single,2,5 +569,19,Female,Undergraduate,India,7.4,WhatsApp,Yes,5.4,5,In Relationship,4,8 +570,21,Male,Graduate,Canada,4.2,Instagram,Yes,8.0,7,Single,3,6 +571,20,Female,Undergraduate,Spain,6.1,Instagram,Yes,7.2,5,Single,4,7 +572,23,Male,Graduate,Denmark,3.8,Twitter,No,7.8,8,In Relationship,2,4 +573,19,Female,Undergraduate,Ireland,5.5,TikTok,Yes,6.8,6,Single,4,8 +574,22,Male,Graduate,India,7.2,Facebook,Yes,5.9,4,Single,5,9 +575,21,Female,Undergraduate,Switzerland,4.2,Instagram,No,7.5,7,In Relationship,2,5 +576,24,Male,Graduate,Turkey,6.8,TikTok,Yes,6.2,5,Single,4,8 +577,20,Female,Undergraduate,USA,5.9,Instagram,Yes,6.7,6,In Relationship,3,7 +578,22,Male,Graduate,Mexico,6.5,Facebook,Yes,6.1,5,Single,4,8 +579,19,Female,Undergraduate,France,4.7,Twitter,No,7.4,7,Single,2,5 +580,23,Male,Graduate,Canada,5.2,Instagram,Yes,7.0,6,In Relationship,3,6 +581,21,Female,Undergraduate,UK,6.3,TikTok,Yes,6.4,5,Single,4,8 +582,24,Male,Graduate,Italy,4.9,Facebook,No,7.3,7,In Relationship,2,5 +583,20,Female,Undergraduate,Russia,6.7,Instagram,Yes,6.0,5,Single,4,8 +584,22,Male,Graduate,China,5.8,WeChat,Yes,6.5,6,In Relationship,3,7 +585,19,Female,Undergraduate,Japan,4.5,Twitter,No,7.6,8,Single,2,4 +586,23,Male,Graduate,Poland,6.4,Facebook,Yes,6.3,5,Single,4,8 +587,21,Female,Undergraduate,Finland,4.1,Instagram,No,7.7,7,In Relationship,2,5 +588,24,Male,Graduate,Spain,6.6,TikTok,Yes,6.2,5,Single,4,8 +589,20,Female,Undergraduate,Denmark,4.4,Instagram,No,7.4,7,In Relationship,2,5 +590,22,Male,Graduate,Ireland,5.7,Twitter,Yes,6.8,6,Single,3,7 +591,19,Female,Undergraduate,India,7.0,Instagram,Yes,5.8,4,Single,5,9 +592,23,Male,Graduate,Switzerland,4.3,Facebook,No,7.5,7,In Relationship,2,5 +593,21,Female,Undergraduate,Turkey,6.9,TikTok,Yes,6.1,5,Single,4,8 +594,24,Male,Graduate,USA,5.6,Instagram,Yes,6.9,6,In Relationship,3,7 +595,20,Female,Undergraduate,Mexico,6.2,Facebook,Yes,6.3,5,Single,4,8 +596,21,Male,Undergraduate,France,5.8,Instagram,Yes,6.7,6,Single,3,7 +597,23,Female,Graduate,Canada,4.9,TikTok,No,7.3,7,In Relationship,2,5 +598,20,Male,Undergraduate,UK,6.4,Facebook,Yes,6.2,5,Single,4,8 +599,22,Female,Graduate,Italy,5.1,Twitter,No,7.1,7,In Relationship,2,5 +600,19,Male,Undergraduate,Russia,6.7,Instagram,Yes,6.0,4,Single,4,8 +601,24,Female,Graduate,China,5.5,WeChat,Yes,6.8,6,In Relationship,3,7 +602,21,Male,Undergraduate,Japan,4.3,Twitter,No,7.6,8,Single,2,4 +603,23,Female,Graduate,Poland,6.2,Instagram,Yes,6.4,5,Single,4,8 +604,20,Male,Undergraduate,Finland,4.5,Facebook,No,7.4,7,In Relationship,2,5 +605,22,Female,Graduate,Spain,6.3,TikTok,Yes,6.3,5,Single,4,8 +606,19,Male,Undergraduate,Denmark,4.7,Instagram,No,7.2,7,In Relationship,2,5 +607,24,Female,Graduate,Ireland,5.9,Twitter,Yes,6.6,6,Single,3,7 +608,21,Male,Undergraduate,India,7.1,Facebook,Yes,5.7,4,Single,5,9 +609,23,Female,Graduate,Switzerland,4.4,Instagram,No,7.4,7,In Relationship,2,5 +610,20,Male,Undergraduate,Turkey,6.6,TikTok,Yes,6.2,5,Single,4,8 +611,22,Female,Graduate,USA,5.4,Instagram,Yes,6.9,6,In Relationship,3,7 +612,19,Male,Undergraduate,Mexico,6.5,Facebook,Yes,6.1,5,Single,4,8 +613,24,Female,Graduate,France,4.8,Twitter,No,7.3,7,In Relationship,2,5 +614,21,Male,Undergraduate,Canada,5.7,Instagram,Yes,6.7,6,Single,3,7 +615,23,Female,Graduate,UK,6.1,TikTok,Yes,6.4,5,Single,4,8 +616,20,Male,Undergraduate,Italy,4.6,Facebook,No,7.2,7,In Relationship,2,5 +617,22,Female,Graduate,Russia,6.8,Instagram,Yes,5.9,4,Single,5,9 +618,19,Male,Undergraduate,China,5.6,WeChat,Yes,6.8,6,In Relationship,3,7 +619,24,Female,Graduate,Japan,4.2,Twitter,No,7.5,8,Single,2,4 +620,21,Male,Undergraduate,Poland,6.3,TikTok,Yes,6.3,5,Single,4,8 +621,23,Female,Graduate,Finland,4.4,Instagram,No,7.4,7,In Relationship,2,5 +622,20,Male,Undergraduate,Spain,6.5,Facebook,Yes,6.2,5,Single,4,8 +623,22,Female,Graduate,Denmark,4.6,Twitter,No,7.3,7,In Relationship,2,5 +624,19,Male,Undergraduate,Ireland,5.8,Instagram,Yes,6.6,6,Single,3,7 +625,24,Female,Graduate,India,7.0,TikTok,Yes,5.8,4,Single,5,9 +626,21,Male,Undergraduate,Switzerland,4.5,Facebook,No,7.3,7,In Relationship,2,5 +627,23,Female,Graduate,Turkey,6.7,Instagram,Yes,6.1,5,Single,4,8 +628,20,Male,Undergraduate,USA,5.5,Twitter,Yes,6.8,6,In Relationship,3,7 +629,22,Female,Graduate,Mexico,6.4,Facebook,Yes,6.2,5,Single,4,8 +630,19,Male,Undergraduate,France,4.7,Instagram,No,7.2,7,In Relationship,2,5 +631,24,Female,Graduate,Canada,5.6,TikTok,Yes,6.7,6,Single,3,7 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+688,22,Male,Graduate,Poland,6.3,TikTok,Yes,6.2,5,Single,4,8 +689,20,Female,Undergraduate,Finland,4.4,Instagram,No,7.4,7,In Relationship,2,5 +690,23,Male,Graduate,Spain,6.5,Facebook,Yes,6.1,5,Single,4,8 +691,21,Female,Undergraduate,Denmark,4.6,Twitter,No,7.3,7,In Relationship,2,5 +692,24,Male,Graduate,Ireland,5.9,Instagram,Yes,6.5,6,Single,3,7 +693,19,Female,Undergraduate,India,7.0,TikTok,Yes,5.8,4,Single,5,9 +694,22,Male,Graduate,Switzerland,4.5,Facebook,No,7.3,7,In Relationship,2,5 +695,20,Female,Undergraduate,Turkey,6.6,Instagram,Yes,6.1,5,Single,4,8 +696,23,Male,Graduate,USA,5.5,Twitter,Yes,6.7,6,In Relationship,3,7 +697,21,Female,Undergraduate,Mexico,6.3,TikTok,Yes,6.2,5,Single,4,8 +698,24,Male,Graduate,France,4.8,Facebook,No,7.1,7,In Relationship,2,5 +699,19,Female,Undergraduate,Canada,5.7,Instagram,Yes,6.6,6,Single,3,7 +700,22,Male,Graduate,UK,6.2,Twitter,Yes,6.3,5,Single,4,8 +701,20,Female,Undergraduate,Italy,4.7,TikTok,No,7.2,7,In Relationship,2,5 +702,23,Male,Graduate,Russia,6.8,Instagram,Yes,5.9,4,Single,5,9 +703,21,Female,Undergraduate,China,5.6,WeChat,Yes,6.7,6,In Relationship,3,7 +704,24,Male,Graduate,Japan,4.3,Twitter,No,7.5,8,Single,2,4 +705,19,Female,Undergraduate,Poland,6.2,Facebook,Yes,6.3,5,Single,4,8 diff --git a/customdatasetweek4.ipynb b/customdatasetweek4.ipynb new file mode 100644 index 0000000..c313788 --- /dev/null +++ b/customdatasetweek4.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "32e6184a-41e1-4948-b870-f7bf309842eb", + "metadata": {}, + "source": [ + "Загрузка и подготовка данных из CSV\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4a781ce0-6c44-49d8-a129-acd160bfcdb2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Student_ID Age Gender Academic_Level Country Avg_Daily_Usage_Hours \\\n", + "0 1 19 Female Undergraduate Bangladesh 5.2 \n", + "1 2 22 Male Graduate India 2.1 \n", + "2 3 20 Female Undergraduate USA 6.0 \n", + "3 4 18 Male High School UK 3.0 \n", + "4 5 21 Male Graduate Canada 4.5 \n", + "\n", + " Most_Used_Platform Affects_Academic_Performance Sleep_Hours_Per_Night \\\n", + "0 Instagram Yes 6.5 \n", + "1 Twitter No 7.5 \n", + "2 TikTok Yes 5.0 \n", + "3 YouTube No 7.0 \n", + "4 Facebook Yes 6.0 \n", + "\n", + " Mental_Health_Score Relationship_Status Conflicts_Over_Social_Media \\\n", + "0 6 In Relationship 3 \n", + "1 8 Single 0 \n", + "2 5 Complicated 4 \n", + "3 7 Single 1 \n", + "4 6 In Relationship 2 \n", + "\n", + " Addicted_Score \n", + "0 8 \n", + "1 3 \n", + "2 9 \n", + "3 4 \n", + "4 7 \n", + "\n", + "RangeIndex: 705 entries, 0 to 704\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Student_ID 705 non-null int64 \n", + " 1 Age 705 non-null int64 \n", + " 2 Gender 705 non-null object \n", + " 3 Academic_Level 705 non-null object \n", + " 4 Country 705 non-null object \n", + " 5 Avg_Daily_Usage_Hours 705 non-null float64\n", + " 6 Most_Used_Platform 705 non-null object \n", + " 7 Affects_Academic_Performance 705 non-null object \n", + " 8 Sleep_Hours_Per_Night 705 non-null float64\n", + " 9 Mental_Health_Score 705 non-null int64 \n", + " 10 Relationship_Status 705 non-null object \n", + " 11 Conflicts_Over_Social_Media 705 non-null int64 \n", + " 12 Addicted_Score 705 non-null int64 \n", + "dtypes: float64(2), int64(5), object(6)\n", + "memory usage: 71.7+ KB\n", + "None\n", + "Train shape: (564, 11), Test shape: (141, 11)\n", + "Class distribution in train:\n", + "Addicted_Label\n", + "1 0.664894\n", + "0 0.335106\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "# Загрузка данных из CSV\n", + "df = pd.read_csv(\"Students Social Media Addiction.csv\")\n", + "\n", + "# Быстрый осмотр\n", + "print(df.head())\n", + "print(df.info())\n", + "\n", + "# Преобразуем категориальные переменные в числовые с помощью LabelEncoder\n", + "label_encoders = {}\n", + "categorical_cols = [\"Gender\", \"Academic_Level\", \"Country\", \"Most_Used_Platform\", \"Relationship_Status\", \"Affects_Academic_Performance\"]\n", + "\n", + "for col in categorical_cols:\n", + " le = LabelEncoder()\n", + " df[col] = le.fit_transform(df[col])\n", + " label_encoders[col] = le\n", + "\n", + "# Целевая переменная: Addicted_Score (например, можно классифицировать как 'high' или 'low')\n", + "# Для простоты создадим бинарную цель: Addicted_Score >= 6 - \"Addicted\", иначе \"Not Addicted\"\n", + "df[\"Addicted_Label\"] = (df[\"Addicted_Score\"] >= 6).astype(int)\n", + "\n", + "# Выделяем признаки и целевую переменную\n", + "X = df.drop(columns=[\"Student_ID\", \"Addicted_Score\", \"Addicted_Label\"])\n", + "y = df[\"Addicted_Label\"]\n", + "\n", + "# Делим на train и test\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", + "\n", + "print(f\"Train shape: {X_train.shape}, Test shape: {X_test.shape}\")\n", + "print(f\"Class distribution in train:\\n{y_train.value_counts(normalize=True)}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ea74112-ba53-45c0-8ede-49155e903890", + "metadata": {}, + "source": [ + "Обучение модели RidgeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d03ccd19-aba5-4fa9-b9a8-13e24039220e", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import RidgeClassifier\n", + "\n", + "clf = RidgeClassifier(tol=1e-2, solver=\"sparse_cg\")\n", + "clf.fit(X_train, y_train)\n", + "\n", + "pred = clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9c85dc8e-1430-4a1f-8b2a-465798ea0f1b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import metrics\n", + "from sklearn.utils.extmath import density\n", + "from time import time\n", + "\n", + "def benchmark(clf, X_train, y_train, X_test, y_test, custom_name=False):\n", + " print(\"_\" * 80)\n", + " print(\"Training: \")\n", + " print(clf)\n", + " t0 = time()\n", + " clf.fit(X_train, y_train)\n", + " train_time = time() - t0\n", + " print(f\"train time: {train_time:.3f}s\")\n", + "\n", + " t0 = time()\n", + " pred = clf.predict(X_test)\n", + " test_time = time() - t0\n", + " print(f\"test time: {test_time:.3f}s\")\n", + "\n", + " score = metrics.accuracy_score(y_test, pred)\n", + " print(f\"accuracy: {score:.3f}\")\n", + "\n", + " if hasattr(clf, \"coef_\"):\n", + " if len(clf.coef_.shape) == 2:\n", + " n_features = clf.coef_.shape[1]\n", + " else:\n", + " n_features = clf.coef_.shape[0]\n", + " print(f\"dimensionality: {n_features}\")\n", + " print(f\"density: {density(clf.coef_)}\")\n", + " print()\n", + "\n", + " print()\n", + " clf_descr = str(custom_name) if custom_name else clf.__class__.__name__\n", + " return clf_descr, score, train_time, test_time\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "79250597-7639-41fb-8c70-d87d58afe8ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "LogisticRegression(C=5, max_iter=1000)\n", + "train time: 0.052s\n", + "test time: 0.001s\n", + "accuracy: 0.965\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "RidgeClassifier(solver='sparse_cg')\n", + "train time: 0.001s\n", + "test time: 0.000s\n", + "accuracy: 0.965\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "KNeighborsClassifier(n_neighbors=100)\n", + "train time: 0.001s\n", + "test time: 0.004s\n", + "accuracy: 0.716\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "RandomForestClassifier()\n", + "train time: 0.060s\n", + "test time: 0.003s\n", + "accuracy: 0.979\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "LinearSVC(C=0.1, dual=False)\n", + "train time: 0.001s\n", + "test time: 0.000s\n", + "accuracy: 0.965\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "SGDClassifier(early_stopping=True, loss='log_loss', n_iter_no_change=3)\n", + "train time: 0.002s\n", + "test time: 0.000s\n", + "accuracy: 0.936\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "NearestCentroid()\n", + "train time: 0.001s\n", + "test time: 0.001s\n", + "accuracy: 0.539\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "ComplementNB(alpha=0.1)\n", + "train time: 0.001s\n", + "test time: 0.000s\n", + "accuracy: 0.837\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression, RidgeClassifier, SGDClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier, NearestCentroid\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.naive_bayes import ComplementNB\n", + "\n", + "classifiers = [\n", + " (LogisticRegression(C=5, max_iter=1000), \"Logistic Regression\"),\n", + " (RidgeClassifier(alpha=1.0, solver=\"sparse_cg\"), \"Ridge Classifier\"),\n", + " (KNeighborsClassifier(n_neighbors=100), \"kNN\"),\n", + " (RandomForestClassifier(), \"Random Forest\"),\n", + " (LinearSVC(C=0.1, dual=False, max_iter=1000), \"Linear SVC\"),\n", + " (SGDClassifier(loss=\"log_loss\", alpha=1e-4, n_iter_no_change=3, early_stopping=True), \"log-loss SGD\"),\n", + " (NearestCentroid(), \"NearestCentroid\"),\n", + " (ComplementNB(alpha=0.1), \"Complement naive Bayes\"),\n", + "]\n", + "\n", + "results = []\n", + "for clf, name in classifiers:\n", + " print(\"=\" * 80)\n", + " results.append(benchmark(clf, X_train, y_train, X_test, y_test, name))\n" + ] + }, + { + "cell_type": "markdown", + "id": "41412766-f5a2-4ca7-aa6c-18f54484b0be", + "metadata": {}, + "source": [ + "Визуализация результатов" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c75ef28c-88d4-489c-9b23-8b9307f106a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "LogisticRegression(max_iter=1000)\n", + "train time: 0.043s\n", + "test time: 0.001s\n", + "accuracy: 0.965\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "RidgeClassifier(solver='sparse_cg')\n", + "train time: 0.001s\n", + "test time: 0.000s\n", + "accuracy: 0.965\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "KNeighborsClassifier(n_neighbors=10)\n", + "train time: 0.001s\n", + "test time: 0.003s\n", + "accuracy: 0.908\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "RandomForestClassifier()\n", + "train time: 0.061s\n", + "test time: 0.003s\n", + "accuracy: 0.979\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "LinearSVC()\n", + "train time: 0.001s\n", + "test time: 0.000s\n", + "accuracy: 0.965\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "SGDClassifier(early_stopping=True, loss='log_loss', n_iter_no_change=3)\n", + "train time: 0.002s\n", + "test time: 0.000s\n", + "accuracy: 0.752\n", + "dimensionality: 11\n", + "density: 1.0\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "NearestCentroid()\n", + "train time: 0.001s\n", + "test time: 0.001s\n", + "accuracy: 0.539\n", + "\n", + "================================================================================\n", + "________________________________________________________________________________\n", + "Training: \n", + "ComplementNB(alpha=0.1)\n", + "train time: 0.001s\n", + "test time: 0.000s\n", + "accuracy: 0.837\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import metrics\n", + "from sklearn.utils.extmath import density\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression, SGDClassifier\n", + "from sklearn.naive_bayes import ComplementNB\n", + "from sklearn.neighbors import KNeighborsClassifier, NearestCentroid\n", + "from sklearn.svm import LinearSVC\n", + "from time import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def benchmark(clf, X_train, y_train, X_test, y_test, custom_name=False):\n", + " print(\"_\" * 80)\n", + " print(\"Training: \")\n", + " print(clf)\n", + " t0 = time()\n", + " clf.fit(X_train, y_train)\n", + " train_time = time() - t0\n", + " print(f\"train time: {train_time:.3f}s\")\n", + "\n", + " t0 = time()\n", + " pred = clf.predict(X_test)\n", + " test_time = time() - t0\n", + " print(f\"test time: {test_time:.3f}s\")\n", + "\n", + " score = metrics.accuracy_score(y_test, pred)\n", + " print(f\"accuracy: {score:.3f}\")\n", + "\n", + " if hasattr(clf, \"coef_\"):\n", + " coef_shape = clf.coef_.shape\n", + " if len(coef_shape) == 1:\n", + " print(f\"dimensionality: {coef_shape[0]}\")\n", + " else:\n", + " print(f\"dimensionality: {coef_shape[1]}\")\n", + " print(f\"density: {density(clf.coef_)}\")\n", + "\n", + " print()\n", + " clf_descr = str(custom_name) if custom_name else clf.__class__.__name__\n", + " return clf_descr, score, train_time, test_time\n", + "\n", + "\n", + "results = []\n", + "classifiers = [\n", + " (LogisticRegression(max_iter=1000), \"Logistic Regression\"),\n", + " (RidgeClassifier(alpha=1.0, solver=\"sparse_cg\"), \"Ridge Classifier\"),\n", + " (KNeighborsClassifier(n_neighbors=10), \"kNN\"),\n", + " (RandomForestClassifier(), \"Random Forest\"),\n", + " (LinearSVC(max_iter=1000), \"Linear SVC\"),\n", + " (SGDClassifier(loss=\"log_loss\", alpha=1e-4, n_iter_no_change=3, early_stopping=True), \"log-loss SGD\"),\n", + " (NearestCentroid(), \"NearestCentroid\"),\n", + " (ComplementNB(alpha=0.1), \"Complement naive Bayes\"),\n", + "]\n", + "\n", + "for clf, name in classifiers:\n", + " print(\"=\" * 80)\n", + " results.append(benchmark(clf, X_train, y_train, X_test, y_test, name))\n", + "\n", + "# Визуализация результатов\n", + "results_arr = np.array(results)\n", + "clf_names, scores, train_times, test_times = results_arr.T\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(10, 8))\n", + "ax1.scatter(scores.astype(float), train_times.astype(float), s=60)\n", + "ax1.set(title=\"Score-training time trade-off\", yscale=\"log\", xlabel=\"Test accuracy\", ylabel=\"Training time (s)\")\n", + "\n", + "fig, ax2 = plt.subplots(figsize=(10, 8))\n", + "ax2.scatter(scores.astype(float), test_times.astype(float), s=60)\n", + "ax2.set(title=\"Score-test time trade-off\", yscale=\"log\", xlabel=\"Test accuracy\", ylabel=\"Test time (s)\")\n", + "\n", + "for i, txt in enumerate(clf_names):\n", + " ax1.annotate(txt, (float(scores[i]), float(train_times[i])))\n", + " ax2.annotate(txt, (float(scores[i]), float(test_times[i])))\n" + ] + }, + { + "cell_type": "markdown", + "id": "35fb3149-bae9-46b0-b64e-bf6e319b68fe", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..5128170 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,114 @@ +anyio==4.9.0 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +asttokens==3.0.0 +async-lru==2.0.5 +attrs==25.3.0 +babel==2.17.0 +beautifulsoup4==4.13.4 +bleach==6.2.0 +certifi==2025.4.26 +cffi==1.17.1 +charset-normalizer==3.4.2 +colorama==0.4.6 +comm==0.2.2 +contourpy==1.3.2 +cycler==0.12.1 +debugpy==1.8.14 +decorator==5.2.1 +defusedxml==0.7.1 +executing==2.2.0 +fastjsonschema==2.21.1 +fonttools==4.58.0 +fqdn==1.5.1 +h11==0.16.0 +httpcore==1.0.9 +httpx==0.28.1 +idna==3.10 +ipykernel==6.29.5 +ipython==9.2.0 +ipython_pygments_lexers==1.1.1 +isoduration==20.11.0 +jedi==0.19.2 +Jinja2==3.1.6 +joblib==1.5.0 +json5==0.12.0 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2025.4.1 +jupyter-events==0.12.0 +jupyter-lsp==2.2.5 +jupyter_client==8.6.3 +jupyter_core==5.7.2 +jupyter_server==2.16.0 +jupyter_server_terminals==0.5.3 +jupyterlab==4.4.2 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +kiwisolver==1.4.8 +liac-arff==2.5.0 +MarkupSafe==3.0.2 +matplotlib==3.10.3 +matplotlib-inline==0.1.7 +minio==7.2.15 +mistune==3.1.3 +nbclient==0.10.2 +nbconvert==7.16.6 +nbformat==5.10.4 +nest-asyncio==1.6.0 +notebook_shim==0.2.4 +numpy==2.2.5 +openml==0.15.1 +overrides==7.7.0 +packaging==25.0 +pandas==2.2.3 +pandocfilters==1.5.1 +parso==0.8.4 +pillow==11.2.1 +platformdirs==4.3.8 +prometheus_client==0.21.1 +prompt_toolkit==3.0.51 +psutil==7.0.0 +pure_eval==0.2.3 +pyarrow==20.0.0 +pycparser==2.22 +pycryptodome==3.22.0 +Pygments==2.19.1 +pyparsing==3.2.3 +python-dateutil==2.9.0.post0 +python-json-logger==3.3.0 +pytz==2025.2 +pywin32==310 +pywinpty==2.0.15 +PyYAML==6.0.2 +pyzmq==26.4.0 +referencing==0.36.2 +requests==2.32.3 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rpds-py==0.25.0 +scikit-learn==1.6.1 +scipy==1.15.3 +Send2Trash==1.8.3 +setuptools==80.7.1 +six==1.17.0 +sniffio==1.3.1 +soupsieve==2.7 +stack-data==0.6.3 +terminado==0.18.1 +threadpoolctl==3.6.0 +tinycss2==1.4.0 +tornado==6.4.2 +tqdm==4.67.1 +traitlets==5.14.3 +types-python-dateutil==2.9.0.20241206 +typing_extensions==4.13.2 +tzdata==2025.2 +uri-template==1.3.0 +urllib3==2.4.0 +wcwidth==0.2.13 +webcolors==24.11.1 +webencodings==0.5.1 +websocket-client==1.8.0 +xmltodict==0.14.2 diff --git a/week4_scikit_learn.ipynb.ipynb b/week4_scikit_learn.ipynb.ipynb new file mode 100644 index 0000000..56b4c41 --- /dev/null +++ b/week4_scikit_learn.ipynb.ipynb @@ -0,0 +1,699 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "48253418-c2ca-4ec6-8857-8ccc78243702", + "metadata": {}, + "source": [ + "Выполнил код, данный по заданию\n", + "Выбранная тема Working with text documents - Classification of text documents using sparce features" + ] + }, + { + "cell_type": "markdown", + "id": "96adf98b-3743-41ec-9917-83edf57535e0", + "metadata": {}, + "source": [ + "Целью работы является изучение и практическая реализация методов представления текстовой информации в виде разреженных векторов и применение алгоритмов машинного обучения для автоматической классификации документов по тематическим категориям. Задача включает освоение обработки текстов, извлечение значимых признаков (например, с помощью TF-IDF), обучение модели классификации, а также оценку её качества и интерпретацию результатов." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9f445cd7-8127-4bad-a7ff-7f5ea7b7d94f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 8\n", + " 1 0.00 0.00 0.00 8\n", + " 2 0.20 0.14 0.17 14\n", + "\n", + " accuracy 0.33 30\n", + " macro avg 0.40 0.38 0.39 30\n", + "weighted avg 0.36 0.33 0.34 30\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\Files\\kobyla4\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# Загрузка и разбиение данных\n", + "X, y = load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", + "\n", + "# Модель MLP — многослойный перцептрон\n", + "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=500)\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# Отчёт о точности\n", + "print(classification_report(y_test, clf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "id": "42057cc2-b54e-4d6d-8ecb-2357d04e44f2", + "metadata": {}, + "source": [ + "Подготовка и загрузка данных из встроенного датасета" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ea600e66-f653-4cb4-8c2e-edbce41d347e", + "metadata": {}, + "outputs": [], + "source": [ + "from time import time\n", + "\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "categories = [\n", + " \"alt.atheism\",\n", + " \"talk.religion.misc\",\n", + " \"comp.graphics\",\n", + " \"sci.space\",\n", + "]\n", + "\n", + "\n", + "def size_mb(docs):\n", + " return sum(len(s.encode(\"utf-8\")) for s in docs) / 1e6\n", + "\n", + "\n", + "def load_dataset(verbose=False, remove=()):\n", + " \"\"\"Load and vectorize the 20 newsgroups dataset.\"\"\"\n", + "\n", + " data_train = fetch_20newsgroups(\n", + " subset=\"train\",\n", + " categories=categories,\n", + " shuffle=True,\n", + " random_state=42,\n", + " remove=remove,\n", + " )\n", + "\n", + " data_test = fetch_20newsgroups(\n", + " subset=\"test\",\n", + " categories=categories,\n", + " shuffle=True,\n", + " random_state=42,\n", + " remove=remove,\n", + " )\n", + "\n", + " # order of labels in `target_names` can be different from `categories`\n", + " target_names = data_train.target_names\n", + "\n", + " # split target in a training set and a test set\n", + " y_train, y_test = data_train.target, data_test.target\n", + "\n", + " # Extracting features from the training data using a sparse vectorizer\n", + " t0 = time()\n", + " vectorizer = TfidfVectorizer(\n", + " sublinear_tf=True, max_df=0.5, min_df=5, stop_words=\"english\"\n", + " )\n", + " X_train = vectorizer.fit_transform(data_train.data)\n", + " duration_train = time() - t0\n", + "\n", + " # Extracting features from the test data using the same vectorizer\n", + " t0 = time()\n", + " X_test = vectorizer.transform(data_test.data)\n", + " duration_test = time() - t0\n", + "\n", + " feature_names = vectorizer.get_feature_names_out()\n", + "\n", + " if verbose:\n", + " # compute size of loaded data\n", + " data_train_size_mb = size_mb(data_train.data)\n", + " data_test_size_mb = size_mb(data_test.data)\n", + "\n", + " print(\n", + " f\"{len(data_train.data)} documents - \"\n", + " f\"{data_train_size_mb:.2f}MB (training set)\"\n", + " )\n", + " print(f\"{len(data_test.data)} documents - {data_test_size_mb:.2f}MB (test set)\")\n", + " print(f\"{len(target_names)} categories\")\n", + " print(\n", + " f\"vectorize training done in {duration_train:.3f}s \"\n", + " f\"at {data_train_size_mb / duration_train:.3f}MB/s\"\n", + " )\n", + " print(f\"n_samples: {X_train.shape[0]}, n_features: {X_train.shape[1]}\")\n", + " print(\n", + " f\"vectorize testing done in {duration_test:.3f}s \"\n", + " f\"at {data_test_size_mb / duration_test:.3f}MB/s\"\n", + " )\n", + " print(f\"n_samples: {X_test.shape[0]}, n_features: {X_test.shape[1]}\")\n", + "\n", + " return X_train, X_test, y_train, y_test, feature_names, target_names" + ] + }, + { + "cell_type": "markdown", + "id": "4f5eae3f-4e01-42b3-a226-01b4d1ea30e5", + "metadata": {}, + "source": [ + "Используем функцию загрузки данных\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8629b553-bbc7-4fc0-b963-60342d327179", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2034 documents - 3.98MB (training set)\n", + "1353 documents - 2.87MB (test set)\n", + "4 categories\n", + "vectorize training done in 0.205s at 19.390MB/s\n", + "n_samples: 2034, n_features: 7831\n", + "vectorize testing done in 0.137s at 20.911MB/s\n", + "n_samples: 1353, n_features: 7831\n" + ] + } + ], + "source": [ + "X_train, X_test, y_train, y_test, feature_names, target_names = load_dataset(\n", + " verbose=True\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4ef4d79c-6865-4c69-9b73-0d400bcd9b9c", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import RidgeClassifier\n", + "\n", + "clf = RidgeClassifier(tol=1e-2, solver=\"sparse_cg\")\n", + "clf.fit(X_train, y_train)\n", + "pred = clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "36a43b0d-e01f-4343-ba76-c73fa1c068ae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ConfusionMatrixDisplay.from_predictions(y_test, pred, ax=ax)\n", + "ax.xaxis.set_ticklabels(target_names)\n", + "ax.yaxis.set_ticklabels(target_names)\n", + "_ = ax.set_title(\n", + " f\"Confusion Matrix for {clf.__class__.__name__}\\non the original documents\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2b64ec4e-0460-4baa-8d8a-e75ae03f91cf", + "metadata": {}, + "source": [ + "Построили матрицу 4х4 для того чтобы посмотреть есть ли закономерности\n" + ] + }, + { + "cell_type": "markdown", + "id": "e88d94b0-9209-4427-bc15-77c6ea5c044b", + "metadata": {}, + "source": [ + "Визиализируем ключевые слова , которые оказывают наибольшее влияние на решения классификатора" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6315e939-c861-40e1-b370-73e708de2c78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 keywords per class:\n", + " alt.atheism comp.graphics sci.space talk.religion.misc\n", + "0 keith graphics space christian\n", + "1 god university nasa com\n", + "2 atheists thanks orbit god\n", + "3 people does moon morality\n", + "4 caltech image access people\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "def plot_feature_effects():\n", + " # learned coefficients weighted by frequency of appearance\n", + " average_feature_effects = clf.coef_ * np.asarray(X_train.mean(axis=0)).ravel()\n", + "\n", + " for i, label in enumerate(target_names):\n", + " top5 = np.argsort(average_feature_effects[i])[-5:][::-1]\n", + " if i == 0:\n", + " top = pd.DataFrame(feature_names[top5], columns=[label])\n", + " top_indices = top5\n", + " else:\n", + " top[label] = feature_names[top5]\n", + " top_indices = np.concatenate((top_indices, top5), axis=None)\n", + " top_indices = np.unique(top_indices)\n", + " predictive_words = feature_names[top_indices]\n", + "\n", + " # plot feature effects\n", + " bar_size = 0.25\n", + " padding = 0.75\n", + " y_locs = np.arange(len(top_indices)) * (4 * bar_size + padding)\n", + "\n", + " fig, ax = plt.subplots(figsize=(10, 8))\n", + " for i, label in enumerate(target_names):\n", + " ax.barh(\n", + " y_locs + (i - 2) * bar_size,\n", + " average_feature_effects[i, top_indices],\n", + " height=bar_size,\n", + " label=label,\n", + " )\n", + " ax.set(\n", + " yticks=y_locs,\n", + " yticklabels=predictive_words,\n", + " ylim=[\n", + " 0 - 4 * bar_size,\n", + " len(top_indices) * (4 * bar_size + padding) - 4 * bar_size,\n", + " ],\n", + " )\n", + " ax.legend(loc=\"lower right\")\n", + "\n", + " print(\"top 5 keywords per class:\")\n", + " print(top)\n", + "\n", + " return ax\n", + "\n", + "\n", + "_ = plot_feature_effects().set_title(\"Average feature effect on the original data\")" + ] + }, + { + "cell_type": "markdown", + "id": "1110ba70-07ce-46bd-b5a3-09e6b3a928e2", + "metadata": {}, + "source": [ + "Модель с очисткой метаданных" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a64afaf2-c5d9-4f38-b241-dc1ce8f2a255", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(\n", + " X_train,\n", + " X_test,\n", + " y_train,\n", + " y_test,\n", + " feature_names,\n", + " target_names,\n", + ") = load_dataset(remove=(\"headers\", \"footers\", \"quotes\"))\n", + "\n", + "clf = RidgeClassifier(tol=1e-2, solver=\"sparse_cg\")\n", + "clf.fit(X_train, y_train)\n", + "pred = clf.predict(X_test)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ConfusionMatrixDisplay.from_predictions(y_test, pred, ax=ax)\n", + "ax.xaxis.set_ticklabels(target_names)\n", + "ax.yaxis.set_ticklabels(target_names)\n", + "_ = ax.set_title(\n", + " f\"Confusion Matrix for {clf.__class__.__name__}\\non filtered documents\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f5a53eee-10fc-49fc-b0ba-b3e3d5ff6bbd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 keywords per class:\n", + " alt.atheism comp.graphics sci.space talk.religion.misc\n", + "0 don graphics space god\n", + "1 people file like christian\n", + "2 say thanks nasa jesus\n", + "3 religion image orbit christians\n", + "4 post does launch wrong\n" + ] + }, + { + "data": { + "image/png": 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XbfXnTqdZOlfP7NOD7fvSx66/H7TjZFzB3/6zFfPnSl9f/aNVUkro74vESOr3O4DYseYMQIrQNvQaGLRls3YFi3nRrob6AcjeMlunCOn1+pdY7S6nXdmcpzQqXZuia1D0BMSxHU/XEt2P/sVYP3Q4/+VaO43F7CZo/0u+VtecTZky5Z796RQfDX2xfbjXaY8PQh+rjlFb4sekz439A5/9L+DOf/HWD38xx620w11s0xztIcZ5jZAeO65pmnGN90FfG10Xo2PQduX6YT+pnsOE7lffe1qt1feetgiPyf7c6vOnYn7Y1qlgMSsO9g/s8Z1GQAOJTmHUjnjO9//yyy/N6xRbZ1BX0Mcd8zHbX/MtW7aYP3TEpNvr+zQ++ocbnRKnFfPYworz6964cWNTldGT2DvfnpBzJuofffT1mDVrVrT3v65ptbfzdz6Ovve1y6Eznb6nvzfsXTH1/aLvG61Wx6xOO7+W+v6LufZOf67iqpw9qIT+vkiMpH6/A4gdlTMAKUJDl4YvnUIYG/2Lq/2E1PYQpv9q+NGW5zpFLma7b21pr9Mdu3fvbha461+J9QOJ/lVbr9cPibE1dHCmH3h16p22JNc1EroWQyt7On3Kee2OfrDX0KXtwLUyZW+lr23hY/5VecyYMWY8uiZGmznoOhSdKqdTC/Wv2Q8ybU6nKmprbG37rWvjdPqnVhj0r/+6+F9PB6BhVtt461/ndUqlNjHQcWm4jW16kj4mrUxqy31tQ6/Tj7QJhrbO1senLdB1rNriXP8Cfr8P10n12uiH3C+++MJ88NWxaLMRXWekYU/3pVUtDU6JlZj9aotybbeuz7v9VAAaGPS51tMDaBME/QCqYVjP46Uf8rXyqg1PtGqgYVjbvuuHcX3fa0jV/euH/bjo+1+fc127o+9H/VnRSo7uS18f5+YfrqTvG23vr+s09edEQ5U+bm0vrz/n+gcYext2DeF6CgWd9qZ/9NBGP/HRnz2tlOvPu/7saDVNT02hoU9P+6DVWKVTJ/V9rc/Tm2++6Wilr5WshKy5058j/dnXY+kUPH2f6+8afV84B3f9edA1slq11/HrtGp9X+i5BLVxi/0PGfo86DYahrSxhp7eQN8PegoEXbNmP2WBVpH0Z0J/l+gUUH08+rNwv+cluX5fJEZSv98BxCGOLo4AkKSaNWtmS58+ve3atWtxbqNt6dOmTetoQa8tpAsVKhRrK2vntvcffPCBae+trZ+zZctmq1Klim3EiBG2iIgIx3a6jx49esS6jy+//NK06db7ly5d2rSx1nbXMX9F6th1H9mzZ7dlzpzZ1rx5c9uhQ4fMdmPGjIm27blz58y2On59TNriXNuET5s27YHbnCu9vz6+DBky2LJkyWLajg8cOND2999/O7bRNvWPPfaY2SZ//vzmdvvpCZzbYF+9etX28ssv27JmzXpPC/Fjx47ZGjRoYJ6TPHny2N5++21z+oPYWunHNdaEvjZx2bNnj61ly5a2HDlymPvr+Fq3bm1bs2bNA7XST8x+1cmTJ01L/Vy5cpnt9DQO+ppqy3q76dOnm+u1Vbz9udm9e7etTZs2tsKFC5v75c6d29a0aVNzGomE0Nb5+j7U940+96+//rrt0qVL0baJ63nXduyxnR4hKVvpnz171pyGQd9/eptzC/crV67YgoODbcWLFzenBciZM6dpgz9u3DjzfnBupT927NhYx6bvPX3e9WdGn4MCBQqY5+/777+Ptt3+/fvNsfX3im6jrfz1ZzkhrfTVwoULbYGBgeY1KlOmjG3RokWxPn/6mN566y3zs6Tj0d8VOnbnFvd2erqDSpUqOd7vOj77aUOUttkfNGiQeV701BbaKl/b8cfVSj/m+9f+e+mff/6Jdr3eV09j8CC/L/TY+nrGpGOPecqE5Hi/A4jOR/8TV3ADAMRP/ypdqVIlmTt3boLaeAMAAMSFNWcAkEC6Viomneao0+Wcm28AAAA8CNacAUACffjhh7Jr1y6zBkXPxaZt/vWia5Liaw0PAACQEExrBIAE0m5u2qxBO7pp0wA9cas2vtBGABrWAAAAHgbhDAAAAAAsgDVnAAAAAGABhDMAAAAAsAAWSSSTu3fvyt9//y1ZsmSJdnJaAAAAAN7FZrOZk7Triem1y3NcCGfJRIMZ3dsAAAAA2J0+fVoKFiwocSGcJROtmNlfAD8/P1cPBwAAAICLREZGmsKNPSPEhXCWTOxTGTWYEc4AAAAA+NxnuRMNQQAAAADAAghnAAAAAGABhDMAAAAAsADCGQAAAABYAA1BAHi0sNKB4s0CD4a5eggAACCBqJwBAAAAgAUQzgAAAADAAjwmnN25c0fu3r3r6mEAAAAAgPuvOfvxxx/l1VdflYsXL0rq1Kll7969UqlSJRk0aJCMGTPGbNO5c2e5ceOGNGjQQPr06SOzZ8+WwYMHy+HDh+Xo0aPi7+8vb775pixbtkyioqKkbt26MnnyZClRooS5/8yZM839vv32W/Pv6dOnpXbt2jJjxgzJly+f2eb27dvSt29fs28dhx7z7NmzEhERIYsXL3bpcwSklKBZQeIJQllzBQAA3ISlKmePP/64XLlyRfbs2WO+X79+veTMmVPWrVvn2Eavq1evnvn6+vXr8sEHH8gXX3whf/zxh+TOnVs6dOggO3fulKVLl8qWLVvEZrNJ48aN5datW4596P3GjRsnc+bMkQ0bNsipU6ekf//+jtt1n/PmzTOBbdOmTRIZGXnfUKZBULdzvgAAAACAW4YzrXpVrFjREcb037feesuEtatXr8pff/1lqmNaDVMauD755BOpWbOmlCpVytyuoUzDmga9ChUqmJCl1zuHK73fZ599JlWrVpXKlStLz549Zc2aNY7bp0yZIsHBwdKiRQspXbq0TJ06VbJmzRrv2EePHm3Gb78UKlQo2Z4nAAAAAJ7HUuFMafDSUKYVr40bN0rLli0lMDBQfvvtN1M1y58/v2OKYrp06aR8+fKO+4aFhUmaNGmkevXqjuty5MhhgpveZpcxY0YpVqyY43udznj+/HnztU5dPHfunFSrVs1xu05trFKlSrzj1jCn97VfdLokAAAAALjlmjOlUxa/+uor2bdvn6RNm9ZUrvQ6DWyXLl1yVM1UhgwZxMfHJ9HH0P06031oGHwYvr6+5gIAAAAAHhHO7OvOJkyY4AhiGs60IYiGs379+sV5X62waTOPbdu2mamOSpuLHDp0SMqUKZOg4+uUxDx58siOHTukTp06jk6Qu3fvNlMuAW8R2j5ULCnE39UjAAAA8I5pjdmyZTNTFXWtmL3xh4YkDUfakdG5chaTTnd87rnnpEuXLmYapFbftPtjgQIFzPUJ1atXL7OGbMmSJSbYafdHDYYPUqUDAAAAALcMZ0oDmFar7OEse/bspvKVN29es34sPtphUdeHNW3aVGrUqGGmK65YseKeqYzx0db9bdq0kXbt2pl9ZM6cWRo2bCjp06d/6McGAAAAALHxsT3sYisvoCe31imTrVu3lnfffTdB99FW+jpFUpuD+Pn5JfsYAa+R2GmNIRHJNRIAAIAkzQaWW3NmBSdPnpRVq1aZCp6ev0xb6Z84cUJefvllVw8NAGELAAB4KEtOa3S1VKlSycyZM+XRRx+VWrVqSWhoqPzyyy+megYAAAAAyYHKWSz0BNKbNm1y9TAAAAAAeBEqZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFpHH1AADELqx0oKuH4BECD4a5eggAAADuVzlbt26d+Pj4yOXLl5P1OOHh4eY4e/fuTdbjAAAAAIBbhLN69epJnz59XDkEAAAAALAES1XOAAAAAMBbuWzNWYcOHWT9+vXmMmnSJHPdjBkzzL+7du2SQYMGyYEDB6RixYrm+lKlSpnbjh07Jn379pWtW7fKtWvXJDAwUEaPHi0NGjRw7DsgIEC6du0qR48elQULFki2bNnknXfeMdfF5s6dO9KlSxfZvHmzrFq1SgoVKiQjRoyQr776Ss6dOyc5cuSQF154QSZPnpwizw2gWCuVNIJmBSXLfkPbhybLfgEAgPdyWeVMA1mNGjVMKDpz5oy5aChSQ4YMkfHjx8vOnTslTZo08tprrznud/XqVWncuLGsWbNG9uzZI40aNZJmzZrJqVOnou1f71+1alWzzRtvvCGvv/66HDp06J5xREVFSatWrcz6s40bN0rhwoVl4cKFMmHCBPn888/lyJEjsnjxYgkKiv8Dnu4nMjIy2gUAAAAALB/O/P39JV26dJIxY0bJmzevuaROndrc9t5770ndunWlTJkyMnjwYFPRunHjhrmtQoUK0q1bNylXrpyUKFFC3n33XSlWrJgsXbo02v41wGkoK168uKnC5cyZU9auXRttGw16TZo0kX/++cfclitXLnO9Bj0dj1bjNKxVq1bNhMj4aPVOH5P9Yg+aAAAAAOC2a87Kly/v+Dpfvnzm3/PnzzsCVf/+/c10xqxZs0rmzJklLCzsnsqZ8z60M6OGLfs+7Nq0aWOmRupURg1UdlpJ+++//6Ro0aImlP3www9y+/bteMccHBwsERERjsvp06cf8lkAAAAA4E0seZ6ztGnTRgtW6u7du+ZfDWarV6+WcePGmapYhgwZzHqwmzdvxrkP+37s+3Curs2dO1e2bNkiTz75pON6rXrpFMhffvnFHEsrcGPHjjXr42Lu187X19dcAFhL6Inof7hJMiH//wcdJIOQCFePAAAA7wpnOq1Rm3EkxqZNm0wzkRYtWjgqaXresgeh69B0euSzzz4ry5cvN1Mp7TT06Vo2vfTo0UNKly4toaGhUrly5Qc6FgAAAABYNpxpV8Vt27aZcKXTE2NWtmKj68wWLVpkQpNWw4YOHZqg+8WlV69eJiA2bdpUfvrpJ6ldu7bMnDnTXFe9enWzJk6raxrWihQp8sDHAQAAAADLrjnTKYraBEQbf2gzjpjrxmLz0Ucfmdb4NWvWNAGtYcOGD13N0hNha+t8neaozUd0Ldv06dOlVq1aZu2aTm9ctmyZaakPAAAAAMnBx2az2ZJlz15OW+lrkxFtDuLn5+fq4QDei7Vh7ok1ZwAAL8wGlmwIAsAaAgYvF3cXPoYP+QAAwD1YspU+AAAAAHgbwhkAAAAAWADhDAAAAAAsgDVnAOIUPqaJq4cAAADgNaicAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALCANK4eALxTWOlAVw8BXiLwYJirhwAAAJAgVM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWIBlG4J8//33MmLECDl69KhkzJhRKlWqJEuWLJEePXrI5cuXzfdTp06VqKgoefnll2Xy5MmSLl06c9+ff/5ZRo0aJb///rukTp1aatSoIZMmTZJixYo59v/nn3/KgAEDZOXKlWYfgYGB8vHHH0v16tXN7XosPf6BAwckf/780r59exkyZIikSWPZp8yt0KQBKSVoVpCrhwAAAFwgtH2ouBtLJo0zZ85ImzZt5MMPP5QWLVrIlStXZOPGjWKz2czta9askfTp08u6deskPDxcOnbsKDly5JD33nvP3H7t2jXp27evlC9fXq5evSrDhg0z+9m7d6+kSpXKXFe3bl0pUKCALF26VPLmzSu7d++Wu3fvmvvrsdq1a2cC3+OPPy7Hjh2Trl27mtuGDx8e65g14OnFLjIyMgWeKQAAAACewsdmTzwWokGpSpUqJngVKVIk2m0dOnSQZcuWyenTp01FTX322WemChYREWHCV0wXLlyQXLlySWhoqJQrV06mTZsm/fv3N/vPnj37Pds3aNBA6tevL8HBwY7r5s6dKwMHDpS///471jGHhISYSltMOiY/P78Heh4APDwqZwAAeKdQC1XOtHDj7+9/32xgyTVnFSpUMOEoKChIWrVqJdOnT5dLly5Fu90ezJROW9RqmAY2deTIEVN5K1q0qHnwAQEB5vpTp06Zf7WCptMiYwtmat++fTJy5EjJnDmz49KlSxdT0bt+/Xqs99Egp0+2/WIfCwAAAAC47bRGXSe2evVq2bx5s6xatUqmTJli1ntt27YtQfdv1qyZqbhpqNP1YjpdUStmN2/eNLdnyJAh3vtr0NMqWMuWLe+5TadTxsbX19dcLCvE39UjAFwiNCTC1UMAAABw33CmfHx8pFatWuaia8Y0bP3www+OytZ///3nCFlbt2411a1ChQrJxYsX5dChQyaY6Xox9dtvv0Xbt65F++KLL+Tff/+NtXpWuXJls4/ixYunyGMFAAAAAEtOa9QK2fvvvy87d+40UxEXLVok//zzj+moqLQC1qlTJ9NJccWKFaZJR8+ePc16s2zZspnmILquTDs9/vrrr6Y5iDOd8qhNQJo3by6bNm2S48ePy8KFC2XLli3mdg2Ds2fPNtWzP/74Q8LCwmT+/PnyzjvvuOT5AAAAAOD5LBnOdJ3Yhg0bpHHjxlKyZEkTisaPHy/PPPOMuV3Xo5UoUULq1KkjL774ojz77LOmIYfSgKZBateuXWYq41tvvSVjx46Ntn9tua/TJXPnzm2OoWvbxowZY6ZTqoYNG8qPP/5otnn00UflsccekwkTJtzTnAQAAAAAPLpbY3y0W6Oe52zx4sViZQntyJJiWHMGb8WaMwAA4CbZwLJrzuCeH1ADBi9PkeMACRXu6gEAAAC487RGAAAAAPA2blc5mzlzpquHAAAAAABJjsoZAAAAAFiA21XOYG3hY5q4eggAAACAW6JyBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMAC0rh6AAAQl7DSgQ+9j8CDYUkyFgAAgORG5QwAAAAALIBwBgAAAAAWQDgDAAAAAAvw6HD2/fffS1BQkGTIkEFy5MghDRo0kGvXrsmOHTvkqaeekpw5c4q/v7/UrVtXdu/e7bjfa6+9Jk2bNo22r1u3bknu3Lnlyy+/dMEjAQAAAODpPLYhyJkzZ6RNmzby4YcfSosWLeTKlSuyceNGsdls5uv27dvLlClTzPfjx4+Xxo0by5EjRyRLlizSuXNnqVOnjtlHvnz5zP5+/PFHuX79urz44ouufmjAPYJmBYlHCn74X1GhSTIQAACA5OfR4ez27dvSsmVLKVKkiLlOq2jqySefjLbttGnTJGvWrLJ+/XpTMatZs6aUKlVK5syZIwMHDjTbzJgxQ1q1aiWZM2eO9XhRUVHmYhcZGZmMjw4AAACAp/HYaY0VKlSQ+vXrm0CmoWr69Oly6dIlc9u5c+ekS5cuUqJECTOt0c/PT65evSqnTp1y3F+rZxrI7Nv/9NNPZrpjXEaPHm32Zb8UKlQoBR4lAAAAAE/hseEsderUsnr1ahOqypQpY6YwajXsxIkTZkrj3r17ZdKkSbJ582bzta5Ju3nzpuP+7dq1k+PHj8uWLVtk7ty58sgjj8jjjz8e5/GCg4MlIiLCcTl9+nQKPVIAAAAAnsBjpzUqHx8fqVWrlrkMGzbMTG/84YcfZNOmTfLJJ5+YdWZKg9SFCxei3VfDWvPmzU31TANax44d4z2Wr6+vuQCuENreoiurQvxdPQIAAAC34bHhbNu2bbJmzRp5+umnTZdF/f6ff/6RwMBAM51R15NVrVrVrA0bMGCA6egYk05t1DVod+7cMdU2AAAAAEguHhvOdB3Zhg0bZOLEiSaAadVMuzI+88wzkjdvXunatatUrlzZrA17//33pX///vfsQ1vva7fGsmXLSv78+V3yOAAAAAB4B48NZ1oh+/nnn2O9rVKlSuZcZ85eeOGFe7bTc6JpE5FOnTol2zgBAAAAwKPD2cO4e/euWYOmlTZtsf/ss8+6ekiAewqJcPUIJGDwclcPAcksfEwTVw8BAIAkQTiLhbbU1+6MBQsWlJkzZ0qaNDxNAAAAAJIXqSMWAQEBYrPZXD0MAAAAAF7EY89zBgAAAADuhHAGAAAAABbAtEYAHo1mEQAAwF1QOQMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAWlcPQAASE5hpQMTvG3gwbBkHQsAAIDbVs7q1asnffr0cXwfEBAgEydOTPD9w8PDxcfHR/bu3ZtMIwQAAAAAL6yc7dixQzJlypTg7QsVKiRnzpyRnDlzJuu4AAAAAMBtw9nNmzclXbp0ibpPrly5ErV96tSpJW/evIkcGQAAAAB4cDjTKYrlypWTNGnSyNy5cyUoKEimTJkiAwYMkI0bN5qK2NNPPy0TJkyIs9Kl0xp1mqN9quPBgwelc+fOsnPnTilatKhMnjxZnnrqKfnhhx+kefPmZlrjI488Inv27JGKFSua+6xfv94cc9++fZI9e3Zp3769jBo1yozLPs7y5ctL+vTp5YsvvjABsnv37hISEpJSTxUQp6BZQa4egtsJZR0ZAABwEym65mzWrFkm7GzatEnGjBkjTz75pFSqVMmEq59//lnOnTsnrVu3TtC+7ty5YwJYxowZZdu2bTJt2jQZMmRIvPf566+/pHHjxvLoo4+acPbpp5/Kl19+acJZzHFqWNT9fvjhhzJy5EhZvXr1Qz12AAAAALDMtMYSJUqYsKM0EGkwe//99x23f/XVV2ad2OHDh6VkyZLx7kvD0rFjx2TdunWOqYvvvfeeqZzF5ZNPPjH7nzp1qmkUUrp0afn7779l0KBBMmzYMEmV6n9ZVStnw4cPd4xZt1+zZk28+46KijIXu8jIyAQ/LwAAAACQopWzKlWqOL7WytXatWslc+bMjouGJaWh634OHTpkgpbzmrJq1arFe5+wsDCpUaOGCWZ2tWrVkqtXr8qff/7puE7DmbN8+fLJ+fPn49336NGjxd/f33HRsQEAAACAJStnzp0WNRA1a9ZMPvjgg3u20zDkSmnTpo32vYa5u3fvxnuf4OBg6du3b7TKGQENAAAAgOW7NVauXFkWLlxomnzYm3EkRqlSpeT06dNmnVqePHkcrfbjExgYaI5ps9kc1TNd/5YlSxYpWLCgPAxfX19zAZJTaPvQlDlQiH/KHAcAAACuPwl1jx495N9//5U2bdqYUKVTGVeuXCkdO3Y0zT7uR9d/FStWzHRb3L9/vwlZ77zzjrnNedqiszfeeMMEul69eplOj0uWLDFry7TiZV9vBgAAAACu4LJEkj9/fhOoNIhpC31tra8t8rNmzZqgoKTnMFu8eLGZHqndF7Wlvr1bo7bBj02BAgVkxYoVsn37dqlQoYJpkd+pUydHqAMAAAAAV/Gx6Rw/D6Fhr3bt2nL06FFTVXMlXXOmjUEiIiLEz8/PpWMBvHpaY0iEq0cAAAC8XGQCs4HL1pwlBT3ZtHZ51Hb3GsjefPNN033R1cEMcHseFGgCBi939RCQjMLHNHH1EAAASDJuHc6uXLlizlF26tQpyZkzpzRo0EDGjx/v6mEBAAAAgHeFs3bt2pkLAAAAALg7WhQCAAAAgAUQzgAAAADAAtx6WiMA3A8NIwAAgLugcgYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDMAAAAAsADCGQAAAABYAOEMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAtK4egAAvEdY6cAUP2bgwbAUPyYAAMCDoHIGAAAAABZAOIvHunXrxMfHRy5fvuzqoQAAAADwcIQzAAAAALAAt19zVq9ePSlXrpz5es6cOZI2bVp5/fXXZeTIkabqdenSJXnzzTdl2bJlEhUVJXXr1pXJkydLiRIlzH1OnjwpPXv2lN9++01u3rwpAQEBMnbsWClTpow88cQTZpts2bKZf9u3by8zZ8504aMFrCFoVtCD3TE45X/lhKb4EQEAALw0nKlZs2ZJp06dZPv27bJz507p2rWrFC5cWLp06SIdOnSQI0eOyNKlS8XPz08GDRokjRs3lgMHDpgg16NHDxPKNmzYIJkyZTLXZ86cWQoVKiQLFy6U559/Xg4dOmTumyFDhjjHoMFPL3aRkZEp9OgBAAAAeAKPCGcapCZMmGAqZaVKlZLQ0FDzvVbVNJRt2rRJatasabadN2+e2X7x4sXSqlUrOXXqlAlgQUH/qwQULVrUsd/s2bObf3Pnzi1Zs2aNdwyjR4+WESNGJOvjBAAAAOC5PGLN2WOPPWaCmV2NGjVMtUyrYGnSpJHq1as7bsuRI4cJcGFh/2uv3bt3bxk1apTUqlVLhg8fLvv373+gMQQHB0tERITjcvr06SR4ZAAAAAC8hUeEs4fRuXNnOX78uLRt29ZU3KpWrSpTpkxJ9H58fX3N1EfnCwAAAAB41bTGbdu2Rft+69atpuGHNvW4ffu2ud0+rfHixYtmDZneZqfTHLt3724uWgGbPn269OrVS9KlS2duv3PnTgo/IsDaQts/YJuNEP+kHgoAAIDH8IjKma4b69u3rwld33zzjal8aYdGDWjPPfecaQyi3Rj37dsnr776qhQoUMBcr/r06SMrV66UEydOyO7du2Xt2rUSGBhobitSpIiZLvnjjz/KP//8I1evXnXxIwUAAADgqTwinLVr107+++8/qVatmum+qMFMOzaqGTNmSJUqVaRp06ZmLZrNZpMVK1aYTo32qpjeRwNZo0aNpGTJkvLJJ5+Y2zTEaZOPwYMHS548eUzLfQAAAABIDj42TStuTDsyVqxYUSZOnChWoq30/f39TXMQ1p8BLpzWGBKR8scEAAB4gGzgEWvOALgJFwSlgMHLU/yYSJjwMU1cPQQAACzFI6Y1AgAAAIC7c/vK2bp161w9BAAAAAB4aFTOAAAAAMAC3L5yBgDxYV0TAABwF1TOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALCCNqwcAAMkprHSgS44beDDMJccFAADui8qZiPj4+MjixYtdPQwAAAAAXoxwBgAAAAAWQDgDAAAAAG9fc1avXj0pV66c+XrOnDmSNm1aef3112XkyJFmqmFUVJQMGTJEvvnmG7l8+bLZ9oMPPjD3s1u4cKEMGzZMjh49Kvny5ZNevXpJv379HLcHBARIp06d5MCBA7J06VLJmjWrvP3229KjR484x3X69Gmzj1WrVkmqVKnk8ccfl0mTJpl9AUi4oFlBrh6ChLL2CwAAuAmXV85mzZoladKkke3bt5sA9NFHH8kXX3xhbuvZs6ds2bJF5s+fL/v375dWrVpJo0aN5MiRI+b2Xbt2SevWreWll16S0NBQCQkJkaFDh8rMmTOjHWPs2LFSoUIF2bNnjwwePFjefPNNWb16dazjuXXrljRs2FCyZMkiGzdulE2bNknmzJnNcW/evBnn49AgGRkZGe0CAAAAAAnlY7PZbOIiWgE7f/68/PHHH6ZSpjQ8aYXr559/lqJFi8qpU6ckf/78jvs0aNBAqlWrJu+//7688sor8s8//5gKl93AgQNl+fLlZp9Kq12BgYHy008/ObbRMKfhacWKFeZ7PfYPP/wgzZs3l7lz58qoUaMkLCzMMSYNZVpx06YhTz/9dKyPRYPhiBEj7rk+IiJC/Pz8kuw5A9yJJSpn7UNdPQQAAODlIiMjxd/f/77ZwOWVs8cee8wRglSNGjVMZUwrYXfu3JGSJUuaypX9sn79ejl27JjZVgNUrVq1ou1Pv9f7632d9+lMv9f7xmbfvn1miqRWzuzHzJ49u9y4ccNx3NgEBwebJ9t+0amRAAAAAOD25zm7evWqpE6d2kxd1H+daWBKzuNWqVJF5s2bd89tuXLlivN+vr6+5gI3FOLv6hF4LGpWAAAAbhTOtm3bFu37rVu3SokSJaRSpUqm+qXTHrUhR2x0uqKuCXOm32u1zTnQ6T5jHkPvG5vKlSvLt99+K7lz52Y6IgAAAIAU4/JpjbqmrG/fvnLo0CHTlXHKlCmmYYcGLF1T1q5dO1m0aJGcOHHCNA0ZPXq0WVOmtKPimjVr5N1335XDhw+b5iJTp06V/v373xPYPvzwQ7PNxx9/LAsWLDDHiI0eM2fOnPLcc8+ZhiB63HXr1knv3r3lzz//TJHnBAAAAID3cXnlTMPXf//9Z5p8aLVLQ1PXrl3NbTNmzDDNOTSE/fXXXyY06Rq1pk2bOqpc3333nWmlrwFNW+lrG/4OHTpEO4bef+fOnaZhh1bDtCOkdmSMTcaMGWXDhg0yaNAgadmypVy5ckUKFCgg9evXp5IGAAAAwHO7NVasWFEmTpyYbMfQbo19+vQxFyt2ZIEFsObMs4VEuHoEAADAy0UmMBu4vHIGuBwf3j1awOD/TYOGdwkf08TVQwAAwP3WnAEAAAAAXFw500YbyS08PDzZjwEAAAAAD4vKGQAAAABYAGvOAHg01h4BAAB3QeUMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDMAAAAAsADCGQAAAABYAOEMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAWkcfUAAHinsNKBKXKcwINhKXIcAACAh0XlDAAAAAAswKvCWUhIiFSsWDHebTp06CDNmzdPsTEBAAAAgGJaYwyTJk0Sm83m+L5evXom0E2cONGl4wIAAADg2bwinGnYunPnToK29ff3T/bxAAAAAIDHhLOoqCgZMGCAzJ8/XyIjI6Vq1aoyYcIEefTRR2XdunXyxBNPyIoVK+Sdd96R0NBQWbVqleO+n3/+uYwaNUouXrwoTZs2lenTpztCmU5rvHz5sixevNh8vX79enPRipo6ceKEBAQEuOxxA54ipRp1BM0KSpHj4H9C24e6eggAALgtt11zNnDgQFm4cKHMmjVLdu/eLcWLF5eGDRvKv//+69hm8ODBMmbMGAkLC5Py5cub644ePSrfffedLFu2TH7++WfZs2ePvPHGG7EeQwNZjRo1pEuXLnLmzBlzKVSoUJxhUUOi8wUAAAAAPDqcXbt2TT799FMZO3asPPPMM1KmTBlT/cqQIYN8+eWXju1GjhwpTz31lBQrVkyyZ89urrtx44bMnj3brCOrU6eOTJkyxVTfzp49e89xtJqWLl06yZgxo+TNm9dcUqdOHeuYRo8ebba3X+IKcQAAAADgMeHs2LFjcuvWLalVq5bjurRp00q1atVMlcxOpzrGVLhwYSlQoIDje62M3b17Vw4dOvRQYwoODpaIiAjH5fTp0w+1PwAAAADexW3XnCVEpkyZUuxYvr6+5gJ4vBD3apoTGhLh6iEAAAB4buVMpynqdMNNmzY5rtNK2o4dO8wUx/icOnVK/v77b8f3W7dulVSpUkmpUqVi3V6Pk9BOjwAAAADgVeFMK2Kvv/666daoTT0OHDhgmnZcv35dOnXqFO9906dPL+3bt5d9+/bJxo0bpXfv3tK6dWuzniw22plx27ZtEh4eLhcuXDBTIAEAAAAgqbnttEbtwqhBqW3btnLlyhWzvmzlypWSLVu2eO+nXR1btmwpjRs3Np0dtZX+J598Euf2/fv3N2FOK3L//fcfrfQBAAAAJAsfm56hGUlOW+lr10ZtDuLn5+fq4QBeu+ZMWHMGAADcJBu4beUMgGeGnYDBy5N0f+FJujcAAIDk45ZrzgAAAADA0xDOAAAAAMACCGcAAAAAYAGsOQNgKeFjmrh6CAAAAC5B5QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABaRx9QAAeJ6w0oFiFYEHw1w9BAAAgAShcgYAAAAAFkA4AwAAAAALIJwBAAAAgAW4ZTirV6+e9O7dWwYOHCjZs2eXvHnzSkhIiOP2jz76SIKCgiRTpkxSqFAheeONN+Tq1auO20+ePCnNmjWTbNmymW3Kli0rK1asMLfduXNHOnXqJI888ohkyJBBSpUqJZMmTXLJ4wQAAADgPdy2IcisWbOkb9++sm3bNtmyZYt06NBBatWqJU899ZSkSpVKJk+ebALW8ePHTTjTIPfJJ5+Y+/bo0UNu3rwpGzZsMOHswIEDkjlzZnPb3bt3pWDBgrJgwQLJkSOHbN68Wbp27Sr58uWT1q1bu/hRA+7BSk04gmYFuXoISKTQ9qGuHgIAAC7hY7PZbOKGlTOtcG3cuNFxXbVq1eTJJ5+UMWPG3LP9999/L927d5cLFy6Y78uXLy/PP/+8DB8+PEHH69mzp5w9e9bsJy5RUVHmYhcZGWmqdhEREeLn55fIRwggqRDO3A/hDADgaTQb+Pv73zcbuOW0RnvAcqaVrfPnz5uvf/nlF6lfv74UKFBAsmTJIm3btpWLFy/K9evXze06JXLUqFGm0qYBbf/+/dH29fHHH0uVKlUkV65cpqI2bdo0OXXqVLzjGT16tHnC7RcNZgAAAACQUG4bztKmTRvtex8fHzMlMTw8XJo2bWrC28KFC2XXrl0mbCmdyqg6d+5spjtqaAsNDZWqVavKlClTzG3z58+X/v37m3Vnq1atkr1790rHjh0d941LcHCwScL2y+nTp5PtsQMAAADwPG675iwuGsY0pI0fP96sPVPffffdPdtpZUunOupFg9X06dOlV69esmnTJqlZs6ZZp2Z37Nix+x7X19fXXACvEeIv7iA0JMLVQwAAAPDsyllcihcvLrdu3TKVMK2OzZkzRz777LNo2/Tp00dWrlwpJ06ckN27d8vatWslMDDQ3FaiRAnZuXOnuf3w4cMydOhQ2bFjh4seDQAAAABv4XHhrEKFCqaV/gcffCDlypWTefPmmfVgzrSZiHZs1EDWqFEjKVmypKOTY7du3aRly5by4osvSvXq1c1aNecqGgAAAAAkB7fs1uhJHVkAt+Um0xqFaY0AAMBNsoHHrTkDYM3QEzB4ubhCuEuOCgAAkHgeN60RAAAAANwR4QwAAAAALIBwBgAAAAAWQDgDAAAAAAugIQiAFBE+pomrhwAAAGBpVM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWEAaVw8AQMoJKx0o3ibwYJirhwAAAODdlbN69epJnz59zNcBAQEyceJEx20+Pj6yePFiF44OAAAAALywcrZjxw7JlCmTq4cBAAAAAN4dznLlyuXqIQAAAABAvLwinOm0Rp3iaJ/mGNPw4cNl2rRpsnLlSilfvrz89ttvEhwcLDt37pScOXNKixYtZPTo0VTf4Pa8cf1V0KwgVw8BFhHaPtTVQwAAwDvXnCWEzWaTXr16yezZs2Xjxo0mmB07dkwaNWokzz//vOzfv1++/fZbE9Z69uzp6uECAAAA8GBeUTmLze3bt+XVV1+VPXv2mPBVoEABc71WyF555RVHla1EiRIyefJkqVu3rnz66aeSPn36WPcXFRVlLnaRkZEp9EgAAAAAeAKvDWdvvfWW+Pr6ytatW83URbt9+/aZitm8efOiVdju3r0rJ06ckMDA2FuRa6gbMWJEiowdAAAAgOfx2mmNTz31lPz1119mnZmzq1evSrdu3WTv3r2Oiwa2I0eOSLFixeLcn65Ri4iIcFxOnz6dAo8CAAAAgKfw2srZs88+K82aNZOXX35ZUqdOLS+99JK5vnLlynLgwAEpXrx4ovanVTi9AEgCIf5JtqvQkIgk2xcAAEBy8trKmdIujHPmzJGOHTvK999/b64bNGiQbN682TQA0aqZVsyWLFlCQxAAAAAAycprK2d2L7zwgllP1rZtW0mVKpW0bNlS1q9fL0OGDJHHH3/crDfT6Ywvvviiq4cKAAAAwIP52DR9IMlpt0Z/f3+z/szPz8/VwwG8dlqjMK0RAAC4STbw+soZgLgFDF7uoiN/nWR7Ck+yPQEAACQvr15zBgAAAABWQTgDAAAAAAsgnAEAAACABRDOAAAAAMACaAgCIE7hY5q4eggAAABeg8oZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAtI4+oBAEBcwkoHPvQ+Ag+GJclYAAAAPL5yVq9ePenTp49YRXh4uPj4+MjevXtdPRQAAAAAXsTl4QwAAAAAQDgDAAAAAEuw1JqzOXPmyKRJk+TQoUOSKVMmefLJJ2XixImSO3duc/vMmTPNFMjLly877rN48WJp0aKF2Gw2831ISIi5rl+/fjJ06FC5dOmSPPPMMzJ9+nTJkiWL2ebu3bsybtw4mTZtmpw+fVry5Mkj3bp1kyFDhjj2e/z4cXnrrbdk27ZtUqJECfnss8+kRo0aKf6cAN4sKdaLBc0KSpKxeJLQ9qGuHgIAALB65ezWrVvy7rvvyr59+0zA0vVfHTp0SPR+jh07Zu7/448/msv69etlzJgxjtuDg4PN9xreDhw4IF9//bUJaM40qPXv39+sPStZsqS0adNGbt++Hecxo6KiJDIyMtoFAAAAANyycvbaa685vi5atKhMnjxZHn30Ubl69apkzpw5wfvRyphW2eyVsrZt28qaNWvkvffekytXrpjq3NSpU6V9+/bm9mLFiknt2rWj7UODWZMmTczXI0aMkLJly8rRo0eldOnSsR5z9OjRZjsAAAAAcPvK2a5du6RZs2ZSuHBhE6zq1q1rrj916lSi9hMQEOAIZipfvnxy/vx583VYWJipctWvXz/efZQvXz7a/ZV9H7HRalxERITjotMlAQAAAMDtKmfXrl2Thg0bmsu8efMkV65cJpTp9zdv3jTbpEqVyrG2zHkqZExp06aN9r22xtdqmsqQIUOCxuO8D72/su8jNr6+vuYCAAAAAG4dzg4ePCgXL140a8EKFSpkrtu5c2e0bTSw6bREDXLaMEQl9nxk2txDA5pOc+zcuXMSPgIA8Qrxd8lhQ0MiXHJcAAAAt53WqFMZ06VLJ1OmTDGdEpcuXWqagzirXr26ZMyYUd5++23T9EMbeejassRInz69DBo0SAYOHCizZ882+9m6dat8+eWXSfyIAAAAAMANw5lWxTRoLViwQMqUKWMqaNru3ln27Nll7ty5smLFCgkKCpJvvvnGtM5PLO3SqK32hw0bJoGBgfLiiy/Gu54MAAAAAJKbjy3mIi4kCW2l7+/vb5qD+Pn5uXo4gNdOaxSmNQIAADfJBpZZcwbAwyUwJAUMXp6khw1P0r0BAAB4wbRGAAAAAPBmhDMAAAAAsADCGQAAAABYAGvOAFhK+Jgmrh4CAACAS1A5AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDMAAAAAsADCGQAAAABYAOEMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDMAAAAAsIA0rh4AACSnsNKBrh6Cxwk8GObqIQAA4JHcqnLWoUMHad68uauHAQAAAADeXTmbNGmS2Gw2Vw8DAAAAALw7nPn7+7t6CAAAAACQLNK427TGy5cvy+LFi+Xu3bvywQcfyLRp0+Ts2bNSsmRJGTp0qLzwwgtm20uXLknPnj1l1apVcvXqVSlYsKC8/fbb0rFjR1m3bp088cQTZpusWbOa7ffu3SuVKlWSEydOSEBAgJw8edLc/7fffpObN2+a68aOHSuNGzd28bMAuL+gWUEpdqxQ1kcBAAA34VbhzNno0aNl7ty58tlnn0mJEiVkw4YN8uqrr0quXLmkbt26JqgdOHBAfvrpJ8mZM6ccPXpU/vvvvwTvv0ePHiaU6X4zZcpk9pU5c+Y4t4+KijIXu8jIyId+jAAAAAC8h1uGMw1B77//vvzyyy9So0YNc13RokVNlevzzz834ezUqVOmEla1alVzu1a+EkPv//zzz0tQUJBj//cLiyNGjHjgxwQAAADAu7lVt0Y7rYJdv35dnnrqKVPNsl9mz54tx44dM9u8/vrrMn/+fKlYsaIMHDhQNm/enKhj9O7dW0aNGiW1atWS4cOHy/79++PdPjg4WCIiIhyX06dPP9RjBAAAAOBd3LJypmvI1PLly6VAgQLRbvP19TX/PvPMM2bd2IoVK2T16tVSv359M1Vx3LhxkirV/zKpc+fHW7duRdtP586dpWHDhuYYum5NK2Pjx4+XXr16xTomPa792ADiF9o+9N4rQ2j4AwAAvJtbVs7KlCljgpBOPSxevHi0S6FChRzb6fqz9u3bm7VpEydONM1D7NerM2fOOLbVhiAx6b66d+8uixYtkn79+sn06dNT5PEBAAAA8D5uWTnLkiWL9O/fX9566y3TtbF27dpmKuGmTZvEz8/PBLJhw4ZJlSpVpGzZsmaN2o8//iiBgYHm/vYQFxISIu+9954cPnzYVMWc9enTx1TftAukdnVcu3at4/4AAAAAkNTcMpypd99911TAdLrh8ePHTUv8ypUrm3b5Kl26dGYdWHh4uGTIkEEef/xxswZNpU2bVr755huzLq18+fLy6KOPmvVlrVq1cuz/zp07Zhrkn3/+aQJfo0aNZMKECS57vAAAAAA8m4/NeeGVxbVp00ZSp05tpilanbbS15Nma0VPwx2A+0iuNWchEcmzXwAAgCTOBm5RObt9+7aZerhlyxbp1q2bq4cD4D4CBi9/gHt9nQwjEQlPlr0CAAB4aUOQ33//3ZyvTNePaYMOAAAAAPA0blE503OV6XnNAAAAAMBTuUXlDAAAAAA8nVtUzgC4l/AxTVw9BAAAALdD5QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABaRx9QAAQIWVDkyW/QYeDEuW/QIAACQ1KmcAAAAAYAGWCmf16tWTPn36uHoYAAAAAODd0xoXLVokadOmdfUwAAAAAMC7w1n27NldPQQAAAAAcIk0VpvWWLFiRZk4caIEBARI586d5fDhw6ailiNHDpkyZYrUqFHDXL9mzRopWrSofPXVV1K1alVz/4sXL0rPnj1lw4YNcunSJSlWrJi8/fbb0qZNG8cxrly5It27d5fFixeLn5+fDBw4UJYsWeI4roqKipIhQ4bIN998I5cvX5Zy5crJBx98YMYHeJqgWUFiCcHJ8+soNFn2CgAA4OFrzmKaMGGC1KpVS/bs2SNNmjSRtm3bSrt27eTVV1+V3bt3m/Cl39tsNrP9jRs3pEqVKrJ8+XL5/fffpWvXruY+27dvd+yzb9++smnTJlm6dKmsXr1aNm7caPblTAPeli1bZP78+bJ//35p1aqVNGrUSI4cORLnWDXQRUZGRrsAAAAAQEL52OzJxoKVs8cff1zmzJljbjt79qzky5dPhg4dKiNHjjTXbd261VTSzpw5I3nz5o11n02bNpXSpUvLuHHjTNVMK3Bff/21vPDCC+b2iIgIyZ8/v3Tp0sUc99SpU6Yip//q9XYNGjSQatWqyfvvvx/rcUJCQmTEiBH3XK/71wodYFWWqZwlk9D21M4AAIBraeHG39//vtnAUtMaYypfvrzj6zx58ph/g4KC7rnu/PnzJpzduXPHhKfvvvtO/vrrL7l586apaGXMmNFsd/z4cbl165YJWXb6JJUqVcrxfWhoqNlPyZIlo41F96PBLi7BwcGmKuf8AhQqVOghnwEAAAAA3sLS4cy5c6OPj0+c1929e9f8O3bsWJk0aZKpgGmIy5Qpk2nNryEtoa5evSqpU6eWXbt2mX+dZc6cOc77+fr6mgvgbjy+shTi7+oRILFCIlw9AgAAXMLS4SyxdC3Zc889Z9ak2UObNhQpU6aM+V6nK2q427FjhxQuXNhcp6VF3aZOnTrm+0qVKpnKmVbjdFolAAAAAIi3NwRJrBIlSpgmH5s3b5awsDDp1q2bnDt3znF7lixZpH379jJgwABZu3at/PHHH9KpUydJlSqVowqn0xlfeeUV02hEu0SeOHHCNBQZPXq0aTQCAAAAAMnBo8LZO++8I5UrV5aGDRua5iK6Dq158+bRtvnoo49MExFtFKJNPrQbZGBgoKRPn96xzYwZM0w469evn1mPpvtwrrYBAAAAgEd3a3SFa9euSYECBWT8+PGmipbSHVkAJDPWnLkf1pwBADyMR3RrTA56zrSDBw+ajo365Njb8utaNQDJJ2Cwa6YFh4/hgz4AAHAPXhfOlJ7z7NChQ5IuXTpz0mo9EXXOnDldPSwAAAAAXszrwpl2Y9Q2+QAAAABgJR7VEAQAAAAA3JXXVc4AuEb4mCauHgIAAIClUTkDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAFpXD0AAEkrrHSgq4dgKYEHw1w9BAAAAO+qnHXo0EGaN28e7zYBAQEyceLEFBsTAAAAACSUV1XOduzYIZkyZXL1MAAAAADAWuHs5s2bki5duhQ7Xq5cuVLsWAAAAADgsmmNV65ckVdeecVUp/LlyycTJkyQevXqSZ8+fRzTCt99911p166d+Pn5SdeuXc31gwYNkpIlS0rGjBmlaNGiMnToULl165ZjvyEhIVKxYkX5/PPPpVChQma71q1bS0RExD1jGDdunDl2jhw5pEePHtH2E3Na4+XLl6Vbt26SJ08eSZ8+vZQrV05+/PFHc9vJkyelWbNmki1bNvN4ypYtKytWrEjKpwsAAAAAkqdy1rdvX9m0aZMsXbrUBJ5hw4bJ7t27TbByDk96/fDhwx3XZcmSRWbOnCn58+eX0NBQ6dKli7lu4MCBjm2OHj0q3333nSxbtkwiIyOlU6dO8sYbb8i8efMc26xdu9YEM/1Xt3/xxRfNsXV/Md29e1eeeeYZEyjnzp0rxYoVkwMHDkjq1KnN7RrstLK3YcMGE870tsyZMyfl04UUFjQrSLxCsFfNVr6vUFcPAAAAIIGS7FOchpxZs2bJ119/LfXr1zfXzZgxwwQuZ08++aT069cv2nXvvPNOtOpW//79Zf78+dHC2Y0bN2T27NlSoEAB8/2UKVOkSZMmMn78eMmbN6+5TqtcU6dONQGrdOnS5vY1a9bEGs5++eUX2b59u4SFhZmqndKqnd2pU6fk+eefl6CgoHtui01UVJS52GmABAAAAIAUn9Z4/PhxM4WwWrVqjuv8/f2lVKlS0barWrXqPff99ttvpVatWiZkaXVKw5qGI2eFCxd2BDNVo0YNU/06dOiQ4zqdemivfCmtop0/fz7W8e7du1cKFizoCGYx9e7dW0aNGmXGpVW+/fv3x/v4R48ebR6v/aLTLwEAAADAsq30Y3ZL3LJli1mn1rhxY7Pea8+ePTJkyBAzpTCx0qZNG+17Hx8fE+BikyFDhnj31blzZxM427Zta6ZaaqjUal1cgoODzRo4++X06dOJHj8AAAAA75Vk0xp12p+GI21Xr1UupSHl8OHDUqdOnTjvt3nzZilSpIgJZHbajCMmraT9/fffjmmSW7dulVSpUt1TmUuo8uXLy59//mnGF1f1TKtf3bt3NxcNX9OnT5devXrFuq2vr6+5wLpC27P6yCuF+Lt6BABcLeTeBmIA4NHhTBt4tG/fXgYMGCDZs2eX3Llzm+mAGqC0ghWXEiVKmOCla8weffRRWb58ufzwww/3bKfdFHX/2lBE13PptEPt2Ghfb5ZYdevWNaFR15V99NFHUrx4cTl48KAZa6NGjUyHSW0YosHt0qVLpslIYGDgAx0LAAAAAFJ0WqOGHF0L1rRpU2nQoIFZr6WBRoNVXJ599ll56623pGfPnqazolbStJV+TBqeWrZsaaY/Pv3006by9cknnzzUeBcuXGgCYZs2baRMmTKmAcmdO3fMbfqvdmzU8WtY05D2sMcDAAAAgLj42Gw2mySTa9eumSYe2lFRW98/KD3P2eLFi00TD3eh1T1tDKJTO/WcbgBchGmNAJjWCMBNskGSnhBJm3no1EDt2KgHHjlypLn+ueeeS8rDAHgAAYOXizcKH8OHMgAA4B6S/Gy1uiZM29unS5dOqlSpIhs3bpScOXMm9WEAAAAAwKMk67RGb8a0RliN91bOmrh6CAAAwMtFJjAbpPh5zgAAAAAA9yKcAQAAAIAnrjkDYE1M7wMAALA2KmcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALCCNqwcAAMkprHSgq4cALxJ4MMzVQwAAuDEqZwk0c+ZMyZo1q6uHAQAAAMBDEc4AAAAAwAIIZwAAAABgAR655uzKlSvSvXt3Wbx4sfj5+cnAgQNlyZIlUrFiRZk4caJcunRJ3nzzTVm2bJlERUVJ3bp1ZfLkyVKiRIlo0xiHDRsmFy5ckIYNG0rt2rVd+phwr6BZQa4eAtxAKGuAAACAm/DIylnfvn1l06ZNsnTpUlm9erVs3LhRdu/e7bi9Q4cOsnPnTnP7li1bxGazSePGjeXWrVvm9m3btkmnTp2kZ8+esnfvXnniiSdk1KhRLnxEAAAAADxdGk+sms2aNUu+/vprqV+/vrluxowZkj9/fvP1kSNHTCjT8FazZk1z3bx586RQoUKm0taqVSuZNGmSNGrUyFTcVMmSJWXz5s3y888/x3lcrcDpxS4yMjKZHykAAAAAT+JxlbPjx4+bCli1atUc1/n7+0upUqXM12FhYZImTRqpXr264/YcOXKY2/U2+zbOt6saNWrEe9zRo0eb49gvGvYAAAAAwGvDmasEBwdLRESE43L69GlXDwkAAACAG/G4aY1FixaVtGnTyo4dO6Rw4cLmOg1Lhw8fljp16khgYKDcvn3brCuzT2u8ePGiHDp0SMqUKWO+1230dmdbt26N97i+vr7mgpQT2j5ULCvE39UjAAAAgJvxuHCWJUsWad++vQwYMECyZ88uuXPnluHDh0uqVKnEx8fHdGR87rnnpEuXLvL555+b7QcPHiwFChQw16vevXtLrVq1ZNy4cea6lStXxrveDAAAAAAelkdOa/zoo4/MGrGmTZtKgwYNTNDSalj69OkdDUKqVKlibtfttFvjihUrTMVNPfbYYzJ9+nTTGKRChQqyatUqeeedd1z8qAAAAAB4Mh+bJhMPd+3aNVMZGz9+vGmRnxK0W6M2BtEplXquNXgZpjVaR0iEq0cAAAC8XGQCs4HHTWtUe/bskYMHD5qOjfoEjBw50lxvn7YIJDsCgWUEDF7u6iG4nfAxTVw9BAAAvJJHhjOl68W0yUe6dOnMFEY9EXXOnDldPSwAAAAA8J5wVqlSJdm1a5erhwEAAAAA3t0QBAAAAADcDeEMAAAAACzAI6c1AoAdzS0AAIC7oHIGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwALSuHoAANxXWOlAsbrAg2GuHgIAAECCeEXlzGazSdeuXSV79uzi4+MjWbNmlT59+jhuDwgIkIkTJ7p0jAAAAAC8m1dUzn7++WeZOXOmrFu3TooWLSqpUqWSDBkyuHpYAAAAAOBd4ezYsWOSL18+qVmzpquHAgAAAADeGc46dOggs2bNMl/rlMYiRYqYaYwVK1aMcyrj5cuXpX///rJkyRKJioqSqlWryoQJE6RChQopPHrg4QXNCkq+nQdb/1dIqKsHAAAAkEAev+Zs0qRJMnLkSClYsKCcOXNGduzYcd/7tGrVSs6fPy8//fST7Nq1SypXriz169eXf//9N877aIiLjIyMdgEAAACAhPL4cObv7y9ZsmSR1KlTS968eSVXrlzxbv/bb7/J9u3bZcGCBaZiVqJECRk3bpxpIvL999/Heb/Ro0ebY9kvhQoVSoZHAwAAAMBTeXw4S6x9+/bJ1atXJUeOHJI5c2bH5cSJE2btWlyCg4MlIiLCcTl9+nSKjhsAAACAe7P+gpEUpsFMm4doZ8eYtHoWF19fX3MBAAAAgAdBOItB15edPXtW0qRJYxqHAO4utL2Xt8QI8Xf1COBqIRGuHgEAAAnCtMYYGjRoIDVq1JDmzZvLqlWrJDw8XDZv3ixDhgyRnTt3unp4AAAAADwU4SwGbbe/YsUKqVOnjnTs2FFKliwpL730kpw8eVLy5Mnj6uEBAAAA8FA+NpvN5upBeCJtpa9dG7U5iJ+fn6uHA3gvpjWCaY0AADfJBqw5A2AJAYOXJ8t+w8fwwRwAALgHpjUCAAAAgAUQzgAAAADAAghnAAAAAGABrDkDYAnhY5q4eggAAAAuReUMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDMAAAAAsADCGQAAAABYAOEMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAtK4egAAXCOsdKB4g8CDYa4eAgAAQIJ4TeWsXr160qdPH1cPAwAAAAC8O5wBAAAAgJURzgAAAADAAjxyzdm1a9fk9ddfl0WLFkmWLFmkf//+0W6/dOmSvPnmm7Js2TKJioqSunXryuTJk6VEiRLm9pkzZ5opkN9++6359/Tp01K7dm2ZMWOG5MuXz0WPCki+tVhBs4LEU4W6egAAAADeXDkbMGCArF+/XpYsWSKrVq2SdevWye7dux23d+jQQXbu3ClLly6VLVu2iM1mk8aNG8utW7cc21y/fl3GjRsnc+bMkQ0bNsipU6fuCXnONORFRkZGuwAAAACA11bOrl69Kl9++aXMnTtX6tevb66bNWuWFCxY0Hx95MgRE8o2bdokNWvWNNfNmzdPChUqJIsXL5ZWrVqZ6zSoffbZZ1KsWDHzfc+ePWXkyJFxHnf06NEyYsSIFHiEAAAAADyRx1XOjh07Jjdv3pTq1as7rsuePbuUKlXKfB0WFiZp0qSJdnuOHDnM7XqbXcaMGR3BTOl0xvPnz8d53ODgYImIiHBcdCokAAAAAHht5SyppE2bNtr3Pj4+ZvpjXHx9fc0FcEeh7eNYmRXin9JDAQAA8FoeVznTapcGq23btkVrAHL48GHzdWBgoNy+fTva7RcvXpRDhw5JmTJlXDJmAAAAAPC4ylnmzJmlU6dOpimITlfMnTu3DBkyRFKl+l8O1Y6Mzz33nHTp0kU+//xz081x8ODBUqBAAXM9AAAAALiCx4UzNXbsWNMYpFmzZiZ89evXz6wDs9OW+NpKv2nTpmZ9Wp06dWTFihX3TGUEAAAAgJTiY4tvIRUemLbS9/f3N6HQz8/P1cMBHownrDkL+f8/zAAAAFg5G3hk5QxAEvGAYBMweLmrhwDcI3xME1cPAQBgQR7XEAQAAAAA3BHhDAAAAAAsgHAGAAAAABbAmjMAHo21PQAAwF1QOQMAAAAACyCcAQAAAIAFMK0RAAAAbuXOnTty69YtVw8DcEibNq2kTp1aHhbhDAAAAG7BZrPJ2bNn5fLly64eCnCPrFmzSt68ecXHx0ceFOEMAAAAbsEezHLnzi0ZM2Z8qA/BQFL+0eD69ety/vx5832+fPkeeF+EMwAAALjFVEZ7MMuRI4erhwNEkyFDBvOvBjR9jz7oFEcaggAAAMDy7GvMtGIGWJH9vfkw6yEJZwAAAHAbTGWEJ783CWcAAAAAYAGEMwAAAMBFwsPDTcVl7969KXK8devWmeM9bMdL3cfixYuTbFz4HxqCAAAAwK0FDF6eYscKH9Mk2cPTE088IZcuXTKt2R9GvXr1pGLFijJx4kRJamfOnJFs2bIl+X69HeEMAAAAQKLo+byQ9AhngBcJKx0o3ibwYJirhwAA8HI///yzjBo1Sn7//XfTYr1GjRoyadIkKVas2D1THLVqpuxVqfbt28vMmTPv2efFixelZ8+esmHDBlNl0329/fbb0qZNG3N7hw4dZP369eaix1InTpxw3H/Xrl0yaNAgOXDggKmuzZgxQ0qVKuW4fcmSJTJixAhze/78+c04hgwZImnSpHFMa/zhhx+kefPmcvPmTenbt68sXLjQjCVPnjzSvXt3CQ4Odmz72WefybJly+TXX3+VIkWKyFdffSW5cuWSzp07y44dO6RChQoyZ86ce54Tb8OaMwAAACAZXbt2zYSXnTt3ypo1ayRVqlTSokULuXv3brTtChUqZAKOOnTokJk6aA9WMd24cUOqVKkiy5cvN6Gva9eu0rZtW9m+fbu5Xe+nIbBLly5mP3rR/dtp0Bo/frwZkwau1157zXHbxo0bpV27dvLmm2+acPb555+bgPjee+/FOpbJkyfL0qVL5bvvvjPjnjdvngQEBETb5t133zX71LV1pUuXlpdfflm6detmApyOQU/k3LNnT/F2HlU5S855tQAAAMCDeP7556N9b68aafDJnDmz43qtqmXPnt18rScyjm/NWYECBaR///6O73v16iUrV640AalatWri7+8v6dKlM+feim0KogatunXrmq8HDx4sTZo0MYEvffr0pmKm12m1TBUtWtSEq4EDB8rw4cPv2depU6ekRIkSUrt2bVMl08pYTB07dpTWrVubr7Vip8Fx6NCh0rBhQ3OdBsGOHTuKt6NyBgAAACSjI0eOmOmGGnL8/PwcVSUNNQ/qzp07JjAFBQWZQKchT8NZQvdZvnx5x9f58uUz/54/f978u2/fPhk5cqTZp/1ir8Bdv379nn3pFEqtiOm0yN69e8uqVaviPZ5Oe1Q6dufrbty4IZGRkeLNPKpyBgAAAFhNs2bNTDVp+vTpZv2WTmcsV66cWav1oMaOHWumLuqMMQ05mTJlkj59+iR4n2nTpr3n5Mn2aZZXr1411bOWLVvecz+trMVUuXJls57tp59+kl9++cVUyBo0aCDff/99vMeLbwzeKo07z919/fXXZdGiRZIlS5ZoZV2lixG1PKoLD6OiokzZVufDasnV7rfffnPMc82ZM6eZ+zt69Gjz5laffPKJTJgwQU6fPm1Kw48//ni0Nxngbc0xgmb9/1+43EWoqwcAAPBq2rhD12FpMNPPkvbPoHHRqYj2ylh8Nm3aJM8995y8+uqrjlBz+PBhKVOmTLR93W8/sdGwpWMuXrx4gu+jFcEXX3zRXF544QVp1KiR/Pvvv45pmvDwaY0DBgww3We0k4yWTvWcELt3745WXtXQpYsTt2zZYhYZNm7cWG7dumVuP3bsmHnT6Bzg/fv3y7fffmt+UOwLEfW+WpbVkq6+ObXLTp06deIcjwZALcM6XwAAAODdtOtijhw5ZNq0aXL06FHTrVCbg8RFK2xaRfrxxx/ln3/+MVUsNXXqVKlfv75jOy04rF69WjZv3ixhYWGmuca5c+ei7UunT27bts10gbxw4UKCq1LDhg2T2bNnm+rZH3/8YfY/f/58eeedd2Ld/qOPPpJvvvlGDh48aALiggULzDq3hz1Pmzdyy3Cmb9Ivv/xSxo0bZ96kWsqdNWuW3L592zGvV0PZF198Yf5Coa05tWvMX3/95TiTuVbIXnnlFVP+1Td3zZo1TWVN34g631Xn62oFrWnTpuaHpFKlSiasxUX3p9U1+8W5Gw4AAAC8k3Zm1GCjret1KuNbb71lpiTG1+jD3pBD12HZCwcarrS4YKdBSStc2lBDm+JpGNK29s50Zpk2GdFqmjYgSeh6NN2nhkMtgDz66KPy2GOPmdlksTX6UDqL7cMPP5SqVaua7TUMrlixwjx2JI6PTUtKbkYXKWpXxpMnT0rhwoUd12uA0umLTz75pKmIacjSN6Tz7Tp1Uf8aoG8crZg5z3XVp0IXOWrnnIIFC0qtWrXMwketsOlF76sdb+KqnOnFTitnGtAiIiJMmRfwBG45rbE9ExsBwBPo5zpd1/TII4/Euu4JsPJ7VLOBFnDulw3cds1ZUlTftPwbWzVMA5/O0dVpkjpdUv9qoIEuJCTEnCQvthKtr6+vuQCezC2DToi/q0eAxAqJcPUIAABwCbesNeqZw7XipXNonRuA6BxXFRgYaKY4Ot9uX4xpXySpZWCtkOlCx5gX+0JMPSGfdprRMq1W2bREq/OEAQAAACCpuWXlTM+10KlTJ9MURBdY6kn69Czn9nmtuoZMu9fo+Rj0jOY6D1bn7eocXr3efvI7nT+r83g7d+5s1pdpWNOFlbrgUufZHj9+3DQB0YWcOm9WF1Hq+RsAAAAAIKm5ZThTupBSpybqeSM0fPXr18/M4bSbMWOGaaWvDT30fA8asjRg2deY6YnwtNujhjptGqLrzbQip+0/lU5d1Db9OpVR549q4NMuNGXLlnXZYwYAAADgudyyIYg7SOiiPwDJjDVn7oc1ZwBiQUMQWB0NQQDAScDg5fdcFz6GD/oAAMA9uGVDEAAAAADwNIQzAAAAALAAwhkAAAAAWABrzgB4jPAxTVw9BAAA8IDq1asnFStWlIkTJ8a5jY+Pj/zwww/SvHlz8USEMwAAALi3lOzMS0dZlzpz5ow5B7GnIpwBAAAAeCC3bt1ynEc4JeTNm1c8GWvOAAAAgGR09+5d+fDDD6V48eLi6+srhQsXlvfee8/cFhoaKk8++aRkyJBBcuTIIV27dpWrV6867tuhQwczhe/999+XPHnySNasWWXkyJFy+/ZtGTBggGTPnl0KFiwoM2bMcNwnPDzcTP+bP3++1KxZ05xzq1y5crJ+/fr7VqWaNGlixqLn6vr6668lICAg2jRD3e+nn34qzz77rGTKlMk8jjt37kinTp3MffS+pUqVkkmTJkXbt/1xjBgxQnLlymXO9dW9e3e5efPmPc/VwIEDzePSIBYSEhLtdj3+4sWLHd//+eef0qZNG7O9jqdq1aqybds2c9u+ffvkiSeekCxZspjjValSRXbu3ClWRuUMAAAASEbBwcEyffp0mTBhgtSuXduEoIMHD8q1a9ekYcOGUqNGDdmxY4ecP39eOnfuLD179pSZM2c67v/rr7+aALZhwwbZtGmTCUKbN2+WOnXqmCDy7bffSrdu3eSpp54y29lpeNNgVaZMGfnoo4+kWbNm5iTJGgJj065dO7lw4YKsW7fOVMP69u1rxhSTBqYxY8aYfadJk8YEKj3uggULzL51bBoy8+XLJ61bt3bcb82aNSYo6v41QHbs2NFsbw+qatasWea4+ri2bNliQl2tWrXMY4tJQ2zdunWlQIECsnTpUhPmdu/ebcajXnnlFalUqZIJk6lTp5a9e/emaJXvQfjYbDabqwfhiRJ6FnAAAADc340bN0yw0OqMfsB3lzVnV65cMZWiqVOnmuDlTAPboEGD5PTp06bqo1asWGFC1N9//20qZRpONMwcP35cUqX636S30qVLS+7cuU1YU1q50s+dX3zxhbz00ksm+OjzpAFK96+00qbX9erVy1SmYtKwGBgYaEKiVp/U0aNHpUSJEiZU9unTx1G50q/1uvhowDx79qx8//335nt9HMuWLTOPNWPGjOa6zz77zARI/bysj61evXrmsWzcuNGxn2rVqpnKoj6WmA1Bpk2bJv379zePVytnMeln8ClTpkj79u3F1e/RhGYDpjUCAAAAySQsLEyioqKkfv36sd5WoUIFRzBTWiXSys+hQ4cc15UtW9YRzJSGtqCgIMf3WhXSClTMKpdW5Oy0wqWhS48ZGz2eblO5cmXHdToNM7bmG/bw5uzjjz820wY1iGbOnNkEp1OnTkXbRh+rPZjZx6fVLw1sduXLl492H62+xVa9U1oJ08pYbMFMaQVOA3GDBg1MuDt27JhYHeEMAAAASCa6ButhxZyKp9Wj2K6zT+dLbs5hUunaNq1g6XTLVatWmdCkUxZjridLiLSJeFz3e251+uUff/xh1tHp1FCd3qlVNysjnAEAAADJRKcFaojQ9VYx6TRCbVqha8/sdE2ZVsm0qcbD2rp1q+Nrnda4a9cuc8zY6PF0mz179jiu02mNly5duu9xdMzaeOSNN94wlSytuMVWpdLH+t9//0Ubn1bZChUq9ACPTkyVTYPgv//+G+c2JUuWlLfeesuExpYtW0ZrnGJFhDMAAAAgmejaI133peu8Zs+ebUKLhpIvv/zSNKzQ23VN1O+//y5r1641a8Latm1rpi4+LJ1qqJUiXU/Wo0cPE7Ree+01x+26ds1eSdKvdfqfNvLYvn27CWn6tQZLrV7dL4BqF8SVK1fK4cOHZejQoWbtWkxaSdPq2oEDB8zauuHDh5u1ac5TNhNDuzRqExBdf6YBUdflLVy40DQS0RCo+9b1eidPnjS365jiCqdWQbdGAAAAIBlpWNH1XMOGDTONPnQdlbaR1/VXGmjefPNNefTRR833zz//vOmsmBR0nZVetLqk1SztaJgzZ85o68y0QYWdhkcNT9oFUkPP6NGjzbTAexqwxKCdIjXMvfjiiybIaWjSKtpPP/0UbTtdd6dBTvev6/B0u5it8hMjXbp0piLWr18/ady4san86dRFDaW6Du/ixYumA+W5c+fM49bKmbbytzK6NSYTujUCAACkULdGRGPv1qiBqWLFig+8Hz2HmE45/OWXX2JtaJIY2q3x8uXL0c5R5mluJEG3RipnAAAAAEzTDO2eqJ0g9VxsOhVTT0KtlS6kDMIZAI8WVtrac8vdSeDB2NsvAwA8w61bt+Ttt982a7eyZMlimnzMmzfP8idu9iRprFA+fdgyZ1KVbgEAAAB3p9WuB1m51LBhQ3NJDjNnzkyW/XoaS1TOJk2alOA3UGxBTufCaunVeYEjAAAAALgTl4azO3fumI4uujjuYWg3Fu0oAwAAAADuKtEnFdAzdH/44YemHaevr68ULlxY3nvvPcftOkf1iSeeMK1AK1SoYM4z4FzOzJo1q2njqW0u9f6nTp0y1TA9P4Hd999/bxYi6nkVcuTIYc65oCfn01abs2bNkiVLlphQpxc9d4FOa9SvtU2oPfRpG1Cd6qj70JPqaXXOmf2Y48aNM+1M9Th6/geda2v3ySefmHaf2m1FzzXxwgsvJP4ZBgAAAIDkqJwFBwfL9OnTZcKECVK7dm0znVBPbGc3ZMgQE3g01OjXev4CPbu4nttBXb9+XT744AP54osvTCDKnTt3tP3r/vQ+GgBbtGghV65ckY0bN5ppj/3795ewsDDTitJ+du/s2bOb80XEDJAFCxaUBQsWmGNs3rzZnERPQ1jr1q0d2+mJ/vQ6/VfHqOdm0DVrXbp0MSfS6927t8yZM8cshtQzj+s4ACSfoFlBSb7PUJpYAAAATwxnGpS0AjV16lRzJnNVrFgxE9K0eqU0QDVp0sR8rSd5K1u2rAk+etZxpZUprUhpVS02Gs70BHJ6krgiRYqY67SKZqeVMD1pXXzTGLWjjPMJ5rSCphW87777Llo4y5Ytm3ksOi1Sx6fjXrNmjQlnWtHLlCmTNG3a1HSr0bFUqlQpzmPqmPRipwESAAAAAJJlWqNWrTSAxHcSuvLlyzu+1qqUOn/+fLQzeTtvE5OGNt2/BrJWrVqZKt2lS5cksfTM4FWqVJFcuXJJ5syZZdq0aSZwOdPgqMHMebz2sT711FMmkBUtWlTatm1r2ohq1S8uegZ1XTtnv2iTEgAAAABIlnCmVav7cT4Pgq4Ds08zdN6H/frYaFhavXq1/PTTT2Zd2pQpU8yaMT3bdkLNnz/fVPB03dmqVavMWrSOHTvKzZs34xyrfbz2sWq1bPfu3fLNN9+Y0DZs2DATHLVTZFzTPfWM3/bL6dOnEzxeAAAAICb9bPqgp5qCF0xr1HVkGq506l/nzp2T9Y1Yq1Ytc9FQpBWsH374Qfr27Wsqb9rwIz6bNm0y68TeeOMNx3XHjh1L9Dh0nZw2I9HL8OHDTTMTPXO6TrmMSZub6AXAgwttH5qwDUMersMrAMCzJMea5Yf+f1US0OU+ugwH3iNR4Uy7Fg4aNEgGDhxoQpKGp3/++Uf++OOPeKc6Jsa2bdtM+Hv66adNsxD9Xo8RGBjoOKneypUr5dChQ6bZR2xt+DVEzp4922yn6820qceOHTvM1wn1448/ms6TderUMT8UK1asMFU1reIBAAAAyY1TRXmfRLfSHzp0qPTr189UtDQwaYdD5zVlD8vPz082bNggjRs3lpIlS8o777wj48ePl2eeecbcrs06NCBVrVrVrCfTKllM3bp1M9UtHVv16tXl4sWL0apoCaFVskWLFsmTTz5pHudnn31mpjjqOjUAAAAgoeI6TZT66quvzOdLnYGlS2l69uyZ4GmN8e3XftoobZKnn5n1M3b37t2jLfP5+eefTWM//dyr99dGeDFnm/3555+mk7p2SNdmefoZXIsndnqKq8qVK5sijvZq0ONpcz88GB+b9qhHktNujVrV0/Vn+sMAIAklZlpjSERyjgQAkEJu3LhhehDoTCgNAu4yrVGnJup5gWOeJqpdu3Zmdpcu2xkzZowpROjnRi089OnTxxHOdGmP8/mAE7JfbYan4WzhwoWmG7kWV7SzuvZg0EKH/RzFerseQ5v1Xb161RRfdDvt15AqVSpznfZcKFCggLz//vumkqc9GbTxXY0aNczxNNBNnjxZHn/8cRPs9PRVemxdEuRtbsTzHk1oNiCcJRPCGTxFwODl4s7Cx/zv1B4AAPfmruFMw4x2ENfQYz9NlJ2GHg1Mo0aNivW+8YWz+ParNCAtW7bMNKnLmDGjuU5ngg0YMMB8PtXwFdOFCxdMlS00NFTKlStnup1rkz09hlbOYtJKnS5t0sZ4dnPnzjVLoGKeh9gb3EiCcJboaY0AAAAAEiau00TpsiANMA/atyEhp5/SbezBTGm1S6th9q7iR44cMVMWdTqiBgbt7aDsp5/SCpqe5ze2YKb27dsnI0eONJU6+0Urc1rVi+8UVIgb4QwAAABIJnGdJurcuXPJst/EnH6qWbNm8u+//5pgp+vI7GvJ7OvS7ncaLQ16usZMQ5z9olU3DX0xK0dIGMIZAAAAkIzsp4nSILNnzx7T9VyDlVaqtEt5Uu5Xp0E6V7b+++8/x/dbt2411S1dM6YN87T7uTbf0wqcNsCLWXnTtWgauDTAxUYbgeg+ihcvfs8ltmmTSOJW+gAAAACS5jRRISEhpoOiXq8NQbSphzYE6dWrV6z70hClzT+0o+P9Tj9lr4B16tTJBDBdN6ZNOvS+Gpz0VFHaoVHXlWmXSJ3KOHjw4GjH0ymP2ghE17yNHj3abKchMH/+/GaKpDYQ0YYg2pjkhRdeMPvVQPj777/HuY4O8SOcAYgXDTUAAHj400RNnDjRNIXQ5h3Op4nSJhITJkwwjTdy5sxpQk5ctBuiNu1IyH7tYU7P/6vn7Y2KijJhSwOh0iA1f/586d27t2n+oVMitetivXr1HPfXStyqVavMabT0NFfaIl+nUH788cfm9oYNG5pzA+u6sw8++EDSpk0rpUuXls6dOyfb8+np6NaYTOjWCAAAkDKd8CCxdmu8fPlyvOdJQ9KiWyMAAAAAeAjCGQAAAABYAGvOAAAAAA8zc+ZMVw8BD4DKGQAAAABYAOEMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAgMV06NBBmjdv7vi+Xr160qdPn2Q/7rp168THx0cuX77saMmfNWvWhxq7lXSw8NgU5zkDAACAWwsrHZhixwo8GJbo+2iwqlixokycOFHczYsvviiNGzdO1H0mTZokNptNrGiShcemCGcAPFpK/g8bSfdBBgCQtDSQ3LlzR9KkSdzH/wwZMphLYvj7+4tV+Vt4bCk2rTE8PNyUR/fu3ZvipceHOTYAAADwMPSz7Pr1603FRj+T6uXYsWPSqVMneeSRR0zwKVWqlLk9MZYvX26Cxrx58+KdnvjTTz9JlSpVxNfXV3777Te5e/eujB492nHsChUqyPfffx/ncWKb1jhq1CjJnTu3ZMmSRTp37iyDBw82lcG4Pr9HRUVJ7969zX3Sp08vtWvXlh07dtwz1jVr1kjVqlUlY8aMUrNmTTl06NB9P+N/99138vjjj5vH8uijj8rhw4fNvnU/mTNnlmeeeUb++eefOMemjz0oKMjcP0eOHNKgQQO5du2a4/avvvpKypYta56/fPnySc+ePcWr15zpG1XfFA8a5AoVKiRnzpyRcuXKJdMIAQAAgLg/y9aoUUO6dOliPpPqpWDBguayYMECOXDggAwbNkzefvttEzQS4uuvv5Y2bdqYYPbKK6/Eu60GpzFjxkhYWJiUL1/eBLPZs2fLZ599Jn/88Ye89dZb8uqrr5oAmRB6zPfee08++OAD2bVrlxQuXFg+/fTTeO8zcOBAWbhwocyaNUt2794txYsXl4YNG8q///4bbbshQ4bI+PHjZefOnabC99prr913PMOHD5d33nnH7Ffv8/LLL5vj6fO+ceNGOXr0qHl+Y6OvhT6Pehx9fjQktmzZ0jHtUR9Xjx49pGvXrhIaGipLly41Y/fKaY1adtU0/LClx9SpU0vevHmTbFwAAABAQuln2XTp0plqkPNn0hEjRji+1irWli1bTDhr3bp1vPv7+OOPTYhZtmyZ1K1b977HHzlypDz11FOOCtb7778vv/zyiwmMqmjRoqai9vnnnydof1OmTDFVv44dO5rvNfisWrVKrl69Guv2WoXSkKPFFq1iqenTp8vq1avlyy+/lAEDBji2fe+99xxj0FDZpEkTuXHjhqm2xaV///4m6Kk333zThC2twNWqVctcp2ONq9Cj4ez27dsmkBUpUsRcp1U05wphv379zH7ttDrnNuFMy6Tjxo2TadOmyenTpyVPnjzSrVs3R6I/fvy4Sefbtm2TEiVKmMRuf2Pok6YdaDTJ64uhJUlNuiEhIaZbzOLFix2lR30z6236Jq9UqZIsWbJExo4da9K40lCn1q5dKwEBAeYNv2fPHlNu1dCn6ffXX3+Vs2fPmrT/xhtvRHvStQKnx9SSq6b3mzdvyksvvWQWcaZNmzYpnzIASSRo1v//MnUWynonAIAFacjSKXOnTp2S//77z3zedJ4aGBv9HHz+/HnZtGlTgkOCTu+z08/P169fd4Q1Oz22fqZOCJ1qqJ+dnVWrVs18to6NTuG8deuWIywp/Tyt99FqlbPy5cs7vtYphEofr35ej4vzfTR7xAxYep3uIzY6pbN+/fpmew14Tz/9tLzwwguSLVs2c5+///7b3J6SkjScBQcHmyQ8YcIEE2w0jR48eNBxu6Z8DW8azPRrTbb6JrEvTNQ3i5ZIv/jiCzPnU+elxlZ6/PDDD6VFixZy5coVU67U0qOmZn2BIyMjZcaMGWb77Nmzmyc1ZoC0l5H1GJs3bzZhTd8Azn+p0GCn1+m/OkbtVKM/MFqSBgAAAB7U/PnzzWdXLQJooULXbmmhQQsY8dEApdP3NNRp6LIXJOKTKVMmx9f26pauVytQoEC07XRNlas5F0Hsj00/uyf2PjGvi2sfOsNOK3iaB7T6p1VBzSj6OuTMmVNcIcnCmQYlnds5depUad++vbmuWLFiJqTpgj2lb0ItTyqtfuniOg0+pUuXNtdpqv7kk09Min2Q0qMu5NNybXzTGPXFSkgZWROzPhZ90XR8Om4tkcYVzvS4erHTkAgAAADotEadvWWnlS9teOFcgdIK0/3oZ2sNdNqaXz+j6mfVxChTpowJYVqtS8gUxtho8xJtuNGuXTvHdc7NPWIbsz5+fcz2z+/6mV/vkxLnbbsfDW9a1dOLTtHUMf7www/St29fMwNPP/8/8cQTklKSrCGIVq00nMRX+ourVGmnL5zzNvGVHlu1amWqdJcuXXqgMrJ2rcmVK5fp4qLTMPVN6kyDo77pnccbV0lU6eJKnVNsv2gjEgAAAEA/5Gs1RgsWFy5cMLPItOnFypUrzVKeoUOHxhtwnJUsWdLM7NIGG87hRgOFveARF63QabFElxnpciANhFqJ04qRfXnQ/fTq1cusFdPtjxw5YtZl7d+/P84qnlbuXn/9dbO27OeffzYNULTYoTPmdD1YQm3fvt08vr/++kuSir4mugZPXwvNAosWLTKdHQMD/3caHl1epWF48uTJ5rHanyu3CGcJOf/B/UqVuo/4yrP20qO2BNXkr0+OpvcTJ04kuoysbwYtX2qLfV3QqHNt4xqrfbzxlVV1SmdERITjomvuAAAAAP3sqZ9j9fOrFgd0fZPOBNNlM9WrV5eLFy/es44rPvr5V9d4ffPNN6ZhhdLPn/G1nrd79913TRjUwoKGkEaNGplpjjqbLCG0l4R+7tXHVLlyZfM5XPs1xNe0Q7tFPv/889K2bVtzH505p8FUZ6ol1PXr183j06pbUvHz85MNGzaYk2xr6NWujxrG7I1LdDag9pzQmX1auGnatKkJacnJx5ZEp8jWTiq6xkuTpZ7vwJn+lcC5KYfShhv6gmjy19KsvSGIXu/M3pzD3hDEmZaHtfSoZUe96Noxnfqo3WviOramfU3sWqK00/MZ6F8x7OdCi+2YOja9XVtsJoROa9QKmv6g6AsP4D5CkumkkCERybNfAECK0s+aGgT0c118QQApTxuM6LKiOXPmiDe7Ec97NKHZIMnWnOkABg0aZM4roNMTdd6mlgX1/AlJ1eVES48aqrSTijYL0e+dS49aMtYUrqlam33E1oZfy8jaEVK30ydO30RaRk7oXwsAAAAAb6UVLO24rtU/rQZq9U5b8+vsNlisW6OWSLXzoi6m0y6Juk6re/fuSV561PKipk+tmjmXHnX+qla2tHuNdqOxt9J3pq39tYqmZWSdqqjdH7WMrFMlAQAAAMRNPz+vWLHCnJNMK0U6xVLXv+lMNFhoWiOiY1ojkEhMawQAxINpjbA6S01rBGANAYOXi3v6Oln2+r8TeQAAAFhfknVrBAAAAAA8OMIZAAAA3EZ8pzYC3P29ybRGAAAAWJ52A0+VKpVpOqfnCtPv4zs/LpBStIWHnjNZu8jre1Tfmw+KcAYAAADL0w+92mhBz2mrAQ2wmowZM0rhwoXNe/VBEc4ADxM+pomrhwAAQLLQioR++L19+7bcuXPH1cMBHPScb3pKsYet5hLOAAAA4Db0w2/atGnNBfA0NAQBAAAAAAsgnAEAAACABRDOAAAAAMACWHOWjC01VWRkpKuHAgAAAMCF7JnAnhHiQjhLJleuXDH/FipUyNVDAQAAAGCRjODv7x/n7T62+8U3PPAZwvUcHFmyZOEEiRb6i4WG5dOnT4ufn5+rh4MUwGvunXjdvROvu3fidfc+kW76mmvk0mCWP3/+eM+DRuUsmeiTXrBgQVcPA7HQH2R3+mHGw+M190687t6J19078bp7Hz83fM3jq5jZ0RAEAAAAACyAcAYAAAAAFkA4g9fw9fWV4cOHm3/hHXjNvROvu3fidfdOvO7ex9fDX3MaggAAAACABVA5AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDN4jH///VdeeeUVc0LCrFmzSqdOneTq1avx3ufGjRvSo0cPyZEjh2TOnFmef/55OXfuXLRtevfuLVWqVDFdgSpWrJjMjwL38/HHH0tAQICkT59eqlevLtu3b493+wULFkjp0qXN9kFBQbJixYpot2tPpGHDhkm+fPkkQ4YM0qBBAzly5EgyPwq4+nVftGiRPP300+Zn38fHR/bu3ZvMjwCufM1v3bolgwYNMtdnypRJ8ufPL+3atZO///47BR4JXPmzHhISYm7X1z1btmzmd/y2bduS+VHA1a+7s+7du5vf8xMnThS3oN0aAU/QqFEjW4UKFWxbt261bdy40Va8eHFbmzZt4r1P9+7dbYUKFbKtWbPGtnPnTttjjz1mq1mzZrRtevXqZZs6daqtbdu2Zv9wnfnz59vSpUtn++qrr2x//PGHrUuXLrasWbPazp07F+v2mzZtsqVOndr24Ycf2g4cOGB75513bGnTprWFhoY6thkzZozN39/ftnjxYtu+fftszz77rO2RRx6x/ffffyn4yJDSr/vs2bNtI0aMsE2fPl07Ftv27NmTgo8IKf2aX7582dagQQPbt99+azt48KBty5YttmrVqtmqVKmSwo8MKf2zPm/ePNvq1attx44ds/3++++2Tp062fz8/Gznz59PwUeGlH7d7RYtWmQ+u+XPn982YcIEmzsgnMEj6A+nfsDasWOH47qffvrJ5uPjY/vrr79ivY/+z1p/mBcsWOC4LiwszOxH/8cd0/DhwwlnLqYfpnr06OH4/s6dO+YX7ujRo2PdvnXr1rYmTZpEu6569eq2bt26ma/v3r1ry5s3r23s2LHR3he+vr62b775JtkeB1z7ujs7ceIE4czLXnO77du3m9f+5MmTSThyWP11j4iIMK/7L7/8koQjhxVf9z///NNWoEABE8qLFCniNuGMaY3wCFu2bDFTGatWreq4TqcupEqVKs7pC7t27TJTXXQ7Oy2RFy5c2OwP1nLz5k3zmjm/Xvr66vdxvV56vfP2qmHDho7tT5w4IWfPno22jb+/v5lSwXvAc193WFtKveYRERFmqpP+vwPe8brrMaZNm2Z+z1eoUCGJHwGs9LrfvXtX2rZtKwMGDJCyZcuKOyGcwSPoB+zcuXNHuy5NmjSSPXt2c1tc90mXLt09/2POkydPnPeB61y4cEHu3LljXp+Evl56fXzb2/9NzD7h/q87rC0lXnNdb6xr0Nq0aWPWKcOzX/cff/zRrCvX9UkTJkyQ1atXS86cOZPhUcAqr/sHH3xgPgdq3wB3QziDpQ0ePNj8ZTO+y8GDB109TACAm9AZE61btzbNgD799FNXDwcp4IknnjBNfzZv3iyNGjUyr//58+ddPSwkk127dsmkSZNk5syZ5nOiu0nj6gEA8enXr5906NAh3m2KFi0qefPmvecX7e3bt00HR70tNnq9ltMvX74crXqm3Rrjug9cR//KmTp16nu6acb3eun18W1v/1ev026NztvQmdNzX3d472tuD2YnT56UX3/9laqZl7zu2qmxePHi5vLYY49JiRIl5Msvv5Tg4OBkeCRw9eu+ceNG85lQl6nYaXVOP1Nqx8bw8HCxMipnsLRcuXKZdWDxXXRqYo0aNUzI0r+W2On/eHXOsa4fio22x0+bNq2sWbPGcd2hQ4fk1KlTZn+wFn2d9TVzfr309dXv43q99Hrn7ZVOZ7Fv/8gjj5hf5s7bREZGmnWKvAc893WHd77m9mCmp8r45ZdfzGkU4J0/67rfqKioJBo5rPa6t23bVvbv32+qpfaLnj5D15+tXLlSLM/VHUmApGylX6lSJdu2bdtsv/32m61EiRLRWulr155SpUqZ251b6RcuXNj266+/mlb6NWrUMBdnR44cMZ3ctAtQyZIlzdd6iYqKStHHh/+129VOijNnzjQdOrt27Wra7Z49e9bcrqc7GDx4cLR2u2nSpLGNGzfOdOLUjpuxtdLXfSxZssS2f/9+23PPPUcrfS943S9evGh+jpcvX246t+kx9PszZ8645DEieV/zmzdvmtNkFCxY0LZ3717zOtsv/C733Nf96tWrtuDgYNOBOTw83Px/vmPHjuYY2sEPnvs7PiZ36tZIOIPH0A9bGsYyZ85szmGiv4CvXLlyT8vstWvXOq7TD+BvvPGGLVu2bLaMGTPaWrRocc+Hs7p165r7xbzo/pDypkyZYgK1nhNF2+/qee2cX6v27dtH2/67774zoVq3L1u2rPkw7kzb6Q8dOtSWJ08e8z+H+vXr2w4dOpRijweued1nzJgR68+1/k8envea23//x3Zx/n8CPOt11//H6//XtS273p4vXz4T0vU0CvDs3/HuHM589D+urt4BAAAAgLdjzRkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDAAAAAAsgHAGAAAAABZAOAMAAAAAcb3/A3at5+FlpQsWAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_feature_effects().set_title(\"Average feature effects on filtered documents\")" + ] + }, + { + "cell_type": "markdown", + "id": "f6f4e3a3-12bb-4e25-95ec-d65ff166c8da", + "metadata": {}, + "source": [ + "Сравнительный анализ классификаторов" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "706e3f1a-ceee-4310-b32c-b8f2c46e0ea6", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import metrics\n", + "from sklearn.utils.extmath import density\n", + "\n", + "\n", + "def benchmark(clf, custom_name=False):\n", + " print(\"_\" * 80)\n", + " print(\"Training: \")\n", + " print(clf)\n", + " t0 = time()\n", + " clf.fit(X_train, y_train)\n", + " train_time = time() - t0\n", + " print(f\"train time: {train_time:.3}s\")\n", + "\n", + " t0 = time()\n", + " pred = clf.predict(X_test)\n", + " test_time = time() - t0\n", + " print(f\"test time: {test_time:.3}s\")\n", + "\n", + " score = metrics.accuracy_score(y_test, pred)\n", + " print(f\"accuracy: {score:.3}\")\n", + "\n", + " if hasattr(clf, \"coef_\"):\n", + " print(f\"dimensionality: {clf.coef_.shape[1]}\")\n", + " print(f\"density: {density(clf.coef_)}\")\n", + " print()\n", + "\n", + " print()\n", + " if custom_name:\n", + " clf_descr = str(custom_name)\n", + " else:\n", + " clf_descr = clf.__class__.__name__\n", + " return clf_descr, score, train_time, test_time" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cf0be004-cd1d-4f7e-bb67-52e411c32d70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "Logistic Regression\n", + "________________________________________________________________________________\n", + "Training: \n", + "LogisticRegression(C=5, max_iter=1000)\n", + "train time: 0.142s\n", + "test time: 0.000592s\n", + "accuracy: 0.772\n", + "dimensionality: 5316\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "Ridge Classifier\n", + "________________________________________________________________________________\n", + "Training: \n", + "RidgeClassifier(solver='sparse_cg')\n", + "train time: 0.013s\n", + "test time: 0.00034s\n", + "accuracy: 0.76\n", + "dimensionality: 5316\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "kNN\n", + "________________________________________________________________________________\n", + "Training: \n", + "KNeighborsClassifier(n_neighbors=100)\n", + "train time: 0.000381s\n", + "test time: 0.0628s\n", + "accuracy: 0.752\n", + "\n", + "================================================================================\n", + "Random Forest\n", + "________________________________________________________________________________\n", + "Training: \n", + "RandomForestClassifier()\n", + "train time: 1.01s\n", + "test time: 0.0311s\n", + "accuracy: 0.693\n", + "\n", + "================================================================================\n", + "Linear SVC\n", + "________________________________________________________________________________\n", + "Training: \n", + "LinearSVC(C=0.1, dual=False)\n", + "train time: 0.0142s\n", + "test time: 0.000421s\n", + "accuracy: 0.752\n", + "dimensionality: 5316\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "log-loss SGD\n", + "________________________________________________________________________________\n", + "Training: \n", + "SGDClassifier(early_stopping=True, loss='log_loss', n_iter_no_change=3)\n", + "train time: 0.0161s\n", + "test time: 0.000508s\n", + "accuracy: 0.771\n", + "dimensionality: 5316\n", + "density: 1.0\n", + "\n", + "\n", + "================================================================================\n", + "NearestCentroid\n", + "________________________________________________________________________________\n", + "Training: \n", + "NearestCentroid()\n", + "train time: 0.0724s\n", + "test time: 1.45s\n", + "accuracy: 0.748\n", + "\n", + "================================================================================\n", + "Complement naive Bayes\n", + "________________________________________________________________________________\n", + "Training: \n", + "ComplementNB(alpha=0.1)\n", + "train time: 0.00173s\n", + "test time: 0.000376s\n", + "accuracy: 0.779\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression, SGDClassifier\n", + "from sklearn.naive_bayes import ComplementNB\n", + "from sklearn.neighbors import KNeighborsClassifier, NearestCentroid\n", + "from sklearn.svm import LinearSVC\n", + "\n", + "results = []\n", + "for clf, name in (\n", + " (LogisticRegression(C=5, max_iter=1000), \"Logistic Regression\"),\n", + " (RidgeClassifier(alpha=1.0, solver=\"sparse_cg\"), \"Ridge Classifier\"),\n", + " (KNeighborsClassifier(n_neighbors=100), \"kNN\"),\n", + " (RandomForestClassifier(), \"Random Forest\"),\n", + " # L2 penalty Linear SVC\n", + " (LinearSVC(C=0.1, dual=False, max_iter=1000), \"Linear SVC\"),\n", + " # L2 penalty Linear SGD\n", + " (\n", + " SGDClassifier(\n", + " loss=\"log_loss\", alpha=1e-4, n_iter_no_change=3, early_stopping=True\n", + " ),\n", + " \"log-loss SGD\",\n", + " ),\n", + " # NearestCentroid (aka Rocchio classifier)\n", + " (NearestCentroid(), \"NearestCentroid\"),\n", + " # Sparse naive Bayes classifier\n", + " (ComplementNB(alpha=0.1), \"Complement naive Bayes\"),\n", + "):\n", + " print(\"=\" * 80)\n", + " print(name)\n", + " results.append(benchmark(clf, name))" + ] + }, + { + "cell_type": "markdown", + "id": "ae49d695-615b-4c4d-8720-525512115ecc", + "metadata": {}, + "source": [ + "Точность, время обучения и тестирования каждого классификатора" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "66a0f892-9100-42d2-b58e-645d1d8705ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "indices = np.arange(len(results))\n", + "\n", + "results = [[x[i] for x in results] for i in range(4)]\n", + "\n", + "clf_names, score, training_time, test_time = results\n", + "training_time = np.array(training_time)\n", + "test_time = np.array(test_time)\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(10, 8))\n", + "ax1.scatter(score, training_time, s=60)\n", + "ax1.set(\n", + " title=\"Score-training time trade-off\",\n", + " yscale=\"log\",\n", + " xlabel=\"test accuracy\",\n", + " ylabel=\"training time (s)\",\n", + ")\n", + "fig, ax2 = plt.subplots(figsize=(10, 8))\n", + "ax2.scatter(score, test_time, s=60)\n", + "ax2.set(\n", + " title=\"Score-test time trade-off\",\n", + " yscale=\"log\",\n", + " xlabel=\"test accuracy\",\n", + " ylabel=\"test time (s)\",\n", + ")\n", + "\n", + "for i, txt in enumerate(clf_names):\n", + " ax1.annotate(txt, (score[i], training_time[i]))\n", + " ax2.annotate(txt, (score[i], test_time[i]))" + ] + }, + { + "cell_type": "markdown", + "id": "18f870fd-0e71-4393-8d37-f413609ea9d4", + "metadata": {}, + "source": [ + "Как видно из всего вышепроделанного, модель успешно справилась с задачей" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}