degtyarev_mikhail_lab_6/Readme.md

@@ -0,0 +1,60 @@
+# Лабораторная 6
+## Вариант 9
+
+## Задание
+Использовать нейронную сеть MLPClassifier для данных из таблицы 1 по варианту, самостоятельно сформулировав задачу. Интерпретировать результаты и оценить, насколько хорошо она подходит для решения сформулированной вами задачи
+
+Задача:
+
+Использовать MLPClassifier для прогнозирования заработной платы на основе опыта работы (experience_level), типа занятости (employment_type), местоположения компании (company_location) и размера компании (company_size). Оценить, насколько хорошо нейронная сеть подходит для решения этой задачи.
+## Описание Программы
+Программа представляет собой пример использования MLPClassifier 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.
+  - `MLPClassifier`: Классификатор многослойного персептрона (нейронная сеть).
+  - `accuracy_score`: Используется для оценки точности классификации.
+
+### Шаги программы
+
+1. **Загрузка данных:**
+   - Загружаются данные из файла `ds_salaries.csv` с использованием библиотеки pandas.
+
+2. **Определение категорий заработной платы:**
+   - Создаются категории заработной платы на основе бинов с использованием `pd.cut`.
+
+3. **Добавление столбца с категориями:**
+   - Добавляется столбец с категориями в данные.
+
+4. **Предварительная обработка данных:**
+   - Категориальные признаки ('experience_level', 'employment_type', 'job_title', 'employee_residence', 'company_location', 'company_size') обрабатываются с использованием OneHotEncoder.
+   - Числовые признаки ('work_year', 'remote_ratio') нормализуются с помощью StandardScaler.
+   - Эти шаги объединяются в ColumnTransformer и используются в качестве предварительного обработчика данных.
+
+5. **Выбор признаков:**
+   - Определены признаки, которые будут использоваться для обучения модели.
+
+6. **Разделение данных:**
+   - Данные разделены на обучающий и тестовый наборы в соотношении 80/20 с использованием функции `train_test_split`.
+
+7. **Обучение модели:**
+   - Используется MLPClassifier, объединенный с предварительным обработчиком данных в рамках Pipeline.
+
+8. **Оценка производительности модели:**
+   - Вычисляется и выводится точность модели с использованием метрики `accuracy_score`.
+
+### Запуск программы
+- Склонировать или скачать код `main.py`.
+- Запустите файл в среде, поддерживающей выполнение Python. `python main.py`
+
+### Результаты
+
+- Точность модели оценивается метрикой accuracy, которая может быть выведена в консоль или использована для визуализации.
+
+В данном случае accuracy получилось: 0.5901639344262295
+
+Чем ближе результат к единице, тем лучше, но данный результат в 59% можно считать средним.
+

degtyarev_mikhail_lab_6/ds_salaries.csv

@@ -0,0 +1,608 @@
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degtyarev_mikhail_lab_6/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()