Create Klas.py

Klas.py

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+# 1. Установка и импорт библиотек
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import joblib
+
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay
+
+# 2. Загрузка и первичный анализ данных
+df = pd.read_csv("dataset.csv")
+print(df.head(), df.info(), df.isnull().sum(), df.describe())
+
+# 3. Предобработка данных
+df.fillna(df.mean(), inplace=True)
+df.fillna(df.mode().iloc[0], inplace=True)
+
+le = LabelEncoder()
+df['target'] = le.fit_transform(df['target'])
+df = pd.get_dummies(df, drop_first=True)
+
+X = df.drop(columns=['target'])
+y = df['target']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
+
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# 4. Обучение моделей
+models = {
+    "Logistic Regression": LogisticRegression(),
+    "Decision Tree": DecisionTreeClassifier(),
+    "Random Forest": RandomForestClassifier(n_estimators=100, random_state=42),
+    "Gradient Boosting": GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, random_state=42)
+}
+
+predictions = {}
+for name, model in models.items():
+    model.fit(X_train, y_train)
+    predictions[name] = model.predict(X_test)
+
+# 5. Оценка качества моделей
+def evaluate_model(name, y_true, y_pred):
+    print(f"=== {name} ===")
+    print("Accuracy:", accuracy_score(y_true, y_pred))
+    print("Classification Report:\n", classification_report(y_true, y_pred))
+    print("Confusion Matrix:\n", confusion_matrix(y_true, y_pred))
+    print("\n")
+
+for name, y_pred in predictions.items():
+    evaluate_model(name, y_test, y_pred)
+
+# 6. Подбор гиперпараметров для случайного леса
+param_grid = {
+    'n_estimators': [50, 100, 200],
+    'max_depth': [None, 10, 20],
+    'min_samples_split': [2, 5, 10]
+}
+
+grid_search = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=5, scoring='accuracy', n_jobs=-1)
+grid_search.fit(X_train, y_train)
+
+print("Лучшие параметры:", grid_search.best_params_)
+best_model = grid_search.best_estimator_
+y_pred_best = best_model.predict(X_test)
+evaluate_model("Best Random Forest", y_test, y_pred_best)
+
+# 7. Визуализация матрицы ошибок
+fig, ax = plt.subplots(figsize=(5, 5))
+ConfusionMatrixDisplay.from_estimator(best_model, X_test, y_test, ax=ax, cmap='Blues')
+plt.show()
+
+# 8. Сохранение и загрузка модели
+joblib.dump(best_model, "best_model.pkl")
+loaded_model = joblib.load("best_model.pkl")