diff --git a/degtyarev_mikhail_lab_3/Readme.md b/degtyarev_mikhail_lab_3/Readme.md
new file mode 100644
index 0000000..e531fd9
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@@ -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 Scientist,190000,USD,190000,US,100,US,S
+7,2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
+8,2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
+9,2020,SE,FT,Lead Data Engineer,125000,USD,125000,NZ,50,NZ,S
+10,2020,EN,FT,Data Scientist,45000,EUR,51321,FR,0,FR,S
+11,2020,MI,FT,Data Scientist,3000000,INR,40481,IN,0,IN,L
+12,2020,EN,FT,Data Scientist,35000,EUR,39916,FR,0,FR,M
+13,2020,MI,FT,Lead Data Analyst,87000,USD,87000,US,100,US,L
+14,2020,MI,FT,Data Analyst,85000,USD,85000,US,100,US,L
+15,2020,MI,FT,Data Analyst,8000,USD,8000,PK,50,PK,L
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+17,2020,SE,FT,Big Data Engineer,100000,EUR,114047,PL,100,GB,S
+18,2020,EN,FT,Data Science Consultant,423000,INR,5707,IN,50,IN,M
+19,2020,MI,FT,Lead Data Engineer,56000,USD,56000,PT,100,US,M
+20,2020,MI,FT,Machine Learning Engineer,299000,CNY,43331,CN,0,CN,M
+21,2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L
+22,2020,SE,FT,Data Engineer,42000,EUR,47899,GR,50,GR,L
+23,2020,MI,FT,BI Data Analyst,98000,USD,98000,US,0,US,M
+24,2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
+25,2020,EX,FT,Director of Data Science,325000,USD,325000,US,100,US,L
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+27,2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
+28,2020,EN,CT,Business Data Analyst,100000,USD,100000,US,100,US,L
+29,2020,SE,FT,Machine Learning Manager,157000,CAD,117104,CA,50,CA,L
+30,2020,MI,FT,Data Engineering Manager,51999,EUR,59303,DE,100,DE,S
+31,2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
+32,2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
+33,2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
+34,2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
+35,2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
+36,2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
+37,2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
+38,2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
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+40,2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
+41,2020,EX,FT,Data Engineering Manager,70000,EUR,79833,ES,50,ES,L
+42,2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
+43,2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
+44,2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
+45,2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
+46,2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
+47,2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
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+50,2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
+51,2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
+52,2020,EN,FT,AI Scientist,300000,DKK,45896,DK,50,DK,S
+53,2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
+54,2020,SE,FL,Computer Vision Engineer,60000,USD,60000,RU,100,US,S
+55,2020,SE,FT,Principal Data Scientist,130000,EUR,148261,DE,100,DE,M
+56,2020,MI,FT,Data Scientist,34000,EUR,38776,ES,100,ES,M
+57,2020,MI,FT,Data Scientist,118000,USD,118000,US,100,US,M
+58,2020,SE,FT,Data Scientist,120000,USD,120000,US,50,US,L
+59,2020,MI,FT,Data Scientist,138350,USD,138350,US,100,US,M
+60,2020,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
+61,2020,MI,FT,Data Engineer,130800,USD,130800,ES,100,US,M
+62,2020,EN,PT,Data Scientist,19000,EUR,21669,IT,50,IT,S
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+64,2020,SE,FT,Machine Learning Engineer,40000,EUR,45618,HR,100,HR,S
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+66,2020,EN,FT,Data Scientist,43200,EUR,49268,DE,0,DE,S
+67,2020,SE,FT,Data Science Manager,190200,USD,190200,US,100,US,M
+68,2020,EN,FT,Data Scientist,105000,USD,105000,US,100,US,S
+69,2020,SE,FT,Data Scientist,80000,EUR,91237,AT,0,AT,S
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+192,2021,MI,FT,Big Data Engineer,18000,USD,18000,MD,0,MD,S
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+196,2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
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+500,2022,SE,FT,Machine Learning Engineer,57000,EUR,62651,NL,100,NL,L
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+518,2022,MI,FT,Data Scientist,115000,CHF,122346,CH,0,CH,L
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diff --git a/degtyarev_mikhail_lab_3/img.png b/degtyarev_mikhail_lab_3/img.png
new file mode 100644
index 0000000..86c8311
Binary files /dev/null and b/degtyarev_mikhail_lab_3/img.png differ
diff --git a/degtyarev_mikhail_lab_3/main.py b/degtyarev_mikhail_lab_3/main.py
new file mode 100644
index 0000000..63c4d76
--- /dev/null
+++ b/degtyarev_mikhail_lab_3/main.py
@@ -0,0 +1,53 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score, mean_squared_error
+from 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())