Готовые модели.ipynb

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    "## Классические алгоритмы без ансамблирования\n",
    "В этом ноутбуке вам нужно обучить модели на датасете классификации из предыдущего ноутбука и сравнить результаты. Вам будет предоставлен baseline, на основе которого вы будете доделывать предсказывающие модели. Оценка лабы будет зависеть от ROC-AUC на тестовых данных по следующим критериям:\n",
    "\\\n",
    "AUC - на тестовых данных\n",
    "- $AUC \\leq 0.75$ - 0 баллов\n",
    "- $0.75 < AUC \\leq 0.76$ - 2 балла\n",
    "- $0.76 < AUC \\leq 0.77$ - 4 балла\n",
    "- $0.77 < AUC \\leq 0.78$ - 6 баллов\n",
    "- $0.78 < AUC \\leq 0.79$ - 8 баллов\n",
    "- $AUC > 0.79$ - 10 баллов\n",
    "\n",
    "\\\n",
    "В этой работе запрещено использовать ансамбли моделей (лес, бустинги и т.д.)!"
   ]
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   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.metrics import roc_auc_score, precision_score, recall_score, roc_curve, accuracy_score\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "cell_type": "code",
   "execution_count": null,
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    "data = pd.read_csv('german.csv', sep=';')\n",
    "print(data.head())\n",
    "\n",
    "X = data.iloc[:, 1:].to_numpy()\n",
    "y = data.iloc[:, 0].to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "plt.hist(y_train, bins=2, edgecolor='k')\n",
    "plt.xticks([0, 1])\n",
    "plt.xlabel('Class (0: Non-Creditworthy, 1: Creditworthy)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Classes in Training Data')\n",
    "plt.show()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Создание модели Logistic Regression\n",
    "logistic_regression_model = LogisticRegression(?)\n",
    "logistic_regression_model.fit(?, ?)\n",
    "\n",
    "# Создание модели Decision Tree\n",
    "decision_tree_model = DecisionTreeClassifier(?)\n",
    "decision_tree_model.fit(?, ?)\n",
    "\n",
    "# Создание модели K-Nearest Neighbors\n",
    "knn_model = KNeighborsClassifier(?)\n",
    "knn_model.fit(?, ?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ebd8b0-3f81-4365-9f84-44bf50bbcbe4",
   "metadata": {},
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   "source": [
    "y_prob_logistic = logistic_regression_model.predict_proba(X_test)[:, 1]\n",
    "y_prob_decision_tree = decision_tree_model.predict_proba(X_test)[:, 1]\n",
    "y_prob_knn = knn_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "y_pred_logistic = logistic_regression_model.predict(X_test)\n",
    "y_pred_decision_tree = decision_tree_model.predict(X_test)\n",
    "y_pred_knn = knn_model.predict(X_test)\n",
    "\n",
    "accuracy_logistic = accuracy_score(y_test, y_pred_logistic)\n",
    "accuracy_decision_tree = accuracy_score(y_test, y_pred_decision_tree)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "\n",
    "roc_auc_logistic = roc_auc_score(y_test, y_prob_logistic)\n",
    "roc_auc_decision_tree = roc_auc_score(y_test, y_prob_decision_tree)\n",
    "roc_auc_knn = roc_auc_score(y_test, y_prob_knn)\n",
    "\n",
    "precision_logistic = precision_score(y_test, y_pred_logistic)\n",
    "precision_decision_tree = precision_score(y_test, y_pred_decision_tree)\n",
    "precision_knn = precision_score(y_test, y_pred_knn)\n",
    "\n",
    "recall_logistic = recall_score(y_test, y_pred_logistic)\n",
    "recall_decision_tree = recall_score(y_test, y_pred_decision_tree)\n",
    "recall_knn = recall_score(y_test, y_pred_knn)\n",
    "\n",
    "print(f'Accuracy of Logistic Regression: {accuracy_logistic}')\n",
    "print(f'Accuracy of Decision Tree: {accuracy_decision_tree}')\n",
    "print(f'Accuracy of K-Nearest Neighbors: {accuracy_knn}')\n",
    "\n",
    "print(f'ROC AUC of Logistic Regression: {roc_auc_logistic}')\n",
    "print(f'ROC AUC of Decision Tree: {roc_auc_decision_tree}')\n",
    "print(f'ROC AUC of K-Nearest Neighbors: {roc_auc_knn}')\n",
    "\n",
    "print(f'Precision of Logistic Regression: {precision_logistic}')\n",
    "print(f'Precision of Decision Tree: {precision_decision_tree}')\n",
    "print(f'Precision of K-Nearest Neighbors: {precision_knn}')\n",
    "\n",
    "print(f'Recall of Logistic Regression: {recall_logistic}')\n",
    "print(f'Recall of Decision Tree: {recall_decision_tree}')\n",
    "print(f'Recall of K-Nearest Neighbors: {recall_knn}')"
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   "cell_type": "markdown",
   "id": "d2d6eb00-77fd-40dc-a3c5-35c1fe0200c0",
   "metadata": {},
   "source": [
    "## Экспериментируйте\n",
    "Для получения лучшего качества придется поэкспериментировать. Подсказка: попробуйте оптимизировать гиперпараметры модели"
   ]
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