diff --git a/degtyarev_mikhail_lab_5/Readme.md b/degtyarev_mikhail_lab_5/Readme.md
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+# Лабораторная 5
+## Вариант 9
+
+## Задание
+Использовать Ласо-регрессию, самостоятельно  сформулировав  задачу.  Оценить, насколько хорошо она подходит для решения сформулированной вами задачи.
+
+Задача:
+
+Можно использовать регрессию для прогнозирования заработной платы на основе опыта работы (experience_level), типа занятости (employment_type), местоположения компании (company_location) и размера компании (company_size).
+
+## Описание Программы
+Программа представляет собой пример использования Lasso регрессии для прогнозирования заработной платы на основе различных признаков.
+
+### Используемые библиотеки
+- `pandas`: Библиотека для обработки и анализа данных, используется для загрузки и предобработки данных.
+- `scikit-learn`:
+
+train_test_split: Используется для разделения данных на обучающий и тестовый наборы.
+
+StandardScaler: Применяется для нормализации числовых признаков.
+
+OneHotEncoder: Используется для кодирования категориальных признаков.
+
+Lasso: Линейная модель Lasso для обучения регрессии.
+
+Pipeline: Позволяет объединять шаги предварительной обработки данных и обучения модели в пайплайн.
+
+- `matplotlib`: Используется для визуализации коэффициентов модели в виде горизонтальной столбчатой диаграммы.
+- `numpy`: Использована для работы с числовыми данными.
+
+### Шаги программы
+
+**Загрузка данных:**
+
+Используется библиотека pandas для загрузки данных из файла ds_salaries.csv.
+
+**Предварительная обработка данных:**
+
+Категориальные признаки ('experience_level', 'employment_type', 'company_location', 'company_size') обрабатываются с использованием OneHotEncoder, а числовые признаки ('work_year') нормализуются с помощью StandardScaler. Эти шаги объединены в ColumnTransformer и используются в качестве предварительного обработчика данных.
+
+**Выбор признаков:**
+
+Определены признаки, которые будут использоваться для обучения модели.
+
+**Разделение данных:**
+
+Данные разделены на обучающий и тестовый наборы в соотношении 80/20 с использованием train_test_split.
+
+**Обучение модели:**
+
+Используется линейная модель Лассо-регрессия, объединенная с предварительным обработчиком данных в рамках Pipeline.
+
+**Оценка точности модели:**
+
+Вычисляется коэффициент детерминации (R^2 Score) и среднеквадратичная ошибка (Mean Squared Error) для оценки точности модели.
+
+**Вывод предсказанных и фактических значений:**
+
+Создается DataFrame с фактическими и предсказанными значениями и выводится в консоль.
+
+**Визуализация весов (коэффициентов) модели:**
+
+Строится горизонтальная столбчатая диаграмма для визуализации весов (коэффициентов) модели.
+
+### Запуск программы
+- Склонировать или скачать код `main.py`.
+- Запустите файл в среде, поддерживающей выполнение Python. `python main.py`
+
+### Результаты
+![](img.png)
+
+![](cli_res.png)
+
+Точность модели составляет всего 39%, что является довольно низким показателем
+
+MSE довольно высок, что указывает на то, что модель не слишком хорошо соответствует данным и допускает ошибки в предсказаниях
+
+Фактические и предсказанные значения: видно, что модель часто недооценивает или переоценивает заработную плату. Например, для индексов 563 и 289 фактическая заработная плата выше, чем предсказанная.
+
+Изменение alfa не особо улучшает общую картину, поэтому, можно сделать вывод, что следует выбрать другой алгоритм.
diff --git a/degtyarev_mikhail_lab_5/cli_res.png b/degtyarev_mikhail_lab_5/cli_res.png
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index 0000000..dfe538d
Binary files /dev/null and b/degtyarev_mikhail_lab_5/cli_res.png differ
diff --git a/degtyarev_mikhail_lab_5/ds_salaries.csv b/degtyarev_mikhail_lab_5/ds_salaries.csv
new file mode 100644
index 0000000..4f56347
--- /dev/null
+++ b/degtyarev_mikhail_lab_5/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
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+5,2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
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+7,2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
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diff --git a/degtyarev_mikhail_lab_5/img.png b/degtyarev_mikhail_lab_5/img.png
new file mode 100644
index 0000000..3ac88a6
Binary files /dev/null and b/degtyarev_mikhail_lab_5/img.png differ
diff --git a/degtyarev_mikhail_lab_5/main.py b/degtyarev_mikhail_lab_5/main.py
new file mode 100644
index 0000000..75bb6ac
--- /dev/null
+++ b/degtyarev_mikhail_lab_5/main.py
@@ -0,0 +1,60 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import Lasso
+from sklearn.metrics import mean_squared_error
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+import matplotlib.pyplot as plt
+
+# Загрузка данных
+file_path = 'ds_salaries.csv'
+data = pd.read_csv(file_path)
+
+# Предварительная обработка данных
+categorical_features = ['experience_level', 'employment_type', 'company_location', 'company_size']
+numeric_features = ['work_year']
+
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), numeric_features),
+        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
+    ])
+
+# Выбор признаков
+features = ['work_year', 'experience_level', 'employment_type', 'company_location', 'company_size']
+X = data[features]
+y = data['salary_in_usd']
+
+# Разделение данных на обучающий и тестовый наборы
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Создание и обучение модели с использованием предварительного обработчика данных
+alpha = 0.01
+lasso_model = Pipeline([
+    ('preprocessor', preprocessor),
+    ('lasso', Lasso(alpha=alpha))
+])
+
+lasso_model.fit(X_train, y_train)
+
+# Получение прогнозов
+y_pred = lasso_model.predict(X_test)
+
+# Оценка точности модели
+accuracy = lasso_model.score(X_test, y_test)
+mse = mean_squared_error(y_test, y_pred)
+
+print(f"R^2 Score: {accuracy:.2f}")
+print(f"Mean Squared Error: {mse:.2f}")
+
+# Вывод предсказанных и фактических значений
+predictions_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
+print(predictions_df)
+
+# Визуализация весов (коэффициентов) модели
+coefficients = pd.Series(lasso_model.named_steps['lasso'].coef_, index=numeric_features + list(lasso_model.named_steps['preprocessor'].transformers_[1][1].get_feature_names(categorical_features)))
+plt.figure(figsize=(10, 6))
+coefficients.sort_values().plot(kind='barh')
+plt.title('Lasso Regression Coefficients')
+plt.show()