diff --git a/lab_2/csv/5.medical_insurance.csv b/lab_2/csv/5.medical_insurance.csv
new file mode 100644
index 0000000..7e9cc0f
--- /dev/null
+++ b/lab_2/csv/5.medical_insurance.csv
@@ -0,0 +1,2773 @@
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\ No newline at end of file
diff --git a/lab_2/csv/8.ds_salaries.csv b/lab_2/csv/8.ds_salaries.csv
new file mode 100644
index 0000000..2339741
--- /dev/null
+++ b/lab_2/csv/8.ds_salaries.csv
@@ -0,0 +1,3756 @@
+work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size
+2023,SE,FT,Principal Data Scientist,80000,EUR,85847,ES,100,ES,L
+2023,MI,CT,ML Engineer,30000,USD,30000,US,100,US,S
+2023,MI,CT,ML Engineer,25500,USD,25500,US,100,US,S
+2023,SE,FT,Data Scientist,175000,USD,175000,CA,100,CA,M
+2023,SE,FT,Data Scientist,120000,USD,120000,CA,100,CA,M
+2023,SE,FT,Applied Scientist,222200,USD,222200,US,0,US,L
+2023,SE,FT,Applied Scientist,136000,USD,136000,US,0,US,L
+2023,SE,FT,Data Scientist,219000,USD,219000,CA,0,CA,M
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+2023,SE,FT,Data Scientist,90700,USD,90700,US,0,US,M
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+2023,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
+2023,EN,FT,Applied Scientist,213660,USD,213660,US,0,US,L
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+2023,SE,FT,Data Modeler,90700,USD,90700,US,0,US,M
+2023,SE,FT,Data Scientist,170000,USD,170000,US,0,US,M
+2023,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M
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+2023,MI,FT,Data Analyst,110000,USD,110000,US,100,US,M
+2023,SE,FT,Research Engineer,275000,USD,275000,DE,0,DE,M
+2023,SE,FT,Research Engineer,174000,USD,174000,DE,0,DE,M
+2023,SE,FT,Analytics Engineer,230000,USD,230000,GB,100,GB,M
+2023,SE,FT,Analytics Engineer,143200,USD,143200,GB,100,GB,M
+2023,SE,FT,Business Intelligence Engineer,225000,USD,225000,US,0,US,M
+2023,SE,FT,Business Intelligence Engineer,156400,USD,156400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,130000,USD,130000,US,0,US,M
+2023,SE,FT,Data Strategist,90000,USD,90000,CA,0,CA,M
+2023,SE,FT,Data Strategist,72000,USD,72000,CA,0,CA,M
+2023,SE,FT,Data Engineer,253200,USD,253200,US,0,US,M
+2023,SE,FT,Data Engineer,90700,USD,90700,US,0,US,M
+2023,SE,FT,Computer Vision Engineer,342810,USD,342810,US,0,US,M
+2023,SE,FT,Computer Vision Engineer,184590,USD,184590,US,0,US,M
+2023,MI,FT,Data Engineer,162500,USD,162500,US,0,US,M
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+2023,MI,FT,Data Engineer,75000,USD,75000,US,100,US,M
+2023,MI,FT,Data Engineer,60400,USD,60400,US,100,US,M
+2023,EN,FT,Data Analyst,85000,USD,85000,US,100,US,M
+2023,EN,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Engineer,252000,USD,252000,US,0,US,M
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+2023,MI,FT,Data Analyst,52500,USD,52500,US,100,US,M
+2023,SE,FT,Data Scientist,201036,USD,201036,US,0,US,M
+2023,SE,FT,Data Scientist,134024,USD,134024,US,0,US,M
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+2023,SE,FT,Data Analyst,140000,USD,140000,US,100,US,M
+2023,EN,FT,Data Engineer,62000,USD,62000,US,100,US,M
+2023,EN,FT,Data Engineer,58000,USD,58000,US,100,US,M
+2023,SE,FT,Data Scientist,172000,USD,172000,US,0,US,M
+2023,SE,FT,Data Scientist,115000,USD,115000,US,0,US,M
+2023,EN,FT,Data Engineer,125000,USD,125000,US,0,US,M
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+2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
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+2023,SE,FT,Data Architect,138000,USD,138000,GB,100,GB,M
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+2023,MI,FT,Data Analyst,112000,USD,112000,US,0,US,M
+2023,MI,FT,Data Engineer,162500,USD,162500,US,0,US,M
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+2023,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M
+2023,SE,FT,Data Science Manager,245100,USD,245100,US,0,US,M
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+2023,SE,FT,Data Engineer,147100,USD,147100,US,0,US,M
+2023,SE,FT,Data Engineer,90700,USD,90700,US,0,US,M
+2023,EN,FT,Data Engineer,115100,USD,115100,US,0,US,M
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+2023,SE,FT,Data Engineer,168400,USD,168400,US,0,US,M
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+2023,SE,FT,Applied Scientist,222200,USD,222200,US,0,US,L
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+2023,MI,FT,Data Analyst,85000,USD,85000,US,0,US,M
+2023,MI,FT,Data Engineer,70000,GBP,85066,GB,100,GB,M
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+2023,MI,FT,Data Engineer,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,185000,USD,185000,US,100,US,M
+2023,SE,FT,Data Analyst,120250,USD,120250,US,100,US,M
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+2023,SE,FT,Research Scientist,94192,USD,94192,US,0,US,M
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+2023,SE,FT,Data Infrastructure Engineer,143000,USD,143000,US,100,US,M
+2023,SE,FT,Data Analyst,70000,USD,70000,US,0,US,M
+2023,SE,FT,Data Analyst,55000,USD,55000,US,0,US,M
+2023,SE,FT,Analytics Engineer,275300,USD,275300,US,100,US,M
+2023,SE,FT,Analytics Engineer,183500,USD,183500,US,100,US,M
+2023,MI,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,MI,FT,Data Analyst,65000,USD,65000,US,100,US,M
+2023,MI,FT,ML Engineer,160000,USD,160000,US,0,US,M
+2023,MI,FT,ML Engineer,147000,USD,147000,US,0,US,M
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+2023,SE,FT,Data Architect,113900,USD,113900,US,100,US,M
+2023,MI,FT,Software Data Engineer,100000,SGD,75020,SG,100,SG,L
+2023,SE,FT,Data Analyst,180180,USD,180180,US,0,US,M
+2023,SE,FT,Data Analyst,106020,USD,106020,US,0,US,M
+2023,MI,FT,Data Scientist,1400000,INR,17022,IN,100,IN,L
+2023,EN,FT,AI Programmer,70000,USD,70000,IN,0,AU,L
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+2023,MI,FT,Machine Learning Engineer,150000,USD,150000,US,0,US,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Data Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Data Scientist,105000,USD,105000,US,0,US,M
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+2023,EX,FT,Data Engineer,116704,USD,116704,US,100,US,M
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+2023,EN,FT,Data Analyst,60000,USD,60000,US,100,US,M
+2023,MI,FT,Analytics Engineer,185700,USD,185700,US,0,US,M
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+2023,SE,FT,Data Analyst,110600,USD,110600,US,0,US,M
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+2023,SE,FT,Data Operations Engineer,136850,USD,136850,US,100,US,M
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+2023,SE,FT,Data Engineer,178500,USD,178500,US,100,US,M
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+2023,MI,FT,Data Scientist,45000,EUR,48289,ES,0,ES,M
+2023,MI,FT,Data Engineer,70000,EUR,75116,SI,100,SI,M
+2023,MI,FT,Data Engineer,45000,EUR,48289,SI,100,SI,M
+2023,SE,FT,Machine Learning Engineer,161000,GBP,195652,GB,0,GB,M
+2023,SE,FT,Machine Learning Engineer,83300,GBP,101228,GB,0,GB,M
+2023,SE,FT,Data Engineer,112700,GBP,136956,GB,0,GB,M
+2023,SE,FT,Data Engineer,83300,GBP,101228,GB,0,GB,M
+2023,SE,FT,Data Engineer,252000,USD,252000,US,0,US,M
+2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M
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+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,ML Engineer,260000,USD,260000,CA,100,CA,M
+2023,SE,FT,ML Engineer,110000,USD,110000,CA,100,CA,M
+2023,SE,FT,Analytics Engineer,170000,USD,170000,US,100,US,M
+2023,SE,FT,Analytics Engineer,130000,USD,130000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M
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+2023,SE,FT,Data Scientist,239748,USD,239748,US,0,US,M
+2023,SE,FT,Data Scientist,159832,USD,159832,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,126000,USD,126000,US,0,US,M
+2023,MI,FT,Data Scientist,128750,USD,128750,US,0,US,M
+2023,MI,FT,Data Scientist,106250,USD,106250,US,0,US,M
+2023,SE,FT,Data Engineer,252000,USD,252000,US,0,US,M
+2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M
+2023,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M
+2023,SE,FT,Data Science Manager,245100,USD,245100,US,0,US,M
+2023,MI,FT,Data Analytics Manager,155000,USD,155000,US,0,US,M
+2023,MI,FT,Data Analytics Manager,140000,USD,140000,US,0,US,M
+2023,EX,FT,Data Engineer,167500,USD,167500,US,0,US,M
+2023,EX,FT,Data Engineer,106500,USD,106500,US,0,US,M
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+2023,SE,FT,Data Analyst,138000,USD,138000,US,100,US,M
+2023,MI,FT,Data Analyst,130000,USD,130000,CA,100,CA,M
+2023,MI,FT,Data Analyst,100000,USD,100000,CA,100,CA,M
+2023,SE,FT,Deep Learning Researcher,115000,EUR,123405,DE,0,DE,L
+2023,SE,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,BI Analyst,160000,USD,160000,US,0,US,M
+2023,SE,FT,BI Analyst,135000,USD,135000,US,0,US,M
+2023,MI,FT,Data Science Manager,104500,USD,104500,US,0,US,M
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+2023,MI,FT,Data Science Consultant,90000,USD,90000,US,0,US,M
+2023,MI,FT,Data Science Consultant,70000,USD,70000,US,0,US,M
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+2023,MI,FT,Research Scientist,125000,USD,125000,US,100,US,M
+2023,SE,FT,Data Analyst,127000,USD,127000,US,100,US,M
+2023,SE,FT,Data Analyst,94000,USD,94000,US,100,US,M
+2023,SE,FT,Data Scientist,210550,USD,210550,US,0,US,M
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+2023,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
+2023,SE,FT,Data Analyst,161500,USD,161500,US,100,US,M
+2023,SE,FT,Data Analyst,119500,USD,119500,US,100,US,M
+2023,SE,FT,Data Analyst,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Analyst,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Scientist,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Scientist,148750,USD,148750,US,0,US,M
+2023,SE,FT,Data Analytics Specialist,105000,USD,105000,US,0,US,M
+2023,SE,FT,Data Analytics Specialist,85000,USD,85000,US,0,US,M
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+2023,EN,FT,AI Developer,200000,EUR,214618,DE,100,DE,L
+2023,MI,FT,Data Engineer,72000,USD,72000,MX,100,MX,M
+2023,MI,FT,Data Engineer,60000,USD,60000,MX,100,MX,M
+2023,SE,FT,Machine Learning Engineer,153400,USD,153400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,122700,USD,122700,US,0,US,M
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+2023,MI,FT,Data Engineer,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Scientist,186300,USD,186300,US,100,US,M
+2023,SE,FT,Data Scientist,123900,USD,123900,US,100,US,M
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+2023,SE,FT,Machine Learning Engineer,122700,USD,122700,US,0,US,M
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+2023,SE,FT,Data Analyst,185900,USD,185900,US,0,US,M
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+2023,SE,FT,Data Analyst,106800,USD,106800,US,0,US,M
+2023,SE,FL,Software Data Engineer,50000,USD,50000,NG,50,AU,M
+2023,EN,FT,Data Analyst,100000,USD,100000,UZ,100,US,L
+2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M
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+2023,EX,FT,Data Engineer,310000,USD,310000,US,100,US,M
+2023,EX,FT,Data Engineer,239000,USD,239000,US,100,US,M
+2023,SE,FT,Data Analyst,125000,USD,125000,US,0,US,M
+2023,SE,FT,Data Analyst,110000,USD,110000,US,0,US,M
+2023,EN,FT,Data Analyst,150000,USD,150000,US,0,US,M
+2023,EN,FT,Data Analyst,100000,USD,100000,US,0,US,M
+2023,SE,FT,Data Scientist,149076,USD,149076,US,0,US,M
+2023,SE,FT,Data Scientist,82365,USD,82365,US,0,US,M
+2023,MI,FT,Data Engineer,146000,USD,146000,US,0,US,M
+2023,MI,FT,Data Engineer,75000,USD,75000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M
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+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
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+2023,MI,FT,Data Analyst,40000,GBP,48609,GB,0,GB,M
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+2023,SE,FT,Data Analyst,75000,USD,75000,US,100,US,M
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+2023,SE,FT,Data Engineer,78000,USD,78000,US,100,US,M
+2023,MI,FT,Data Engineer,154000,USD,154000,US,0,US,M
+2023,MI,FT,Data Engineer,116000,USD,116000,US,0,US,M
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+2023,SE,FT,Data Scientist,136000,USD,136000,US,0,US,M
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+2023,MI,FT,Data Analytics Manager,155000,USD,155000,US,0,US,M
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+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
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+2023,SE,FT,Machine Learning Engineer,128280,USD,128280,US,0,US,M
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+2023,SE,FT,Research Engineer,100000,EUR,107309,DE,100,DE,S
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+2023,SE,FT,Machine Learning Engineer,174000,USD,174000,DE,0,DE,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
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+2023,SE,FT,Data Analyst,85000,USD,85000,US,100,US,M
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+2023,SE,FT,Data Engineer,124740,USD,124740,US,0,US,M
+2023,SE,FT,Data Engineer,65488,USD,65488,US,0,US,M
+2023,SE,FT,Data Quality Analyst,72200,USD,72200,US,0,US,M
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+2023,SE,FT,Data Analyst,86466,USD,86466,US,100,US,M
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+2023,MI,FT,Insight Analyst,42000,GBP,51039,GB,0,GB,M
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+2023,SE,FT,Data Engineer,185000,USD,185000,US,0,US,M
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+2023,MI,FT,AI Scientist,36000,EUR,38631,ES,50,ES,L
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
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+2023,SE,FT,Data Engineer,167580,USD,167580,US,0,US,M
+2023,SE,FT,Data Engineer,87980,USD,87980,US,0,US,M
+2023,SE,FT,Data Engineer,202000,USD,202000,US,100,US,M
+2023,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
+2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Data Engineer,104000,USD,104000,US,100,US,M
+2023,SE,FT,Data Engineer,65000,USD,65000,US,100,US,M
+2023,SE,FT,Research Engineer,230000,USD,230000,US,0,US,M
+2023,SE,FT,Research Engineer,148000,USD,148000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,269000,USD,269000,CA,100,CA,M
+2023,SE,FT,Machine Learning Engineer,158000,USD,158000,CA,100,CA,M
+2023,SE,FT,Analytics Engineer,197000,USD,197000,US,0,US,M
+2023,SE,FT,Analytics Engineer,106000,USD,106000,US,0,US,M
+2023,MI,FT,Deep Learning Engineer,150000,USD,150000,US,100,US,M
+2023,MI,FT,Deep Learning Engineer,100000,USD,100000,US,100,US,M
+2023,SE,FT,Data Engineer,290000,USD,290000,US,100,US,M
+2023,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
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+2023,SE,FT,Data Engineer,172800,USD,172800,US,0,US,M
+2023,SE,FT,Data Scientist,300240,USD,300240,US,0,US,M
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+2023,SE,FT,Data Scientist,200160,USD,200160,US,0,US,M
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+2023,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M
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+2023,SE,FT,Data Engineer,66000,USD,66000,US,100,US,M
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+2023,MI,FT,Machine Learning Engineer,126277,USD,126277,US,0,US,M
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+2023,MI,FT,Data Analyst,60000,USD,60000,US,0,US,M
+2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M
+2023,SE,FT,Data Engineer,146000,USD,146000,US,100,US,M
+2023,SE,FT,Data Engineer,200000,USD,200000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M
+2023,EX,FT,Data Engineer,310000,USD,310000,US,100,US,M
+2023,EX,FT,Data Engineer,239000,USD,239000,US,100,US,M
+2023,SE,FT,Data Analyst,142000,USD,142000,US,100,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,100,US,M
+2023,SE,FT,Data Scientist,155000,USD,155000,US,0,US,M
+2023,SE,FT,Data Scientist,139500,USD,139500,US,0,US,M
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+2023,MI,FT,Data Engineer,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M
+2023,SE,FT,Data Engineer,146000,USD,146000,US,100,US,M
+2023,MI,FT,Data Analyst,90000,GBP,109371,HR,0,HR,M
+2023,MI,FT,Data Analyst,60000,GBP,72914,HR,0,HR,M
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+2023,SE,FT,Data Science Lead,172200,USD,172200,US,0,US,M
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+2023,SE,FT,Data Scientist,156400,USD,156400,US,0,US,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Data Engineer,109400,USD,109400,US,0,US,M
+2023,MI,FT,Data Engineer,125000,USD,125000,US,0,US,M
+2023,MI,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,100000,EUR,107309,FR,100,FR,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,70000,EUR,75116,FR,100,FR,M
+2023,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Data Analyst,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Analyst,110600,USD,110600,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,140000,USD,140000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,140000,USD,140000,US,100,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,MI,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,MI,FT,Data Science Lead,60000,GBP,72914,GB,0,GB,M
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+2023,EN,FT,Data Analyst,60000,USD,60000,US,100,US,M
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+2023,EN,FT,Research Engineer,60000,USD,60000,GB,100,GB,M
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+2023,SE,FT,Machine Learning Engineer,90700,USD,90700,US,0,US,M
+2023,SE,FT,Data Engineer,230000,USD,230000,US,0,US,M
+2023,SE,FT,Data Engineer,170000,USD,170000,US,0,US,M
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+2023,SE,FT,Data Analyst,108000,USD,108000,US,0,US,M
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+2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
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+2023,SE,FT,Data Analyst,52000,EUR,55800,ES,100,ES,M
+2023,SE,FT,Data Analyst,48000,EUR,51508,ES,100,ES,M
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+2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M
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+2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M
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+2023,SE,FT,Data Engineer,63000,USD,63000,US,100,US,M
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+2023,SE,FT,Data Engineer,124740,USD,124740,US,0,US,M
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+2023,SE,FT,Data Engineer,95000,EUR,101943,IE,100,IE,M
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+2023,EX,FT,Data Engineer,235000,USD,235000,US,0,US,M
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+2023,MI,FT,Data Analyst,90000,GBP,109371,GB,0,GB,M
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+2023,MI,FT,Machine Learning Engineer,80000,GBP,97218,GB,0,GB,M
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+2023,MI,FT,Machine Learning Engineer,100000,GBP,121523,GB,0,GB,M
+2023,SE,FT,Computer Vision Software Engineer,50000,EUR,53654,NL,100,CA,L
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+2023,SE,FT,Data Engineer,81500,USD,81500,US,0,US,M
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+2023,MI,FT,Data Engineer,55000,EUR,59020,ES,100,ES,M
+2023,SE,FT,Director of Data Science,170000,CAD,125686,CA,50,CA,M
+2023,SE,FT,Azure Data Engineer,100000,USD,100000,NL,50,NL,L
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+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
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+2023,SE,FT,Data Engineer,167200,USD,167200,US,100,US,M
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+2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
+2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+2022,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
+2022,SE,FT,Data Analyst,69000,USD,69000,US,100,US,M
+2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
+2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+2022,SE,FT,Data Analyst,150075,USD,150075,US,100,US,M
+2022,SE,FT,Data Analyst,110925,USD,110925,US,100,US,M
+2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
+2022,SE,FT,Data Engineer,25000,USD,25000,US,100,US,M
+2022,SE,FT,Data Analyst,126500,USD,126500,US,100,US,M
+2022,SE,FT,Data Analyst,106260,USD,106260,US,100,US,M
+2022,SE,FT,Data Engineer,220110,USD,220110,US,100,US,M
+2022,SE,FT,Data Engineer,160080,USD,160080,US,100,US,M
+2022,SE,FT,Data Analyst,105000,USD,105000,US,100,US,M
+2022,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
+2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
+2022,SE,FT,Data Analyst,135000,USD,135000,US,100,US,M
+2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M
+2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+2022,MI,FT,Data Analyst,135000,USD,135000,US,100,US,M
+2022,MI,FT,Data Analyst,50000,USD,50000,US,100,US,M
+2022,SE,FT,Data Scientist,220000,USD,220000,US,100,US,M
+2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+2022,MI,FT,Data Scientist,140000,GBP,172386,GB,0,GB,M
+2022,MI,FT,Data Scientist,70000,GBP,86193,GB,0,GB,M
+2022,SE,FT,Machine Learning Engineer,220000,USD,220000,US,100,US,M
+2022,SE,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,M
+2022,MI,FT,Data Scientist,200000,USD,200000,US,100,US,M
+2022,MI,FT,Data Scientist,120000,USD,120000,US,100,US,M
+2022,SE,FT,Machine Learning Engineer,120000,USD,120000,AE,100,AE,S
+2022,SE,FT,Machine Learning Engineer,65000,USD,65000,AE,100,AE,S
+2022,EX,FT,Data Engineer,324000,USD,324000,US,100,US,M
+2022,EX,FT,Data Engineer,216000,USD,216000,US,100,US,M
+2022,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
+2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
+2022,SE,FT,Data Scientist,185100,USD,185100,US,100,US,M
+2022,SE,FT,Data Scientist,104890,USD,104890,US,100,US,M
+2022,SE,FT,Data Engineer,105000,USD,105000,US,100,US,M
+2022,SE,FT,Data Engineer,80000,USD,80000,US,100,US,M
+2022,MI,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M
+2020,SE,FT,Machine Learning Manager,157000,CAD,117104,CA,50,CA,L
+2022,EX,FT,Director of Data Science,250000,CAD,192037,CA,50,CA,L
+2022,MI,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,S
+2022,MI,FT,Business Data Analyst,1400000,INR,17805,IN,100,IN,M
+2022,MI,FT,Data Scientist,2400000,INR,30523,IN,100,IN,L
+2022,MI,FT,Machine Learning Infrastructure Engineer,53000,EUR,55685,PT,50,PT,L
+2022,MI,PT,Data Engineer,50000,EUR,52533,DE,50,DE,L
+2022,EN,FT,Data Scientist,1400000,INR,17805,IN,100,IN,M
+2022,MI,FT,Applied Machine Learning Scientist,75000,USD,75000,BO,100,US,L
+2022,MI,FT,Applied Data Scientist,157000,USD,157000,US,100,US,L
+2022,MI,FT,Business Data Analyst,90000,CAD,69133,CA,50,CA,L
+2022,EN,FT,Data Engineer,65000,USD,65000,US,100,US,S
+2022,SE,FT,Machine Learning Engineer,65000,EUR,68293,IE,100,IE,S
+2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L
+2022,MI,FT,Data Scientist,88000,CAD,67597,CA,100,CA,M
+2022,EN,FT,Computer Vision Engineer,10000,USD,10000,PT,100,LU,M
+2022,MI,FT,Data Analyst,20000,USD,20000,GR,100,GR,S
+2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L
+2022,SE,FT,Head of Data,200000,USD,200000,MY,100,US,M
+2022,EN,FT,Data Scientist,66500,CAD,51081,CA,100,CA,L
+2022,MI,FT,Data Scientist,78000,USD,78000,US,100,US,M
+2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
+2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
+2022,MI,FT,Machine Learning Engineer,121000,AUD,83864,AU,100,AU,L
+2022,EN,FT,Data Scientist,40000,USD,40000,JP,100,MY,L
+2022,MI,FT,Head of Data,30000,EUR,31520,EE,100,EE,S
+2022,SE,FT,Machine Learning Engineer,57000,EUR,59888,NL,100,NL,L
+2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
+2022,SE,FT,Research Scientist,85000,EUR,89306,FR,50,FR,L
+2022,EN,FT,Data Scientist,120000,AUD,83171,AU,50,AU,M
+2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M
+2022,MI,FT,Machine Learning Scientist,153000,USD,153000,US,50,US,M
+2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M
+2022,SE,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M
+2022,MI,FT,Data Scientist,150000,PLN,33609,PL,100,PL,L
+2022,MI,FT,Principal Data Analyst,75000,USD,75000,CA,100,CA,S
+2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S
+2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,30469,TN,100,CZ,M
+2021,MI,FT,Research Scientist,69999,USD,69999,CZ,50,CZ,L
+2022,EN,FT,Data Engineer,52800,EUR,55475,PK,100,DE,M
+2022,MI,FT,Research Scientist,59000,EUR,61989,AT,0,AT,L
+2022,SE,FT,Data Science Manager,152500,USD,152500,US,100,US,M
+2022,EN,FT,Research Scientist,120000,USD,120000,US,100,US,L
+2022,MI,FT,Data Scientist,135000,USD,135000,US,100,US,L
+2022,SE,FT,Data Analytics Lead,405000,USD,405000,US,100,US,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,0,US,L
+2021,SE,FT,Data Science Manager,240000,USD,240000,US,0,US,L
+2021,MI,FT,Data Analyst,135000,USD,135000,US,100,US,L
+2021,EN,FT,Data Scientist,80000,USD,80000,US,100,US,M
+2022,SE,FT,Applied Data Scientist,380000,USD,380000,US,100,US,L
+2022,MI,FT,Data Scientist,115000,CHF,120402,CH,0,CH,L
+2022,SE,FT,Applied Data Scientist,177000,USD,177000,US,100,US,L
+2022,MI,FT,Data Engineer,62000,EUR,65141,FR,100,FR,M
+2022,MI,FT,Data Scientist,48000,USD,48000,RU,100,US,S
+2022,EN,FT,Data Analytics Engineer,20000,USD,20000,PK,0,PK,M
+2021,SE,FT,Principal Data Scientist,220000,USD,220000,US,0,US,L
+2021,MI,FT,ML Engineer,8500000,JPY,77364,JP,50,JP,S
+2021,MI,FT,ML Engineer,7000000,JPY,63711,JP,50,JP,S
+2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S
+2021,MI,FT,Data Analyst,90000,USD,90000,US,100,US,M
+2022,MI,FL,Data Scientist,100000,USD,100000,CA,100,US,M
+2021,EN,FT,Data Scientist,100000,USD,100000,US,0,US,S
+2022,EN,PT,Data Scientist,100000,USD,100000,DZ,50,DZ,M
+2022,SE,FT,Research Scientist,144000,USD,144000,US,50,US,L
+2022,SE,FT,Principal Data Scientist,148000,EUR,155499,DE,100,DE,M
+2021,SE,FT,Computer Vision Engineer,24000,USD,24000,BR,100,BR,M
+2021,MI,FT,Applied Machine Learning Scientist,38400,USD,38400,VN,100,US,M
+2022,EN,FT,Financial Data Analyst,100000,USD,100000,US,50,US,L
+2021,MI,FT,Data Scientist,82500,USD,82500,US,100,US,S
+2021,EN,FT,Data Scientist,42000,EUR,49646,FR,50,FR,M
+2021,SE,FT,Lead Data Scientist,3000000,INR,40570,IN,50,IN,L
+2022,EN,FT,Data Engineer,120000,USD,120000,US,100,US,M
+2022,SE,FT,Lead Machine Learning Engineer,80000,EUR,84053,DE,0,DE,M
+2021,EN,FT,Machine Learning Engineer,20000,USD,20000,IN,100,IN,S
+2022,EN,FT,Computer Vision Engineer,125000,USD,125000,US,0,US,M
+2021,MI,FT,Data Scientist,700000,INR,9466,IN,0,IN,S
+2021,SE,FT,Machine Learning Scientist,120000,USD,120000,US,50,US,S
+2021,EN,PT,Data Analyst,8760,EUR,10354,ES,50,ES,M
+2021,EN,FT,Applied Data Scientist,80000,GBP,110037,GB,0,GB,L
+2022,EN,FT,ML Engineer,20000,EUR,21013,PT,100,PT,L
+2021,EN,FT,Data Analyst,50000,USD,50000,US,100,US,M
+2021,SE,FT,Principal Data Engineer,200000,USD,200000,US,100,US,M
+2021,MI,FT,Big Data Engineer,60000,USD,60000,ES,50,RO,M
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,EN,FT,Machine Learning Developer,100000,USD,100000,IQ,50,IQ,S
+2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L
+2021,SE,FT,Machine Learning Engineer,70000,EUR,82744,BE,50,BE,M
+2020,MI,FT,Data Engineer,51999,EUR,59303,DE,100,DE,S
+2021,MI,FT,Research Scientist,53000,EUR,62649,FR,50,FR,M
+2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L
+2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L
+2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L
+2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S
+2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L
+2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L
+2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
+2021,EX,FT,Director of Data Science,110000,EUR,130026,DE,50,DE,M
+2021,EN,FT,Data Science Consultant,54000,EUR,63831,DE,50,DE,L
+2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
+2021,EX,FT,Head of Data Science,85000,USD,85000,RU,0,RU,M
+2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L
+2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S
+2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M
+2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
+2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
+2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
+2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L
+2021,SE,FT,Data Science Manager,144000,USD,144000,US,100,US,L
+2021,EN,FT,Data Scientist,13400,USD,13400,UA,100,UA,L
+2021,MI,FT,Data Scientist,95000,CAD,75774,CA,100,CA,L
+2021,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
+2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
+2021,SE,FT,Data Engineer,153000,USD,153000,US,100,US,L
+2021,MI,FT,Data Engineer,90000,USD,90000,US,100,US,L
+2021,EN,FT,Data Analyst,90000,USD,90000,US,100,US,S
+2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S
+2021,MI,FT,Data Scientist,50000,USD,50000,NG,100,NG,L
+2021,EN,PT,AI Scientist,12000,USD,12000,PK,100,US,M
+2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409,IN,50,IN,M
+2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L
+2021,MI,FT,Applied Data Scientist,68000,CAD,54238,GB,50,CA,L
+2021,MI,FT,Machine Learning Engineer,40000,EUR,47282,ES,100,ES,S
+2021,EX,FT,Director of Data Science,130000,EUR,153667,IT,100,PL,L
+2021,MI,FT,Data Engineer,110000,PLN,28476,PL,100,PL,L
+2021,MI,FT,Data Analytics Engineer,110000,USD,110000,US,100,US,L
+2021,EN,FT,Research Scientist,60000,GBP,82528,GB,50,GB,L
+2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
+2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
+2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
+2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
+2020,EN,FT,Machine Learning Engineer,138000,USD,138000,US,100,US,S
+2021,MI,FT,Data Engineer,140000,USD,140000,US,100,US,L
+2021,SE,FT,Data Analytics Engineer,67000,EUR,79197,DE,100,DE,L
+2021,SE,FT,Lead Data Analyst,170000,USD,170000,US,100,US,L
+2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M
+2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
+2021,MI,FT,BI Data Analyst,100000,USD,100000,US,100,US,M
+2021,SE,FT,Data Scientist,45000,EUR,53192,FR,50,FR,L
+2021,EX,FT,Head of Data,235000,USD,235000,US,100,US,L
+2021,EX,FT,BI Data Analyst,150000,USD,150000,IN,100,US,L
+2020,EX,FT,Data Engineer,70000,EUR,79833,ES,50,ES,L
+2021,EN,FT,Machine Learning Scientist,225000,USD,225000,US,100,US,L
+2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,100,DE,S
+2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
+2021,SE,FT,Marketing Data Analyst,75000,EUR,88654,GR,100,DK,L
+2021,SE,FT,Lead Data Engineer,75000,GBP,103160,GB,100,GB,S
+2021,SE,FT,Data Engineer,82500,GBP,113476,GB,100,GB,M
+2021,SE,FT,Machine Learning Engineer,80000,EUR,94564,DE,50,DE,L
+2021,EN,FT,Data Engineer,2250000,INR,30428,IN,100,IN,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
+2021,SE,FT,Data Engineer,115000,USD,115000,US,100,US,S
+2021,MI,FT,Research Scientist,235000,CAD,187442,CA,100,CA,L
+2021,MI,FT,Data Analyst,37456,GBP,51519,GB,50,GB,L
+2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
+2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
+2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,L
+2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
+2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746,DE,100,US,S
+2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000,US,100,US,M
+2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
+2021,MI,FT,Cloud Data Engineer,120000,SGD,89294,SG,50,SG,L
+2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L
+2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L
+2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
+2021,SE,FT,Cloud Data Engineer,160000,USD,160000,BR,100,US,S
+2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,SE,FT,Data Engineer,174000,USD,174000,US,100,US,L
+2021,MI,FT,Data Analyst,93000,USD,93000,US,100,US,L
+2021,EN,FT,Data Scientist,2100000,INR,28399,IN,100,IN,M
+2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L
+2021,EN,FT,Data Scientist,90000,USD,90000,US,100,US,S
+2020,MI,FT,Data Engineer,61500,EUR,70139,FR,50,FR,L
+2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
+2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
+2021,SE,FT,Principal Data Analyst,170000,USD,170000,US,100,US,M
+2021,SE,FT,Data Engineer,70000,GBP,96282,GB,50,GB,L
+2021,MI,FT,Data Engineer,108000,TRY,12103,TR,0,TR,M
+2021,EN,FT,Data Scientist,31000,EUR,36643,FR,50,FR,L
+2021,MI,FT,Data Engineer,52500,GBP,72212,GB,50,GB,L
+2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
+2021,SE,FT,Big Data Architect,125000,CAD,99703,CA,50,CA,M
+2021,SE,FT,Data Scientist,165000,USD,165000,US,100,US,L
+2021,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L
+2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L
+2021,EN,FT,Data Engineer,1600000,INR,21637,IN,50,IN,M
+2020,EN,FT,Research Scientist,42000,USD,42000,NL,50,NL,L
+2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
+2021,MI,FT,Research Scientist,80000,CAD,63810,CA,100,CA,M
+2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S
+2021,MI,FT,Head of Data Science,110000,USD,110000,US,0,US,S
+2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L
+2021,SE,FT,Data Analyst,200000,USD,200000,US,100,US,L
+2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M
+2021,SE,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S
+2021,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
+2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L
+2021,EN,FT,Data Engineer,72500,USD,72500,US,100,US,L
+2021,SE,FT,Machine Learning Engineer,185000,USD,185000,US,50,US,L
+2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L
+2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L
+2021,MI,FT,Data Engineer,112000,USD,112000,US,100,US,L
+2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L
+2021,SE,FT,Data Scientist,180000,TRY,20171,TR,50,TR,L
+2021,SE,FT,AI Scientist,55000,USD,55000,ES,100,ES,L
+2021,EN,FT,Data Scientist,58000,USD,58000,US,50,US,L
+2021,EN,FT,Data Scientist,100000,USD,100000,US,100,US,M
+2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M
+2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S
+2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,0,DE,L
+2021,SE,CT,Staff Data Scientist,105000,USD,105000,US,100,US,M
+2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
+2021,EN,FT,Data Engineer,55000,EUR,65013,DE,50,DE,M
+2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M
+2021,MI,FT,Data Engineer,111775,USD,111775,US,0,US,M
+2021,MI,FT,Data Engineer,93150,USD,93150,US,0,US,M
+2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S
+2021,MI,FT,Data Scientist,21600,EUR,25532,RS,100,DE,S
+2021,SE,FT,Machine Learning Engineer,4900000,INR,66265,IN,0,IN,L
+2021,MI,FT,Data Scientist,1250000,INR,16904,IN,100,IN,S
+2021,SE,FT,Data Analyst,54000,EUR,63831,DE,50,DE,L
+2020,SE,FT,Lead Data Scientist,190000,USD,190000,US,100,US,S
+2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L
+2021,EN,FT,Big Data Engineer,1200000,INR,16228,IN,100,IN,L
+2021,SE,FT,Data Analyst,90000,CAD,71786,CA,100,CA,M
+2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
+2021,SE,FT,Data Scientist,135000,USD,135000,US,0,US,L
+2021,EN,FT,Machine Learning Engineer,21000,EUR,24823,DE,50,DE,M
+2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L
+2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L
+2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
+2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L
+2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L
+2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L
+2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
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diff --git a/lab_2/csv/9.Stores.csv b/lab_2/csv/9.Stores.csv
new file mode 100644
index 0000000..d5cd92c
--- /dev/null
+++ b/lab_2/csv/9.Stores.csv
@@ -0,0 +1,897 @@
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diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb
new file mode 100644
index 0000000..877bd43
--- /dev/null
+++ b/lab_2/lab2.ipynb
@@ -0,0 +1,2374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Датасет 1. Зарплата специалистов по обработке данных в 2023 году\n",
+    "https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary\n",
+    "## Анализ сведений\n",
+    "### Краткое описание\n",
+    "Этот датасет посвящен анализу факторов, влияющих на уровень заработных плат специалистов в области Data Science. Включенные данные позволяют исследовать взаимосвязь между различными характеристиками сотрудников и их доходами.\n",
+    "### Проблемная область\n",
+    "Датасет касается анализа факторов, влияющих на заработную плату специалистов в области Data Science, что является важным аспектом для понимания экономических и профессиональных тенденций на рынке труда в этой сфере. Проблемная область включает:\n",
+    "- Анализ влияния опыта, типа занятости, географического положения и других факторов на размер заработной платы специалистов.\n",
+    "- Определение ключевых факторов, влияющих на рост зарплаты в профессии Data Scientist.\n",
+    "- Выявление тенденций, которые могут помочь работодателям и специалистам принимать решения о карьере, зарплате и условиях работы.\n",
+    "\n",
+    "### Актуальность\n",
+    "- **Рост профессии**: Data Science — это одна из самых востребованных и динамично развивающихся областей на рынке труда. Понимание факторов, влияющих на зарплату, важно для профессионалов и компаний.\n",
+    "- **Тенденции на рынке труда**: В условиях глобализации и удаленной работы важно понять, как тип занятости и местоположение компании влияют на оплату труда.\n",
+    "- **Оптимизация карьерных решений**: Анализ данных поможет специалистам принимать обоснованные решения при выборе карьерных путей, а работодателям — разрабатывать конкурентоспособные предложения по зарплате и условиям работы.\n",
+    "\n",
+    "### Объекты наблюдений\n",
+    "Объектами наблюдения являются **Data Scientists**, то есть специалисты, занимающиеся анализом данных. Каждый объект представляет собой запись, которая отражает характеристики работы конкретного специалиста в определенный год.\n",
+    "\n",
+    "### Атрибуты объектов\n",
+    "Каждый объект имеет следующие атрибуты:\n",
+    "- **work_year** — год, в котором была выплачена зарплата. Позволяет отслеживать изменения зарплат в разные годы.\n",
+    "- **experience_level** — уровень опыта сотрудника (Entry-level, Mid-level, Senior-level, Executive-level). Это важный атрибут, который влияет на зарплату.\n",
+    "- **employment_type** — тип занятости (Part-time, Full-time, Contract, Freelance). Определяет, является ли работа постоянной или временной.\n",
+    "- **job_title** — должность, занимаемая сотрудником. Важно для анализа различий между зарплатами для разных специализаций.\n",
+    "- **salary** — общая сумма заработной платы.\n",
+    "- **salary_currency** — валюта, в которой выплачена зарплата.\n",
+    "- **salaryinusd** — зарплата в долларах США. Этот атрибут используется для стандартизации данных.\n",
+    "- **employee_residence** — страна проживания сотрудника. Влияет на размер зарплаты и может быть важным для анализа глобальных различий.\n",
+    "- **remote_ratio** — доля работы, выполняемой удаленно. Важно для анализа влияния удаленной работы на уровень зарплаты.\n",
+    "- **company_location** — страна, где находится основная офисная локация компании. Это атрибут, который позволяет анализировать региональные различия в зарплатах.\n",
+    "- **company_size** — размер компании, выраженный через медиану числа сотрудников. Размер компании может влиять на оплату труда, так как крупные компании часто предлагают более высокие зарплаты.\n",
+    "\n",
+    "### Связь между объектами\n",
+    "Связь между объектами заключается в том, что все атрибуты в совокупности описывают профессиональную деятельность и условия работы каждого специалиста. Например:\n",
+    "- **experience_level** и **job_title** могут быть взаимосвязаны, так как более высокие должности (например, Senior или Executive) соответствуют большему опыту.\n",
+    "- **salary** напрямую зависит от **experience_level**, **employment_type**, **employee_residence**, **company_location**, и **company_size**, а также от уровня удаленности работы (**remote_ratio**).\n",
+    "- **salaryinusd** служит для нормализации и сопоставления зарплат между различными странами и валютами.\n",
+    "- **employee_residence** и **company_location** могут быть связаны с различиями в заработной плате, так как зарплаты могут варьироваться в зависимости от страны проживания и местоположения компании.\n",
+    "\n",
+    "## Качество набора данных\n",
+    "### Информативность\n",
+    "Датасет содержит разнообразные атрибуты, которые предоставляют полезную информацию для анализа факторов, влияющих на зарплату специалистов в области Data Science. Включенные переменные, такие как **уровень опыта**, **тип занятости**, **зарплата**, **географическое расположение** и **удаленная работа**, позволяют провести многогранный анализ и выявить значимые закономерности. Однако, отсутствие информации о дополнительной квалификации или навыках специалистов (например, знание конкретных технологий или инструментов) может ограничить глубину анализа.\n",
+    "\n",
+    "### Степень покрытия\n",
+    "Датасет охватывает достаточно широкий спектр факторов, влияющих на зарплату, включая географические данные (страна проживания, местоположение компании) и рабочие условия (удаленная работа, тип занятости). Однако степень покрытия может быть ограничена:\n",
+    "- Данные охватывают только одну профессиональную категорию (Data Science), что не позволяет делать выводы о других областях.\n",
+    "- Пропущенные данные по некоторым атрибутам могут снизить полноту информации (например, отсутствие данных по размеру компании или типу работы для некоторых записей).\n",
+    "\n",
+    "### Соответствие реальным данным\n",
+    "Датасет в целом отражает реальные условия рынка труда для специалистов в области Data Science. Он содержит важные атрибуты, такие как уровень опыта и зарплата, которые широко используются в исследованиях зарплат. Однако стоит учитывать, что в реальной жизни могут существовать дополнительные переменные, которые не учтены в наборе данных, такие как текущее состояние отрасли или специфические тренды (например, спрос на специалистов в определенных областях).\n",
+    "\n",
+    "### Согласованность меток\n",
+    "Метки в датасете, такие как **experience_level** (уровень опыта), **employment_type** (тип занятости), и **company_size** (размер компании), имеют четкие и логичные категории, что способствует легкости их интерпретации. Однако для некоторых меток могут возникнуть проблемы с точностью классификации, например:\n",
+    "- В разных странах или компаниях могут существовать различные способы определения уровней опыта, и это может не всегда совпадать с метками в датасете.\n",
+    "- Некоторые метки могут требовать дополнительного пояснения, например, категориальные значения для **remote_ratio** или **job_title** могут быть варьироваться в зависимости от контекста.\n",
+    "\n",
+    "## Бизнес-цели\n",
+    "### 1. **Определение конкурентоспособных уровней зарплат для специалистов в области Data Science**\n",
+    "\n",
+    "**Эффект на бизнес:**\n",
+    "Датасет поможет компаниям, работающим в сфере Data Science, определять конкурентоспособные уровни зарплат для специалистов в зависимости от уровня опыта, типа занятости и географического положения. Это способствует привлечению и удержанию талантливых специалистов, улучшая стратегию найма и оптимизируя расходы на оплату труда.\n",
+    "\n",
+    "**Примеры целей технического проекта:**\n",
+    "- **Цель проекта:** Создание модели для предсказания конкурентоспособных зарплат для специалистов по Data Science в зависимости от их уровня опыта и местоположения.\n",
+    "  - **Что поступает на вход:** Данные о годе работы, уровне опыта, типе занятости, местоположении компании и специалиста.\n",
+    "  - **Целевой признак:** Прогнозируемая зарплата (в долларах США или эквивалент в локальной валюте).\n",
+    "\n",
+    "### 2. **Определение факторов, влияющих на рост зарплат в сфере Data Science**\n",
+    "\n",
+    "**Эффект на бизнес:**\n",
+    "Анализ факторов, влияющих на рост зарплат, позволит компаниям лучше понимать, какие характеристики (например, удаленная работа, опыт работы в крупных компаниях) способствуют повышению заработной платы. Это может помочь в построении программ карьерного роста и мотивации для сотрудников.\n",
+    "\n",
+    "**Примеры целей технического проекта:**\n",
+    "- **Цель проекта:** Разработка модели для анализа факторов, которые влияют на рост зарплат в сфере Data Science.\n",
+    "  - **Что поступает на вход:** Данные о годе работы, уровне опыта, типе занятости, удаленной работе, размере компании и других характеристиках.\n",
+    "  - **Целевой признак:** Изменение зарплаты за год (прибавка к зарплате или её снижение).\n",
+    "\n",
+    "### 3. **Улучшение стратегии удаленной работы и гибких условий занятости**\n",
+    "\n",
+    "**Эффект на бизнес:**\n",
+    "Датасет поможет компаниям понять, как удаленная работа или гибкие условия занятости влияют на уровень зарплаты специалистов. Это даст возможность оптимизировать политику гибкости в работе и предложить лучшие условия для сотрудников, что повышает их удовлетворенность и снижает текучесть кадров.\n",
+    "\n",
+    "**Примеры целей технического проекта:**\n",
+    "- **Цель проекта:** Создание модели для анализа влияния удаленной работы и типа занятости на уровень зарплаты в сфере Data Science.\n",
+    "  - **Что поступает на вход:** Данные о проценте удаленной работы, типе занятости (фриланс, контракт, полная или частичная занятость).\n",
+    "  - **Целевой признак:** Зарплата в зависимости от удаленности работы и типа занятости (фиксированная сумма или разница в зарплатах для разных типов занятости)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выполним все необходимые импорты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Any\n",
+    "from math import ceil\n",
+    "\n",
+    "import pandas as pd\n",
+    "from pandas import DataFrame, Series\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import ADASYN, SMOTE\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Считаем данные для первого датасета"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3755 entries, 0 to 3754\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column              Non-Null Count  Dtype \n",
+      "---  ------              --------------  ----- \n",
+      " 0   work_year           3755 non-null   int64 \n",
+      " 1   experience_level    3755 non-null   object\n",
+      " 2   employment_type     3755 non-null   object\n",
+      " 3   job_title           3755 non-null   object\n",
+      " 4   salary              3755 non-null   int64 \n",
+      " 5   salary_currency     3755 non-null   object\n",
+      " 6   salary_in_usd       3755 non-null   int64 \n",
+      " 7   employee_residence  3755 non-null   object\n",
+      " 8   remote_ratio        3755 non-null   int64 \n",
+      " 9   company_location    3755 non-null   object\n",
+      " 10  company_size        3755 non-null   object\n",
+      "dtypes: int64(4), object(7)\n",
+      "memory usage: 322.8+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>work_year</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>2022.373635</td>\n",
+       "      <td>0.691448</td>\n",
+       "      <td>2020.0</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>2023.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>salary</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>190695.571771</td>\n",
+       "      <td>671676.500508</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>138000.0</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>30400000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>137570.389880</td>\n",
+       "      <td>63055.625278</td>\n",
+       "      <td>5132.0</td>\n",
+       "      <td>95000.0</td>\n",
+       "      <td>135000.0</td>\n",
+       "      <td>175000.0</td>\n",
+       "      <td>450000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>remote_ratio</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>46.271638</td>\n",
+       "      <td>48.589050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count           mean            std     min       25%  \\\n",
+       "work_year      3755.0    2022.373635       0.691448  2020.0    2022.0   \n",
+       "salary         3755.0  190695.571771  671676.500508  6000.0  100000.0   \n",
+       "salary_in_usd  3755.0  137570.389880   63055.625278  5132.0   95000.0   \n",
+       "remote_ratio   3755.0      46.271638      48.589050     0.0       0.0   \n",
+       "\n",
+       "                    50%       75%         max  \n",
+       "work_year        2022.0    2023.0      2023.0  \n",
+       "salary         138000.0  180000.0  30400000.0  \n",
+       "salary_in_usd  135000.0  175000.0    450000.0  \n",
+       "remote_ratio        0.0     100.0       100.0  "
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('csv/8.ds_salaries.csv')\n",
+    "df.info()\n",
+    "df.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Метод проверки пустых значений в датафрейме"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Проверка пропущенных данных\n",
+    "def check_null_columns(dataframe: DataFrame) -> None:\n",
+    "    print('Присутствуют ли пустые значения признаков в колонке:')\n",
+    "    print(dataframe.isnull().any(), '\\n')\n",
+    "\n",
+    "    if any(dataframe.isnull().any()):\n",
+    "        print('Количество пустых значений признаков в колонке:')\n",
+    "        print(dataframe.isnull().sum(), '\\n')\n",
+    "\n",
+    "        print('Процент пустых значений признаков в колонке:')\n",
+    "        for column in dataframe.columns:\n",
+    "            null_rate: float = dataframe[column].isnull().sum() / len(dataframe) * 100\n",
+    "            if null_rate > 0:\n",
+    "                print(f\"{column} процент пустых значений: {null_rate:.2f}%\")            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим на пустые значения в колонках"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Присутствуют ли пустые значения признаков в колонке:\n",
+      "work_year             False\n",
+      "experience_level      False\n",
+      "employment_type       False\n",
+      "job_title             False\n",
+      "salary                False\n",
+      "salary_currency       False\n",
+      "salary_in_usd         False\n",
+      "employee_residence    False\n",
+      "remote_ratio          False\n",
+      "company_location      False\n",
+      "company_size          False\n",
+      "dtype: bool \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_null_columns(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на наличие выборосов и зашумленности данных\n",
+    "\n",
+    "Зашумленность – это наличие случайных ошибок или вариаций в данных, которые могут затруднить выявление истинных закономерностей.\n",
+    "\n",
+    "Выбросы – это значения, которые значительно отличаются от остальных наблюдений в наборе данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Функция возвращает список числовых колонок датафрейма"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_numeric_columns(dataframe: DataFrame) -> list[str]:\n",
+    "    w = []\n",
+    "    for column in dataframe.columns:\n",
+    "        if not pd.api.types.is_numeric_dtype(dataframe[column]):\n",
+    "            continue\n",
+    "        w.append(column)\n",
+    "    return w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Метод для проверки датафрейма на наличие выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_outliers(dataframe: DataFrame) -> list[str]:\n",
+    "    w = []\n",
+    "    for column in get_numeric_columns(dataframe):\n",
+    "        Q1: float = dataframe[column].quantile(0.25)\n",
+    "        Q3: float = dataframe[column].quantile(0.75)\n",
+    "        IQR: float = Q3 - Q1\n",
+    "\n",
+    "        lower_bound: float = Q1 - 1.5 * IQR\n",
+    "        upper_bound: float = Q3 + 1.5 * IQR\n",
+    "\n",
+    "        outliers: DataFrame = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
+    "        outlier_count: int = outliers.shape[0]\n",
+    "\n",
+    "        if outlier_count > 0:\n",
+    "            w.append(column)\n",
+    "\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
+    "        print(f\"\\tМинимальное значение: {dataframe[column].min()}\")\n",
+    "        print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
+    "        print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
+    "        print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
+    "    return w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Метод для визуализации выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_outliers(dataframe: DataFrame) -> None:\n",
+    "    columns = get_numeric_columns(dataframe)\n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "    rows: int = ceil(len(columns) / 3)\n",
+    "    for index, column in enumerate(columns, 1):\n",
+    "        plt.subplot(rows, 3, index)\n",
+    "        plt.boxplot(dataframe[column], vert=True, patch_artist=True)\n",
+    "        plt.title(f\"Диаграмма размахов для \\\"{column}\\\"\")\n",
+    "        plt.xlabel(column)\n",
+    "            \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим на наличие выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка work_year:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 76\n",
+      "\tМинимальное значение: 2020\n",
+      "\tМаксимальное значение: 2023\n",
+      "\t1-й квартиль (Q1): 2022.0\n",
+      "\t3-й квартиль (Q3): 2023.0\n",
+      "\n",
+      "Колонка salary:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 113\n",
+      "\tМинимальное значение: 6000\n",
+      "\tМаксимальное значение: 30400000\n",
+      "\t1-й квартиль (Q1): 100000.0\n",
+      "\t3-й квартиль (Q3): 180000.0\n",
+      "\n",
+      "Колонка salary_in_usd:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 63\n",
+      "\tМинимальное значение: 5132\n",
+      "\tМаксимальное значение: 450000\n",
+      "\t1-й квартиль (Q1): 95000.0\n",
+      "\t3-й квартиль (Q3): 175000.0\n",
+      "\n",
+      "Колонка remote_ratio:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0\n",
+      "\tМаксимальное значение: 100\n",
+      "\t1-й квартиль (Q1): 0.0\n",
+      "\t3-й квартиль (Q3): 100.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "columns_with_outliers = check_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Визуализируем выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Метод устраняет выбросы в заданных колонках, задавая значениям выше максимального значение максимума, а ниже минимального - значение минимума."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
+    "    print('Колонки с выбросами:', *columns, sep='\\n')\n",
+    "    for column in columns:\n",
+    "        Q1: float = dataframe[column].quantile(0.25)\n",
+    "        Q3: float = dataframe[column].quantile(0.75)\n",
+    "        IQR: float = Q3 - Q1\n",
+    "\n",
+    "        lower_bound: float = Q1 - 1.5 * IQR\n",
+    "        upper_bound: float = Q3 + 1.5 * IQR\n",
+    "\n",
+    "        dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
+    "    \n",
+    "    return dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Устраняем выбросы, если они имеются"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонки с выбросами:\n",
+      "work_year\n",
+      "salary\n",
+      "salary_in_usd\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = remove_outliers(df, columns_with_outliers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим наличие выбросов и визуализируем"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка work_year:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 2020.5\n",
+      "\tМаксимальное значение: 2023.0\n",
+      "\t1-й квартиль (Q1): 2022.0\n",
+      "\t3-й квартиль (Q3): 2023.0\n",
+      "\n",
+      "Колонка salary:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 6000.0\n",
+      "\tМаксимальное значение: 300000.0\n",
+      "\t1-й квартиль (Q1): 100000.0\n",
+      "\t3-й квартиль (Q3): 180000.0\n",
+      "\n",
+      "Колонка salary_in_usd:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 5132.0\n",
+      "\tМаксимальное значение: 295000.0\n",
+      "\t1-й квартиль (Q1): 95000.0\n",
+      "\t3-й квартиль (Q3): 175000.0\n",
+      "\n",
+      "Колонка remote_ratio:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0\n",
+      "\tМаксимальное значение: 100\n",
+      "\t1-й квартиль (Q1): 0.0\n",
+      "\t3-й квартиль (Q3): 100.0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "check_outliers(df)\n",
+    "visualize_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Разбиение набора данных на выборки:¶\n",
+    "Групповое разбиение данных – это метод разделения данных на несколько групп или подмножеств на основе определенного признака или характеристики. При этом наблюдения для одного объекта должны попасть только в одну выборку.\n",
+    "\n",
+    "Основные виды выборки данных:\n",
+    "- Обучающая выборка (60-80%). Обучение модели (подбор коэффициентов некоторой математической функции для аппроксимации).\n",
+    "- Контрольная выборка (10-20%). Выбор метода обучения, настройка гиперпараметров.\n",
+    "- Тестовая выборка (10-20% или 20-30%). Оценка качества модели перед передачей заказчику.\n",
+    "\n",
+    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Функция для создания выборок"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    ") -> tuple[Any, Any, Any]:\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X: DataFrame = df_input\n",
+    "    y: DataFrame = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]\n",
+    "\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, \n",
+    "        stratify=y, \n",
+    "        test_size=(1.0 - frac_train), \n",
+    "        random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    relative_frac_test: float = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Функция оценки сбалансированности по колонке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_balance(dataframe: DataFrame, dataframe_name: str, column: str) -> None:\n",
+    "    counts: Series[int] = dataframe[column].value_counts()\n",
+    "    print(dataframe_name + \": \", dataframe.shape)\n",
+    "    print(f\"Распределение выборки данных по классам в колонке \\\"{column}\\\":\\n\", counts)\n",
+    "    total_count: int = len(dataframe)\n",
+    "    for value in counts.index:\n",
+    "        percentage: float = counts[value] / total_count * 100\n",
+    "        print(f\"Процент объектов класса \\\"{value}\\\": {percentage:.2f}%\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Функция определения необходимости аугментации данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def need_augmentation(dataframe: DataFrame,\n",
+    "                      column: str, \n",
+    "                      first_value: Any, second_value: Any) -> bool:\n",
+    "    counts: Series[int] = dataframe[column].value_counts()\n",
+    "    ratio: float = counts[first_value] / counts[second_value]\n",
+    "    return ratio > 1.5 or ratio < 0.67"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Метод визуализации сбалансированности классов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_balance(dataframe: DataFrame,\n",
+    "                      column: str) -> None:\n",
+    "   fig, axes = plt.subplots(1, 1, figsize=(15, 5))\n",
+    "\n",
+    "   counts_train: Series[int] = dataframe[column].value_counts()\n",
+    "   axes.pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
+    "   axes.set_title(f\"Распределение классов \\\"{column}\\\"\\n\")\n",
+    "\n",
+    "   plt.tight_layout()\n",
+    "   plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
+    "\n",
+    "Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
+    "\n",
+    "Чтобы решить эту проблему введём категории для значения зарплаты. Вместо того, чтобы использовать точные значения зарплаты для стратификации, мы создадим категории зарплат, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении зарплаты. Это позволит создать более крупные классы, что устранит проблему с редкими значениями\n",
+    "\n",
+    "Категории для разбиения зарплат:\n",
+    "- Низкая зарплата: зарплаты ниже первого квартиля (25%) — это значения меньше 95000.\n",
+    "- Средняя зарплата: зарплаты между первым квартилем (25%) и третьим квартилем (75%) — это зарплаты от 95000 до 175000.\n",
+    "- Высокая зарплата: зарплаты выше третьего квартиля (75%) и до максимального значения — это зарплаты выше 175000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "salary_in_usd\n",
+      "100000.0    99\n",
+      "150000.0    98\n",
+      "120000.0    91\n",
+      "160000.0    84\n",
+      "130000.0    82\n",
+      "            ..\n",
+      "39916.0      1\n",
+      "26005.0      1\n",
+      "22611.0      1\n",
+      "5679.0       1\n",
+      "40038.0      1\n",
+      "Name: count, Length: 1002, dtype: int64 \n",
+      "\n",
+      "Статистическое описание целевого признака:\n",
+      "count      3755.000000\n",
+      "mean     136959.779760\n",
+      "std       61098.121137\n",
+      "min        5132.000000\n",
+      "25%       95000.000000\n",
+      "50%      135000.000000\n",
+      "75%      175000.000000\n",
+      "max      295000.000000\n",
+      "Name: salary_in_usd, dtype: float64 \n",
+      "\n",
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "salary_category\n",
+      "medium    1867\n",
+      "low        956\n",
+      "high       932\n",
+      "Name: count, dtype: int64 \n",
+      "\n",
+      "Проверка сбалансированности:\n",
+      "Весь датасет:  (3755, 12)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
+      " salary_category\n",
+      "medium    1867\n",
+      "low        956\n",
+      "high       932\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 49.72%\n",
+      "Процент объектов класса \"low\": 25.46%\n",
+      "Процент объектов класса \"high\": 24.82%\n",
+      "\n",
+      "Проверка необходимости аугментации:\n",
+      "Для датасета аугментация данных ТРЕБУЕТСЯ\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df.salary_in_usd.value_counts(), '\\n')\n",
+    "\n",
+    "# Статистическое описание целевого признака\n",
+    "print('Статистическое описание целевого признака:')\n",
+    "print(df['salary_in_usd'].describe().transpose(), '\\n')\n",
+    "\n",
+    "# Определим границы для каждой категории зарплаты\n",
+    "bins: list[float] = [df['salary_in_usd'].min() - 1, \n",
+    "                     df['salary_in_usd'].quantile(0.25), \n",
+    "                     df['salary_in_usd'].quantile(0.75), \n",
+    "                     df['salary_in_usd'].max() + 1]\n",
+    "labels: list[str] = ['low', 'medium', 'high']\n",
+    "\n",
+    "# Создаем новую колонку с категориями зарплат#\n",
+    "df['salary_category'] = pd.cut(df['salary_in_usd'], bins=bins, labels=labels)\n",
+    "\n",
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df['salary_category'].value_counts(), '\\n')\n",
+    "\n",
+    "# Проверка сбалансированности\n",
+    "print('Проверка сбалансированности:')\n",
+    "check_balance(df, 'Весь датасет', 'salary_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации\n",
+    "print('Проверка необходимости аугментации:')\n",
+    "print(f\"Для датасета аугментация данных {'НЕ ' if not need_augmentation(df, 'salary_category', 'low', 'medium') else ''}ТРЕБУЕТСЯ\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df, 'salary_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать низкие или высокие зарплаты (low или high), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
+    "\n",
+    "Для получения более сбалансированных данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Метод приращения с избытком (oversampling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def oversample(df: DataFrame, column: str) -> DataFrame:\n",
+    "    X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
+    "    y: DataFrame = df[column] # type: ignore\n",
+    "    \n",
+    "    adasyn = ADASYN()\n",
+    "    X_resampled, y_resampled = adasyn.fit_resample(X, y) # type: ignore\n",
+    "    \n",
+    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
+    "    return df_resampled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Проверка сбалансированности выборок после применения метода oversampling:\n",
+      "Весь датасет:  (5601, 279)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
+      " salary_category\n",
+      "high      1868\n",
+      "medium    1867\n",
+      "low       1866\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"high\": 33.35%\n",
+      "Процент объектов класса \"medium\": 33.33%\n",
+      "Процент объектов класса \"low\": 33.32%\n",
+      "\n",
+      "Проверка необходимости аугментации выборок после применения метода oversampling:\n",
+      "Для всего датасета аугментация данных НЕ ТРЕБУЕТСЯ\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Приращение данных (oversampling)\n",
+    "df_oversampled: DataFrame = oversample(df, 'salary_category')\n",
+    "\n",
+    "# Проверка сбалансированности\n",
+    "print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
+    "check_balance(df_oversampled, 'Весь датасет', 'salary_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации\n",
+    "print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
+    "print(f\"Для всего датасета аугментация данных {'НЕ ' if not need_augmentation(df_oversampled, 'salary_category', 'low', 'medium') else ''}ТРЕБУЕТСЯ\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_oversampled, 'salary_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разделим датасет на выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Тренировочная выборка\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Контрольная выборка\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Тестовая выборка\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   df_oversampled,\n",
+    "   stratify_colname=\"salary_category\", \n",
+    "   frac_train=0.60, \n",
+    "   frac_val=0.20, \n",
+    "   frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print('Тренировочная выборка')\n",
+    "visualize_balance(df_train, 'salary_category')\n",
+    "print('Контрольная выборка')\n",
+    "visualize_balance(df_val, 'salary_category')\n",
+    "print('Тестовая выборка')\n",
+    "visualize_balance(df_test, 'salary_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Датасет 2. Анализ продаж филиалов супермаркетов\n",
+    "https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data\n",
+    "## Анализ сведений о датасете\n",
+    "\n",
+    "### **Проблемная область**  \n",
+    "Датасет описывает производственные и экономические характеристики магазинов супермаркетов с целью анализа их деятельности и выявления факторов, влияющих на прибыльность. Задачи включают:\n",
+    "- Оценку производительности магазинов;\n",
+    "- Поиск факторов, которые могут улучшить прибыль и эффективность;\n",
+    "- Определение взаимосвязи между различными характеристиками магазинов.\n",
+    "\n",
+    "### **Актуальность**  \n",
+    "Анализ эффективности супермаркетов актуален в сфере розничной торговли, поскольку помогает:\n",
+    "- Повышать прибыльность магазинов;\n",
+    "- Улучшать распределение ресурсов (например, товаров или пространства);\n",
+    "- Оптимизировать маркетинговые и операционные стратегии;\n",
+    "- Оценивать влияние внешних факторов (например, площади магазина или ассортимента товаров) на продажи.\n",
+    "\n",
+    "### **Объекты наблюдений**  \n",
+    "Объектами наблюдения являются **магазины супермаркетов**, каждый из которых представлен в датасете через уникальный идентификатор (Store ID). Для каждого магазина представлены различные параметры, которые отражают его физическую структуру и экономическую деятельность.\n",
+    "\n",
+    "### **Атрибуты объектов**  \n",
+    "Каждое наблюдение (магазин) имеет следующие атрибуты:\n",
+    "- **Store ID** — уникальный идентификатор магазина (индекс);\n",
+    "- **Store_Area** — физическая площадь магазина в квадратных ярдах (меряет размер магазина);\n",
+    "- **Items_Available** — количество различных товаров, доступных в магазине (ассортимент);\n",
+    "- **Daily_Customer_Count** — среднее количество клиентов, посещающих магазин ежедневно (популярность);\n",
+    "- **Store_Sales** — объем продаж магазина в долларах США (экономическая эффективность).\n",
+    "\n",
+    "### **Связь между объектами**  \n",
+    "Связь между атрибутами объектов (магазинов) может быть следующей:\n",
+    "- **Store_Area ↔ Items_Available**: Большее количество товаров может требовать большей площади для их размещения.\n",
+    "- **Store_Area ↔ Store_Sales**: Большая площадь магазина может свидетельствовать о большем объеме продаж, поскольку позволяет разместить больше товаров и обслуживать больше клиентов.\n",
+    "- **Items_Available ↔ Daily_Customer_Count**: Магазины с большим ассортиментом товаров могут привлекать больше клиентов, особенно если товары соответствуют потребительским ожиданиям.\n",
+    "- **Daily_Customer_Count ↔ Store_Sales**: Прямая зависимость — большее количество клиентов может привести к большему объему продаж.\n",
+    "\n",
+    "Для дальнейшего анализа можно использовать корреляционные методы, чтобы понять, как различные факторы (площадь, ассортимент, количество клиентов) влияют на продажи.\n",
+    "\n",
+    "### Качество набора данных\n",
+    "\n",
+    "1. **Информативность**:  \n",
+    "   Датасет содержит несколько ключевых атрибутов, которые отражают как физические характеристики магазинов, так и их экономическую эффективность. Эти атрибуты (площадь, ассортимент товаров, количество клиентов и продажи) достаточно информативны для начального анализа производительности супермаркетов.\n",
+    "\n",
+    "2. **Степень покрытия**:  \n",
+    "   Датасет охватывает информацию по нескольким магазинам компании, однако он может не быть репрезентативным для всей розничной сети, так как данные собраны только для определенных магазинов с их уникальными характеристиками. Это может ограничить выводы, если не все магазины покрыты в данных.\n",
+    "\n",
+    "3. **Соответствие реальным данным**:  \n",
+    "   Данные, представленные в датасете, соответствуют реальной практической ситуации, поскольку информация о площади магазинов, количестве товаров и клиентском потоке довольно типична для анализа розничных торговых точек.\n",
+    "\n",
+    "4. **Согласованность меток**:  \n",
+    "   Метки данных (например, Store ID, Store_Area, Items_Available и т.д.) хорошо согласованы и имеют понятные и логичные наименования. Однако для полной уверенности в корректности данных потребуется проверка на наличие пропусков или аномалий (например, если площадь магазина или количество товаров кажется необычно низким или высоким).\n",
+    "\n",
+    "### Бизнес цели, которые может решить датасет:\n",
+    "\n",
+    "1. **Оптимизация ассортимента товаров и пространства**  \n",
+    "   **Цель**: Разработать стратегию по оптимальному размещению товаров и выбору ассортимента в зависимости от площади магазина и его клиентской базы.  \n",
+    "   **Эффект на бизнес**: Поможет увеличить продажи путем улучшения доступности популярных товаров и оптимизации использования пространства в магазинах.  \n",
+    "   \n",
+    "   **Цели технического проекта**:\n",
+    "   - **Входные данные**: Площадь магазина, количество товаров, ежедневное количество клиентов.\n",
+    "   - **Целевой признак**: Объем продаж (Store_Sales).\n",
+    "\n",
+    "2. **Увеличение продаж через улучшение привлечения клиентов**  \n",
+    "   **Цель**: Разработать стратегию по увеличению потока клиентов в магазины на основе текущего количества покупателей и их корреляции с объемом продаж.  \n",
+    "   **Эффект на бизнес**: Увеличение количества клиентов может прямо повлиять на рост продаж и прибыльность, особенно если будет применена стратегия привлечения дополнительного потока потребителей.  \n",
+    "   \n",
+    "   **Цели технического проекта**:\n",
+    "   - **Входные данные**: Количество товаров в магазине, площадь магазина, среднее количество клиентов.\n",
+    "   - **Целевой признак**: Объем продаж (Store_Sales).\n",
+    "\n",
+    "3. **Предсказание и управление производительностью магазинов**  \n",
+    "   **Цель**: Оценить, какие факторы (площадь, ассортимент, количество клиентов) влияют на эффективность магазина и как прогнозировать его продажи в будущем.  \n",
+    "   **Эффект на бизнес**: Ожидаемый результат — повышение точности прогнозов продаж и улучшение стратегического планирования для различных магазинов сети.  \n",
+    "   \n",
+    "   **Цели технического проекта**:\n",
+    "   - **Входные данные**: Площадь магазина, количество товаров, ежедневное количество клиентов.\n",
+    "   - **Целевой признак**: Объем продаж (Store_Sales).\n",
+    "\n",
+    "### Примеры целей технического проекта для каждой бизнес-цели:\n",
+    "\n",
+    "1. **Оптимизация ассортимента товаров и пространства**\n",
+    "   - **Задача**: Построить модель, которая на основе площади магазина и ассортимента товаров будет предсказывать оптимальный объем продаж.\n",
+    "   - **Вход**: Площадь магазина (Store_Area), Количество товаров (Items_Available).\n",
+    "   - **Цель**: Прогнозировать объем продаж (Store_Sales).\n",
+    "\n",
+    "2. **Увеличение продаж через улучшение привлечения клиентов**\n",
+    "   - **Задача**: Разработать алгоритм, который будет анализировать связи между количеством клиентов и продажами для оценки эффективности маркетинговых усилий.\n",
+    "   - **Вход**: Среднее количество клиентов (Daily_Customer_Count), Количество товаров (Items_Available), Площадь магазина (Store_Area).\n",
+    "   - **Цель**: Прогнозировать объем продаж (Store_Sales).\n",
+    "\n",
+    "3. **Предсказание и управление производительностью магазинов**\n",
+    "   - **Задача**: Построить модель для предсказания объемов продаж на основе характеристик магазинов, чтобы заранее прогнозировать производительность и принимать меры по улучшению результатов.\n",
+    "   - **Вход**: Площадь магазина (Store_Area), Среднее количество клиентов (Daily_Customer_Count), Количество товаров (Items_Available).\n",
+    "   - **Цель**: Прогнозировать объем продаж (Store_Sales)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 896 entries, 0 to 895\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column                Non-Null Count  Dtype\n",
+      "---  ------                --------------  -----\n",
+      " 0   Store ID              896 non-null    int64\n",
+      " 1   Store_Area            896 non-null    int64\n",
+      " 2   Items_Available       896 non-null    int64\n",
+      " 3   Daily_Customer_Count  896 non-null    int64\n",
+      " 4   Store_Sales           896 non-null    int64\n",
+      "dtypes: int64(5)\n",
+      "memory usage: 35.1 KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Store ID</th>\n",
+       "      <td>896.0</td>\n",
+       "      <td>448.500000</td>\n",
+       "      <td>258.797218</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>224.75</td>\n",
+       "      <td>448.5</td>\n",
+       "      <td>672.25</td>\n",
+       "      <td>896.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Store_Area</th>\n",
+       "      <td>896.0</td>\n",
+       "      <td>1485.409598</td>\n",
+       "      <td>250.237011</td>\n",
+       "      <td>775.0</td>\n",
+       "      <td>1316.75</td>\n",
+       "      <td>1477.0</td>\n",
+       "      <td>1653.50</td>\n",
+       "      <td>2229.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Items_Available</th>\n",
+       "      <td>896.0</td>\n",
+       "      <td>1782.035714</td>\n",
+       "      <td>299.872053</td>\n",
+       "      <td>932.0</td>\n",
+       "      <td>1575.50</td>\n",
+       "      <td>1773.5</td>\n",
+       "      <td>1982.75</td>\n",
+       "      <td>2667.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Daily_Customer_Count</th>\n",
+       "      <td>896.0</td>\n",
+       "      <td>786.350446</td>\n",
+       "      <td>265.389281</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>600.00</td>\n",
+       "      <td>780.0</td>\n",
+       "      <td>970.00</td>\n",
+       "      <td>1560.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Store_Sales</th>\n",
+       "      <td>896.0</td>\n",
+       "      <td>59351.305804</td>\n",
+       "      <td>17190.741895</td>\n",
+       "      <td>14920.0</td>\n",
+       "      <td>46530.00</td>\n",
+       "      <td>58605.0</td>\n",
+       "      <td>71872.50</td>\n",
+       "      <td>116320.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      count          mean           std      min       25%  \\\n",
+       "Store ID              896.0    448.500000    258.797218      1.0    224.75   \n",
+       "Store_Area            896.0   1485.409598    250.237011    775.0   1316.75   \n",
+       "Items_Available       896.0   1782.035714    299.872053    932.0   1575.50   \n",
+       "Daily_Customer_Count  896.0    786.350446    265.389281     10.0    600.00   \n",
+       "Store_Sales           896.0  59351.305804  17190.741895  14920.0  46530.00   \n",
+       "\n",
+       "                          50%       75%       max  \n",
+       "Store ID                448.5    672.25     896.0  \n",
+       "Store_Area             1477.0   1653.50    2229.0  \n",
+       "Items_Available        1773.5   1982.75    2667.0  \n",
+       "Daily_Customer_Count    780.0    970.00    1560.0  \n",
+       "Store_Sales           58605.0  71872.50  116320.0  "
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('csv/9.Stores.csv')\n",
+    "df.info()\n",
+    "df.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удалим колонку с ID магазинов, она нам вряд ли понадобится"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 896 entries, 0 to 895\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column                Non-Null Count  Dtype\n",
+      "---  ------                --------------  -----\n",
+      " 0   Store_Area            896 non-null    int64\n",
+      " 1   Items_Available       896 non-null    int64\n",
+      " 2   Daily_Customer_Count  896 non-null    int64\n",
+      " 3   Store_Sales           896 non-null    int64\n",
+      "dtypes: int64(4)\n",
+      "memory usage: 28.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"Store ID \" in df.columns:\n",
+    "    df = df.drop(columns=[\"Store ID \"])\n",
+    "\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Присутствуют ли пустые значения признаков в колонке:\n",
+      "Store_Area              False\n",
+      "Items_Available         False\n",
+      "Daily_Customer_Count    False\n",
+      "Store_Sales             False\n",
+      "dtype: bool \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_null_columns(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим на наличие выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка Store_Area:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 5\n",
+      "\tМинимальное значение: 775\n",
+      "\tМаксимальное значение: 2229\n",
+      "\t1-й квартиль (Q1): 1316.75\n",
+      "\t3-й квартиль (Q3): 1653.5\n",
+      "\n",
+      "Колонка Items_Available:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 5\n",
+      "\tМинимальное значение: 932\n",
+      "\tМаксимальное значение: 2667\n",
+      "\t1-й квартиль (Q1): 1575.5\n",
+      "\t3-й квартиль (Q3): 1982.75\n",
+      "\n",
+      "Колонка Daily_Customer_Count:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 3\n",
+      "\tМинимальное значение: 10\n",
+      "\tМаксимальное значение: 1560\n",
+      "\t1-й квартиль (Q1): 600.0\n",
+      "\t3-й квартиль (Q3): 970.0\n",
+      "\n",
+      "Колонка Store_Sales:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 1\n",
+      "\tМинимальное значение: 14920\n",
+      "\tМаксимальное значение: 116320\n",
+      "\t1-й квартиль (Q1): 46530.0\n",
+      "\t3-й квартиль (Q3): 71872.5\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "columns_with_outliers = check_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Визуализируем выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Устраняем выбросы, если они имеются"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонки с выбросами:\n",
+      "Store_Area\n",
+      "Items_Available\n",
+      "Daily_Customer_Count\n",
+      "Store_Sales\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = remove_outliers(df, columns_with_outliers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим наличие выбросов и визуализируем"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка Store_Area:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 811.625\n",
+      "\tМаксимальное значение: 2158.625\n",
+      "\t1-й квартиль (Q1): 1316.75\n",
+      "\t3-й квартиль (Q3): 1653.5\n",
+      "\n",
+      "Колонка Items_Available:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 964.625\n",
+      "\tМаксимальное значение: 2593.625\n",
+      "\t1-й квартиль (Q1): 1575.5\n",
+      "\t3-й квартиль (Q3): 1982.75\n",
+      "\n",
+      "Колонка Daily_Customer_Count:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 45.0\n",
+      "\tМаксимальное значение: 1525.0\n",
+      "\t1-й квартиль (Q1): 600.0\n",
+      "\t3-й квартиль (Q3): 970.0\n",
+      "\n",
+      "Колонка Store_Sales:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 14920.0\n",
+      "\tМаксимальное значение: 109886.25\n",
+      "\t1-й квартиль (Q1): 46530.0\n",
+      "\t3-й квартиль (Q3): 71872.5\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "check_outliers(df)\n",
+    "visualize_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
+    "\n",
+    "Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
+    "\n",
+    "Чтобы решить эту проблему введём категории для значения объема продаж. Вместо того, чтобы использовать точные значения объема продаж для стратификации, мы создадим категории, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении. Это позволит создать более крупные классы, что устранит проблему с редкими значениями\n",
+    "\n",
+    "Категории для разбиения:\n",
+    "- Низкая: значения ниже первого квартиля (25%) — это значения меньше 46530.0\n",
+    "- Средняя: значения между первым квартилем (25%) и третьим квартилем (75%) — это значения от 46530.0 до 71872.5\n",
+    "- Высокая: значения выше третьего квартиля (75%) и до максимального значения — это значения выше 71872.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "Store_Sales\n",
+      "63540.0    3\n",
+      "54590.0    3\n",
+      "76530.0    2\n",
+      "54370.0    2\n",
+      "82350.0    2\n",
+      "          ..\n",
+      "70620.0    1\n",
+      "82080.0    1\n",
+      "76440.0    1\n",
+      "96610.0    1\n",
+      "54340.0    1\n",
+      "Name: count, Length: 816, dtype: int64 \n",
+      "\n",
+      "Статистическое описание целевого признака:\n",
+      "count       896.000000\n",
+      "mean      59344.125279\n",
+      "std       17168.248608\n",
+      "min       14920.000000\n",
+      "25%       46530.000000\n",
+      "50%       58605.000000\n",
+      "75%       71872.500000\n",
+      "max      109886.250000\n",
+      "Name: Store_Sales, dtype: float64 \n",
+      "\n",
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "Store_Sales_category\n",
+      "medium    448\n",
+      "low       224\n",
+      "high      224\n",
+      "Name: count, dtype: int64 \n",
+      "\n",
+      "Проверка сбалансированности:\n",
+      "Весь датасет:  (896, 5)\n",
+      "Распределение выборки данных по классам в колонке \"Store_Sales_category\":\n",
+      " Store_Sales_category\n",
+      "medium    448\n",
+      "low       224\n",
+      "high      224\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 50.00%\n",
+      "Процент объектов класса \"low\": 25.00%\n",
+      "Процент объектов класса \"high\": 25.00%\n",
+      "\n",
+      "Проверка необходимости аугментации:\n",
+      "Для датасета аугментация данных ТРЕБУЕТСЯ\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df.Store_Sales.value_counts(), '\\n')\n",
+    "\n",
+    "# Статистическое описание целевого признака\n",
+    "print('Статистическое описание целевого признака:')\n",
+    "print(df['Store_Sales'].describe().transpose(), '\\n')\n",
+    "\n",
+    "# Определим границы для каждой категории зарплаты\n",
+    "bins: list[float] = [df['Store_Sales'].min() - 1, \n",
+    "                     df['Store_Sales'].quantile(0.25), \n",
+    "                     df['Store_Sales'].quantile(0.75), \n",
+    "                     df['Store_Sales'].max() + 1]\n",
+    "labels: list[str] = ['low', 'medium', 'high']\n",
+    "\n",
+    "# Создаем новую колонку с категориями\n",
+    "df['Store_Sales_category'] = pd.cut(df['Store_Sales'], bins=bins, labels=labels)\n",
+    "\n",
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df['Store_Sales_category'].value_counts(), '\\n')\n",
+    "\n",
+    "# Проверка сбалансированности\n",
+    "print('Проверка сбалансированности:')\n",
+    "check_balance(df, 'Весь датасет', 'Store_Sales_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации\n",
+    "print('Проверка необходимости аугментации:')\n",
+    "print(f\"Для датасета аугментация данных {'НЕ ' if not need_augmentation(df, 'Store_Sales_category', 'low', 'medium') else ''}ТРЕБУЕТСЯ\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df, 'Store_Sales_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ради интереса можно сначала разбить на выборки, а потом провести аугментацию"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_balance_three_pies(dataframe_train: DataFrame,\n",
+    "                      dataframe_val: DataFrame,\n",
+    "                      dataframe_test: DataFrame, \n",
+    "                      column: str) -> None:\n",
+    "   fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "\n",
+    "   counts_train: Series[int] = dataframe_train[column].value_counts()\n",
+    "   axes[0].pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
+    "   axes[0].set_title(f\"Распределение классов \\\"{column}\\\"\\nв обучающей выборке\")\n",
+    "\n",
+    "   counts_val: Series[int] = dataframe_val[column].value_counts()\n",
+    "   axes[1].pie(counts_val, labels=counts_val.index, autopct='%1.1f%%', startangle=90)\n",
+    "   axes[1].set_title(f\"Распределение классов \\\"{column}\\\"\\nв контрольной выборке\")\n",
+    "\n",
+    "   counts_test: Series[int] = dataframe_test[column].value_counts()\n",
+    "   axes[2].pie(counts_test, labels=counts_test.index, autopct='%1.1f%%', startangle=90)\n",
+    "   axes[2].set_title(f\"Распределение классов \\\"{column}\\\"\\nв тренировочной выборке\")\n",
+    "\n",
+    "   plt.tight_layout()\n",
+    "   plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   df,\n",
+    "   stratify_colname=\"Store_Sales_category\", \n",
+    "   frac_train=0.60, \n",
+    "   frac_val=0.20, \n",
+    "   frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "visualize_balance_three_pies(df_train, df_val, df_test, 'Store_Sales_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Произведем оверсэмплинг\n",
+    "\n",
+    "Однако непонятно, зачем вообще проводить аугментацию контрольной и тестовой выборок? Ведь они не используется для обучения, только для контроля качества - соответственно, это просто потеря данных.. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train_oversampled = oversample(df_train, 'Store_Sales_category')\n",
+    "df_val_oversampled = oversample(df_val, 'Store_Sales_category')\n",
+    "df_test_oversampled = oversample(df_test, 'Store_Sales_category')\n",
+    "\n",
+    "visualize_balance_three_pies(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'Store_Sales_category')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Датасет 3. Прогнозирование стоимости медицинского страхования\n",
+    "https://www.kaggle.com/datasets/harishkumardatalab/medical-insurance-price-prediction\n",
+    "## Анализ сведений о датасете\n",
+    "\n",
+    "### **Проблемная область**:  \n",
+    "Задача прогнозирования медицинских расходов на основе различных факторов, влияющих на стоимость страхования. Это важно для компаний медицинского страхования для оптимизации ценообразования и управления рисками.\n",
+    "\n",
+    "### **Актуальность**:  \n",
+    "Прогнозирование медицинских расходов является ключевым элементом для страховых компаний, чтобы правильно оценить риски, установить справедливые страховые взносы и обеспечить финансовую устойчивость компании. Актуальность такого анализа возрастает с увеличением потребности в персонализированном страховании.\n",
+    "\n",
+    "### **Объекты наблюдений**:  \n",
+    "Каждый объект наблюдения представляет собой запись о человеке, который является клиентом медицинской страховой компании.\n",
+    "\n",
+    "### **Атрибуты объектов**:\n",
+    "- **Age (возраст)** — числовой атрибут, показывает возраст клиента.\n",
+    "- **Sex (пол)** — категориальный атрибут (мужчина/женщина), который может повлиять на тип медицинских услуг и расходы.\n",
+    "- **BMI (индекс массы тела)** — числовой атрибут, который может быть важным для оценки здоровья клиента и возможных заболеваний.\n",
+    "- **Children (дети)** — числовой атрибут, который может показывать потребность в медицинских услугах для детей.\n",
+    "- **Smoker (курящий)** — булев атрибут, показывающий, является ли человек курильщиком, что влияет на его здоровье и расходы.\n",
+    "- **Region (регион)** — текстовый атрибут, который может учитывать различия в стоимости медицинских услуг в разных регионах.\n",
+    "- **Charges (расходы)** — целевой числовой атрибут, показывающий медицинские расходы, которые следует предсказать.\n",
+    "\n",
+    "### **Связь между объектами**:\n",
+    "  Атрибуты данных взаимосвязаны. Например, возраст, ИМТ и курение могут быть связанными с увеличением медицинских расходов, так как старение и ожирение повышают риски заболеваний. Регион может определять базовый уровень расходов, а наличие детей может указывать на дополнительные расходы на медицинские услуги для детей.\n",
+    "\n",
+    "## Качество набора данных\n",
+    "\n",
+    "### **Информативность**:  \n",
+    "Набор данных содержит важные параметры для оценки медицинских расходов, такие как возраст, ИМТ, статус курящего и наличие детей. Однако дополнительные параметры, такие как хронические заболевания, история медицинских визитов или история страховки, могут улучшить модель.\n",
+    "\n",
+    "### **Степень покрытия**:  \n",
+    "Набор данных охватывает несколько ключевых факторов (возраст, пол, ИМТ, количество детей, курение, регион), которые являются важными для прогнозирования расходов. Однако для более точных прогнозов могут быть полезны дополнительные данные, такие как образ жизни или медицинская история.\n",
+    "\n",
+    "### **Соответствие реальным данным**:  \n",
+    "Данные вполне могут соответствовать реальной ситуации в медицинском страховании, так как параметры, такие как курение, возраст и ИМТ, действительно влияют на здоровье и, следовательно, на расходы на лечение. Однако важно, чтобы данные были сбалансированы и не содержали искажений.\n",
+    "\n",
+    "### **Согласованность меток**:  \n",
+    "Метки, такие как пол, курящий/не курящий, и регион, должны быть корректно представлены. Необходимо убедиться в отсутствии противоречий в данных (например, отсутствие значений для категориальных переменных или неверных числовых значений).\n",
+    "\n",
+    "## Бизнес-цели, которые может решить этот датасет\n",
+    "\n",
+    "1. **Оптимизация ценообразования на медицинское страхование**\n",
+    "   - **Эффект на бизнес**: Компании смогут более точно оценивать потенциальные расходы на медицинские услуги для клиентов, что позволит устанавливать адекватные страховые взносы, минимизируя риски и обеспечивая прибыльность.\n",
+    "\n",
+    "2. **Оценка рисков клиентов**\n",
+    "   - **Эффект на бизнес**: Страховые компании смогут выявлять группы клиентов с высоким риском, что поможет предсказать, какие клиенты могут потребовать больше затрат на лечение, и соответственно, предлагать им более высокие премии или дополнительные услуги.\n",
+    "\n",
+    "3. **Разработка персонализированных предложений для клиентов**\n",
+    "   - **Эффект на бизнес**: Возможность предложить клиентам индивидуальные страховые планы и дополнительные услуги, основанные на их рисках и потребностях, повысит их удовлетворенность и лояльность, а также улучшит финансовые результаты компании.\n",
+    "\n",
+    "## Примеры целей технического проекта для каждой бизнес-цели\n",
+    "\n",
+    "1. **Оптимизация ценообразования на медицинское страхование**\n",
+    "   - **Цель технического проекта**: Построить модель регрессии для прогнозирования медицинских расходов на основе демографических данных (возраст, пол, ИМТ, курение и т.д.).\n",
+    "   - **Что поступает на вход**: Возраст, пол, ИМТ, количество детей, курение, регион.\n",
+    "   - **Целевой признак**: Расходы (charges).\n",
+    "\n",
+    "2. **Оценка рисков клиентов**\n",
+    "   - **Цель технического проекта**: Разработать модель классификации для оценки уровня риска клиента (низкий, средний, высокий риск).\n",
+    "   - **Что поступает на вход**: Возраст, пол, ИМТ, количество детей, курение, регион.\n",
+    "   - **Целевой признак**: Риск (классификация на категории: низкий, средний, высокий).\n",
+    "\n",
+    "3. **Разработка персонализированных предложений для клиентов**\n",
+    "   - **Цель технического проекта**: Создать систему рекомендаций, которая будет предлагать персонализированные страховые планы и услуги на основе характеристик клиента.\n",
+    "   - **Что поступает на вход**: Все атрибуты клиента (возраст, пол, ИМТ, дети, курение, регион).\n",
+    "   - **Целевой признак**: Рекомендуемый план страхования или дополнительная услуга.\n",
+    "\n",
+    "Каждый из этих проектов направлен на повышение прибыльности компании, улучшение персонализированного подхода к клиентам и снижение финансовых рисков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2772 entries, 0 to 2771\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   age       2772 non-null   int64  \n",
+      " 1   sex       2772 non-null   object \n",
+      " 2   bmi       2772 non-null   float64\n",
+      " 3   children  2772 non-null   int64  \n",
+      " 4   smoker    2772 non-null   object \n",
+      " 5   region    2772 non-null   object \n",
+      " 6   charges   2772 non-null   float64\n",
+      "dtypes: float64(2), int64(2), object(3)\n",
+      "memory usage: 151.7+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>39.109668</td>\n",
+       "      <td>14.081459</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>26.000</td>\n",
+       "      <td>39.00000</td>\n",
+       "      <td>51.0000</td>\n",
+       "      <td>64.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bmi</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>30.701349</td>\n",
+       "      <td>6.129449</td>\n",
+       "      <td>15.9600</td>\n",
+       "      <td>26.220</td>\n",
+       "      <td>30.44750</td>\n",
+       "      <td>34.7700</td>\n",
+       "      <td>53.13000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>children</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>1.101732</td>\n",
+       "      <td>1.214806</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>5.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>charges</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>13261.369959</td>\n",
+       "      <td>12151.768945</td>\n",
+       "      <td>1121.8739</td>\n",
+       "      <td>4687.797</td>\n",
+       "      <td>9333.01435</td>\n",
+       "      <td>16577.7795</td>\n",
+       "      <td>63770.42801</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count          mean           std        min       25%         50%  \\\n",
+       "age       2772.0     39.109668     14.081459    18.0000    26.000    39.00000   \n",
+       "bmi       2772.0     30.701349      6.129449    15.9600    26.220    30.44750   \n",
+       "children  2772.0      1.101732      1.214806     0.0000     0.000     1.00000   \n",
+       "charges   2772.0  13261.369959  12151.768945  1121.8739  4687.797  9333.01435   \n",
+       "\n",
+       "                 75%          max  \n",
+       "age          51.0000     64.00000  \n",
+       "bmi          34.7700     53.13000  \n",
+       "children      2.0000      5.00000  \n",
+       "charges   16577.7795  63770.42801  "
+      ]
+     },
+     "execution_count": 285,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('csv/5.medical_insurance.csv')\n",
+    "df.info()\n",
+    "df.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим на наличие пустых значений в колонках - все отлично, таких нет"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Присутствуют ли пустые значения признаков в колонке:\n",
+      "age         False\n",
+      "sex         False\n",
+      "bmi         False\n",
+      "children    False\n",
+      "smoker      False\n",
+      "region      False\n",
+      "charges     False\n",
+      "dtype: bool \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_null_columns(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим на наличие выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка age:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 18\n",
+      "\tМаксимальное значение: 64\n",
+      "\t1-й квартиль (Q1): 26.0\n",
+      "\t3-й квартиль (Q3): 51.0\n",
+      "\n",
+      "Колонка bmi:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 14\n",
+      "\tМинимальное значение: 15.96\n",
+      "\tМаксимальное значение: 53.13\n",
+      "\t1-й квартиль (Q1): 26.22\n",
+      "\t3-й квартиль (Q3): 34.77\n",
+      "\n",
+      "Колонка children:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0\n",
+      "\tМаксимальное значение: 5\n",
+      "\t1-й квартиль (Q1): 0.0\n",
+      "\t3-й квартиль (Q3): 2.0\n",
+      "\n",
+      "Колонка charges:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 296\n",
+      "\tМинимальное значение: 1121.8739\n",
+      "\tМаксимальное значение: 63770.42801\n",
+      "\t1-й квартиль (Q1): 4687.797\n",
+      "\t3-й квартиль (Q3): 16577.7795\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns_with_outliers = check_outliers(df)\n",
+    "visualize_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Устраняем выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонки с выбросами:\n",
+      "bmi\n",
+      "charges\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = remove_outliers(df, columns_with_outliers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Устраняем выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка age:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 18\n",
+      "\tМаксимальное значение: 64\n",
+      "\t1-й квартиль (Q1): 26.0\n",
+      "\t3-й квартиль (Q3): 51.0\n",
+      "\n",
+      "Колонка bmi:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 15.96\n",
+      "\tМаксимальное значение: 47.59500000000001\n",
+      "\t1-й квартиль (Q1): 26.22\n",
+      "\t3-й квартиль (Q3): 34.77\n",
+      "\n",
+      "Колонка children:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0\n",
+      "\tМаксимальное значение: 5\n",
+      "\t1-й квартиль (Q1): 0.0\n",
+      "\t3-й квартиль (Q3): 2.0\n",
+      "\n",
+      "Колонка charges:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 1121.8739\n",
+      "\tМаксимальное значение: 34412.75325000001\n",
+      "\t1-й квартиль (Q1): 4687.797\n",
+      "\t3-й квартиль (Q3): 16577.7795\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "check_outliers(df)\n",
+    "visualize_outliers(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разделим выборку данных на 5 групп и проанализируем качество распределения данных.\n",
+    "\n",
+    "Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
+    "\n",
+    "Чтобы решить эту проблему введём категории для значения **суммы страховки**. Вместо того, чтобы использовать точные значения для стратификации, мы создадим 5 категорий. Это позволит создать более крупные классы, что устранит проблему с редкими значениями\n",
+    "\n",
+    "Категории для разбиения:\n",
+    "- Очень низкая: значения ниже 1 / 6\n",
+    "- Низкая: значения между 1 / 6 и 1 / 3\n",
+    "- Средняя: значения между 1 / 3 и 1 / 2\n",
+    "- Высокая: значения между 1 / 2 и 2 / 3\n",
+    "- Очень высокая: значения выше 2 / 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "charges\n",
+      "34412.75325    296\n",
+      "2913.56900       4\n",
+      "12032.32600      4\n",
+      "13470.80440      4\n",
+      "6289.75490       4\n",
+      "              ... \n",
+      "1731.67700       2\n",
+      "1163.46270       2\n",
+      "19496.71917      2\n",
+      "7201.70085       2\n",
+      "11093.62290      2\n",
+      "Name: count, Length: 1197, dtype: int64 \n",
+      "\n",
+      "Статистическое описание целевого признака:\n",
+      "count     2772.000000\n",
+      "mean     12455.464566\n",
+      "std      10174.073271\n",
+      "min       1121.873900\n",
+      "25%       4687.797000\n",
+      "50%       9333.014350\n",
+      "75%      16577.779500\n",
+      "max      34412.753250\n",
+      "Name: charges, dtype: float64 \n",
+      "\n",
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "charges_category\n",
+      "very high    924\n",
+      "very low     462\n",
+      "low          462\n",
+      "medium       462\n",
+      "high         462\n",
+      "Name: count, dtype: int64 \n",
+      "\n",
+      "Проверка сбалансированности:\n",
+      "Весь датасет:  (2772, 8)\n",
+      "Распределение выборки данных по классам в колонке \"charges_category\":\n",
+      " charges_category\n",
+      "very high    924\n",
+      "very low     462\n",
+      "low          462\n",
+      "medium       462\n",
+      "high         462\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"very high\": 33.33%\n",
+      "Процент объектов класса \"very low\": 16.67%\n",
+      "Процент объектов класса \"low\": 16.67%\n",
+      "Процент объектов класса \"medium\": 16.67%\n",
+      "Процент объектов класса \"high\": 16.67%\n",
+      "\n",
+      "Проверка необходимости аугментации:\n",
+      "Для датасета аугментация данных НЕ ТРЕБУЕТСЯ\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df.charges.value_counts(), '\\n')\n",
+    "\n",
+    "# Статистическое описание целевого признака\n",
+    "print('Статистическое описание целевого признака:')\n",
+    "print(df['charges'].describe().transpose(), '\\n')\n",
+    "\n",
+    "q = 1 / 6\n",
+    "\n",
+    "# Определим границы для каждой категории зарплаты\n",
+    "bins: list[float] = [df['charges'].min() - 1, \n",
+    "                     df['charges'].quantile(q * 1),\n",
+    "                     df['charges'].quantile(q * 2),\n",
+    "                     df['charges'].quantile(q * 3),\n",
+    "                     df['charges'].quantile(q * 4), \n",
+    "                     df['charges'].max() + 1]\n",
+    "\n",
+    "labels: list[str] = ['very low', 'low', 'medium', 'high', 'very high']\n",
+    "\n",
+    "# Создаем новую колонку с категориями\n",
+    "df['charges_category'] = pd.cut(df['charges'], bins=bins, labels=labels)\n",
+    "\n",
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df['charges_category'].value_counts(), '\\n')\n",
+    "\n",
+    "# Проверка сбалансированности\n",
+    "print('Проверка сбалансированности:')\n",
+    "check_balance(df, 'Весь датасет', 'charges_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации\n",
+    "print('Проверка необходимости аугментации:')\n",
+    "print(f\"Для датасета аугментация данных {'НЕ ' if not need_augmentation(df, 'charges_category', 'low', 'medium') else ''}ТРЕБУЕТСЯ\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df, 'charges_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разделим на три выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   df,\n",
+    "   stratify_colname=\"charges_category\",\n",
+    "   frac_train=0.60,\n",
+    "   frac_val=0.20, \n",
+    "   frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "visualize_balance_three_pies(df_train, df_val, df_test, 'charges_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проведем аугментацию только для обучающей выборки, остальные оставим как есть (так захотелось:3)\n",
+    "\n",
+    "Так как классов у нас 5, то ADASYN не подошел, используем SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def oversample(df: DataFrame, column: str) -> DataFrame:\n",
+    "    X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
+    "    y: DataFrame = df[column] # type: ignore\n",
+    "    \n",
+    "    smote = SMOTE()\n",
+    "    X_resampled, y_resampled = smote.fit_resample(X, y) # type: ignore\n",
+    "    \n",
+    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
+    "    return df_resampled\n",
+    "\n",
+    "df_train_oversampled = oversample(df_train, 'charges_category')\n",
+    "\n",
+    "visualize_balance_three_pies(df_train_oversampled, df_val, df_test, 'charges_category')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lab_2/requirements.txt b/lab_2/requirements.txt
new file mode 100644
index 0000000..883c37d
Binary files /dev/null and b/lab_2/requirements.txt differ