From cfb610ad02e766d2fd9e3656c311ebcf3fe7b96e Mon Sep 17 00:00:00 2001 From: sodaler Date: Sun, 3 Dec 2023 15:08:35 +0400 Subject: [PATCH] degtyarev_mikhail_lab_3_is_ready --- degtyarev_mikhail_lab_3/Readme.md | 71 +++ degtyarev_mikhail_lab_3/ds_salaries.csv | 608 ++++++++++++++++++++++++ degtyarev_mikhail_lab_3/img.png | Bin 0 -> 30081 bytes degtyarev_mikhail_lab_3/main.py | 53 +++ 4 files changed, 732 insertions(+) create mode 100644 degtyarev_mikhail_lab_3/Readme.md create mode 100644 degtyarev_mikhail_lab_3/ds_salaries.csv create mode 100644 degtyarev_mikhail_lab_3/img.png create mode 100644 degtyarev_mikhail_lab_3/main.py diff --git a/degtyarev_mikhail_lab_3/Readme.md b/degtyarev_mikhail_lab_3/Readme.md new file mode 100644 index 0000000..e531fd9 --- /dev/null +++ b/degtyarev_mikhail_lab_3/Readme.md @@ -0,0 +1,71 @@ +# Лабораторная 3 +## Вариант 9 + +## Задание +Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта»на 99% ваших данных. +Проверьте работу модели на оставшемся проценте, сделайте вывод. + +## Описание Программы +Данная программа предназначена для построения и оценки модели дерева решений с использованием данных о зарплатах в области Data Science. +1) Задача, решаемая деревом решений: +Можно использовать дерево решений для классификации должностей на основе опыта работы (experience_level), типа занятости (employment_type), местоположения компании (company_location) и размера компании (company_size). Например, можно предсказать категории должностей, такие как "Junior Data Scientist," "Senior Data Analyst," и т.д. + +### Используемые библиотеки +- `pandas`: Библиотека для обработки и анализа данных, используется для загрузки и предобработки данных. +- `scikit-learn`: Библиотека для машинного обучения, включает в себя реализацию дерева решений (DecisionTreeClassifier), метрики оценки модели (accuracy_score, mean_squared_error) и кодировщик категориальных переменных (LabelEncoder). + +### Шаги программы + +**Загрузка данных:** + +Данные о зарплатах в области Data Science загружаются из файла 'ds_salaries.csv'. +Столбец 'Unnamed: 0', предположительно, содержащий индекс или идентификатор, удаляется. + +**Предобработка данных:** + +Определены признаки (features) и целевая переменная (target). +Категориальные признаки преобразованы в числовой формат с использованием LabelEncoder. +Целевая переменная также преобразована в числовой формат. + +**Разделение данных:** + +Данные разделены на обучающий (99%) и тестовый (1%) наборы с использованием train_test_split. + +**Построение и обучение модели:** + +Создана и обучена модель дерева решений с использованием DecisionTreeClassifier. + +**Предсказание и оценка:** + +Выполнено предсказание категорий должностей на тестовом наборе данных. +Оценена точность модели с использованием accuracy_score. +Рассчитана средняя квадратичная ошибка в процентах с использованием mean_squared_error. + +**Анализ важности признаков:** + +Выведена важность каждого признака в модели. +Вывод первых 5 строк тестового набора данных: + +Выведены первые 5 строк тестового набора данных для ознакомления с фактическими и предсказанными значениями. + +### Запуск программы +- Склонировать или скачать код `main.py`. +- Запустите файл в среде, поддерживающей выполнение Python. `python main.py` + +### Результаты +![](img.png) + +На основе результатов, предоставленных моделью дерева решений, можно сделать вывод, что текущая модель не идеально подходит для задачи классификации должностей на основе предоставленных данных. + +**Точность модели 14.29%** + +Низкая точность может свидетельствовать о том, что модель недостаточно эффективна в предсказании категорий должностей. Возможные причины низкой точности могут включать в себя неоптимальный выбор признаков, недостаточную обработку данных или неоптимальную настройку параметров модели. + +**Средняя квадратичная ошибка: 165.86%** + +Высокая средняя квадратичная ошибка также указывает на значительное отклонение предсказанных значений от фактических значений. Это говорит о том, что модель недостаточно точно предсказывает категории должностей. + +**Важность признаков** + +'company_location' оказывает наибольшее влияние на модель, тогда как 'employment_type' - наименьшее. + diff --git a/degtyarev_mikhail_lab_3/ds_salaries.csv b/degtyarev_mikhail_lab_3/ds_salaries.csv new file mode 100644 index 0000000..4f56347 --- /dev/null +++ b/degtyarev_mikhail_lab_3/ds_salaries.csv @@ -0,0 +1,608 @@ +,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size +0,2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L +1,2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S +2,2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M +3,2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S +4,2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L +5,2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L +6,2020,SE,FT,Lead Data 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Science,110000,EUR,130026,DE,50,DE,M +165,2021,SE,FT,Data Specialist,165000,USD,165000,US,100,US,L +166,2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L +167,2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L +168,2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S +169,2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L +170,2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L +171,2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L +172,2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S +173,2021,SE,FT,Principal Data Scientist,235000,USD,235000,US,100,US,L +174,2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L +175,2021,SE,FT,Data Engineering Manager,174000,USD,174000,US,100,US,L +176,2021,MI,FT,Data Scientist,58000,MXN,2859,MX,0,MX,S +177,2021,MI,FT,Data Scientist,30400000,CLP,40038,CL,100,CL,L +178,2021,EN,FT,Machine Learning Engineer,81000,USD,81000,US,50,US,S +179,2021,MI,FT,Data Scientist,420000,INR,5679,IN,100,US,S +180,2021,MI,FT,Big Data Engineer,1672000,INR,22611,IN,0,IN,L +181,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L +182,2021,MI,FT,Data Engineer,22000,EUR,26005,RO,0,US,L +183,2021,SE,FT,Finance Data Analyst,45000,GBP,61896,GB,50,GB,L +184,2021,MI,FL,Machine Learning Scientist,12000,USD,12000,PK,50,PK,M +185,2021,MI,FT,Data Engineer,4000,USD,4000,IR,100,IR,M +186,2021,SE,FT,Data Analytics Engineer,50000,USD,50000,VN,100,GB,M +187,2021,EX,FT,Data Science Consultant,59000,EUR,69741,FR,100,ES,S +188,2021,SE,FT,Data Engineer,65000,EUR,76833,RO,50,GB,S +189,2021,MI,FT,Machine Learning Engineer,74000,USD,74000,JP,50,JP,S +190,2021,SE,FT,Data Science Manager,152000,USD,152000,US,100,FR,L +191,2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M +192,2021,MI,FT,Big Data Engineer,18000,USD,18000,MD,0,MD,S +193,2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L +194,2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L +195,2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L +196,2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S +197,2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L +198,2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L +199,2021,EN,FT,Data Science Consultant,90000,USD,90000,US,100,US,S +200,2021,MI,FT,Data Scientist,52000,EUR,61467,DE,50,AT,M +201,2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M +202,2021,MI,FT,Data Scientist,32000,EUR,37825,ES,100,ES,L +203,2021,SE,FT,Research Scientist,50000,USD,50000,FR,100,US,S +204,2021,MI,FT,Data Scientist,160000,USD,160000,US,100,US,L +205,2021,MI,FT,Data Scientist,69600,BRL,12901,BR,0,BR,S +206,2021,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,L +207,2021,SE,FT,Data Engineer,165000,USD,165000,US,0,US,M +208,2021,MI,FL,Data Engineer,20000,USD,20000,IT,0,US,L +209,2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,0,US,L +210,2021,MI,FT,Machine Learning Engineer,21000,EUR,24823,SI,50,SI,L +211,2021,MI,FT,Research Scientist,48000,EUR,56738,FR,50,FR,S +212,2021,MI,FT,Data Engineer,48000,GBP,66022,HK,50,GB,S +213,2021,EN,FT,Big Data Engineer,435000,INR,5882,IN,0,CH,L +214,2021,EN,FT,Machine Learning Engineer,21000,EUR,24823,DE,50,DE,M +215,2021,SE,FT,Principal Data Engineer,185000,USD,185000,US,100,US,L +216,2021,EN,PT,Computer Vision Engineer,180000,DKK,28609,DK,50,DK,S +217,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L +218,2021,MI,FT,Machine Learning Engineer,75000,EUR,88654,BE,100,BE,M +219,2021,SE,FT,Data Analytics Manager,140000,USD,140000,US,100,US,L +220,2021,MI,FT,Machine Learning Engineer,180000,PLN,46597,PL,100,PL,L +221,2021,MI,FT,Data Scientist,85000,GBP,116914,GB,50,GB,L +222,2021,MI,FT,Data Scientist,2500000,INR,33808,IN,0,IN,M +223,2021,MI,FT,Data Scientist,40900,GBP,56256,GB,50,GB,L +224,2021,SE,FT,Machine Learning Scientist,225000,USD,225000,US,100,CA,L +225,2021,EX,CT,Principal Data Scientist,416000,USD,416000,US,100,US,S +226,2021,SE,FT,Data Scientist,110000,CAD,87738,CA,100,CA,S +227,2021,MI,FT,Data Scientist,75000,EUR,88654,DE,50,DE,L +228,2021,SE,FT,Data Scientist,135000,USD,135000,US,0,US,L +229,2021,SE,FT,Data Analyst,90000,CAD,71786,CA,100,CA,M +230,2021,EN,FT,Big Data Engineer,1200000,INR,16228,IN,100,IN,L +231,2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S +232,2021,SE,FT,Director of Data Engineering,200000,USD,200000,US,100,US,L +233,2021,SE,FT,Data Analyst,200000,USD,200000,US,100,US,L +234,2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L +235,2021,MI,FT,Head of Data Science,110000,USD,110000,US,0,US,S +236,2021,MI,FT,Research Scientist,80000,CAD,63810,CA,100,CA,M +237,2021,MI,FT,Data Scientist,39600,EUR,46809,ES,100,ES,M +238,2021,EN,FT,Data Scientist,4000,USD,4000,VN,0,VN,M +239,2021,EN,FT,Data Engineer,1600000,INR,21637,IN,50,IN,M +240,2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L +241,2021,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L +242,2021,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L +243,2021,SE,FT,Data Scientist,165000,USD,165000,US,100,US,L +244,2021,EN,FT,AI Scientist,1335000,INR,18053,IN,100,AS,S +245,2021,MI,FT,Data Engineer,52500,GBP,72212,GB,50,GB,L +246,2021,EN,FT,Data Scientist,31000,EUR,36643,FR,50,FR,L +247,2021,MI,FT,Data Engineer,108000,TRY,12103,TR,0,TR,M +248,2021,SE,FT,Data Engineer,70000,GBP,96282,GB,50,GB,L +249,2021,SE,FT,Principal Data Analyst,170000,USD,170000,US,100,US,M +250,2021,MI,FT,Data Scientist,115000,USD,115000,US,50,US,L +251,2021,EN,FT,Data Scientist,90000,USD,90000,US,100,US,S +252,2021,EX,FT,Principal Data Engineer,600000,USD,600000,US,100,US,L +253,2021,EN,FT,Data Scientist,2100000,INR,28399,IN,100,IN,M +254,2021,MI,FT,Data Analyst,93000,USD,93000,US,100,US,L +255,2021,SE,FT,Big Data Architect,125000,CAD,99703,CA,50,CA,M +256,2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L +257,2021,SE,FT,Principal Data Scientist,147000,EUR,173762,DE,100,DE,M +258,2021,SE,FT,Machine Learning Engineer,185000,USD,185000,US,50,US,L +259,2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L +260,2021,MI,FT,Data Scientist,130000,USD,130000,US,50,US,L +261,2021,SE,FT,Data Analyst,54000,EUR,63831,DE,50,DE,L +262,2021,MI,FT,Data Scientist,1250000,INR,16904,IN,100,IN,S +263,2021,SE,FT,Machine Learning Engineer,4900000,INR,66265,IN,0,IN,L +264,2021,MI,FT,Data Scientist,21600,EUR,25532,RS,100,DE,S +265,2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S +266,2021,MI,FT,Data Engineer,93150,USD,93150,US,0,US,M +267,2021,MI,FT,Data Engineer,111775,USD,111775,US,0,US,M +268,2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M +269,2021,EN,FT,Data Engineer,55000,EUR,65013,DE,50,DE,M +270,2021,EN,FT,Data Engineer,72500,USD,72500,US,100,US,L +271,2021,SE,FT,Computer Vision Engineer,102000,BRL,18907,BR,0,BR,M +272,2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,0,DE,L +273,2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S +274,2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M +275,2021,EN,FT,Data Scientist,100000,USD,100000,US,100,US,M +276,2021,EN,FT,Data Scientist,58000,USD,58000,US,50,US,L +277,2021,SE,FT,AI Scientist,55000,USD,55000,ES,100,ES,L +278,2021,SE,FT,Data Scientist,180000,TRY,20171,TR,50,TR,L +279,2021,EN,FT,Business Data Analyst,50000,EUR,59102,LU,100,LU,L +280,2021,MI,FT,Data Engineer,112000,USD,112000,US,100,US,L +281,2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L +282,2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L +283,2021,SE,CT,Staff Data Scientist,105000,USD,105000,US,100,US,M +284,2021,MI,FT,Research Scientist,69999,USD,69999,CZ,50,CZ,L +285,2021,SE,FT,Data Science Manager,7000000,INR,94665,IN,50,IN,L +286,2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L +287,2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L +288,2021,MI,FT,Machine Learning Engineer,43200,EUR,51064,IT,50,IT,L +289,2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M +290,2022,SE,FT,Data Analyst,155000,USD,155000,US,100,US,M +291,2022,SE,FT,Data Analyst,120600,USD,120600,US,100,US,M +292,2022,MI,FT,Data Scientist,130000,USD,130000,US,0,US,M +293,2022,MI,FT,Data Scientist,90000,USD,90000,US,0,US,M +294,2022,MI,FT,Data Engineer,170000,USD,170000,US,100,US,M +295,2022,MI,FT,Data Engineer,150000,USD,150000,US,100,US,M +296,2022,SE,FT,Data Analyst,102100,USD,102100,US,100,US,M +297,2022,SE,FT,Data Analyst,84900,USD,84900,US,100,US,M +298,2022,SE,FT,Data Scientist,136620,USD,136620,US,100,US,M +299,2022,SE,FT,Data Scientist,99360,USD,99360,US,100,US,M +300,2022,SE,FT,Data Scientist,90000,GBP,117789,GB,0,GB,M +301,2022,SE,FT,Data Scientist,80000,GBP,104702,GB,0,GB,M +302,2022,SE,FT,Data Scientist,146000,USD,146000,US,100,US,M +303,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M +304,2022,EN,FT,Data Engineer,40000,GBP,52351,GB,100,GB,M +305,2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M +306,2022,SE,FT,Data Analyst,116000,USD,116000,US,0,US,M +307,2022,MI,FT,Data Analyst,106260,USD,106260,US,0,US,M +308,2022,MI,FT,Data Analyst,126500,USD,126500,US,0,US,M +309,2022,EX,FT,Data Engineer,242000,USD,242000,US,100,US,M +310,2022,EX,FT,Data Engineer,200000,USD,200000,US,100,US,M +311,2022,MI,FT,Data Scientist,50000,GBP,65438,GB,0,GB,M +312,2022,MI,FT,Data Scientist,30000,GBP,39263,GB,0,GB,M +313,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,0,GB,M +314,2022,MI,FT,Data Engineer,40000,GBP,52351,GB,0,GB,M +315,2022,SE,FT,Data Scientist,165220,USD,165220,US,100,US,M +316,2022,EN,FT,Data Engineer,35000,GBP,45807,GB,100,GB,M +317,2022,SE,FT,Data Scientist,120160,USD,120160,US,100,US,M +318,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M +319,2022,SE,FT,Data Engineer,181940,USD,181940,US,0,US,M +320,2022,SE,FT,Data Engineer,132320,USD,132320,US,0,US,M +321,2022,SE,FT,Data Engineer,220110,USD,220110,US,0,US,M +322,2022,SE,FT,Data Engineer,160080,USD,160080,US,0,US,M +323,2022,SE,FT,Data Scientist,180000,USD,180000,US,0,US,L +324,2022,SE,FT,Data Scientist,120000,USD,120000,US,0,US,L +325,2022,SE,FT,Data Analyst,124190,USD,124190,US,100,US,M +326,2022,EX,FT,Data Analyst,130000,USD,130000,US,100,US,M +327,2022,EX,FT,Data Analyst,110000,USD,110000,US,100,US,M +328,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M +329,2022,MI,FT,Data Analyst,115500,USD,115500,US,100,US,M +330,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M +331,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M +332,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M +333,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M +334,2022,SE,FT,Data Engineer,165400,USD,165400,US,100,US,M +335,2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M +336,2022,MI,FT,Data Analyst,167000,USD,167000,US,100,US,M +337,2022,SE,FT,Data Engineer,243900,USD,243900,US,100,US,M +338,2022,SE,FT,Data Analyst,136600,USD,136600,US,100,US,M +339,2022,SE,FT,Data Analyst,109280,USD,109280,US,100,US,M +340,2022,SE,FT,Data Engineer,128875,USD,128875,US,100,US,M +341,2022,SE,FT,Data Engineer,93700,USD,93700,US,100,US,M +342,2022,EX,FT,Head of Data Science,224000,USD,224000,US,100,US,M +343,2022,EX,FT,Head of Data Science,167875,USD,167875,US,100,US,M +344,2022,EX,FT,Analytics Engineer,175000,USD,175000,US,100,US,M +345,2022,SE,FT,Data Engineer,156600,USD,156600,US,100,US,M +346,2022,SE,FT,Data Engineer,108800,USD,108800,US,0,US,M +347,2022,SE,FT,Data Scientist,95550,USD,95550,US,0,US,M +348,2022,SE,FT,Data Engineer,113000,USD,113000,US,0,US,L +349,2022,SE,FT,Data Analyst,135000,USD,135000,US,100,US,M +350,2022,SE,FT,Data Science Manager,161342,USD,161342,US,100,US,M +351,2022,SE,FT,Data Science Manager,137141,USD,137141,US,100,US,M +352,2022,SE,FT,Data Scientist,167000,USD,167000,US,100,US,M +353,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M +354,2022,SE,FT,Data Engineer,60000,GBP,78526,GB,0,GB,M +355,2022,SE,FT,Data Engineer,50000,GBP,65438,GB,0,GB,M +356,2022,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M +357,2022,SE,FT,Data Scientist,211500,USD,211500,US,100,US,M +358,2022,SE,FT,Data Architect,192400,USD,192400,CA,100,CA,M +359,2022,SE,FT,Data Architect,90700,USD,90700,CA,100,CA,M +360,2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M +361,2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M +362,2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M +363,2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M +364,2022,SE,FT,Data Engineer,160000,USD,160000,US,0,US,L +365,2022,SE,FT,Data Scientist,138600,USD,138600,US,100,US,M +366,2022,SE,FT,Data Engineer,136000,USD,136000,US,0,US,M +367,2022,MI,FT,Data Analyst,58000,USD,58000,US,0,US,S +368,2022,EX,FT,Analytics Engineer,135000,USD,135000,US,100,US,M +369,2022,SE,FT,Data Scientist,170000,USD,170000,US,100,US,M +370,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M +371,2022,SE,FT,Machine Learning Engineer,189650,USD,189650,US,0,US,M +372,2022,SE,FT,Machine Learning Engineer,164996,USD,164996,US,0,US,M +373,2022,MI,FT,ETL Developer,50000,EUR,54957,GR,0,GR,M +374,2022,MI,FT,ETL Developer,50000,EUR,54957,GR,0,GR,M +375,2022,EX,FT,Lead Data Engineer,150000,CAD,118187,CA,100,CA,S +376,2022,SE,FT,Data Analyst,132000,USD,132000,US,0,US,M +377,2022,SE,FT,Data Engineer,165400,USD,165400,US,100,US,M +378,2022,SE,FT,Data Architect,208775,USD,208775,US,100,US,M +379,2022,SE,FT,Data Architect,147800,USD,147800,US,100,US,M +380,2022,SE,FT,Data Engineer,136994,USD,136994,US,100,US,M +381,2022,SE,FT,Data Engineer,101570,USD,101570,US,100,US,M +382,2022,SE,FT,Data Analyst,128875,USD,128875,US,100,US,M +383,2022,SE,FT,Data Analyst,93700,USD,93700,US,100,US,M +384,2022,EX,FT,Head of Machine Learning,6000000,INR,79039,IN,50,IN,L +385,2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M +386,2022,EN,FT,Machine Learning Engineer,28500,GBP,37300,GB,100,GB,L +387,2022,SE,FT,Data Analyst,164000,USD,164000,US,0,US,M +388,2022,SE,FT,Data Engineer,155000,USD,155000,US,100,US,M +389,2022,MI,FT,Machine Learning Engineer,95000,GBP,124333,GB,0,GB,M +390,2022,MI,FT,Machine Learning Engineer,75000,GBP,98158,GB,0,GB,M +391,2022,MI,FT,AI Scientist,120000,USD,120000,US,0,US,M +392,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M +393,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M +394,2022,SE,FT,Data Analytics Manager,145000,USD,145000,US,100,US,M +395,2022,SE,FT,Data Analytics Manager,105400,USD,105400,US,100,US,M +396,2022,MI,FT,Machine Learning Engineer,80000,EUR,87932,FR,100,DE,M +397,2022,MI,FT,Data Engineer,90000,GBP,117789,GB,0,GB,M +398,2022,SE,FT,Data Scientist,215300,USD,215300,US,100,US,L +399,2022,SE,FT,Data Scientist,158200,USD,158200,US,100,US,L +400,2022,SE,FT,Data Engineer,209100,USD,209100,US,100,US,L +401,2022,SE,FT,Data Engineer,154600,USD,154600,US,100,US,L +402,2022,SE,FT,Data Analyst,115934,USD,115934,US,0,US,M +403,2022,SE,FT,Data Analyst,81666,USD,81666,US,0,US,M +404,2022,SE,FT,Data Engineer,175000,USD,175000,US,100,US,M +405,2022,MI,FT,Data 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sklearn.preprocessing import LabelEncoder + +# Загрузка данных и удаление столбца 'Unnamed: 0' +data = pd.read_csv('ds_salaries.csv').drop('Unnamed: 0', axis=1) + +# Определение признаков и целевой переменной +features = ['experience_level', 'employment_type', 'company_location', 'company_size'] +target = 'job_title' + +# Преобразование категориальных признаков в числовые +label_encoder = LabelEncoder() +for feature in features: + data[feature] = label_encoder.fit_transform(data[feature]) + +# Преобразование целевой переменной в числовой формат +data[target] = label_encoder.fit_transform(data[target]) + +# Разделение данных на обучающий (99%) и тестовый (1%) наборы +train_data, test_data = train_test_split(data, test_size=0.01, random_state=42) + +# Создание модели дерева решений +model = DecisionTreeClassifier(random_state=42) + +# Обучение модели +model.fit(train_data[features], train_data[target]) + +# Предсказание на тестовом наборе +predictions = model.predict(test_data[features]) + +# Обратное преобразование числовых предсказаний в строковый формат +predictions_str = label_encoder.inverse_transform(predictions) + +# Оценка точности модели на тестовом наборе +accuracy = accuracy_score(test_data[target], predictions) +print(f'Accuracy: {accuracy * 100:.2f}%') + +# Средняя квадратичная ошибка в процентах +mse = mean_squared_error(test_data[target], predictions) +print(f'Mean Squared Error: {mse:.2f}%') + +feature_importance = model.feature_importances_ +feature_importance_dict = dict(zip(features, feature_importance)) +sorted_feature_importance = sorted(feature_importance_dict.items(), key=lambda x: x[1], reverse=True) +print("Feature Importance:") +for feature, importance in sorted_feature_importance: + print(f"{feature}: {importance}") + +print("First 5 rows of test data:") +print(test_data.head())